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5 compelling examples of research projects

Last updated

3 April 2024

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Creative and innovative minds dream up big ideas that build the trends of tomorrow, but the research behind the scenes is often the secret sauce to company success. Businesses need a way to learn how their products or services will resonate with the market and where to invest their marketing efforts. 

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• Research project examples

Data collected from research products can help you verify theories, understand customer behavior, and quantify KPIs for a clear picture of how to improve business practices. 

Many types of research projects can help businesses find ways to fuel growth and adapt to market changes. These five examples of market research projects highlight the various ways businesses can use research and measurable data to grow successfully and avoid poor investments.  

Example 1: Competitive analysis

It's important for businesses of all sizes to understand the competitive landscape and where they stand in comparison to direct competitors. By identifying your competitors and evaluating their strengths and weaknesses, you can find ways to position your company for greater success. 

Competitive analysis can be used to better understand the market, improve marketing methods, and identify underserved customers.

The goals of competitive analysis may include:

Identifying your company's position in the market

Uncovering industry trends

Finding new marketing techniques

Identifying a new target customer base

Planning for new product innovation

Competitive research is conducted by identifying competitors and analyzing their performance. After identifying your direct competitors and gathering data about their products and services, you can dig deeper to learn more about how they serve customers. This may include gathering information about sales and marketing strategies, customer engagement , and social media strategies.

When analyzing direct competitors, organizing information about your competitors' attributes, strategies, strengths, and weaknesses will help you reveal themes that give you greater insight into the market.

Competitor analysis templates

Example 2: market segmentation.

Every business relies on customers for success. Researching your target audience and your potential position in the market is essential to developing strong marketing plans. 

Market segmentation can be used to plan marketing campaigns, identify ideal product prices, and personalize your brand.

The goals of market segmentation research may include: 

Identifying the target audience

Planning for new products or services

Expanding to a new location

Improving marketing efforts

Personalizing communications with customers

Improving customer satisfaction

There are many ways to collect and organize data for market segmentation research. Depending on your products and services, you might choose to divide your target population into groups based on demographics, location, behavior patterns, lifestyle aspects, etc. Organizing such data allows you to create buyer personas and test marketing strategies.

Example 3: New product development research

Companies must invest significant time and money into the development of a new product . Product development research is an important part of promoting a successful launch of a new product. 

The goals of product development research may include:

Forecasting the usage of products

Identifying accurate pricing

How products compare to competitors

Potential barriers to success

How customers will respond to new or updated products

Product development research includes studies conducted during the planning phase all the way through prototype testing and market planning. Research may include online surveys to determine which demographics would be most interested in the product or how a new product might be used. Advanced studies can include product testing to gather feedback about issues customers are having or features that could be improved.

Example 4: Customer satisfaction

According to the CallMiner Churn Index 2020 , U.S. companies lose $168 billion per year due to avoidable consumer switching. Customer satisfaction leads to loyalty and repeat purchases. Furthermore, happy customers leave good reviews and act as natural brand ambassadors. 

Findings from customer satisfaction surveys can help companies get a better understanding of the customer journey and develop new processes.

The goals of customer satisfaction research may include: 

Understanding overall customer satisfaction

Finding bottlenecks or points along the customer journey that decrease the level of customer satisfaction

Measuring the level of likelihood to recommend to others ( Net Promoter Score )

Measuring customer satisfaction may include surveys to determine satisfaction with the company, opinions about the sales process, or about a specific process like the user-friendliness of an app or company website. This can be achieved by organizing data derived from customer interviews, customer satisfaction surveys, reviews, and customer loyalty programs. 

Example 5: Brand research

No product or business is without competition. Establishing your brand in the market can help you stand out from the crowd. Brand research can help you understand whether your marketing campaigns are reaching their goals and how customers perceive your brand. 

Some goals of brand research may include:

Positioning your brand more competitively in the marketplace

Measuring the effectiveness of brand marketing

Determining the public perception of your brand

Developing new marketing campaigns

Tracking brand success on a regular basis

There are a variety of ways to conduct research about how consumers perceive your brand. In-person focus groups can help you get an in-depth view of how your brand is perceived and why. Surveys can help you gather data surrounding brand preference, brand loyalty, and what people associate with your brand. Ongoing research in these areas can help you build your brand value over time and find ways to share your company mission and personality with consumers.

• How to find ideas for your next research project

Successfully running a business requires you to be well-informed on product development, branding, customer service, industry trends, marketing, sales, organizational processes, employee satisfaction , and more. 

Various research products can help you stay informed and up-to-date in all these areas. However, determining where to focus your efforts and invest your capital can be challenging. These actions can help you find ideas for your next research project.

Identify problems or issues

Remember, research is conducted to satisfy a question or reach a goal. Identify problems that impact customer retention , sales, or company performance. Use these problems to determine which types of research topics are most likely to help your company achieve greater success. If performance is low, consider a research project to determine employee satisfaction levels and identify how to improve them. If sales are low, consider research into sales processes or customer satisfaction. 

Confirm the potential for a new idea

New products or services help companies grow and attract more customers. However, they require a big upfront investment from your organization. You can prove that your next big idea will be a hit by developing research projects around the need for a new product and your target customers. Solid data is often needed to convince company leaders and stakeholders to invest in a new product or service.

Check out the competition

Where do you stand in comparison to your competitors? If you're unsatisfied with your position in the market, learning more about what your competitors are doing right can help you determine how to improve. 

• Characteristics of a good market research idea

Shallow or vague research topics can lead to lackluster results that don't really add value to your studies. To conduct a successful research project, it's important to develop a plan that will yield productive data. When choosing a topic for your next research project, look for these characteristics. 

The topic is relevant to your current position

The idea is manageable (research can be conducted with your resources and budget)

The project has a specific and focused goal

You can clearly define and outline the scope of the project

The subject matter isn't too broad or narrow to yield useful results

While research can be science-based or for academic purposes, market research is conducted for a variety of reasons to help businesses grow or reach new levels of success. Understanding market research goals is the key to developing highly effective research projects that yield useful data. By examining examples of different research projects and your organizational goals, you can more easily decide where to focus your efforts.

Which topic is best for a research project?

There isn't a single topic that provides the best research project for every researcher. The best research topics serve a purpose like gaining a deeper understanding of a specific phenomenon, solving problems, improving processes, generating ideas, etc. Finding the best topic for research requires an investigation into what type of research project is likely to yield the most effective results.

How do you structure a research project?

The structure of your research project should clarify what you will investigate, why it is important, and how you will conduct your research. To get funding or approval for a research project, researchers are often required to submit a research proposal which acts as a blueprint and guide for a research plan. Any formal or informal research plan should include these features.

The identity and position of the researcher

An introduction of the topic and why it's relevant

The objective of the project and why you think the research is worth doing

An overview of existing knowledge on the topic

A detailed list of practical steps for how you will reach your objective, including gathering data and how you'll gain insights from the data you obtain

A clear timeline of the project and the planned project budget

What's the difference between a project and a research project?

A project is a planned set of activities with a specific outcome, while a research project is the investigation of data, sources, and facts to reach new conclusions. In a business context, a project may be the development of a marketing campaign, planning a new product or service, or establishing new policies. Research projects use relevant data to fuel business projects and activities.

What are some examples of practical research topics?

Practical research projects can range across a variety of subjects and purposes. Research is often conducted to further medical knowledge, change and adapt laws, address economic changes, advance academic studies, or improve business success. Here are a few examples.

How eating a diet high in fruits and vegetables affects advanced Crohn's disease

How to improve customer satisfaction by 20% in six weeks

The impact of increasing voter turnout by 25% on the presidential election

The percentage increase of new customers with the addition of online enrollment for banking services

The most effective way to improve employee retention in a company with 1,000 employees

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• Product-led Growth

What is Product Research: A Guide for Founders

I don’t think there’s ever been a founder who succeeded with the first iteration of their product idea. For most of us, building a product means constant discovery and iteration, or product research , for short. 

In this guide, I’ll explain what product research is and show you the best methods to do product research for your startup.

And there’s a very good reason for you to keep on reading…

We, founders, have really just one objective, after all – To create a product that will pretty much rock our customers’ worlds. 

Sure, we can say that we want to exit and get a bucket load of cash for our product. 

Or that we want to conquer the world. 

Or build another SaaS unicorn…

And even if that is the case, we still need to build that perfect product first. Perfect in a sense that our customers won’t be able to live without it. 

The thing is – It’s almost impossible to do it right off the bat. No matter what ideas we have, we still need more insights, data, and feedback to fine-tune it. And the only way to get it is through product research. 

Below, I’ve included what I know about it, and what I’ve done to create Refiner. Full disclaimer, I also plugged my product there because, let’s be fair, it does help with product research too. 

But on the whole, the below guide contains what you need to know about product research to refine your idea and build a product that’ll delight your audience. 

So, let’s get on with it, then. 

What does it really mean to conduct product research?

I ask this because the term – product research – could, at first, be misleading.

It’s easy to consider it referring to the process of researching products to buy, for example but that’s not it. 

So what is product research? Various definitions call it a process focusing on gathering insights and information that, once analyzed, can help us build and improve our products.

There is far more in making it happen, of course. However, on the whole, I think that’s probably the easiest way to explain what product research is. 

There are several other reasons to conduct product research beyond just learning what your users want. 

• Product research is your gateway to becoming a user-centric company. It’s how you understand the target audience, their needs, pain points, and desires, and develop products that meet those requirements, rather than do what you’d like them to do.
• Secondly, product research also helps you validate ideas and assumptions. This last item is of particular interest here. After all, how often do we come up with ideas, and then rationalize them in our minds by assuming certain things about our audience or their needs? Now, these assumptions may as well be true. But you can only know that if you’ve validated them through product research. 
• Thanks to different product research methods (more on those in just a moment), we get to find out which features our audience really want us to build. And needless to say, that’s a huge help that can also prevent us from spending time and effort on the wrong feature. 
• Finally, product analysis can tell us a whole lot about our competitors , far more than other research methods could uncover. Why? Because through product research, you learn what your customers think of the competition. You discover how they perceive other similar products on the market, what value they (meaning, your customers) think those products deliver, and the reasons why they could consider using those products. 

Worth to note – Product research is an iterative process. It’s not something you do once and then, forget about it but a continuous process that helps you refine anything from questions you ask to insights you collect.

As a result, product research is also quite an undertaking. It’s not something you should be doing on a whim, or to plug leaks in your funnel. Instead, product research is a process you should do continuously, and tap into those insights when you need data to fuel a specific project (like plugging leaks in a funnel, for example.)

In other words, product research is where you continuously collect data that you then turn to when needed, not the other way around. 

So, how do you do it, then? How do you collect product research data?

Product research methods

Let me start by saying that there is no single, ideal product research method. It’s also impossible to tell which product research method is better than others, and so on. All of the methods I’ve listed below work exceptionally well in their respective best use case scenarios. 

But as you can imagine, I’m still a little biased towards surveys. That’s what my product, Refiner , does, after all. Surveys are also the method I’ve used the most in the past, and that’s what helped me drive the development of Refiner.

Nonetheless, here are all the most popular product research methods, including surveys, of course. 

Surveys: Surveys rely on you sending questionnaires to a pre-selected audience segment to collect quantitative and qualitative data about those people’s opinions, insights, and so on. 

What makes surveys so incredible for product research is their:

• Wide reach – You can send a survey to a large number of people, and collect insights from them without much effort. 
• Scalability – Similarly, surveys don’t require additional time or effort from you to scale the research. 
• Low cost – Again, surveys are relatively inexpensive to run. 

Some of the most common product research surveys include:

• NPS – a survey that allows you to evaluate the customers’ attitudes towards your product. 
• CSAT , which helps you learn more about your customer satisfaction and draw actionable insights based on that.
• CES which reveals how easy (or not) customers find your product to use. 

Here’s an example of an NPS survey used in product research.

FEATURED READING: How to run a perfect customer survey for a digital product

User Interviews: This method is all about sitting down with your customers one-on-one to discuss their needs, uncover pain points, or their product preferences. User interviews are all about asking open-ended questions and letting the person reveal their opinions. Unfortunately, this makes it a time-consuming method for product research, particularly if compared with surveys. 

Focus groups: Running a focus group is like conducting user interviews at scale. In this method, you bring a small group of users together to discuss your product, provide feedback on issues you want to research, and so on.

Usability testing: This method focuses on observing users as they interact with the product (or its prototypes.) By observing how users complete predefined actions in the product, you can identify usability issues and gather insights for improvement.

User behavior tracking: In this method, you’re also drawing conclusions based on user behavior. However, unlike usability testing, behavioral tracking focuses on analyzing data that you collect using different user behavior tracking tools (like Hotjar, for example) to understand user behavior, spot patterns, and collect benchmarks.

A/B testing: A/B or split tests allow you to uncover the audience’s preferences by eliminating product or UI elements that fail to engage them successfully. In this product research method, you present different versions of a product to different user segments and monitor their engagement with those to determine which variation performs better.

Customer journey mapping: This method relies less on collecting specific insights than using your data so far to understand the entire customer journey, from initial product discovery to post-purchase experience. This is an important product research method, as it allows you to map pain points to specific stages of the buyer’s journey, and spot opportunities for improvement.

How product research works in practice – Key elements of a product research project

It’s quite easy to assume that to launch a product research project, you just need to, a.) pick a research method, and b.) figure out how to use it, and off you go but no, that’s not how it works.

Successful product research strategy relies on several key elements:

Clearly defined objectives

It’s as obvious as it sounds, actually. For your project to work out, you need to set specific objectives and communicate them in a clear and understandable way. These objectives will guide the entire project, from selecting the target audience, research method, to what you’re going to do with the data. 

Specific target audience

Again, quite an obvious element but also, often one that we tend to forget about. Naturally, we always have an audience for research. There is no such project without it, after all. But at the same time, we often tend to jump in and invite all customers for research, whereas we should be gathering insights from a specific audience or customer segment only.

This product research element focuses on selecting the right people who have the insights and knowledge that you seek. For example, if you’re evaluating advanced product functionality, you should focus exclusively on experienced users. New users mightn’t have even discovered those advanced features. And even if they did, their level of product knowledge mightn’t be sufficient for them to provide any meaningful insights for your research. 

Research method

In most cases, once you set clear objectives, and pick the target audience that has insights to help you achieve those, choosing the research method is relatively easy. But you still have to do it. And I recommend that you evaluate all potential options (we’ve covered them above,) and select the method that’s the most appropriate for your goals and the audience. 

TIP: It pays off to select several research methods sometimes. For example, if your goal is to improve the usability of your interface, you might start by tracking user behavior to identify potential challenges users experience. Then, hold usability sessions to confirm your assumptions, and finally, interview most engaged users about their challenges. 

Notice how each method in this example allows you to go deeper into the problem. You start by collecting data that allows you to make hypotheses about potential usability problems. You, then, observe how users engage with those tasks in real-life, and finally, you get their opinion about those challenges. 

Research instruments

I have to be honest – I’m not particularly fond of this label, research instrument. It’s quite misleading. But that’s what it is so I’m sticking with it here too. However, what we really mean by research instruments are materials that you’re going to use in the research methods you’ve selected. 

These can be questions that you’ll be asking customers to answer in a survey, tasks for the usability session, questions for user interviews, and so on. 

And needless to say, these are hugely important to prepare upfront, test, validate, and only put into use when you’re absolutely certain that they can deliver the insights you’re after without causing any form of bias.

Panel recruitment

In an earlier step, you’ve selected your target audience. Now, you need to recruit at least some of those people to participate in your product research. How you’re going to do it will largely depend on the research method you’ve selected. 

For example, if you’re using behavioral data like heatmaps, then you don’t really need to recruit anyone for the study. Whatever heatmap software you’ll be using will collect the data for you. 

If you run a survey, then you, most likely, will email it or display the survey in-app to your customer segment. Again, it’s not going to take much time and effort. 

But the situation is different if you plan to run a user group, for example. In this case, you need to go through a formal process of approaching participants, getting their consent to participate in the study, and so on. 

IMPORTANT: Note I mentioned collecting user consent before research. This is a hugely important step to remember, particularly in the face of GDPR and similar privacy laws. 

As Phil Hesketh of ConsentKit explains :

“To ensure your research panel is compliant with data protection regulations in your country or state, it’s essential that you get consent from each panelist to collect and store their personal information.

You’ll need to get consent when a person agrees to join your panel for the purposes of recruiting for research, and you’ll need to ask for consent again when they agree to take part in a specific project.”

Data collection

This element is all about launching your research method to collect the data. Simply. 

Data analysis and insights synthesis

In a typical product research project, you collect data for a specified period of time, after which you begin to analyze and synthesize that information. The goal here is to identify patterns, trends, and specific insights within the data, and document those so that you have a clear record of your findings.

Actionable insights

The project, typically, concludes not with documented data but a list of actionable recommendations drawn from it. In other words, with product research, you complete a full circle, starting with a problem, conducting research to generate data, and ending up facing the same problem again. This time, however, you’re equipped with insights and actionable recommendations to tackle it successfully. 

And that’s it

That’s a typical product research project in a nutshell. 

Naturally, there is more to each of its elements than I was able to fit into this guide. But I’m hoping that I was able to provide you with a solid foundation and general understanding of the process. 

Also, if you’re interested in digging deeper into how to use surveys for product research, I recommend the my other guides:

• Product discovery process – the do’s and don’ts
• How to collect and measure user feedback for a digital product
• How to use UX research surveys for product development
• In-app feedback – How to capture user feedback within your app
• 15 best product feedback tools to help you improve your app

And if you want to see how Refiner, my survey software, could help you run successful product research surveys, sign up for a free trial or check out these live survey demos . 

Related posts

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What is a Good CSAT Score for SaaS

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CSAT Survey Analysis: How to Analyze Customer Satisfaction Survey Data

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Product Experience

Product Research

What is product research?

Why is product research important, how to conduct product research.

• Using quantitative and qualitative product research

Areas of product research

• Top product research tips
• Qualtrics’ product research tools

See how ProductXM works

26 min read Don’t skimp on your product research. The insights you gather and act upon can mean the difference between selling products that are a roaring success or an abject failure.

You’re in the process of developing a new product idea with a view to launch it on the market . Or maybe you’re reviewing and updating an existing product that’s already on the market in the hope of making it better.

It’s a risk – get it wrong and you could make an expensive mistake; get it right and you could have a successful, profitable product, service or experience delivering amazing returns on your investment. So how can you make sure you end up in the second category?

This is where product research comes in. It helps you:

• Evaluate and prioritize your ideas
• Test and validate concepts
• Assess names and packaging designs
• Check out the competition
• Set the right selling price
• Gauge customer satisfaction and monitor product-market fit post-launch
• Continually improve the product

Product research is the term most often used to describe this process, but it’s not just about physical products. The research process we’re describing here could apply equally to a physical product such as a smartphone, a service like cloud storage, or an experience like a tour or hotel stay.

Product research goes hand-in-hand with market research, which identifies a target customer, develops typical buyer personas, and analyses purchasing behavior. Together, the two help you make informed decisions about how to find the right fit between what people need or want, and what you can offer them.

Get started with our free product research survey template

A product, service or experience idea can be brilliant and original, but if nobody wants to buy it, there’s no way it can be successful. At best, it will be an impressive oddity, and at worst, a total waste of time. Likewise, if you’re proposing changes to an existing product or service but they’re not the ones your customers value, you could end up doing more harm than good.

Product research will tell you whether your idea resonates with potential customers. Because a product is only worth what people are willing to pay for it, you’ll also be able to extend your research process to set a product price accordingly.

And if your competitors are doing better than you, you need to find out why, identify any gaps that they’ve missed and uncover emerging trends so you can get ahead.

Sure, you can start your research online. Amazon, review sites, and social media will give you an idea of what’s out there and how buyers react to products, but if you want to dig deeper and own your niche, you need to invest in user experience (UX) research .

UX research focuses solely on the user of a particular product, looking at how human beings interact with products and services and learning from their experiences.

UX research typically covers the following areas:

Target demographic

Demographic targeting or segmentation helps you understand who your particular product or service is targeting. Demographic information includes everything from gender and age to education and income.

Uncovering a need is one of the main drivers for creating a winning product. For example, if you’re selling into a particular market, your UX research can help you identify flaws in particular products or services and what your potential customers might want from something new. Likewise, researching customer needs helps you to identify what you shouldn’t focus on.

When you research products and UX, you should also examine what your target market wants out of the product or service. Want differs from need as a product need is a requirement, whereas a want could specify certain features of capabilities. They can also differ in priority based on each customer.

For example, this could be improving how your product or service works, or the UX outcomes — e.g. does the product make their job easier or give them more time to focus on other activities?

This should all form part of your UX research and can give you a huge advantage if you get it right.

What do your customers think about you or your product? What do they think about your competitors , and how do you compare to those competitors in their eyes?

What do your customers think about the problem they have? Or how can they solve it? Do you think about their problem the same way they do?

You need to understand how your customers think and what they think if you’re going to convince them to buy your product.

Understanding your customers’ behavior should form a big part of your product research process. Not just in terms of how they go about their lives or work — which can help you understand how your products fit in — but also how they do their product research when looking for a solution. Do they base their purchase decisions on word-of-mouth? Do they typically buy online, or are they more likely to want to visit a retailer to see, hear and touch their potential purchase?

Think about the use case for your product or service. If it’s a gadget, will they use it in the home or out and about? This consideration might inform whether you build in a battery power feature or rely on a wall socket. If it’s a shoe-shine service, would they prefer to be able to walk in off the street or is an appointment system more appealing?

Understanding their behavior can help with your product development to meet a need, and also with your marketing strategy and sales messaging.

Motivations

What drives your customers to find and buy products or services? This is something you need in your product research so you can create solutions that further their success.

Customer motivations are either conscious or subconscious, e.g. they’re aware of a problem and need a solution, or driven by changes or demand in the market (for example, a government requirement to implement certain technologies for business operations).

Product research can help you to understand these motivators, subsequently enabling you to tailor your market messaging and create amazing products.

Using quantitative and qualitative methods for product research

Product research relies heavily upon qualitative and quantitative research methods — this includes capturing feedback , observing how people use products, and analyzing existing data (or new data) to uncover trends or opportunities.

Combining both qualitative and quantitative research methods will help you to identify the most pivotal market trends, as well as understand the specific thoughts and beliefs of customers. Here’s a quick breakdown of how each product research method works:

Quantitative UX research

Quantitative research is a starting point. It’s all about crunching the numbers that translate into informative statistics. Surveys and polls (online, mobile, paper, telephone) are the most commonly used methods, although you can add data from analytics platforms to the mix.

Qualitative UX research

Qualitative research joins the dots of the quantitative data by revealing what people think, believe, and feel about a product. Rather than ticking boxes, people say or write what they think either in open text boxes on surveys, or during interviews and focus groups. Qualitative UX research provides context, painting a picture using the data.

Breaking product research down into manageable stages will increase its effectiveness. These stages are:

Market research

Understanding the current marketplace can help you not only identify any trends in particular buying habits or fads that are attracting short-term attention. It can also help you identify areas of opportunity that you could exploit.

Market research helps with pricing decisions based on whether your market is stable or growing. From the research, you’ll uncover market size and competition, as well as answers to the following questions:

• What are the best-selling products already in your market and where do you fit in compared to them?
• Is there low competition in your market, or is it saturated?
• Are you selling a product that’s got high demand? And how long does that high demand last?
• Is your product something that can be sold year-round? Or is your product seasonal?

Customer research

We’ve already touched on this, but your product research should include your customers.

For example, research demographics to understand who they are, use psychographic information to understand their wants and needs, and leverage specific customer segmentation to ensure you’re targeting the right section of the market with your product or services. All of this will help you to build a better picture of your audience.

Your customer research should also include where you plan to sell your products based on your customers’ preferences. Will you need to sell online? Or is your product something your customers will want to see in person first?

If you plan to sell online, you’ll need to factor in other elements of your selling and marketing strategy. This will be everything from keyword research for marketing and understanding the search volume for your products, to shipping costs if you’re going to sell nationally, or internationally.

New product development

Finally, you should examine your specific product idea. This includes whether there’s a demand for it, what the pricing landscape is like and where you should position yourself, minute product details like the size and weight, or whether you’re selling perishable goods or consumables.

Furthermore, if you’re selling the same product as the competition, how are you planning to compete? Are you going to compete on price and if so, what will your selling price need to be to gain an advantage while generating a profit margin?

Segmentation Research

Segmentation is the strategic lens through which you view your market landscape. It’s used to identify who to target so that you can develop, maintain, or adjust branding and marketing tactics and identify product optimization and innovation opportunities. The most profitable products are those that are targeted at specific segments of a market and tackle a particular need or problem.

Ask yourself the following:

• How large is that segment?
• Are your customers early adopters or more traditional? Are they open to new ways to do things or will they need to be convinced?

How people perceive your brand speaks volumes about what they are prepared to buy from you. Just as you wouldn’t buy a computer from a supermarket, you wouldn’t buy your groceries from a technology store (more importantly, it’s highly likely that neither would sell either of those products).

Concept testing

Concept testing should be conducted in an agile environment.

Begin early in the process with an MVP to test on existing and potential customers. A series of small studies done throughout the product innovation cycle will ensure that your new product is refined by customer input.

It is always more cost-effective to refine a new offering as it is being developed than to have to drop or make significant changes to a product that has already consumed a great deal of investment.

This continues as you roll out your new product. You must stay in touch with your target audience as they use the product, and take on board comments and suggestions for improvement. There are many benefits to concept testing:

• It’s cost-effective and flexible: You can send out simple, quick surveys if you want high-level rapid feedback, or longer ones if you want to dig deeper into the detail
• You’ll be able to optimize your product: You’ll gather useful information on things like branding, pricing, and market status that will make a real impact on your development decisions
• Continuous quality assurance: You can use the same audience to give feedback on your improvements, or survey a new audience to get fresh insights on your product development.
• Great brand loyalty: You’ll build up good customer relationships and increase your brand equity by including potential customers in your product’s design and development.

Improve your concept testing with our Introduction to Concept testing eBook

Naming research

What’s in a name? A best selling product, we hope.

Product naming is the process of coming up with compelling, unique names for your new products.

We would always recommend using qualitative research, with its emphasis on verbal expression, to test product names with your prospective customers, and using a text analytics tool to categorize text responses by both topic and sentiment automatically.

When deciding on product names (we recommend between 3 and 15 options) to run past your respondents, remember these six golden rules:

• It should be easy to remember: consumers must be able to recall the name easily
• It should be memorable: There’s a lot of product ‘noise’, and your name needs to be heard above it
• It must be easy to pronounce: Word of mouth is important, and if your customers can’t say it, they won’t mention it to others
• It must be easily understood: It helps if the name hints at what the product does unless you have a colossal marketing budget to explain a more leftfield name
• It must translate well with international audiences: We’ve all heard the apocryphal story that Coca Cola originally transliterated as ‘bite the wax tadpole’ in Chinese. Whatever the truth, make sure audiences around the world can pronounce your product name and it doesn’t mean anything problematic.
• It has to be a name you can own. No one wants to discover after a comprehensive research program that the name everyone loves is not available for use.

Provide your respondents with a product description, and ideally images of the product. Break your testing between:

• Overall name questions: How does each name compare with the others? Rank the names in order of preference, or against criteria such as trustworthiness, appeal or creativity. Do respondents have any names of their own? The results of this testing will give you the top names overall and in every category.
• Individual name questions: Would respondents buy this product with this specific name? How does this name make them feel? Individual name analysis should reveal name sentiment, as well as data about a consumer’s likelihood to purchase or consider your product.

Feature research

You use this to identify which product features your customer’s value so that you can add or improve them.

But you always need to keep in the back of your mind that a product is more than just the sum of its features – it’s how they work together to give a seamless experience that’s important. Research will help you do that.

There are three areas of feature research that you’ll need to undertake:

• Identifying customers’ wants and needs : Customer needs analysis will give you insights into personal values, purchasing decisions, and pricing tolerance . Conjoint analysis, with its multiple product attribute comparison/trade-off scenarios, will inform which features customers consider most valuable.
• Internal development : Once you’ve analyzed what customers want, you need to bring your feature back in-house and seek the expert opinion of your product managers, business analysts, marketers, designers, engineers, and customer experience teams.
• Test with customers : You’ve created a feature that customers want, and your in-house teams have approved them. Now you can use product feature prioritization to understand the features your customers’ value (and don’t). Survey: usage (where and how the customer uses the feature); ‘top of mind’ negative and positive associations with the feature; product categorization (comparing with the competition to see which features make a product more or less ‘swappable’).

Pricing research

A product is only worth what people are prepared to pay for it. You need to ensure that its price is low enough for people to feel they’ve got good value, and you also make sufficient profit, but not so cheap that its quality is questioned.

You also need to consider how many units you’ll need to sell based on your product prices to have a good profit margin.

When you conduct pricing research , you’ll discover:

• How willing the market is to buy your product
• The highest return on your development investment
• How to maintain your brand’s value
• When and how to alter your pricing effectively
• The costs involved in producing your product

Pricing research is done through a combination of market research, competitor research , market analysis, and testing in the marketplace.

Use product pricing research tools. These use one or more of the following methodologies to ask survey respondents:

• Van Westendorp price sensitivity meter is a type of direct pricing research that asks survey respondents four simple questions to gauge whether your product is too expensive or a bargain
• Gabor-Granger pricing methodology uses predefined price points to determine the highest price a respondent would pay for your product
• Conjoint analysis gives respondents a choice between product packages and then asks them to choose one of the feature/price configurations to create the ideal option. Each option comes with trade-offs and the best time for use

Free eBook: 16 Research Methods to Maximize Product Success

Top new product development tips

You could come up with an amazing product or service, but without product research, you’ll never know if there’s a market for it. Furthermore, you might price it wrong and/or target the wrong customers, severely limiting (or preventing entirely) your sales.

The good news is that there are a lot of product research tips and product research tools we can share with you:

Research existing products on the market

The easiest way to do product research is to examine what products are already available in your marketplace and identify the top-selling products on the market that you’re competing with.

If you’re not already offering this product, you can create your own version, and with the right research, you can identify areas of improvement to differentiate your offering.

Look at product descriptions to see how existing products are being sold and use this to create products that fill a gap.

The easiest way to do product research is to get out and act as a customer. Go to popular online marketplaces like eBay and Amazon to see what people are looking at. Get out onto the high street and visit brick and mortar stores to see what product categories are already on the market.

All of the above will help you understand the current landscape and how you can fit in or disrupt it .

You can also look online at Google Trends and queries for particular products in your marketplace. Understanding these trends and the search volume for a particular keyword related to your products can help with your launch, as well as the optimization of product pages.

These trends can be particularly useful if you’re selling a seasonal product, as you can use real-time search data to see trending products, or changes to your market size when there might be more people looking for your product at certain times of the year.

Look at online reviews

The best feedback on products will always come from customers. And the best place to find honest feedback is by looking at online reviews — whether they’re on Google or Amazon.

Especially if you’re an online business, these Amazon and online reviews are a trove of ideas that can help you with your product research.

For example, paying particular attention to poor reviews can help you identify gaps in a competitor’s offering that you can take advantage of.

Identify gaps in the market

You don’t have to reinvent the wheel with your product ideas, often you can simply take an existing product and improve on it.

Online reviews are incredibly useful for product research because customers will typically share their ideas for improvement as part of the submission.

Quantitative product research

Using quantitative research with online polls or product research tools can help you reach a large portion of your potential marketplace in a cost-effective way.

You can use this as a starting point to understand the market as a whole before drilling down into specifics.

Qualitative product research

As well as analyzing the wider market, you should get specific reviews and opinions of your products from customers using qualitative research .

Through focus groups or individual interviews, you can start to understand the sentiment towards your products and identify opportunities to capitalize on.

In the long run, this data can help you to develop new product ideas, especially if you uncover downsides or holes in your product development.

Ongoing product research

While research can help test and optimize your product idea before launch, it shouldn’t stop once the product is on the market.

You should continue to do your research as an ongoing project.

This will give you more information about how your product or service is perceived and can also help you identify ongoing room for improvement for your products to help you make more sales in the future.

Conducting thorough product research is key to more sales

Researching your products can make the difference between success and failure when it comes to launching winning products and having success in the long term.

It should produce a complete picture of your market, product, and customers and will produce the roadmap for your launch and ongoing sales .

Your research will help with everything from product development, to your marketing campaigns, to selling prices and ongoing development.

By doing in-depth research you’ll be able to make better, more informed decisions about your product ideas and help you create and sell more profitable products.

Find success with Qualtrics’ product research tools

By bringing customers into your product development process, you can identify and solve problems while uncovering new opportunities. Today’s digital tools make it easier than ever.

With the right research platform, you can accelerate your product development cycle using real insights from your customers and easily identify gaps in the market. This enables you to launch new, customer-oriented products, services, and solutions, or disrupt existing categories with offerings that have new or improved features.

You can also get instant access to feedback from multiple channels and data sources like Google Search and other search engines and social media sites.

Then, use smart analysis at every step of the product development lifecycle to launch products you know your customers will love. Concept testing enables you to validate every aspect of your offering, from features and branding to messaging and price, to set it up for success. You can also use conjoint analysis to find your customers’ ideal product configurations, e.g. packing, pricing, design and features.

Finally, close the product experience gap, instantly gather real-time feedback that you can use, and automate the process using automated actions.

Start your PX journey today with our free product research survey template

Related resources

Product feature research 10 min read, product analysis 13 min read.

Pricing Research

Product Price Optimization 12 min read

Product presentation 11 min read.

Buyer Personas

Customer Targeting 12 min read

Product Development

Product Development 11 min read

Product concept 12 min read, request demo.

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Product research: the key to building a product people will love

You want to build a long-term vision for your product, and work on something that your users will buy and love—but can you really do that if you don’t understand your customers?

Probably not.

Enter product research, the key to leading your business to success through data-backed insights and smart, customer-centric product decisions.

But how can you make sure your product research is effective, and that it will benefit your customers and business? Keep reading to find out!

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Hotjar gives you the tools you need to lead user-driven product research and development processes.

Product research: what it is and why it matters

Product research is a vital first step before introducing new features, a new product, or entering a different stage of the product lifecycle. It enhances your understanding of what the customer wants so you can make user-led product decisions and address customer needs.

What is product research? 

Product research is the process of gathering information about your product's purpose, development direction, and which solutions you should offer to create customer delight. Product research is conducted through surveying and studying users to identify their needs and understand what they demand from your product, and usually happens at these stages in the product lifecycle:

Before launch : to understand which initiatives you should include and prioritize based on customer needs, and to develop a product-market fit.

Testing and feedback: to understand how the customer perceives new iterations, learn what they like or don’t like, and how you can improve the product to delight them.

Soft launch: to analyze how effective and useful your Minimum Viable Product (MVP) is among a segment of customers, and identify changes to make before releasing the full product to market.

Post-launch: to study customers' reactions and behavior after launch for continuous discovery , analyze customer satisfaction, and identify potential bugs or improvement areas.

Why product research matters for product teams

If you don't know what your customers want from your product, even your most brilliant idea risks failure. Here's how product research helps you align your product ideas with customer needs:

Understand user needs and pain points: your product needs to solve the customer's most pressing issues, but how can it do that if you don't know what issues they’re having? Product research helps you gather data and behavioral insights to understand your users’ problems and build the solutions they need.

Align user needs with business goals: understanding customers' needs and how they align with your product and business goals helps you plan a product roadmap that'll serve both the users and your company.

Higher scope for innovation and accuracy: when you have clarity about what your customers need, you can find innovative ways to solve their problems and build a product they'll love.

Gain a competitive edge: researching your competitors will help you understand how to differentiate your product and uncover gaps in the market, which can help you decide what features to build.

Simpler and more impactful prioritization: with product research, your entire team is clear on how to prioritize initiatives to achieve customer delight , making it easier for you to manage your product backlog .

Why and how product research can vary across product teams 

Product research validates your ideas and gives you a better understanding of your user throughout the product development process. But the responsibility to conduct product research doesn’t fall solely on the product manager— people across departments should also be conducting product research.  

Since product research isn't a single, standard process, the purpose and level of contribution can vary at different stages of the product lifecycle and across roles and departments:

1. Product managers

A product manager's primary goals are to understand user needs, learn business goals, and determine market requirements to create a product vision and roadmap. 

They also use product analytics to validate ideas around iterations and product features—all of which require extensive product research. 

A product manager’s main research aims are to ensure that product development decisions are data-informed and customer-centric, and address users' needs to build a great product. 

Common research methods include interviews, surveys, competitor studies, and analyzing user behavior and product experience insights.

2. Product designers

Product designers need to empathize with users to create an intuitive product experience that users will enjoy. 

During product research, designers might observe customers in real-time to note their reactions, responses, and behaviors around different elements of the product's design. For example, they might observe how users interact with the UI to identify product elements or features that seem to slow down or confuse individual users.  

Product designers use these insights to improve the user experience ( UX ) and create a seamless product experience ( PX ) for easy navigation and usage. 

Their research methods include customer feedback forms and behavior tools (like heatmaps and session recordings) to understand where users are facing issues and how design changes can fix them.

3. Researchers

Researchers study user behavior, needs, and motivations to translate insights into new and better product opportunities and better-informed product decisions. 

Researchers constantly conduct product research to monitor trends over time and see how user behavior patterns are changing in response to product iterations—and how to improve them.

But, here’s the catch: this data is not readily available. So, researchers use a combination of quantitative and qualitative research methods like surveys, feedback forms, and customer interviews to get recurring data.

4. Product research by lifecycle stage

Product research processes, methods, and findings will change as your product reaches new stages of development:

If you're developing new features for an existing product , you want to understand the customer's current needs, how they've changed over time, and their reaction to recent iterations. This helps you understand which initiatives and ideas you should prioritize and introduce next.

When you're developing a completely new product, product research will be different. Here you don't have historical information about user response and behavior patterns from your previous developments, so you need to perform in-depth product research to understand your target customer's needs and pain points.

4 elements of successful product research

Product research is necessary to avoid misguided product development decisions, identify potential issues with your product, and get an in-depth insight into your customer's mind. This research helps you create a well-thought-out strategy for building a product customers love.

But you need to take some proactive steps to make your research successful:

1. Use accurate and unbiased data collection methods

The primary goal of your research is to collect accurate data that tells you about how your customers experience your product—what they like or don't like, what they want or need, and what issues they encounter.

But your research won't be useful or actionable if you use unreliable data collection methods .

The best way to ensure the data you collect is accurate and unbiased is to use reliable methods like surveys, customer interviews , and tools that provide consistent real-time product experience insights (like Hotjar!). 

Only with accurate data can you be confident in making truly customer-centric decisions to build the best product.

Pro tip: use Hotjar Heatmaps and Session Recordings to study customers' behavior patterns on your site. These tools give you an unbiased look at how your customers scroll, click, move, and navigate your website, which can help you identify potential issues and improvement areas.

For example, if you use heatmaps and notice that users aren’t scrolling down your home screen to where you’ve included testimonials and product use-cases, you can use this information in your research to place them further up the page.

2. Conduct thorough competitive and comparative analysis

Product research isn't just relevant for studying customers and their needs—it's also key to understanding your competitors and where you stand in the market. 

Conduct a thorough analysis of your competitors' products, audience, and processes. This will help you analyze what's working for your audience, what gaps you can fill, and how to create a better, more efficient product for your customers.

You can complement your research efforts by carrying comparative analysis of what you're missing out on. Study your competitors and identify which features they’re providing that you’re not and what makes them unique. This will tell you where you’re lacking and help you create an optimization plan for better results and customer satisfaction.

For example, if you understand how your competitors are launching features—and how their customers are responding to them—you can use those insights to develop and introduce your next feature, and build a better product that delights your customers and stands apart from the crowd.

3. Leverage existing research material

Marketplace and trade reports—analysis reports by institutions and organizations in your industry—give you valuable insight into product processes used by companies over the years and indicate how consumer trends have changed.

This goes beyond your first-hand information and adds a historical touch to your research, so you can discover new product opportunities by taking inspiration from what’s worked before (or learn from what hasn't).

For example, you may come across an innovative way to collect customer feedback or an efficient way to test product features that might not have crossed your mind. You can explore this idea with the help of historical data.

4. Segment results based on business goals

Your product research is irrelevant if you can’t use it to make more effective decisions and product improvements. 

Enter segmentation, which is when you categorize your research findings based on business goals, so information doesn't get mixed up or lost in translation. You can also document your findings so product team members can refer to them from time to time for guided decisions.

Segmentation can also help you align your short-term and long-term goals to make the research more valuable for use in the future.

For example, you may want to study your customers' major pain points around a specific feature, at first—but later, when you're introducing a suite of new products, you might want to look at the issues your previous product didn’t solve, potential initiatives that can complement your new products, or gaps in your past marketing strategy. This will help you better address these areas for your new product.

How to measure the success of your product research

Measuring the success of your product research isn't exactly straightforward: tangible product results come much later when you receive feedback for the product, so it’s challenging to gauge the effectiveness of your research process in the beginning.

You can get some clarity around how research is translating into benefits for your users and business by attaching Key Performance Indicators (KPIs) to your product during the initial stages.

It's important to know what success means to you before starting product research. Be clear on the question you are trying to answer first.

Much like a scientific experiment, you should identify the aims and objectives and develop a hypothesis to test. Design your research methodology around the hypothesis.

You might choose a survey, a literature review, or something else. The results, once analyzed, should illustrate a statistical significance in your findings to prove or disprove your hypothesis—a true measure of research success.

Here's a list of questions you should answer to determine the success of your product research:

Do you understand the major triggers and pain points of your customer?

Have you analyzed your products in comparison to competitors and identified gaps?

Have you converted your research into data points and findings?

Have you used the research report to introduce modifications in your product roadmap or created a new one from scratch?

Did this research give you a good idea of what the customer wants from your product?

Do you understand which initiatives you need to prioritize?

How you answer these questions will tell you whether you have sufficient information or need to change your product research strategy and collect additional data.

Ultimately though, the best measure of product research success is the knowledge you gain about your customers—and how you use that knowledge to build a product they'll love.

3 ways Hotjar can assist your product research for better results and efficiency

1. use recordings and heatmaps after releasing features to get user behavior insights.

One of the most direct ways to get feedback and insights about your customers' needs, pain points, and responses to product features is to understand their behavior in context as they experience your product.

Heatmaps give you insight into the elements of your page that drive the most clicks and conversions while highlighting things you can optimize for better results. These findings can be used in your research to identify potential areas for improvement.

Session Recordings give you a play-by-play of individual user activity in your product to show you their navigation path, mouse scrolls, and clicks. Real-time behavior patterns tell you what the customer is struggling with presently , so you can improve product design, navigation, and experience.

2. Leverage surveys to get validation and feedback

If you want to know exactly what your customers are thinking and let voice of the customer (VoC) data guide your product strategy, use qualitative tools like Hotjar's Incoming Feedback widget and Surveys.

Try placing surveys and feedback widgets on high-traffic visitor points of your product to get direct, unbiased, and genuine feedback from the customer at the best time: when they’re experiencing your site. This helps you understand user needs more intuitively so you can validate ideas and features.

3. Use Hotjar integrations to prioritize features and get buy-in for ideas

Product research is a comprehensive process, and it’s challenging to do it regularly. However, the process becomes more efficient when stakeholders and team members have access to customer feedback as soon as it’s available.

Hotjar integrates with tools like Slack and Zapier to help you seamlessly communicate with stakeholders and get buy-in for your ideas then and there, so you can move forward with your product improvements.

Final thoughts

Product research lies at the very core of product management. If research isn't conducted throughout the entire development process, you risk misalignment and a resulting product that doesn't meet users' needs.

You need to understand what users need right now to build a truly user-centric product—and product research can help you achieve just that.

Use Hotjar's qualitative and quantitative product insights tools to organize your product research efforts and really understand how your customers experience your product.

FAQs about product research

What’s the difference between product research and market research.

The primary difference is that product research involves studying the product (customer needs, feedback, pain points, issues) while market research involves analyzing the market (competitors, consumers, demand).

How do you conduct product research?

Define your product and its vision

Identify your target customer

Understand your customer's needs and pain points

Conduct research using qualitative and quantitative methods

Convert the research into data findings and insights

Use the analysis to guide your product strategy

How can using product research tools help?

Product research tools help you identify consumer trends, study user patterns, and analyze user behavior for data collection. Some useful product research tools are Hotjar, Zendesk, Product Plan, Jira, and ProductBoard.

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Product Research: The Building Blocks of a User-Centered Solution

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The product research process for building winning solutions

In the previous chapter, we covered the key elements of product research. Now, you need a robust process to understand how your user will engage with your product—and the alternatives currently on the market. Here’s how to prep, conduct, and access the results of your product research.

What to consider before starting the product research process

Before adopting a product research process at your organization, you need to align with other stakeholders and ensure everyone’s on the same page about what you’re trying to achieve, why, and how you’re going to do it.

Some key things to bear in mind are:

Adopt a continuous product research mindset

When you have a continuous product discovery mindset, user insights aren’t just gathered at the beginning of a project, but are collected regularly using various product discovery techniques throughout the product lifecycle.

Think of research as something that happens during product development, not as a precursor to it. That way, you’ll always be working towards delivering a product that your users really want—and can easily use. “This iterative approach helps refine the product, enhance user experience, and identify future opportunities,” explains Sonal Srivastava , Senior UX Researcher at Amazon.

Ideally, you’ll conduct research on an ongoing basis, but at the very least, you could think about how to implement it during the following stages:

• Before development: To understand user needs, identify opportunities and define product features
• During product development: To test various concepts and refine the product through user feedback
• Post-launch: To evaluate performance, gather user satisfaction insights, and identify areas for improvement

Adapting a continuous product discovery mindset before you begin research is helpful for several reasons. Firstly, your team will be prepared for a process that isn’t entirely linear. Part of this includes storing user and product research insights in an organized and secure way, so they can be used easily throughout the product lifecycle. You’ll also be able to plan the time and budget accordingly, making sure you’re prepared to conduct studies before every iteration.

Know what you’re looking for and why

Researching without objectives is aimless, and can result in incorrect assumptions and biased products.

To avoid wasting your resources (and your user's time), you need to set clear research goals. This means knowing exactly what you want to achieve with each study, and why you're conducting it. When you combine this with an effort to stay aligned with the business and product strategy, you'll experience the added benefit of getting your decisions signed off more quickly. Your key stakeholders will understand exactly what your research will achieve.

Having clear goals also helps you decide whether you have any relevant context from previous user or market research, or if you're starting from scratch.

Build the type of product your user needs

Keep users at the center of your product decisions by using Maze to conduct remote tests at scale—and throughout the product lifecycle.

8 Steps to conduct effective product research

Before you begin, bear in mind that the product research process can (and should) be cyclical. Below, we lay out the steps in a linear way, but a continuous discovery approach is most effective for ensuring every product decision is based on user insights.

1. Opportunity assessment

Matthieu Dixte , our Product Researcher at Maze, explains the opportunity assessment stage as the place to identify and prioritize opportunities before thinking about the solution. If developing a product were a murder mystery, this would be when the detective starts looking for leads. No assumptions are being made just yet; it's all about gathering clues and fact-finding.

For you, the opportunity assessment means:

• Going through any previous research findings
• Monitoring the market
• Hosting customer feedback sessions
• Doing competitive product analysis or customer voice analysis

During this stage, you can also review your product's performance to spot any interesting trends. For example, Matthieu tells a story of a company he worked with before Maze. His team noticed that people were trying to click on charts on their website—the charts weren't actually clickable, but people wanted to filter them.

"This behavior helped us understand that people wanted to interact with the charts," says Matthieu. "We also detected that sometimes people clicked on the charts to copy and paste information because they wanted to share it with stakeholders."

These are the kinds of insights that can appear during the first stage of your product research process, and highlight which opportunities to explore in depth. “At Maze, we frequently review user feedback and monitor the market to gauge how our competitors are evolving. Each product idea comes from a trend that we detected,” says Matthieu.

2. Problem shaping

At this stage, you’re building on your findings from the opportunity assessment stage and shaping them into hypotheses. This will help identify the specific problem you want to solve.

For example, imagine people are complaining about your existing product not being flexible enough. This could mean a number of things:

• They’re not able to customize the dashboard colors
• They can’t export the source code
• The mobile experience is lacking

During the problem shaping stage, look into all those possibilities, refine the potential hypothesis, and decide which opportunities to explore further using prioritization techniques . You should also think of your target audience and how to conduct research to identify what your customers want.

Take this opportunity to seek stakeholder alignment, set clear objectives in line with your research strategy , and plan what you’ll do with the gathered insights. You can also use this stage to come up with ideas for product experimentation based on your previous research and findings.

3. Solution generation and screening

The solution generation and screening step is for outlining how your new feature, product, or initiative will reach the previously set goals—and improve the customer experience. It’s about defining how you’ll bridge the gap between user problems and a solution.

You should also use this stage to screen the different solutions or ideas and prioritize them. Determine which ones have a better market fit and are feasible, and get rid of unpromising ideas. Here you should also decide the methods, questions, and participants’ requirements—and share the plan with key stakeholders. At this stage, you should seek stakeholders' input and ensure continued alignment.

To properly screen ideas and solutions, you need to keep business goals in mind as well as the users’ pain points, so you can come up with win-win solutions for all your key stakeholders. Speak to users through surveys , focus groups, or five-second tests to see how they understand your idea or solution, then use those insights to decide which solution to develop.

4. Solution definition

It's time to think about how the ideas can come together. The solution definition stage is about further refining and solidifying the chosen idea or concept. The goal is to get a clear concept that designers can turn into a prototype.

During this stage, you should also conduct a feasibility assessment to determine whether you have the resources to go ahead with the solution. This means assessing your internal resources and team capacity, as well as potential revenue. You can use feedback and satisfaction surveys or conduct user interviews to gauge your target customers' opinions and address the desirability of the solution or idea.

You can also take a look at what you already know, or what others have learned to further define your solution. Here's how Sonal does it:

"I draft research questions that guide the study and address our objectives," she explains. "I also conduct a literature review and collaborate with the analytics team to gather relevant data. I define the methodology, participant recruitment criteria, and timelines, sharing them with stakeholders for feedback and alignment. Then, I create interview scripts or survey questions based on the objectives and conduct research sessions to collect data."

Like Sonal, it's vital to collaborate with other team members and stakeholders throughout the process. That way, you get their input as you define your solution and will have their buy-in when you want to make product decisions later.

5. Prototyping

Before going deep into the product development process , you should get your current or potential customers to test a prototype of your solution. Contrary to popular opinion, you should test your prototype when launching any new feature—you don’t need to wait until you have a new product to launch.

Prototyping usually involves designing physical or digital representations of the solution or concept. These can be anything from low-fidelity mockups to high-fidelity, almost fully-functional products. This stage allows you to collect insights on your product’s usability , user experience (UX) , and information architecture (IA). It also identifies any potential design flaws before the development or product launch—remember, it’ll be far more complicated and costly to make edits later.

It’s worth remembering that you can move back-and-forth between this step, validation and testing, and solution definition, until you’ve found a concept that drives customer satisfaction.

Product tip💡

Use a product research tool like Maze to test your prototype or wireframe. Share the tests with your users or access Maze’s Panel of highly qualified participants. Maze can also help you conduct UX research methods like surveys, tree testing, and card sorting.

6. Solution validation and testing

After creating a prototype, you should validate and test your solution to ensure it's effectively solving the problem and meeting the desired goals. During this step, gather feedback from your target market, personas, or potential users to identify any design flaws or areas of improvement.

To conduct proper solution or idea validation and testing, you can gather insights through user interviews, usability tests, focus groups, surveys, copy and content tests , A/B tests , and five-second tests. It's always important to get a mix of quantitative and qualitative results, but it's crucial that you conduct some sort of qualitative research at this stage, like an interview study or survey, so you're able to truly understand your customers' needs and pain points.

7. Development and deployment

The development and deployment stage of product research is when your ideas, concepts, and solutions come to life. Once you’ve validated, tested, and refined your prototype, you can involve the development team. Now’s when you can build the minimum viable product (MVP) or complete a new feature, considering your time and resource constraints.

Depending on your software development methodology, you can also choose to continuously deploy solutions as the team codes them, or wait until you've tested the different components before deploying them. For example, if the update is straightforward and has low user impact, a continuous deployment approach might be more efficient.

Continue to conduct product testing after launch so you can keep iterating and building successful products. If you’re using Maze as your continuous product discovery tool, you can use In-Product Prompts or Live Website Testing to gather insights from real users—after your solution is live.

8. Impact assessment

The last step of the product research process is to analyze the impact of your innovation. Review the results and present them to the product team and stakeholders. If you’re following a continuous research mindset, use this stage to reflect on the metrics and come up with improvement ideas to test and develop in the next sprint.

"I like to present findings in a team meeting, followed by a brainstorming session to discuss the next steps for the team based on research insights," says Sonal. This helps you continue building better products that excite your target demographic. "I often also create multiple versions of the results where I customize the report for different stakeholders to ensure they receive the insights relevant to their roles," she adds.

Product research process: What you need to know

In short, the product research process enables you to make decisions based on product-market interactions. If you want to build user-centric products, adopting a continuous product research mindset is crucial. This means taking user insights as a starting point for doing further research and informing decision-making. Your workflow should also have clear goals aligned with your high-level business and product strategy.

Throughout the product lifecycle, complete the following steps at regular intervals:

• Opportunity assessment
• Problem shaping
• Solution generation and screening
• Solution definition
• Prototyping
• Solution validation and testing
• Development and deployment
• Impact assessment

Keep reading for our chapters on how to experiment with your product to improve customer satisfaction, and how to conduct competitive product analysis to set you apart.

Frequently asked questions about the product research process

What does product research include?

Product research includes finding areas of improvement, testing ideas, and developing solutions to enhance the product experience. The product research process goes like this:

• Validation and testing

How to conduct product research?

To do product research, start by looking at the product's performance, user, and market data to come up with opportunities you want to explore. Then, talk to users to shape the problem space and generate a solution. Conduct further research to define the solution, test your prototype, and validate your assumptions. Only then can you take the final design to the development team, launch the product, and assess the impact of your innovation.

What tools do you need for product research?

These are the tools you need for product research:

• Product testing tools like Maze to test usability, capture customer feedback, and gather user sentiment at scale
• Note-taking apps like Notion to write down interview questions and answers, or keep track of your diary studies
• Video conferencing tools such as Zoom or Google Meet to interview your customers and collect feedback

Product experimentation

Product Management

How to do Product Research [Step-by-Step Guide]

Associate Product Marketer at Zeda.io.

Mahima Arora

August 7, 2023

14 mins read

Transform Insights into Impact

Build Products That Drive Revenue and Delight Customers!

An effective and robust product research process increases the chances of product success. 

Seth W. Godin, an American author, once said – ‘Don’t find customers for your products, find products for your customers.’

The quote subtly hints at the necessity of product research. By performing product research beforehand, product managers can create the ideal product for customers. 

Did you know that 90% of new product research focuses on ‘modifications’ and ‘additions’ rather than new concepts? 

But even improving or adding new features to an existing product requires a proper product research process.   

Product research enables managers to understand the current and future needs of the users. Thus, based on users’ pain points and what they are looking for, product managers can innovate products of a higher value. 

Furthermore, organizations with a strong product research process understand the market better. They stay one step ahead of the competition and survive better in the long run. 

Now that you know how important product research is, you must dive into how to do product research right away!

So, let’s begin!

10 Steps to Product Research

There’s no single product research process that fits all the product development teams. But there are certain key steps in the process that product managers must know about. 

Here are the 10 essential steps to perform a successful product research process. Follow these steps to derive valuable product insights that will guide your product development decisions.  

1. Define your research goals

Why are you performing the research?

Once product managers find an answer to the why, they can set the goals of the research. 

Set the high-level goals first. You can set these goals considering the product strategy and vision, ensuring their alignment with business objectives. 

Next, create SMART (Specific, Measurable, Attainable, Reliable, and Time-bound) goals for the product development team to focus on during each research stage. This may include;

• Performing market research for product adoption before its release
• Finding out the key areas or features of the product to be improved after the launch
• Assessing the product performance through the product’s lifecycle.

Setting clear, measurable, and time-bound goals for the product research process guides the product team’s actions. It helps them to understand what they need to do. Also, the goals help product managers to measure outcomes and make improvements where necessary.  

2. Understand your customer's needs and pain points

An effective product research process is always customer-centric. So, start engaging in user discovery. 

Understand user pain points. Start your user research even before planning the product features. Interact with your existing and potential users to identify their needs and wants. 

Performing open-ended user research will help product managers to; 

• Measure the market demand, 
• Get inspiration for the new product
• Determine the product-market fit
• Product positioning against competitors, and 
• Increase customer satisfaction. 

Since user research is a vital part of the product research process, you can check out the best product discovery questions list . 

After the user research, when product teams develop prototypes, they can start trials and ask for user feedback. Next, the insights from feedback can be used to improve the product. 

3. Perform competitor and comparative analysis

The next step in the product research process is to know the competition. 

• Start with competitive analysis . It involves reviewing the products that are directly similar to yours. For example, if a company sells smartphones, it is directly competing with other companies selling smartphones (like Samsung and Xiaomi). 
• Next, perform a comparative analysis . It involves evaluating the alternative options for a product. For instance, an automobile manufacturer can compare the safety features of multiple car models to measure the sales of each and identify the features that require improvement. 

Performing the analyses will provide insights that product teams can use to make the product better. 

4. Study the market

Besides performing competitive and comparative analyses, product managers must run thorough market research to map the available opportunities.

Here are a few ways to study the market thoroughly;

• Use the historical market records, and research reports by academic institutions, government agencies, and trade associations. 
• Observe and analyze the competitors’ strategies like advertising, pricing, and distribution of products. 
• Read up on blogs, magazines, social media posts, and other specific content related to your space.
• Run keyword research to understand what your users are looking for. This can help you generate product ideas too. 

Once product managers validate a viable market for the product and determine the market saturation, the development teams can focus on the product's USP (Unique Selling Points). 

5. Conduct research using qualitative and quantitative methods

Further, product managers can use both qualitative and quantitative methods of market research. 

Qualitative methods – The qualitative methods of market research aren’t statistically significant. These methods help product teams to understand the potential customers at a deeper level. Individual interviews, focus groups, observations or follow-me-homes, and interviews with professionals or field experts are a few qualitative methods you can utilize for market research.

Quantitative methods – Quantitative methods include conducting surveys, polls, or sending out questionnaires. Through quantitative methods, product managers study a large enough pool of respondents in their target market to have reasonable confidence in the collected data. For organizations with a limited budget, you can rely on the survey reports of other organizations in the relevant field.

6. Know the industry trends

Stay on top of the industry trends by updating your knowledge regularly. Observe the tech trends that may impact users’ expectations of your product or its viability in the long run. 

Engage with the tech cultures – read blogs, news, and magazines, listen to tech podcasts, follow the latest tech updates on social media platforms, forums, etc. Product managers can also use tools like Google Trends, Trend Hunter, etc. 

The IT teams in organizations also serve as a key source of tech information. Product managers can interact and take regular updates from them. 

The industry trend updates can also help product managers to research future projections, disruptive technologies, and the chances of product category obsolescence. Thus, with these insights, the product teams can create products that are likely to be in demand in the future. 

7. Validate product ideas

After thorough research, product teams can test ideas and solutions.

Based on the extensive research data, you can identify the possible products, their key features, or improvisations that can meet the user's needs. Further, you can perform concept testing to examine user experiences with concrete product ideas.

To start testing, identify the key users to test. Get participants for interviews, focus groups, or implement surveys, feedback tools, etc., to test the ideas with the existing users. 

Product managers can also ask questions and assess user responses. Or, they may simply explain to the users the product concept using wireframes and mock-ups.

8. Build your product and test the MVP

A crucial step in the product research process and the most conclusive market research that product managers can perform to ensure product success. It is only after a lot of effort that product teams get to the point of testing MVP (Minimal Viable Product).

Testing MVP is all about creating the MVP and trying to sell the product or the product idea to the target audience. Several types of product testing, like card sorting, tree testing, etc., tell whether or not users can navigate your product easily, to find the different functionalities they are looking for. 

Further, product managers can run a regression analysis, quality assurance, and performance testing to check the MVP functionalities. Running these tests helps the team to identify the areas where changes are needed.  

Multivariate tests and A/B tests are helpful when the user base is split into different groups and each group has different products or product features. These tests help product managers to choose the perfect iteration.

9. Derive findings and insights from the market research data 

The market research data is of no use unless you convert them into findings and insights. 

Products managers and the product team must analyze the data to find out conclusive outcomes that can support their product decisions. 

The team can then start building the final product or improvise the MVP based on the research insights. 

10. Use the analysis to guide your product strategy

The final step in the product research process is to convert the research insights into action. Cut through the noise and gather valuable customer-centric insights .

Then, you can use the research to create a strong product roadmap and strategy to guide the entire product development process. When you perform new research, ensure to compare the strategy and roadmap to keep them updated. 

Further, the research should also be used to make regular decisions, drive product backlog management , and create the basis for your product storytelling. 

7 Tips to Conduct an Effective Product Research

A strategic approach to performing the product research process is essential. But alongside the planned strategy, product managers must consider a few tips or best practices to conduct the product research successfully. 

1. Research highly-demanded products

At the initial stage, when you do not have a product concept, get inspired by the products high in demand. 

Check out trending hashtags, reviews, comments on review sites, and bestsellers list to find out the most popular products in your space. 

Here, the goal is not to imitate the product in demand. It is to keep an open mind, ascertain the demand level, and evaluate if the product idea is awesome or not. The product manager’s goal is to perform an honest evaluation and get back to brainstorming with the collected inspirations.

2. Read about similar products

When performing a competitive analysis, read reviews and case studies on the products. 

Product reviews are gold mines. You can find out what users like about the product and what they do not. Reading the reviews carefully can give you a list of the customer pain points. 

Similarly, product managers must download or buy case studies from companies that sell similar products. The case studies generally include the product-related challenges and how the company solved them. 

Evaluating reviews and case studies allows product managers to think through the potential issues and keep the solutions handy. Also, they can identify the product features that can be made better than that of the competitors. 

3. Host a focus group

Evaluate your product by bringing in people who fit your target market. Give them a product profile – what the product will look like, its features, and benefits. Then, ask relevant questions concerning what they like and dislike about the product.

Though focus groups aren’t effective all the time, they can help product managers to get an idea of what people would say about the product. 

Providing the focus group with an MVP or prototype works better. The feedback received is more valid and meaningful. 

4. Get expert product engineers

Product managers can hire product engineers to get unbiased opinions on the product prototypes. 

The experts work on a contractual basis. They evaluate the product design, and features, test prototypes, and ensure quality and usability. 

If required, product engineers can also assess the market research, build design ideas, and supervise production. 

5. Consider product marketing

Building the product is not the end of the product research process. Not overlooking product marketing is one of the best practices to follow. 

Product managers must give equal importance to product positioning and marketing strategy. They can check out the competitors to understand;

• How they promote their products
• Whether or not their marketing strategy is successful
• How to make improvements in the strategy

Further, considering the target market is a must. Try answering questions like;

• Where do they mostly shop?
• What are their interests?
• What are the social media platforms and communication channels mostly used by the target market?
• Where do they discover the products from?

Considering these aspects, the marketing strategies, campaigns, and distribution channels must be planned. 

6. Go for a soft launch

A trial or soft launch allows product managers to estimate sales. If the trial results aren’t satisfactory, they can modify the product before spending more on its marketing.

Soft launches need not be expensive. You can create a simple landing page for the product and then run a PPC or Pay per Click campaign to assess the demand. 

You can also provide a form that interested users could fill up. Explain the product to those who inquire, maintain communication, and notify them when the product is available. 

7. Continue product research

Continue your research even after the product launch . Ask for customer feedback, measure the outcomes of your marketing campaign, and track metrics like repeat purchases, new customers, etc. 

Further, track competitors too. Observe their strategies and emerging trends. Also, test new strategies like referral programs or loyalty programs. 

Product Research Tools

Building a user-centric product isn’t easy. Product managers must be equipped with the most effective tools. They must take every bit of help available to them. 

A product research tool is something that can help the product teams a great deal. It helps in making product management a more organized and structured process. Further, using these tools, product managers can get data-backed user insights and accurate research findings.

Check out the best 5 product research tools you can invest in.

Zeda.io is one of the best product management tools that you must invest in. It is a platform where you can; 

• Collect feedback, ideas, and feature requests from customers, 
• Analyze the data from a single dashboard, convert them to actionable insights, identify trends
• Plan product roadmaps , create live roadmaps, and share them with teams and customers
• Prioritize product tasks with prioritization frameworks like RICE
• Execute the product development process in collaboration with teams

In a nutshell, Zeda.io is the all-in-one product management software that allows you to build a product seamlessly and in an organized way. 

Also Read: Top AI Tools for Product Managers and Product Teams

Zendesk is a tool that helps you maintain interactions with your customers. It is a platform that allows collecting, understanding, and responding to user feedback .

Using Zendesk, product teams can listen to customer issues, develop a response plan, and deliver solutions to address their concerns.

Simply said, Zendesk ensures carrying out regular customer conversations as they are an integral part of the product research process. These customer conversations provide direct insights into customers' thoughts opinions, suggestions, and challenges. 

Thus, you can learn from customer feedback and incorporate changes, and better features in the product to ensure an incredible user experience. 

3. ProductPlan

In the product research process, the product research eventually converts to a product roadmap. It is the product roadmap that highlights the present and future priorities, workflows, product vision, and product progress. 

Once you have come across the research stage, the focus is on building the product roadmap. ProductPlan is the platform you can use to build visual roadmaps. The tool helps in maintaining flexibility, team collaboration, and efficient addressing of issues.

Here’s why you should get ProductPlan in your product research tool stack.

• It is easy to use
• It allows customizing roadmaps with lists, layouts, and timelines
• The drag-and-drop interface helps in tailoring the roadmaps according to one’s needs
• You can collaborate with teams, tag the members, and also comment within the roadmap

Another must-have product management tool, Jira ensures a hassle-free product journey from prototyping to product launch.

Jira is a project management tool that primarily helps with;

• Organizing project tasks
• Managing scrum and agile teams
• Capturing and recording software bugs 

With Jira, agile product teams can manage their workflow seamlessly. The tool offers 300+ integrations, is highly customizable, great for managing product issues, and overall effective product management. 

5. Proto.io

After you have built a product, you cannot release it directly to the market. User feedback and validation are required. So, instead of building a full-fledged product, you create an MVP or prototype with the basic features for testing the waters first. 

This is where Proto.io comes in. Proto.io is one of the leading prototyping tools that help you build a prototype quickly and easily. 

• Proto.io has a great interactive drag-and-drop interface that lets you create the prototype to test each product feature or idea based on your research. 
• It is user-friendly with integrated icons and easy image management

Thus, Proto.io increases your research efficiency. It helps you to offer customers an amazing product experience resulting in better customer satisfaction. 

Also Read: Choosing the Best Product Discovery Tool: Top 5 Picks

Final Thoughts

How to do product research is a common but complex question. Not all organizations use the same way to perform product research. But the product research process does have a few key steps that are crucial for its success. 

Throughout the process, just remember that product research is all about user research. The main goal is to understand the users, their needs, and their pain points. 

Once product managers implement the user-centric approach, they can build better products – the products that would meet the ever-changing demands of the market. Further, it will increase customer satisfaction and inspire loyalty.

With platforms like Zeda.io , your product research process can get easier. You can seamlessly perform user research using Zeda.io ’s product features like the central dashboard, prioritization framework, building live product roadmap, easy tracking, sharing roadmaps with teams and customers, etc.  

Suggested Read: The Product Management Process: 6 Essential Steps

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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14 Market Research Examples

This article was originally published in the MarketingSherpa email newsletter .

Example #1: National bank’s A/B testing

You can learn what customers want by conducting experiments on real-life customer decisions using A/B testing. When you ensure your tests do not have any validity threats, the information you garner can offer very reliable insights into customer behavior.

Here’s an example from Flint McGlaughlin, CEO of MarketingSherpa and MECLABS Institute, and the creator of its  online marketing course .

A national bank was working with MECLABS to discover how to increase the number of sign-ups for new checking accounts.

Customers who were interested in checking accounts could click on an “Open in Minutes” link on the bank’s homepage.

Creative Sample #1: Anonymized bank homepage

After clicking on the homepage link, visitors were taken to a four-question checking account selector tool.

Creative Sample #2: Original checking account landing page — account recommendation selector tool

After filling out the selector tool, visitors were taken to a results page that included a suggested package (“Best Choice”) along with a secondary option (“Second Choice”). The results page had several calls to action (CTAs). Website visitors were able to select an account and begin pre-registration (“Open Now”) or find out more information about the account (“Learn More”), go back and change their answers (“Go back and change answers”), or manually browse other checking options (“Other Checking Options”).

Creative Sample #3: Original checking account landing page — account recommendation selector tool results page

After going through the experience, the MECLABS team hypothesized that the selector tool wasn’t really delivering on the expectation the customer had after clicking on the “Open in Minutes” CTA. They created two treatments (new versions) and tested them against the control experience.

In the first treatment, the checking selector tool was removed, and instead, customers were directly presented with three account options in tabs from which customers could select.

Creative Sample #4: Checking account landing page Treatment #1

The second treatment’s landing page focused on a single product and had only one CTA. The call-to-action was similar to the CTA customers clicked on the homepage to get to this page — “Open Now.”

Creative Sample #5: Checking account landing page Treatment #2

Both treatments increased account applications compared to the control landing page experience, with Treatment #2 generating 65% more applicants at a 98% level of confidence.

Creative Sample #6: Results of bank experiment that used A/B testing

You’ll note the Level of Confidence in the results. With any research tactic or tool you use to learn about customers, you have to consider whether the information you’re getting really represents most customers, or if you’re just seeing outliers or random chance.

With a high Level of Confidence like this, it is more likely the results actually represent a true difference between the control and treatment landing pages and that the results aren’t just a random event.

The other factor to consider is — testing in and of itself will not produce results. You have to use testing as research to actually learn about the customer and then make changes to better serve the customer.

In the video How to Discover Exactly What the Customer Wants to See on the Next Click: 3 critical skills every marketer must master , McGlaughlin discussed this national bank experiment and explained how to use prioritization, identification and deduction to discover what your customers want.

This example was originally published in Marketing Research: 5 examples of discovering what customers want .

Example #2: Consumer Reports’ market intelligence research from third-party sources

The first example covers A/B testing. But keep in mind, ill-informed A/B testing isn’t market research, it’s just hoping for insights from random guesses.

In other words, A/B testing in a vacuum does not provide valuable information about customers. What you are testing is crucial, and then A/B testing is a means to help better understand whether insights you have about the customer are either validated or refuted by actual customer behavior. So it’s important to start with some research into potential customers and competitors to inform your A/B tests.

For example, when MECLABS and MarketingExperiments (sister publisher to MarketingSherpa) worked with Consumer Reports on a public, crowdsourced A/B test, we provided a market intelligence report to our audience to help inform their test suggestions.

Every successful marketing test should confirm or deny an assumption about the customer. You need enough knowledge about the customer to create marketing messages you think will be effective.

For this public experiment to help marketers improve their split testing abilities, we had a real customer to work with — donors to Consumer Reports.

To help our audience better understand the customer, the MECLABS Marketing Intelligence team created the 26-page ConsumerReports Market Intelligence Research document (which you can see for yourself at that link).

This example was originally published in Calling All Writers and Marketers: Write the most effective copy for this Consumer Reports email and win a MarketingSherpa Summit package and Consumer Reports Value Proposition Test: What you can learn from a 29% drop in clickthrough .

Example #3: Virtual event company’s conversation

What if you don’t have the budget for A/B testing? Or any of the other tactics in this article?

Well, if you’re like most people you likely have some relationships with other human beings. A significant other, friends, family, neighbors, co-workers, customers, a nemesis (“Newman!”). While conducting market research by talking to these people has several validity threats, it at least helps you get out of your own head and identify some of your blind spots.

WebBabyShower.com’s lead magnet is a PDF download of a baby shower thank you card ‘swipe file’ plus some extras. “Women want to print it out and have it where they are writing cards, not have a laptop open constantly,” said Kurt Perschke, owner, WebBabyShower.com.

That is not a throwaway quote from Perschke. That is a brilliant insight, so I want to make sure we don’t overlook it. By better understanding customer behavior, you can better serve customers and increase results.

However, you are not your customer. So you must bridge the gap between you and them.

Often you hear marketers or business leaders review an ad or discuss a marketing campaign and say, “Well, I would never read that entire ad” or “I would not be interested in that promotion.” To which I say … who cares? Who cares what you would do? If you are not in the ideal customer set, sorry to dent your ego, but you really don’t matter. Only the customer does.

Perschke is one step ahead of many marketers and business leaders because he readily understands this. “Owning a business whose customers are 95% women has been a great education for me,” he said.

So I had to ask him, how did he get this insight into his customers’ behavior? Frankly, it didn’t take complex market research. He was just aware of this disconnect he had with the customer, and he was alert for ways to bridge the gap. “To be honest, I first saw that with my wife. Then we asked a few customers, and they confirmed it’s what they did also. Writing notes by hand is viewed as a ‘non-digital’ activity and reading from a laptop kinda spoils the mood apparently,” he said.

Back to WebBabyShower. “We've seen a [more than] 100% increase in email signups using this method, which was both inexpensive and evergreen,” Perschke said.

This example was originally published in Digital Marketing: Six specific examples of incentives that worked .

Example #4: Spiceworks Ziff Davis’ research-informed content marketing

Marketing research isn’t just to inform products and advertising messages. Market research can also give your brand a leg up in another highly competitive space – content marketing.

Don’t just jump in and create content expecting it to be successful just because it’s “free.” Conducting research beforehand can help you understand what your potential audience already receives and where they might need help but are currently being served.

When Spiceworks Ziff Davis (SWZD) published its annual State of IT report, it invested months in conducting primary market research, analyzing year-over-year trends, and finally producing the actual report.

“Before getting into the nuts and bolts of writing an asset, look at market shifts and gaps that complement your business and marketing objectives. Then, you can begin to plan, research, write, review and finalize an asset,” said Priscilla Meisel, Content Marketing Director, SWZD.

This example was originally published in Marketing Writing: 3 simple tips that can help any marketer improve results (even if you’re not a copywriter) .

Example #5: Business travel company’s guerilla research

There are many established, expensive tactics you can use to better understand customers.

But if you don’t have the budget for those tactics, and don’t know any potential customers, you might want to brainstorm creative ways you can get valuable information from the right customer target set.

Here’s an example from a former client of Mitch McCasland, Founding Partner and Director, Brand Inquiry Partners. The company sold a product related to frequent business flyers and was interested in finding out information on people who travel for a living. They needed consumer feedback right away.

“I suggested that they go out to the airport with a bunch of 20-dollar bills and wait outside a gate for passengers to come off their flight,” McCasland said. When people came off the flight, they were politely asked if they would answer a few questions in exchange for the incentive (the $20). By targeting the first people off the flight they had a high likelihood of reaching the first-class passengers.

This example was originally published in Guerrilla Market Research Expert Mitch McCasland Tells How You Can Conduct Quick (and Cheap) Research .

Example #6: Intel’s market research database

When conducting market research, it is crucial to organize your data in a way that allows you to easily and quickly report on it. This is especially important for qualitative studies where you are trying to do more than just quantify the data, but need to manage it so it is easier to analyze.

Anne McClard, Senior Researcher, Doxus worked with Shauna Pettit-Brown of Intel on a research project to understand the needs of mobile application developers throughout the world.

Intel needed to be able to analyze the data from several different angles, including segment and geography, a daunting task complicated by the number of interviews, interviewers, and world languages.

“The interviews were about an hour long, and pretty substantial,” McClard says. So, she needed to build a database to organize the transcripts in a way that made sense.

Different types of data are useful for different departments within a company; once your database is organized you can sort it by various threads.

The Intel study had three different internal sponsors. "When it came to doing the analysis, we ended up creating multiple versions of the presentation targeted to individual audiences," Pettit-Brown says.

The organized database enabled her to go back into the data set to answer questions specific to the interests of the three different groups.

This example was originally published in 4 Steps to Building a Qualitative Market Research Database That Works Better .

Example #7: National security survey’s priming

When conducting market research surveys, the way you word your questions can affect customers’ response. Even the way you word previous questions can put customers in a certain mindset that will skew their answers.

For example, when people were asked if they thought the U.S. government should spend money on an anti-missile shield, the results appeared fairly conclusive. Sixty-four percent of those surveyed thought the country should and only six percent were unsure, according to Opinion Makers: An Insider Exposes the Truth Behind the Polls .

But when pollsters added the option, "...or are you unsure?" the level of uncertainty leaped from six percent to 33 percent. When they asked whether respondents would be upset if the government took the opposite course of action from their selection, 59 percent either didn’t have an opinion or didn’t mind if the government did something differently.

This is an example of how the way you word questions can change a survey’s results. You want survey answers to reflect customer’s actual sentiments that are as free of your company’s previously held biases as possible.

This example was originally published in Are Surveys Misleading? 7 Questions for Better Market Research .

Example #8: Visa USA’s approach to getting an accurate answer

As mentioned in the previous example, the way you ask customers questions can skew their responses with your own biases.

However, the way you ask questions to potential customers can also illuminate your understanding of them. Which is why companies field surveys to begin with.

“One thing you learn over time is how to structure questions so you have a greater likelihood of getting an accurate answer. For example, when we want to find out if people are paying off their bills, we'll ask them to think about the card they use most often. We then ask what the balance was on their last bill after they paid it,” said Michael Marx, VP Research Services, Visa USA.

This example was originally published in Tips from Visa USA's Market Research Expert Michael Marx .

Example #9: Hallmark’s private members-only community

Online communities are a way to interact with and learn from customers. Hallmark created a private members-only community called Idea Exchange (an idea you could replicate with a Facebook or LinkedIn Group).

The community helped the greeting cards company learn the customer’s language.

“Communities…let consumers describe issues in their own terms,” explained Tom Brailsford, Manager of Advancing Capabilities, Hallmark Cards. “Lots of times companies use jargon internally.”

At Hallmark they used to talk internally about “channels” of distribution. But consumers talk about stores, not channels. It is much clearer to ask consumers about the stores they shop in than what channels they shop.

For example, Brailsford clarified, “We say we want to nurture, inspire, and lift one’s spirits. We use those terms, and the communities have defined those terms for us. So we have learned how those things play out in their lives. It gives us a much richer vocabulary to talk about these things.”

This example was originally published in Third Year Results from Hallmark's Online Market Research Experiment .

Example #10: L'Oréal’s social media listening

If you don’t want the long-term responsibility that comes with creating an online community, you can use social media listening to understand how customers talking about your products and industry in their own language.

In 2019, L'Oréal felt the need to upgrade one of its top makeup products – L'Oréal Paris Alliance Perfect foundation. Both the formula and the product communication were outdated – multiple ingredients had emerged on the market along with competitive products made from those ingredients.

These new ingredients and products were overwhelming consumers. After implementing new formulas, the competitor brands would advertise their ingredients as the best on the market, providing almost magical results.

So the team at L'Oréal decided to research their consumers’ expectations instead of simply crafting a new formula on their own. The idea was to understand not only which active ingredients are credible among the audience, but also which particular words they use while speaking about foundations in general.

The marketing team decided to combine two research methods: social media listening and traditional questionnaires.

“For the most part, we conduct social media listening research when we need to find out what our customers say about our brand/product/topic and which words they use to do it. We do conduct traditional research as well and ask questions directly. These surveys are different because we provide a variety of readymade answers that respondents choose from. Thus, we limit them in terms of statements and their wording,” says Marina Tarandiuk, marketing research specialist, L'Oréal Ukraine.

“The key value of social media listening (SML) for us is the opportunity to collect people’s opinions that are as ‘natural’ as possible. When someone leaves a review online, they are in a comfortable environment, they use their ‘own’ language to express themselves, there is no interviewer standing next to them and potentially causing shame for their answer. The analytics of ‘natural’ and honest opinions of our customers enables us to implement the results in our communication and use the same language as them,” Tarandiuk said.

The team worked with a social media listening tool vendor to identify the most popular, in-demand ingredients discussed online and detect the most commonly used words and phrases to create a “consumer glossary.”

Questionnaires had to confirm all the hypotheses and insights found while monitoring social media. This part was performed in-house with the dedicated team. They created custom questionnaires aiming to narrow down all the data to a maximum of three variants that could become the base for the whole product line.

“One of our recent studies had a goal to find out which words our clients used to describe positive and negative qualities of [the] foundation. Due to a change in [the] product’s formula, we also decided to change its communication. Based on the opinions of our customers, we can consolidate the existing positive ideas that our clients have about the product,” Tarandiuk said.

To find the related mentions, the team monitored not only the products made by L'Oréal but also the overall category. “The search query contained both brand names and general words like foundation, texture, smell, skin, pores, etc. The problem was that this approach ended up collecting thousands of mentions, not all of which were relevant to the topic,” said Elena Teselko, content marketing manager, YouScan (L'Oréal’s social media listening tool).

So the team used artificial intelligence-based tagging that divided mentions according to the category, features, or product type.

This approach helped the team discover that customers valued such foundation features as not clogging pores, a light texture, and not spreading. Meanwhile, the most discussed and appreciated cosmetics component was hyaluronic acid.

These exact phrases, found with the help of social media monitoring, were later used for marketing communication.

Creative Sample #7: Marketing communicating for personal care company with messaging based on discoveries from market research

“Doing research and detecting audience’s interests BEFORE starting a campaign is an approach that dramatically lowers any risks and increases chances that the campaign would be appreciated by customers,” Teselko said.

This example was originally published in B2C Branding: 3 quick case studies of enhancing the brand with a better customer experience .

Example #11: Levi’s ethnographic research

In a focus group or survey, you are asking customers to explain something they may not even truly understand. Could be why they bought a product. Or what they think of your competitor.

Ethnographic research is a type of anthropology in which you go into customers’ homes or places of business and observe their actual behavior, behavior they may not understand well enough to explain to you.

While cost prohibitive to many brands, and simply unfeasible for others, it can elicit new insights into your customers.

Michael Perman, Senior Director Cultural Insights, Levi Strauss & Co. uses both quantitative and qualitative research on a broad spectrum, but when it comes to gathering consumer insight, he focuses on in-depth ethnographic research provided by partners who specialize in getting deep into the “nooks and crannies of consumer life in America and around the world.” For example, his team spends time in consumers’ homes and in their closets. They shop with consumers, looking for the reality of a consumer’s life and identifying themes that will enable designers and merchandisers to better understand and anticipate consumer needs.

Perman then puts together multi-sensory presentations that illustrate the findings of research. For example, “we might recreate a teenager’s bedroom and show what a teenage girl might have on her dresser.”

This example was originally published in How to Get Your Company to Pay Attention to Market Research Results: Tips from Levi Strauss .

Example #12: eBags’ ethnographic research

Ethnographic research isn’t confined to a physical goods brand like Levi’s. Digital brands can engage in this form of anthropology as well.

While usability testing in a lab is useful, it does miss some of the real-world environmental factors that play a part in the success of a website. Usability testing alone didn’t create a clear enough picture for Gregory Casey, User Experience Designer and Architect, eBags.

“After we had designed our mobile and tablet experience, I wanted to run some contextual user research, which basically meant seeing how people used it in the wild, seeing how people are using it in their homes. So that’s exactly what I did,” Gregory said.

He found consumers willing to open their home to him and be tested in their normal environment. This meant factors like the television, phone calls and other family members played a part in how they experienced the eBags mobile site.

“During these interview sessions, a lot of times we were interrupted by, say, a child coming over and the mother having to do something for the kid … The experience isn’t sovereign. It’s not something where they just sit down, work through a particular user flow and complete their interaction,” Gregory said.

By watching users work through the site as they would in their everyday life, Gregory got to see what parts of the site they actually use.

This example was originally published in Mobile Marketing: 4 takeaways on how to improve your mobile shopping experience beyond just responsive design .

Example #13: John Deere’s shift from product-centric market research to consumer-centric research

One of the major benefits of market research is to overcome company blind spots. However, if you start with your blind spots – i.e., a product focus – you will blunt the effectiveness of your market research.

In the past, “they’d say, Here’s the product, find out how people feel about it,” explained David van Nostrand, Manager, John Deere's Global Market Research. “A lot of companies do that.” Instead, they should be saying, “Let's start with the customers: what do they want, what do they need?”

The solution? A new in-house program called “Category Experts” brings the product-group employees over as full team members working on specific research projects with van Nostrand’s team.

These staffers handle items that don’t require a research background: scheduling, meetings, logistics, communication and vendor management. The actual task they handle is less important than the fact that they serve as human cross-pollinators, bringing consumer-centric sensibility back to their product- focused groups.

For example, if van Nostrand’s team is doing research about a vehicle, they bring in staffers from the Vehicles product groups. “The information about vehicle consumers needs to be out there in the vehicle marketing groups, not locked in here in the heads of the researchers.”

This example was originally published in How John Deere Increased Mass Consumer Market Share by Revamping its Market Research Tactics .

Example #14: LeapFrog’s market research involvement throughout product development (not just at the beginning and the end)

Market research is sometimes thought of as a practice that can either inform the development of a product, or research consumer attitudes about developed products. But what about the middle?

Once the creative people begin working on product designs, the LeapFrog research department stays involved.

They have a lab onsite where they bring moms and kids from the San Francisco Bay area to test preliminary versions of the products. “We do a lot of hands-on, informal qualitative work with kids,” said Craig Spitzer, VP Marketing Research, LeapFrog. “Can they do what they need to do to work the product? Do they go from step A to B to C, or do they go from A to C to B?”

When designing the LeapPad Learning System, for example, the prototype went through the lab “a dozen times or so,” he says.

A key challenge for the research department is keeping and building the list of thousands of families who have agreed to be on call for testing. “We've done everything from recruiting on the Internet to putting out fliers in local schools, working through employees whose kids are in schools, and milking every connection we have,” Spitzer says.

Kids who test products at the lab are compensated with a free, existing product rather than a promise of the getting the product they're testing when it is released in the future.

This example was originally published in How LeapFrog Uses Marketing Research to Launch New Products .

Related resources

The Marketer’s Blind Spot: 3 ways to overcome the marketer’s greatest obstacle to effective messaging

Get Your Free Test Discovery Tool to Help Log all the Results and Discoveries from Your Company’s Marketing Tests

Marketing Research: 5 examples of discovering what customers want

Online Marketing Tests: How do you know you’re really learning anything?

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Top 10 Product Market Research Templates with Samples and Examples

Deepika Dhaka

Product Market Research is important for businesses to plan and make good choices. It's like a strong base that helps them make smart decisions and grow steadily. Companies can learn important things by looking closely at how the market works, what customers do, and what's popular in the industry. This helps them improve their products, determine who their customers are, and make better marketing plans. 

A compelling illustration of the indispensable role of Product Market Research in achieving business success is evident in the case of Netflix. 

As the streaming industry evolved, Netflix consistently conducted thorough research to comprehend shifting viewer preferences, content consumption habits, and emerging technological trends. This proactive approach allowed the company to shift from its initial DVD rental model to a streaming service, reshaping the entertainment landscape. By analyzing data on user behavior, content engagement, and regional variations, Netflix effectively curated a diverse array of original programming that resonated with global audiences, propelling it to become a household name in the streaming sphere. This example shows how important Product Market Research is for helping companies to be flexible, creative, and stand out in tough and competitive markets.

If you're someone who is in charge of finding out what people want for a new product, you might find it hard. The biggest problem is figuring out how to do this research in a way that helps your new product succeed. Well, that's where we can help!

Here comes Product Market Research Templates!

Best Product Market Research Templates for New Product Development

This blog will walk you through the most popular templates to prepare for effective research that ensures that none of your resources are wasted. Also, the 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure. 

Let’s begin exploring these pre-designed templates!

Template 1: New Product Market Research PowerPoint Presentation

This Complete Deck is your one-stop solution for conducting thorough research for new products. It equips you with well-structured slides on the customer preferences survey form and overview slides for survey outcomes. It contains easy-to-understand graphics, pie charts, and well-formatted tables to assist you best. It will help you save time you would have otherwise wasted drafting the forms and creating the resulting structure. Download this presentation now to have a handy resource. 

Download this template

Template 2: Market Research for New Product PPT Slide

This PowerPoint Slide will help you understand and communicate the two aspects. These are the market research process and the new product development process. The market research process includes steps like primary & secondary research, concept volumetric and packaging testing, consumer usage research, advertising research pre-testing, and marketing monitoring. On the other hand, the new product development process includes identifying consumer views, product concept and packaging development, testing the product, brand positioning and advertising development, and product launch and post-launch. Download this template now for a consolidated view of these crucial processes.

(Leverage our Qualitative and Quantitative Data Analysis Templates to uncover meaningful patterns, translating data into actionable strategies. Download today for a transformative analysis experience!)

Template 3: Product Market Research Outcome Analysis

This PPT Template is ideal if you want to plan and present the product market research outcome analysis. It includes factors like growth drivers, restraints, challenges, and opportunities, which are also abbreviated as DRCO for easy recall. If you're working on a new product or business, you can use this slide to organize your thoughts and plan what you need to focus on. Download this helpful resource now!

Template 4: Budgeting for Product Launch Market Research

If you are struggling to draft a budget for your research, here’s an effective tool to the rescue. This Budgeting framework includes five columns entailing business objective, research objective, priority, methodologies, and the annual budget. This content-ready slide is your one-stop solution to all your budgeting challenges. It will help you give a crystal clear- justification for the money involved. Download it today to create an effective and efficient budget. 

Template 5: Quarterly Market Research Roadmap for Product Launch

Use this PPT Template to create an achievable and practical roadmap for your research. It encompasses a yearly roadmap divided into four quarters and covers activities such as market and community research, identifying target customers, devising a unique value proposition (USP), and determining market strategy. It also includes other tasks such as testing product and overall approach, rolling out marketing campaigns, and monitoring product life cycle. If you want to add or remove the activities as per your requirement, you can easily do it here. Download this PPT Slide now to ensure easy communication of tasks and timeline to the members helping you with the research.

Template 6: New Product Market Research Survey Form Template

Streamline your product development process with this user-friendly template, designed to gather valuable insights on customer preferences and expectations. Easily customizable, this template offers a structured approach to crafting surveys that unveil critical market trends. From identifying target audiences to refining product features, this template empowers you to make informed decisions backed by thorough research. Download this template now!

Template 7: New Product Market Research Survey Outcome Template

Get a comprehensive view of your survey results with this feature-packed slide entailing fool-proof graphics and visuals. If you use this slide, it will just need a glance to understand your customer preferences. It covers crucial questions that you need to know before developing a new product. Download this template now for informed and data-driven decision-making!

Template 8: Product Market Research Survey Result Page

This template presents your survey findings in an elaborated manner, using clear visuals to reveal customer preferences. Streamline decision-making by identifying key trends for well-informed product strategies. It encompasses essential customer details like their professions and highlights customer responses, categorizing them by country. With user-friendly graphics, this PPT template simplifies complex information, making it easily understandable. Get it today!

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Template 9: Survey Result Reporting Dashboard Template

Presenting a comprehensive report derived from a product market research survey, this slide highlights customer feedback. It encompasses vital components, including desired ingredients, new product concept assessment, and demographic insights grouped by gender, marital status, age, and education. It delves into key evaluation factors such as uniqueness, problem-solving ability, effectiveness, trend appeal, and safety. Instantly grasp the survey outcomes with this template. Download now for a concise overview of the report!

Template 10: Market Research Survey Questionnaire Template

If you are struggling to create a survey questionnaire with a well-structured format, this PPT Slide is the best choice. It contains important questions you must ask for a new product development market research, such as “What is your first impression of the product?”, “What is your household income group” and how valuable the product is for the consumer. Each of these questions is an objective type providing easy-to-choose answers for the consumer. Get it now to obtain the real response.

Conduct Your Best Research Ever

By understanding the ever-evolving needs and preferences of our customers, we fortify our foundations, innovate relentlessly, and position ourselves for success. The strategic insights gained from thorough research become the building blocks of our growth, enabling us to craft products that truly resonate with our audience. 

Whether you're a seasoned industry professional or an aspiring entrepreneur, harness the power of informed decisions by leveraging SlideTeam's meticulously crafted templates. Take the first step towards success. Download these templates now!

PS: If you are looking for Product Proposal Templates, here’s a handy guide with the most popular samples and examples.

Explore our Monthly, Semi-annual, and Annual plans here to download the premium templates on any topic.

FAQs on Product Market Research

What is product market research.

Product market research involves studying the market to understand what customers want and need, as well as how they behave. It helps businesses make informed decisions about their products and marketing strategies by collecting and analyzing data about customer preferences, trends, and competition.

How to do market research for new products?

To conduct market research for new products, you can follow these steps:

• Define Your Goals: Understand what you want to learn and achieve.
• Identify Your Target Audience: Determine who your potential customers are.
• Collect Data: Use surveys, interviews, online research, and more to gather information.
• Analyze Data: Look for patterns, preferences, and trends in the collected information.
• Evaluate Competition: Study similar products and their success in the market.
• Make Decisions: Based on your findings, make informed decisions about your new product.

What is the objective of product research?

The objective of product research is to gather insights and information that help in developing, launching, and promoting products successfully. It aims to understand customer needs, preferences, and behaviors, evaluate market demand, identify competitive advantages, and guide strategic decisions throughout the product's lifecycle.

What are the five product levels?

The five product levels represent different layers of a product's offering:

• Core Product: The basic benefit or problem-solving function that the product provides.
• Generic Product: The basic features and attributes of the product.
• Expected Product: The set of attributes that customers expect to receive when they purchase the product.
• Augmented Product: Additional features, benefits, or services that exceed customer expectations.
• Potential Product: The future possibilities and innovations that the product could offer.

Why is product research important in marketing?

Product research is vital in marketing for several reasons:

• Customer Understanding: It helps in comprehending customer needs, preferences, and behaviors.
• Innovation: Research guides the development of new and improved products.
• Market Positioning: It assists in identifying unique selling points and differentiating from competitors.
• Reduced Risk: Research minimizes the risk of product failure by aligning products with customer demands.
• Effective Marketing Strategies: Insights from research inform targeted marketing campaigns and communication.

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Market Research: A How-To Guide and Template

Discover the different types of market research, how to conduct your own market research, and use a free template to help you along the way.

MARKET RESEARCH KIT

5 Research and Planning Templates + a Free Guide on How to Use Them in Your Market Research

Updated: 02/21/24

Published: 02/21/24

Today's consumers have a lot of power. As a business, you must have a deep understanding of who your buyers are and what influences their purchase decisions.

Enter: Market Research.

Whether you're new to market research or not, I created this guide to help you conduct a thorough study of your market, target audience, competition, and more. Let’s dive in.

Table of Contents

What is market research?

Primary vs. secondary research, types of market research, how to do market research, market research report template, market research examples.

Market research is the process of gathering information about your target market and customers to verify the success of a new product, help your team iterate on an existing product, or understand brand perception to ensure your team is effectively communicating your company's value effectively.

Market research can answer various questions about the state of an industry. But if you ask me, it's hardly a crystal ball that marketers can rely on for insights on their customers.

Market researchers investigate several areas of the market, and it can take weeks or even months to paint an accurate picture of the business landscape.

However, researching just one of those areas can make you more intuitive to who your buyers are and how to deliver value that no other business is offering them right now.

How? Consider these two things:

• Your competitors also have experienced individuals in the industry and a customer base. It‘s very possible that your immediate resources are, in many ways, equal to those of your competition’s immediate resources. Seeking a larger sample size for answers can provide a better edge.
• Your customers don't represent the attitudes of an entire market. They represent the attitudes of the part of the market that is already drawn to your brand.

The market research services market is growing rapidly, which signifies a strong interest in market research as we enter 2024. The market is expected to grow from roughly $75 billion in 2021 to $90.79 billion in 2025 .

Free Market Research Kit

• SWOT Analysis Template
• Survey Template
• Focus Group Template

You're all set!

Click this link to access this resource at any time.

Why do market research?

Market research allows you to meet your buyer where they are.

As our world becomes louder and demands more of our attention, this proves invaluable.

By understanding your buyer's problems, pain points, and desired solutions, you can aptly craft your product or service to naturally appeal to them.

Market research also provides insight into the following:

• Where your target audience and current customers conduct their product or service research
• Which of your competitors your target audience looks to for information, options, or purchases
• What's trending in your industry and in the eyes of your buyer
• Who makes up your market and what their challenges are
• What influences purchases and conversions among your target audience
• Consumer attitudes about a particular topic, pain, product, or brand
• Whether there‘s demand for the business initiatives you’re investing in
• Unaddressed or underserved customer needs that can be flipped into selling opportunity
• Attitudes about pricing for a particular product or service

Ultimately, market research allows you to get information from a larger sample size of your target audience, eliminating bias and assumptions so that you can get to the heart of consumer attitudes.

As a result, you can make better business decisions.

To give you an idea of how extensive market research can get , consider that it can either be qualitative or quantitative in nature — depending on the studies you conduct and what you're trying to learn about your industry.

Qualitative research is concerned with public opinion, and explores how the market feels about the products currently available in that market.

Quantitative research is concerned with data, and looks for relevant trends in the information that's gathered from public records.

That said, there are two main types of market research that your business can conduct to collect actionable information on your products: primary research and secondary research.

Primary Research

Primary research is the pursuit of first-hand information about your market and the customers within your market.

It's useful when segmenting your market and establishing your buyer personas.

Primary market research tends to fall into one of two buckets:

• Exploratory Primary Research: This kind of primary market research normally takes place as a first step — before any specific research has been performed — and may involve open-ended interviews or surveys with small numbers of people.
• Specific Primary Research: This type of research often follows exploratory research. In specific research, you take a smaller or more precise segment of your audience and ask questions aimed at solving a suspected problem.

Secondary Research

Secondary research is all the data and public records you have at your disposal to draw conclusions from (e.g. trend reports, market statistics, industry content, and sales data you already have on your business).

Secondary research is particularly useful for analyzing your competitors . The main buckets your secondary market research will fall into include:

• Public Sources: These sources are your first and most-accessible layer of material when conducting secondary market research. They're often free to find and review — like government statistics (e.g., from the U.S. Census Bureau ).
• Commercial Sources: These sources often come in the form of pay-to-access market reports, consisting of industry insight compiled by a research agency like Pew , Gartner , or Forrester .
• Internal Sources: This is the market data your organization already has like average revenue per sale, customer retention rates, and other historical data that can help you draw conclusions on buyer needs.
• Focus Groups
• Product/ Service Use Research
• Observation-Based Research
• Buyer Persona Research
• Market Segmentation Research
• Pricing Research
• Competitive Analysis Research
• Customer Satisfaction and Loyalty Research
• Brand Awareness Research
• Campaign Research

1. Interviews

Interviews allow for face-to-face discussions so you can allow for a natural flow of conversation. Your interviewees can answer questions about themselves to help you design your buyer personas and shape your entire marketing strategy.

2. Focus Groups

Focus groups provide you with a handful of carefully-selected people that can test out your product and provide feedback. This type of market research can give you ideas for product differentiation.

3. Product/Service Use Research

Product or service use research offers insight into how and why your audience uses your product or service. This type of market research also gives you an idea of the product or service's usability for your target audience.

4. Observation-Based Research

Observation-based research allows you to sit back and watch the ways in which your target audience members go about using your product or service, what works well in terms of UX , and which aspects of it could be improved.

5. Buyer Persona Research

Buyer persona research gives you a realistic look at who makes up your target audience, what their challenges are, why they want your product or service, and what they need from your business or brand.

6. Market Segmentation Research

Market segmentation research allows you to categorize your target audience into different groups (or segments) based on specific and defining characteristics. This way, you can determine effective ways to meet their needs.

7. Pricing Research

Pricing research helps you define your pricing strategy . It gives you an idea of what similar products or services in your market sell for and what your target audience is willing to pay.

8. Competitive Analysis

Competitive analyses give you a deep understanding of the competition in your market and industry. You can learn about what's doing well in your industry and how you can separate yourself from the competition .

9. Customer Satisfaction and Loyalty Research

Customer satisfaction and loyalty research gives you a look into how you can get current customers to return for more business and what will motivate them to do so (e.g., loyalty programs , rewards, remarkable customer service).

10. Brand Awareness Research

Brand awareness research tells you what your target audience knows about and recognizes from your brand. It tells you about the associations people make when they think about your business.

11. Campaign Research

Campaign research entails looking into your past campaigns and analyzing their success among your target audience and current customers. The goal is to use these learnings to inform future campaigns.

• Define your buyer persona.
• Identify a persona group to engage.
• Prepare research questions for your market research participants.
• List your primary competitors.
• Summarize your findings.

1. Define your buyer persona.

You have to understand who your customers are and how customers in your industry make buying decisions.

This is where your buyer personas come in handy. Buyer personas — sometimes referred to as marketing personas — are fictional, generalized representations of your ideal customers.

Use a free tool to create a buyer persona that your entire company can use to market, sell, and serve better.

Don't forget to share this post!

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The Essential Guide to Doing Your Research Project

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Home » Research Project – Definition, Writing Guide and Ideas

Research Project – Definition, Writing Guide and Ideas

Table of Contents

Research Project

Definition :

Research Project is a planned and systematic investigation into a specific area of interest or problem, with the goal of generating new knowledge, insights, or solutions. It typically involves identifying a research question or hypothesis, designing a study to test it, collecting and analyzing data, and drawing conclusions based on the findings.

Types of Research Project

Types of Research Projects are as follows:

Basic Research

This type of research focuses on advancing knowledge and understanding of a subject area or phenomenon, without any specific application or practical use in mind. The primary goal is to expand scientific or theoretical knowledge in a particular field.

Applied Research

Applied research is aimed at solving practical problems or addressing specific issues. This type of research seeks to develop solutions or improve existing products, services or processes.

Action Research

Action research is conducted by practitioners and aimed at solving specific problems or improving practices in a particular context. It involves collaboration between researchers and practitioners, and often involves iterative cycles of data collection and analysis, with the goal of improving practices.

Quantitative Research

This type of research uses numerical data to investigate relationships between variables or to test hypotheses. It typically involves large-scale data collection through surveys, experiments, or secondary data analysis.

Qualitative Research

Qualitative research focuses on understanding and interpreting phenomena from the perspective of the people involved. It involves collecting and analyzing data in the form of text, images, or other non-numerical forms.

Mixed Methods Research

Mixed methods research combines elements of both quantitative and qualitative research, using multiple data sources and methods to gain a more comprehensive understanding of a phenomenon.

Longitudinal Research

This type of research involves studying a group of individuals or phenomena over an extended period of time, often years or decades. It is useful for understanding changes and developments over time.

Case Study Research

Case study research involves in-depth investigation of a particular case or phenomenon, often within a specific context. It is useful for understanding complex phenomena in their real-life settings.

Participatory Research

Participatory research involves active involvement of the people or communities being studied in the research process. It emphasizes collaboration, empowerment, and the co-production of knowledge.

Research Project Methodology

Research Project Methodology refers to the process of conducting research in an organized and systematic manner to answer a specific research question or to test a hypothesis. A well-designed research project methodology ensures that the research is rigorous, valid, and reliable, and that the findings are meaningful and can be used to inform decision-making.

There are several steps involved in research project methodology, which are described below:

Define the Research Question

The first step in any research project is to clearly define the research question or problem. This involves identifying the purpose of the research, the scope of the research, and the key variables that will be studied.

Develop a Research Plan

Once the research question has been defined, the next step is to develop a research plan. This plan outlines the methodology that will be used to collect and analyze data, including the research design, sampling strategy, data collection methods, and data analysis techniques.

Collect Data

The data collection phase involves gathering information through various methods, such as surveys, interviews, observations, experiments, or secondary data analysis. The data collected should be relevant to the research question and should be of sufficient quantity and quality to enable meaningful analysis.

Analyze Data

Once the data has been collected, it is analyzed using appropriate statistical techniques or other methods. The analysis should be guided by the research question and should aim to identify patterns, trends, relationships, or other insights that can inform the research findings.

Interpret and Report Findings

The final step in the research project methodology is to interpret the findings and report them in a clear and concise manner. This involves summarizing the results, discussing their implications, and drawing conclusions that can be used to inform decision-making.

Research Project Writing Guide

Here are some guidelines to help you in writing a successful research project:

• Choose a topic: Choose a topic that you are interested in and that is relevant to your field of study. It is important to choose a topic that is specific and focused enough to allow for in-depth research and analysis.
• Conduct a literature review : Conduct a thorough review of the existing research on your topic. This will help you to identify gaps in the literature and to develop a research question or hypothesis.
• Develop a research question or hypothesis : Based on your literature review, develop a clear research question or hypothesis that you will investigate in your study.
• Design your study: Choose an appropriate research design and methodology to answer your research question or test your hypothesis. This may include choosing a sample, selecting measures or instruments, and determining data collection methods.
• Collect data: Collect data using your chosen methods and instruments. Be sure to follow ethical guidelines and obtain informed consent from participants if necessary.
• Analyze data: Analyze your data using appropriate statistical or qualitative methods. Be sure to clearly report your findings and provide interpretations based on your research question or hypothesis.
• Discuss your findings : Discuss your findings in the context of the existing literature and your research question or hypothesis. Identify any limitations or implications of your study and suggest directions for future research.
• Write your project: Write your research project in a clear and organized manner, following the appropriate format and style guidelines for your field of study. Be sure to include an introduction, literature review, methodology, results, discussion, and conclusion.
• Revise and edit: Revise and edit your project for clarity, coherence, and accuracy. Be sure to proofread for spelling, grammar, and formatting errors.
• Cite your sources: Cite your sources accurately and appropriately using the appropriate citation style for your field of study.

Examples of Research Projects

Some Examples of Research Projects are as follows:

• Investigating the effects of a new medication on patients with a particular disease or condition.
• Exploring the impact of exercise on mental health and well-being.
• Studying the effectiveness of a new teaching method in improving student learning outcomes.
• Examining the impact of social media on political participation and engagement.
• Investigating the efficacy of a new therapy for a specific mental health disorder.
• Exploring the use of renewable energy sources in reducing carbon emissions and mitigating climate change.
• Studying the effects of a new agricultural technique on crop yields and environmental sustainability.
• Investigating the effectiveness of a new technology in improving business productivity and efficiency.
• Examining the impact of a new public policy on social inequality and access to resources.
• Exploring the factors that influence consumer behavior in a specific market.

Characteristics of Research Project

Here are some of the characteristics that are often associated with research projects:

• Clear objective: A research project is designed to answer a specific question or solve a particular problem. The objective of the research should be clearly defined from the outset.
• Systematic approach: A research project is typically carried out using a structured and systematic approach that involves careful planning, data collection, analysis, and interpretation.
• Rigorous methodology: A research project should employ a rigorous methodology that is appropriate for the research question being investigated. This may involve the use of statistical analysis, surveys, experiments, or other methods.
• Data collection : A research project involves collecting data from a variety of sources, including primary sources (such as surveys or experiments) and secondary sources (such as published literature or databases).
• Analysis and interpretation : Once the data has been collected, it needs to be analyzed and interpreted. This involves using statistical techniques or other methods to identify patterns or relationships in the data.
• Conclusion and implications : A research project should lead to a clear conclusion that answers the research question. It should also identify the implications of the findings for future research or practice.
• Communication: The results of the research project should be communicated clearly and effectively, using appropriate language and visual aids, to a range of audiences, including peers, stakeholders, and the wider public.

Importance of Research Project

Research projects are an essential part of the process of generating new knowledge and advancing our understanding of various fields of study. Here are some of the key reasons why research projects are important:

• Advancing knowledge : Research projects are designed to generate new knowledge and insights into particular topics or questions. This knowledge can be used to inform policies, practices, and decision-making processes across a range of fields.
• Solving problems: Research projects can help to identify solutions to real-world problems by providing a better understanding of the causes and effects of particular issues.
• Developing new technologies: Research projects can lead to the development of new technologies or products that can improve people’s lives or address societal challenges.
• Improving health outcomes: Research projects can contribute to improving health outcomes by identifying new treatments, diagnostic tools, or preventive strategies.
• Enhancing education: Research projects can enhance education by providing new insights into teaching and learning methods, curriculum development, and student learning outcomes.
• Informing public policy : Research projects can inform public policy by providing evidence-based recommendations and guidance on issues related to health, education, environment, social justice, and other areas.
• Enhancing professional development : Research projects can enhance the professional development of researchers by providing opportunities to develop new skills, collaborate with colleagues, and share knowledge with others.

Research Project Ideas

Following are some Research Project Ideas:

Field: Psychology

• Investigating the impact of social support on coping strategies among individuals with chronic illnesses.
• Exploring the relationship between childhood trauma and adult attachment styles.
• Examining the effects of exercise on cognitive function and brain health in older adults.
• Investigating the impact of sleep deprivation on decision making and risk-taking behavior.
• Exploring the relationship between personality traits and leadership styles in the workplace.
• Examining the effectiveness of cognitive-behavioral therapy (CBT) for treating anxiety disorders.
• Investigating the relationship between social comparison and body dissatisfaction in young women.
• Exploring the impact of parenting styles on children’s emotional regulation and behavior.
• Investigating the effectiveness of mindfulness-based interventions for treating depression.
• Examining the relationship between childhood adversity and later-life health outcomes.

Field: Economics

• Analyzing the impact of trade agreements on economic growth in developing countries.
• Examining the effects of tax policy on income distribution and poverty reduction.
• Investigating the relationship between foreign aid and economic development in low-income countries.
• Exploring the impact of globalization on labor markets and job displacement.
• Analyzing the impact of minimum wage laws on employment and income levels.
• Investigating the effectiveness of monetary policy in managing inflation and unemployment.
• Examining the relationship between economic freedom and entrepreneurship.
• Analyzing the impact of income inequality on social mobility and economic opportunity.
• Investigating the role of education in economic development.
• Examining the effectiveness of different healthcare financing systems in promoting health equity.

Field: Sociology

• Investigating the impact of social media on political polarization and civic engagement.
• Examining the effects of neighborhood characteristics on health outcomes.
• Analyzing the impact of immigration policies on social integration and cultural diversity.
• Investigating the relationship between social support and mental health outcomes in older adults.
• Exploring the impact of income inequality on social cohesion and trust.
• Analyzing the effects of gender and race discrimination on career advancement and pay equity.
• Investigating the relationship between social networks and health behaviors.
• Examining the effectiveness of community-based interventions for reducing crime and violence.
• Analyzing the impact of social class on cultural consumption and taste.
• Investigating the relationship between religious affiliation and social attitudes.

Field: Computer Science

• Developing an algorithm for detecting fake news on social media.
• Investigating the effectiveness of different machine learning algorithms for image recognition.
• Developing a natural language processing tool for sentiment analysis of customer reviews.
• Analyzing the security implications of blockchain technology for online transactions.
• Investigating the effectiveness of different recommendation algorithms for personalized advertising.
• Developing an artificial intelligence chatbot for mental health counseling.
• Investigating the effectiveness of different algorithms for optimizing online advertising campaigns.
• Developing a machine learning model for predicting consumer behavior in online marketplaces.
• Analyzing the privacy implications of different data sharing policies for online platforms.
• Investigating the effectiveness of different algorithms for predicting stock market trends.

Field: Education

• Investigating the impact of teacher-student relationships on academic achievement.
• Analyzing the effectiveness of different pedagogical approaches for promoting student engagement and motivation.
• Examining the effects of school choice policies on academic achievement and social mobility.
• Investigating the impact of technology on learning outcomes and academic achievement.
• Analyzing the effects of school funding disparities on educational equity and achievement gaps.
• Investigating the relationship between school climate and student mental health outcomes.
• Examining the effectiveness of different teaching strategies for promoting critical thinking and problem-solving skills.
• Investigating the impact of social-emotional learning programs on student behavior and academic achievement.
• Analyzing the effects of standardized testing on student motivation and academic achievement.

Field: Environmental Science

• Investigating the impact of climate change on species distribution and biodiversity.
• Analyzing the effectiveness of different renewable energy technologies in reducing carbon emissions.
• Examining the impact of air pollution on human health outcomes.
• Investigating the relationship between urbanization and deforestation in developing countries.
• Analyzing the effects of ocean acidification on marine ecosystems and biodiversity.
• Investigating the impact of land use change on soil fertility and ecosystem services.
• Analyzing the effectiveness of different conservation policies and programs for protecting endangered species and habitats.
• Investigating the relationship between climate change and water resources in arid regions.
• Examining the impact of plastic pollution on marine ecosystems and biodiversity.
• Investigating the effects of different agricultural practices on soil health and nutrient cycling.

Field: Linguistics

• Analyzing the impact of language diversity on social integration and cultural identity.
• Investigating the relationship between language and cognition in bilingual individuals.
• Examining the effects of language contact and language change on linguistic diversity.
• Investigating the role of language in shaping cultural norms and values.
• Analyzing the effectiveness of different language teaching methodologies for second language acquisition.
• Investigating the relationship between language proficiency and academic achievement.
• Examining the impact of language policy on language use and language attitudes.
• Investigating the role of language in shaping gender and social identities.
• Analyzing the effects of dialect contact on language variation and change.
• Investigating the relationship between language and emotion expression.

Field: Political Science

• Analyzing the impact of electoral systems on women’s political representation.
• Investigating the relationship between political ideology and attitudes towards immigration.
• Examining the effects of political polarization on democratic institutions and political stability.
• Investigating the impact of social media on political participation and civic engagement.
• Analyzing the effects of authoritarianism on human rights and civil liberties.
• Investigating the relationship between public opinion and foreign policy decisions.
• Examining the impact of international organizations on global governance and cooperation.
• Investigating the effectiveness of different conflict resolution strategies in resolving ethnic and religious conflicts.
• Analyzing the effects of corruption on economic development and political stability.
• Investigating the role of international law in regulating global governance and human rights.

Field: Medicine

• Investigating the impact of lifestyle factors on chronic disease risk and prevention.
• Examining the effectiveness of different treatment approaches for mental health disorders.
• Investigating the relationship between genetics and disease susceptibility.
• Analyzing the effects of social determinants of health on health outcomes and health disparities.
• Investigating the impact of different healthcare delivery models on patient outcomes and cost effectiveness.
• Examining the effectiveness of different prevention and treatment strategies for infectious diseases.
• Investigating the relationship between healthcare provider communication skills and patient satisfaction and outcomes.
• Analyzing the effects of medical error and patient safety on healthcare quality and outcomes.
• Investigating the impact of different pharmaceutical pricing policies on access to essential medicines.
• Examining the effectiveness of different rehabilitation approaches for improving function and quality of life in individuals with disabilities.

Field: Anthropology

• Analyzing the impact of colonialism on indigenous cultures and identities.
• Investigating the relationship between cultural practices and health outcomes in different populations.
• Examining the effects of globalization on cultural diversity and cultural exchange.
• Investigating the role of language in cultural transmission and preservation.
• Analyzing the effects of cultural contact on cultural change and adaptation.
• Investigating the impact of different migration policies on immigrant integration and acculturation.
• Examining the role of gender and sexuality in cultural norms and values.
• Investigating the impact of cultural heritage preservation on tourism and economic development.
• Analyzing the effects of cultural revitalization movements on indigenous communities.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Products of Research: Publication and Beyond

• First Online: 23 June 2020

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• Carlo Ghezzi 2  

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This chapter discusses the products of research and their dissemination. It focuses on scientific publications and on the peer review process that guarantees their quality. It also discusses other forms of dissemination that are becoming quite common, like artifacts and datasets. It describes the publication world, which includes publishers and professional societies. It also is discusses the current trend towards open science and in particular open access to publications.

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Some journals have been investing considerable efforts to try to counter this phenomenon and compete with the conferences that decide about acceptance/rejection in three to 4 months.

This is just a random list of buzzwords, probably automatically extracted from my previous papers.

Conflict of interests is discussed at length in Chap. 6 .

Unfortunately, reality shows that often these deadlines are not respected.

In some cases, papers may be conditionally accepted, subject to certain modifications to be made in the final manuscript.

Patents may also be generated to protect a researcher’s invention. I provide comments on patents later in this section.

Fees vary a lot depending on the publisher and on the journal. For example, the publishing fees of a well-known publisher range from US$ 150 to US$ 6000.

The term platinum open access is sometimes used to refer to gold open access journals that do not charge any author fees. They are usually financed by a university or research organization.

Unlike Gold open access, the copyright for these papers is usually owned by the publisher and there are restrictions as to how the work can be reused.

The full text may be found here https://www.acm.org/publications/policies/copyright-policy .

ACM: Artifact review and badging URL https://www.acm.org/publications/policies/artifact-review-badging

Krishnamurthi, S., Vitek, J.: The real software crisis: Repeatability as a core value. Commun. ACM 58 (3), 34–36 (2015). https://doi.org/10.1145/2658987 . URL http://doi.acm.org/10.1145/2658987

Ramsey, N.: Learn technical writing in two hours per week (2006). URL https://www.cs.tufts.edu/~nr/pubs/two-abstract.html

for Research & Innovation, E.C.D.G.: H2020 Programme – Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020 (2017). URL http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf

Strunk, W., White, E.: The Elements of Style. Longman (2009)

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Suber, P.: Open Access. The MIT Press (2012)

Zobel, J.: Writing for Computer Science, 3rd edn. Springer Publishing Company, Incorporated (2015)

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Ghezzi, C. (2020). The Products of Research: Publication and Beyond. In: Being a Researcher. Springer, Cham. https://doi.org/10.1007/978-3-030-45157-8_3

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The Ultimate 10 Product Research Tools for New Product Development

Starting a SaaS business nowadays seems easier, with plenty of no-code platforms and strong communities for the support online. All it takes is a good idea and some funds to put it all together. But you may be forgetting the most important part - product research.

To find out if your idea can be successful, you must not forget about your product: how it solves your customers’ pain points and whether they find it useful or not. Product research is an ongoing process, and no matter how big or successful you get, you should always strive to make improvements.

Besides painstakingly long customer interviews, some better ways to do product research. Here are some tools that can make product research quick and easy so you can deliver more value for your customers.

The best way to find out what customers think about your product? Just ask them.

It sounds simple, and with Survicate , it is simple. Our survey software lets you communicate with your customers and collect valuable feedback through many survey types, including the most popular such as NPS, CSAT, and CES. Or surveys that help you build successful product roadmap, like our Requests for new features survey template below:

Survicate can be embedded on your website or in your product, or you can choose to send surveys through emails. The essence is the same - you select a survey type by grabbing a survey template and asking the questions you need to refine your product.

The great thing about surveys is that you can use them for various stages in the sales funnel. For example, you can do a market research survey before launching a product, a CES survey to see if it’s easy to sign up, an NPS survey to gauge your customers’ attitude towards you, and more.

As you’ll read in a minute, Survicate integrates with all your favorite software , including email marketing tools, CRMs, live chat tools, and many others.

There is a 10-day free tria l with all Business plan features and up to 25 survey responses. Paid plans start at $117 per month.

Sounds like something your product could use? Sign up for your free trial today!

The biggest issue with feedback is getting feedback from your customers. They won’t complain to you unless they’re happy. This makes things difficult.

Fullstory allows collecting feedback without actually asking customers to tell you anything. The first way to do so is by tracking user session recordings. 

With Fullstory, you can get session recordings from individual website visitors on your landing pages or within your product. You can use these insights to determine any problems with your UI or UX and to see your customers' paths to get to a certain feature.

That’s one part of the story (pun intended), and the other is the heatmaps. Using this feature, you can analyze the performance of individual pages to see where users spend the most time and which parts they steer clear from.

Suppose you’re wondering about pricing - tough luck because Fullstory does not list any prices on their website. There are two plans; the only way to learn more about each is to sign up for a product demo. A free 14-day trial is available.

It’s hard not to be aware of Intercom, the biggest player in the customer communication market. And despite their increasingly high prices, Intercom delivers an amazing product that they now call the “engagement OS.”

In short, Intercom is an omnichannel communication system that lets your entire team get in touch with your customers. Whether through emails, social media chats, live chat, in-product messages, or something else, all your customer communications go through one main channel for marketing, sales, and customer support.

This is good enough, but Intercom's amazing wealth of integrations for product research stand out. For example, you can integrate Survicate surveys in your Intercom live chat to collect feedback immediately from your product or website. All the information goes straight into your Intercom records.

The pricing plans are not disclosed for larger companies, but you can get a Starter plan for as little as $74 per month, which is a huge value. Beware, though, as pricing scales up as you add new users.

If you want to combine Intercom with the power of surveys, you’ll love our Survicate integration with Intercom . 

Before project management tools became all the rage, Trello was the king of tasks and projects. And even today, when more elaborate solutions like ClickUp exist, Trello is one of the easiest and most intuitive ways to manage projects.

It has a great feature for roadmaps where you can use Trello boards and cards as a roadmap for future product changes - new features or bug fixes. If you’re collecting feedback, it needs to be managed and organized in one spot, and Trello is a great fit for this.

How do you go about this? Well, you could add new cards and comments manually, or you could connect Survicate and Trello . For each new survey answer, you can trigger an action in Trello, such as a new comment or a card, so all of your feedback from Survicate is neatly ordered and in one place.

Trello is pretty affordable, as you can get the cheapest plan for as little as $5 per user per month. This gives you access to unlimited boards and unlimited storage space, which is a tough offer to beat.

A free plan limits you to 10 boards and unlimited files up to 10MB per file. And realistically speaking, the free plan should be more than enough for your basic product research needs.

Productboard

If you’ve done any research on product management before, you’ve heard of Productboard , as it is one of the oldest and most popular tools in this space. Aimed at bigger companies and enterprise audiences, its main use is to collect, manage and organize product feedback .

The most appealing part of Productboard is the ability to centralize feedback . That means connecting your Zendesk, Intercom, emails, social media inboxes, and all other types of product feedback into a single Productboard repository. After that, it gets even easier.

You can prioritize and manage your feedback and determine which product features need to be released and what needs to be scrapped. Besides building a backlog, you can also use it to build detailed product roadmaps to show your customers what you have in store for the upcoming months.

When you consider its advanced features, Productboard is not overly expensive. The most affordable plan is $20 per maker per month, but it only gives you access to product feedback collection and roadmaps. To prioritize and plan, you must pay $80 per maker per month for the more expensive plan.

A free trial is available if you want to give it a test drive. Overall, it’s a very capable tool that can get expensive if you have a large number of people on your product team.

Mailgun is a platform dedicated to delivering emails, and they’re one of the best in the business. Unlike Mailchimp or similar competitors, their focus is on transactional emails. If you want to send product emails, you’re going to look beyond the standard tools and go for something built for the purpose.

You can use it to send product feedback emails when a certain action happens. For example, when someone tries a new feature in your product, they can get an email asking about their impressions.

The way to do that is fairly simple - just use the Mailgun and Survicate integration so you can get in touch with any person on your mailing list in a few clicks.

Mailgun has a free trial that gets you up to 5,000 emails per month, and paid plans start at $35 monthly with up to 50,000 emails.

If you want to analyze what people do on your website and in your product, Google Analytics will be the default choice. But for true product research, you need more in-depth insights and don’t want to spend months interpreting the data Google Analytics throws at you.

With SaaS products in mind, Mixpanel helps you understand what customers do with your product. You can find out if customers are dropping off the product, where it’s happening, and why. You can use it to uncover how many people use a certain feature or aspect of your product so you can drive better product adoption.

Since your customers are already there, you can find common traits - which customers stick and become power users and which groups are more likely to drop off and churn. You can also integrate Survicate and Mixpanel , so your survey results are sent to Mixpanel as user events, making product feedback collection easier.

You can get a free plan with up to 100k monthly users, but you won’t save your reports. After you've completed your trial, you can go for a paid plan, which starts at $25 per month.

Although it’s technically not a product research tool, Zapier allows product feedback from various sources without breaking a sweat. Some of the tools we mentioned above (such as Trello) can be used with Zapier to get feedback from customer-facing tools like Survicate.

You may have heard about Zapier before, but its main selling point is automation and the ability to connect two or more tools. In short, you can get data from one app to another even if there is no native integration available. You can do this with Survicate too .

The list of things that you can do is practically limitless. A good deal of integrations is already available in Survicate, so a Zapier plan might not be necessary.

However, you can get the basic plan for $19.99 per month, which should be good enough for 750 tasks. How you use the app and collect feedback could be enough to get you started.

What does the world’s favorite business communication app do with product research? The reason is simple - Slack is a tool most of us use daily and can’t check. While Slack does not do much product research, it’s the ideal place to receive new product feedback.

Many product research tools have great Slack integrations, including Survicate . When you run different survey types in our tool, you can choose to send new answers to a dedicated Slack channel. And that’s not all.

You can choose a specific channel you want to receive survey responses. You can select which specific surveys have their answers in this channel, as well as what kind of answers you want to receive. For example, you can only choose to receive detractors from NPS surveys.

Slack has a generous free plan, but you will soon encounter issues as your data stays visible for 90 days only. Paid plans start at $7.25 per user per month, which is very little compared to the value you get - both out of the tool and the integrations.

Jira Service Desk

A part of the Atlassian family of products, Jira is one of the most popular projects management apps today. With the right tools, Jira can be a great resource for product research. If you’re a fan of agile sprint planning, this is the tool your developers and product managers will love.

For example, you can connect Survicate and Jira (through Zapier), so new survey responses from your customers come in as requests on a Jira board. Since your best feature requests and bug reports will go to Jira anyways, this is the ideal shortcut for collecting feedback from your most valued customers.

Jira plans start at $7.50 per user, and you’re forced to add 10 users from the very start, which may be too much for your initial needs. A free trial is available too.

Wrapping up

Product research does not have to be a complex and painstaking task. All it takes is a plan and the right set of tools, and with just a fraction of your time, you can collect actionable insights from your customers.

To get there, we suggest giving Survicate a try. Use our surveys to get the product feedback you need today to make smarter decisions tomorrow. Sign up today to get started!

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• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

Tieman, D. et al. A chemical genetic roadmap to improved tomato flavor. Science 355 , 391–394 (2017).

Article   ADS   CAS   PubMed   Google Scholar  

Plutowska, B. & Wardencki, W. Application of gas chromatography–olfactometry (GC–O) in analysis and quality assessment of alcoholic beverages – A review. Food Chem. 107 , 449–463 (2008).

Article   CAS   Google Scholar  

Legin, A., Rudnitskaya, A., Seleznev, B. & Vlasov, Y. Electronic tongue for quality assessment of ethanol, vodka and eau-de-vie. Anal. Chim. Acta 534 , 129–135 (2005).

Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P. & Rayappan, J. B. B. Electronic noses for food quality: A review. J. Food Eng. 144 , 103–111 (2015).

Ahn, Y.-Y., Ahnert, S. E., Bagrow, J. P. & Barabási, A.-L. Flavor network and the principles of food pairing. Sci. Rep. 1 , 196 (2011).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bartoshuk, L. M. & Klee, H. J. Better fruits and vegetables through sensory analysis. Curr. Biol. 23 , R374–R378 (2013).

Article   CAS   PubMed   Google Scholar  

Piggott, J. R. Design questions in sensory and consumer science. Food Qual. Prefer. 3293 , 217–220 (1995).

Article   Google Scholar  

Kermit, M. & Lengard, V. Assessing the performance of a sensory panel-panellist monitoring and tracking. J. Chemom. 19 , 154–161 (2005).

Cook, D. J., Hollowood, T. A., Linforth, R. S. T. & Taylor, A. J. Correlating instrumental measurements of texture and flavour release with human perception. Int. J. Food Sci. Technol. 40 , 631–641 (2005).

Chinchanachokchai, S., Thontirawong, P. & Chinchanachokchai, P. A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations. J. Retail. Consum. Serv. 61 , 1–12 (2021).

Ross, C. F. Sensory science at the human-machine interface. Trends Food Sci. Technol. 20 , 63–72 (2009).

Chambers, E. IV & Koppel, K. Associations of volatile compounds with sensory aroma and flavor: The complex nature of flavor. Molecules 18 , 4887–4905 (2013).

Pinu, F. R. Metabolomics—The new frontier in food safety and quality research. Food Res. Int. 72 , 80–81 (2015).

Danezis, G. P., Tsagkaris, A. S., Brusic, V. & Georgiou, C. A. Food authentication: state of the art and prospects. Curr. Opin. Food Sci. 10 , 22–31 (2016).

Shepherd, G. M. Smell images and the flavour system in the human brain. Nature 444 , 316–321 (2006).

Meilgaard, M. C. Prediction of flavor differences between beers from their chemical composition. J. Agric. Food Chem. 30 , 1009–1017 (1982).

Xu, L. et al. Widespread receptor-driven modulation in peripheral olfactory coding. Science 368 , eaaz5390 (2020).

Kupferschmidt, K. Following the flavor. Science 340 , 808–809 (2013).

Billesbølle, C. B. et al. Structural basis of odorant recognition by a human odorant receptor. Nature 615 , 742–749 (2023).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Smith, B. Perspective: Complexities of flavour. Nature 486 , S6–S6 (2012).

Pfister, P. et al. Odorant receptor inhibition is fundamental to odor encoding. Curr. Biol. 30 , 2574–2587 (2020).

Moskowitz, H. W., Kumaraiah, V., Sharma, K. N., Jacobs, H. L. & Sharma, S. D. Cross-cultural differences in simple taste preferences. Science 190 , 1217–1218 (1975).

Eriksson, N. et al. A genetic variant near olfactory receptor genes influences cilantro preference. Flavour 1 , 22 (2012).

Ferdenzi, C. et al. Variability of affective responses to odors: Culture, gender, and olfactory knowledge. Chem. Senses 38 , 175–186 (2013).

Article   PubMed   Google Scholar  

Lawless, H. T. & Heymann, H. Sensory evaluation of food: Principles and practices. (Springer, New York, NY). https://doi.org/10.1007/978-1-4419-6488-5 (2010).

Colantonio, V. et al. Metabolomic selection for enhanced fruit flavor. Proc. Natl. Acad. Sci. 119 , e2115865119 (2022).

Fritz, F., Preissner, R. & Banerjee, P. VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds. Nucleic Acids Res 49 , W679–W684 (2021).

Tuwani, R., Wadhwa, S. & Bagler, G. BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules. Sci. Rep. 9 , 1–13 (2019).

Dagan-Wiener, A. et al. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure. Sci. Rep. 7 , 1–13 (2017).

Pallante, L. et al. Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Sci. Rep. 12 , 1–11 (2022).

Malavolta, M. et al. A survey on computational taste predictors. Eur. Food Res. Technol. 248 , 2215–2235 (2022).

Lee, B. K. et al. A principal odor map unifies diverse tasks in olfactory perception. Science 381 , 999–1006 (2023).

Mayhew, E. J. et al. Transport features predict if a molecule is odorous. Proc. Natl. Acad. Sci. 119 , e2116576119 (2022).

Niu, Y. et al. Sensory evaluation of the synergism among ester odorants in light aroma-type liquor by odor threshold, aroma intensity and flash GC electronic nose. Food Res. Int. 113 , 102–114 (2018).

Yu, P., Low, M. Y. & Zhou, W. Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends Food Sci. Technol. 71 , 202–215 (2018).

Oladokun, O. et al. The impact of hop bitter acid and polyphenol profiles on the perceived bitterness of beer. Food Chem. 205 , 212–220 (2016).

Linforth, R., Cabannes, M., Hewson, L., Yang, N. & Taylor, A. Effect of fat content on flavor delivery during consumption: An in vivo model. J. Agric. Food Chem. 58 , 6905–6911 (2010).

Guo, S., Na Jom, K. & Ge, Y. Influence of roasting condition on flavor profile of sunflower seeds: A flavoromics approach. Sci. Rep. 9 , 11295 (2019).

Ren, Q. et al. The changes of microbial community and flavor compound in the fermentation process of Chinese rice wine using Fagopyrum tataricum grain as feedstock. Sci. Rep. 9 , 3365 (2019).

Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning. (Springer, New York, NY). https://doi.org/10.1007/978-0-387-21606-5 (2001).

Dietz, C., Cook, D., Huismann, M., Wilson, C. & Ford, R. The multisensory perception of hop essential oil: a review. J. Inst. Brew. 126 , 320–342 (2020).

CAS   Google Scholar  

Roncoroni, Miguel & Verstrepen, Kevin Joan. Belgian Beer: Tested and Tasted. (Lannoo, 2018).

Meilgaard, M. Flavor chemistry of beer: Part II: Flavor and threshold of 239 aroma volatiles. in (1975).

Bokulich, N. A. & Bamforth, C. W. The microbiology of malting and brewing. Microbiol. Mol. Biol. Rev. MMBR 77 , 157–172 (2013).

Dzialo, M. C., Park, R., Steensels, J., Lievens, B. & Verstrepen, K. J. Physiology, ecology and industrial applications of aroma formation in yeast. FEMS Microbiol. Rev. 41 , S95–S128 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Datta, A. et al. Computer-aided food engineering. Nat. Food 3 , 894–904 (2022).

American Society of Brewing Chemists. Beer Methods. (American Society of Brewing Chemists, St. Paul, MN, U.S.A.).

Olaniran, A. O., Hiralal, L., Mokoena, M. P. & Pillay, B. Flavour-active volatile compounds in beer: production, regulation and control. J. Inst. Brew. 123 , 13–23 (2017).

Verstrepen, K. J. et al. Flavor-active esters: Adding fruitiness to beer. J. Biosci. Bioeng. 96 , 110–118 (2003).

Meilgaard, M. C. Flavour chemistry of beer. part I: flavour interaction between principal volatiles. Master Brew. Assoc. Am. Tech. Q 12 , 107–117 (1975).

Briggs, D. E., Boulton, C. A., Brookes, P. A. & Stevens, R. Brewing 227–254. (Woodhead Publishing). https://doi.org/10.1533/9781855739062.227 (2004).

Bossaert, S., Crauwels, S., De Rouck, G. & Lievens, B. The power of sour - A review: Old traditions, new opportunities. BrewingScience 72 , 78–88 (2019).

Google Scholar  

Verstrepen, K. J. et al. Flavor active esters: Adding fruitiness to beer. J. Biosci. Bioeng. 96 , 110–118 (2003).

Snauwaert, I. et al. Microbial diversity and metabolite composition of Belgian red-brown acidic ales. Int. J. Food Microbiol. 221 , 1–11 (2016).

Spitaels, F. et al. The microbial diversity of traditional spontaneously fermented lambic beer. PLoS ONE 9 , e95384 (2014).

Blanco, C. A., Andrés-Iglesias, C. & Montero, O. Low-alcohol Beers: Flavor Compounds, Defects, and Improvement Strategies. Crit. Rev. Food Sci. Nutr. 56 , 1379–1388 (2016).

Jackowski, M. & Trusek, A. Non-Alcohol. beer Prod. – Overv. 20 , 32–38 (2018).

Takoi, K. et al. The contribution of geraniol metabolism to the citrus flavour of beer: Synergy of geraniol and β-citronellol under coexistence with excess linalool. J. Inst. Brew. 116 , 251–260 (2010).

Kroeze, J. H. & Bartoshuk, L. M. Bitterness suppression as revealed by split-tongue taste stimulation in humans. Physiol. Behav. 35 , 779–783 (1985).

Mennella, J. A. et al. A spoonful of sugar helps the medicine go down”: Bitter masking bysucrose among children and adults. Chem. Senses 40 , 17–25 (2015).

Wietstock, P., Kunz, T., Perreira, F. & Methner, F.-J. Metal chelation behavior of hop acids in buffered model systems. BrewingScience 69 , 56–63 (2016).

Sancho, D., Blanco, C. A., Caballero, I. & Pascual, A. Free iron in pale, dark and alcohol-free commercial lager beers. J. Sci. Food Agric. 91 , 1142–1147 (2011).

Rodrigues, H. & Parr, W. V. Contribution of cross-cultural studies to understanding wine appreciation: A review. Food Res. Int. 115 , 251–258 (2019).

Korneva, E. & Blockeel, H. Towards better evaluation of multi-target regression models. in ECML PKDD 2020 Workshops (eds. Koprinska, I. et al.) 353–362 (Springer International Publishing, Cham, 2020). https://doi.org/10.1007/978-3-030-65965-3_23 .

Gastón Ares. Mathematical and Statistical Methods in Food Science and Technology. (Wiley, 2013).

Grinsztajn, L., Oyallon, E. & Varoquaux, G. Why do tree-based models still outperform deep learning on tabular data? Preprint at http://arxiv.org/abs/2207.08815 (2022).

Gries, S. T. Statistics for Linguistics with R: A Practical Introduction. in Statistics for Linguistics with R (De Gruyter Mouton, 2021). https://doi.org/10.1515/9783110718256 .

Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2 , 56–67 (2020).

Ickes, C. M. & Cadwallader, K. R. Effects of ethanol on flavor perception in alcoholic beverages. Chemosens. Percept. 10 , 119–134 (2017).

Kato, M. et al. Influence of high molecular weight polypeptides on the mouthfeel of commercial beer. J. Inst. Brew. 127 , 27–40 (2021).

Wauters, R. et al. Novel Saccharomyces cerevisiae variants slow down the accumulation of staling aldehydes and improve beer shelf-life. Food Chem. 398 , 1–11 (2023).

Li, H., Jia, S. & Zhang, W. Rapid determination of low-level sulfur compounds in beer by headspace gas chromatography with a pulsed flame photometric detector. J. Am. Soc. Brew. Chem. 66 , 188–191 (2008).

Dercksen, A., Laurens, J., Torline, P., Axcell, B. C. & Rohwer, E. Quantitative analysis of volatile sulfur compounds in beer using a membrane extraction interface. J. Am. Soc. Brew. Chem. 54 , 228–233 (1996).

Molnar, C. Interpretable Machine Learning: A Guide for Making Black-Box Models Interpretable. (2020).

Zhao, Q. & Hastie, T. Causal interpretations of black-box models. J. Bus. Econ. Stat. Publ. Am. Stat. Assoc. 39 , 272–281 (2019).

Article   MathSciNet   Google Scholar  

Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning. (Springer, 2019).

Labrado, D. et al. Identification by NMR of key compounds present in beer distillates and residual phases after dealcoholization by vacuum distillation. J. Sci. Food Agric. 100 , 3971–3978 (2020).

Lusk, L. T., Kay, S. B., Porubcan, A. & Ryder, D. S. Key olfactory cues for beer oxidation. J. Am. Soc. Brew. Chem. 70 , 257–261 (2012).

Gonzalez Viejo, C., Torrico, D. D., Dunshea, F. R. & Fuentes, S. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system. Beverages 5 , 33 (2019).

Gonzalez Viejo, C., Fuentes, S., Torrico, D. D., Godbole, A. & Dunshea, F. R. Chemical characterization of aromas in beer and their effect on consumers liking. Food Chem. 293 , 479–485 (2019).

Gilbert, J. L. et al. Identifying breeding priorities for blueberry flavor using biochemical, sensory, and genotype by environment analyses. PLOS ONE 10 , 1–21 (2015).

Goulet, C. et al. Role of an esterase in flavor volatile variation within the tomato clade. Proc. Natl. Acad. Sci. 109 , 19009–19014 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Borisov, V. et al. Deep Neural Networks and Tabular Data: A Survey. IEEE Trans. Neural Netw. Learn. Syst. 1–21 https://doi.org/10.1109/TNNLS.2022.3229161 (2022).

Statista. Statista Consumer Market Outlook: Beer - Worldwide.

Seitz, H. K. & Stickel, F. Molecular mechanisms of alcoholmediated carcinogenesis. Nat. Rev. Cancer 7 , 599–612 (2007).

Voordeckers, K. et al. Ethanol exposure increases mutation rate through error-prone polymerases. Nat. Commun. 11 , 3664 (2020).

Goelen, T. et al. Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid. Oikos 129 , 1415–1428 (2020).

Article   ADS   Google Scholar  

Reher, T. et al. Evaluation of hop (Humulus lupulus) as a repellent for the management of Drosophila suzukii. Crop Prot. 124 , 104839 (2019).

Stein, S. E. An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom. 10 , 770–781 (1999).

American Society of Brewing Chemists. Sensory Analysis Methods. (American Society of Brewing Chemists, St. Paul, MN, U.S.A., 1992).

McAuley, J., Leskovec, J. & Jurafsky, D. Learning Attitudes and Attributes from Multi-Aspect Reviews. Preprint at https://doi.org/10.48550/arXiv.1210.3926 (2012).

Meilgaard, M. C., Carr, B. T. & Carr, B. T. Sensory Evaluation Techniques. (CRC Press, Boca Raton). https://doi.org/10.1201/b16452 (2014).

Schreurs, M. et al. Data from: Predicting and improving complex beer flavor through machine learning. Zenodo https://doi.org/10.5281/zenodo.10653704 (2024).

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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DOI : https://doi.org/10.1038/s41467-024-46346-0

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How is the OND-RP funded regulatory science research different from other types of research?

Regulatory science research is much more targeted than basic scientific research, which provides fundamental knowledge about the nature and behavior of living systems. Basic scientific research asks broad questions that may take a long time to answer. The OND-RP, meanwhile, is focused on addressing specific scientific questions that will produce immediate impacts on how CDER and FDA make drug approval decisions. Because our research is so targeted, our projects are relatively short, about three to four years.

We have comparatively limited funding, so our research projects use creative methods to generate high-quality clinical or scientific information. A strategy we like to use is “data mining” of existing large data sets. For example, OND often repurposes raw data submitted in new drug applications to address questions. We also leverage electronic health records and patient registries to address clinical knowledge gaps. We hope the research produces tangible end-products that will support FDA’s mission, such as guidances for industry, internal review documents, updates to regulatory policies, and Drug Development Tools . Like most scientific researchers, we share our findings with the community through presentations and published journal articles.

What are the two main ways that the OND-RP funds research?

While our OND review staff typically identify the research question, they don’t have the bandwidth to complete the study alone. Instead, the OND-RP funds both extramural (investigators from outside FDA) and intramural (investigators from within FDA) research. That is why we published two reports — one highlighting our extramural research projects and another focused on intramural research.

On the extramural front, we use several mechanisms to fund research. These include FDA’s Broad Agency Announcement program, the agency’s Centers for Excellence in Regulatory Science and Innovation program, inter-agency agreements with other government entities, and public-private partnerships. We also use agreements such as memorandums of understanding, research collaboration agreements, and cooperative research and development agreements to formalize our research partnerships. Our external investigators provide the capabilities, expertise, or equipment to conduct extramural research. These investigators come from industry, academia, or other government agencies.

We fund intramural research primarily through the Oak Ridge Institute for Science and Education (ORISE) fellowship program . In this program, college students or recent graduates (up to five years after graduating) can conduct research with an FDA mentor on a regulatory science project. It’s a great opportunity for young investigators to get hands-on experience in regulatory science and contribute to our mission. People interested in the fellowship program can search and apply here. We regularly post new full-year ORISE fellowship opportunities.

What are examples of research projects that the OND-RP has funded?

One example from our extramural report is an effort to develop blood-based biomarkers, or molecules that signal a normal or abnormal biologic process, for traumatic brain injury. Drug development in this area is difficult because people’s symptoms vary and there are few objective ways to determine prognosis. A biomarker would help investigators sort individuals into clinical trials by disease severity, ultimately fostering drug development.

Another extramural research project focuses on the challenges in interpreting electrocardiograms (ECGs) in children, making it difficult to evaluate drugs targeting serious and life-threatening heart rhythm abnormalities. In this project, researchers are collecting real-world ECG data to better understand how to interpret readings and perform pediatric cardiac safety evaluations.

One project highlighted in our ORISE report focused on enhancing the evaluation of bulk drug substances. Compounders use these substances to create custom drugs for individual patients. For example, if a patient has an allergy to an ingredient in an FDA-approved drug, compounders can use bulk drug substances to compound a drug without the allergen. In this project, ORISE fellows helped create internal resources to streamline our review of bulk substances. This will enhance the safety of compounded drugs, which are not FDA-approved and don’t have the same safety assurances as approved drugs.

In another example, ORISE fellows contributed to the development of a list of molecular targets related to the growth or progression of pediatric cancers. This list is published on the FDA website , and it’s helping to spur discussion on initial Pediatric Study Plans (iPSPs) for more than 100 drug and biologic candidates. iPSPs are outlines of pediatric stud(ies) that drug sponsors plan to conduct to show that a drug can be used in children. This is important because if a drug will be used to treat children, FDA needs data from pediatric clinical trials to assess safety and efficacy. In oncology, some molecular targets are unlikely to be associated with the growth of pediatric cancers, meaning that studying the drug in children may not be necessary. Industry uses the lists created from this research project to inform their iPSPs for oncology drugs.

I encourage you to read our extramural and ORISE outcomes reports to learn more about our work in advancing regulatory science research. For more information about the OND’s regulatory science research, please visit Office of New Drugs Regulatory Science Research . If you have suggestions for regulatory science knowledge gaps in new drug development, send us an email at [email protected] .

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What is Product Prototype? Types, Purpose, Examples & Tips

Home Blog Web Development What is Product Prototype? Types, Purpose, Examples & Tips

This blog post is all about product prototyping, and I'm goin g to delve deep into this topic as we go along with the discussion on various aspects of it. So, let me start by describing what it is and why it's needed. Product prototyping is a primary model of a product, serving as a real representation of an idea before mass production.

It acts as a bridge between concept and reality, enabling me as a developer, along with designers and stakeholders, to explore the feasibility, configuration, functionality, and user knowledge of a new product. Prototype product development vary in sophistication, fidelity, and commitment, ranging from simple mock-ups to highly functional, near-final products.

They are crucial for visualizing the product concept, identifying potential issues, collecting feedback, and making necessary adjustments before completing the design and beginning production.

What is a Product Prototype?

As per my understanding, product prototyping is an early example, instance, or release of a product built to test a concept or function. It's a tool I use for visualizing how a product will look or function in the real world. Prototyping helps in refining and validating the design and functionality of a product, serving as a crucial step in our product development process.

They can be developed at various fidelity levels, from low-fidelity sketches or models that focus on the basic size and shape to high-fidelity prototypes that closely mimic the final product in terms of appearance, materials, and working mechanisms.

This iterative process helps us and our team of designers and engineers experiment with different approaches, make improvements, and solve problems before committing to large-scale production.

The Purpose of a Prototype

The primary purpose of a prototype is to bring product ideas to life, allowing us to explore design concepts, test functionality, and identify potential issues. Prototypes enable me to evaluate the product's feasibility, assess user interaction, and gather feedback from stakeholders or potential customers.

This early testing helps ensure that the product meets the needs of its intended market, fits within our budget constraints, and can be manufactured within technological limitations. By identifying and addressing flaws early in the development process, prototypes significantly reduce the risk of costly errors and rework later on.

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What is Typically Included in a Product Prototype?

A product prototyping typically includes elements that allow it to be tested for functionality, usability, and marketability. 

These elements vary depending on the prototype's fidelity but can include a physical model or a digital simulation of the product. For physical prototypes, materials may range from basic foam or cardboard for early-stage models to more sophisticated, production-like materials for advanced prototypes. 

Functional aspects, such as moving parts or electronics, are integrated to mimic the product's final operation. Aesthetics, including color, shape, and texture, are also considered to evaluate the product's appeal to potential customers. Documentation, such as design sketches or technical specifications, accompanies the prototype of the product to guide its development and testing.

When is a Product Prototype Ready?

Determining when our product prototype is ready depends on the goals, I've set for what it needs to demonstrate or prove. Our product design and prototyping are ready when they sufficiently address the core functionality, design, and user interaction elements that allow for effective testing and feedback collection.

It doesn't have to be perfect or final but should be advanced enough to provide valuable insights into how our product performs in realistic scenarios.

Key indicators that from prototype to final product is ready include achieving desired design specifications, functioning according to initial requirements, and being able to withstand user testing without significant issues. KnowledgeHut has a wide variety of courses for front-end engineers and developers. You might consider this course to upskill your knowledge Web Development course syllabus .

Types of Product Prototyping

Here are the different types of prototypes:

• Feasibility Prototypes
• Sketches and Diagrams
• 3D Printing or Rapid Model
• Physical Model
• Virtual or Augmented Reality
• Working Model
• Video Prototype
• Horizontal Prototype
• Vertical Prototype
• Storyboard Prototype
• Simulations
• Wizard of Oz Prototypes
• User-Driven Prototypes
• Mock-up Prototype

How To Make a Product Prototype?

When we create a product prototype, then it becomes an essential step in the development of a new product. It allows inventors and companies to explore the feasibility of their ideas, identify potential issues, and present a tangible model to stakeholders and investors. Here’s a step-by-step guide to making a product prototype.

1. Conduct Extensive Research

Before we dive into the prototype, it's crucial to conduct thorough market and technical research. We need to understand the needs of your target audience, existing solutions, and where there might be gaps in the market. This stage should also involve researching materials, technology, and methods relevant to your product concept. 

2. Create Design Sketches

The next step is to bring our ideas to paper through design sketches. This process transforms our research and ideas into visual representations, making it easier to conceptualize the product. These sketches should detail the product’s arrival, functionality, and mechanics, serving as a blueprint for further evolution stages.

3. Develop and Test the Proof of Concept (POC)

When we develop a Proof of Concept (POC), we create a simplified version of our product to test its feasibility. The POC focuses on the core functionality of the product, allowing us to assess whether the idea can be transformed into a viable product. This step is crucial for identifying any technical or design flaws early in the development process.

4. Create a Physical Product Prototype

With a validated POC, our next phase is to create a physical prototype. This involves selecting materials and production methods and integrating all the design elements. The physical prototype should closely correspond to the final product in terms of design, functionality, and user experience. 

5. Test the Prototype

Now in our Testing, the prototype is critical to understanding how it performs under real-world conditions and identifying any areas for improvement. This involves both technical testing to ensure the product works as intended and user testing to gather feedback on usability, design, and user experience. We should do thorough and iterative testing, with each round of feedback used to refine the prototype further, enhancing its design and functionality.

6. Create a Production-Ready Prototype

Once we have tested and refined our prototype application, the next step is to develop a production-ready prototype. This version of the prototype addresses all identified issues and incorporates user feedback. It should be as close to the final product as possible, meeting all the necessary specifications for mass production.

7. Protect Your Creation

Before we move forward with mass production or public disclosure, it’s important to protect your intellectual property. This can involve filing for patents, trademarks, or design rights, depending on the nature of your product. It’s advisable to consult with a patent attorney or intellectual property specialist to ensure that our creation is fully protected and that all legal needs are met.

Tips for Prototyping Your Product

I’m listing down a few important tips for prototyping for product architecture – 

• Start Simple: Begin with low-fidelity prototypes to test fundamental concepts before moving to more detailed models.
• Iterate Frequently: Use feedback to make ongoing improvements. Don't be afraid to go through multiple iterations.
• Focus on Core Features: Focus on prototyping the most critical aspects of your product to validate its primary functions.
• Gather Diverse Feedback: Get input from a variety of stakeholders, including potential users, designers, and technical experts.
• Consider Materials and Manufacturing: Use materials and processes similar to those in the final product to identify possible production challenges early.
• Test Thoroughly: Ensure your product design and prototyping experience rigorous testing to identify and fix any issues.
• Keep an Open Mind: Be prepared to revise your concept based on a prototype of the product findings. Flexibility is key to successful product development.

Prototype Examples

Examples of prototypes can range from the tech industry's electronic devices, where initial versions might focus on hardware configuration, to the fashion sector, where early garment samples test aesthetics and functionality. 

In software development, prototypes often take the form of wireframes or beta versions to evaluate user interfaces and experience. Consumer goods may start with 3D printed models to assess design and ergonomics. 

Each of these examples emphasizes the versatility of prototyping in validating various aspects of development design and usability across various industries. If you are aspiring to become a software engineer or want to explore some courses in the field of information technology , you can try out this course to  learn UI/UX Design . 

Common Prototyping Mistakes to Avoid

So, it’s good to have best practices but also note below prototyping mistakes that one must avoid having a solid product prototype maker design.

• Overcomplicating the Design: Focusing on too many features too early can dilute the primary purpose of the prototype.
• Ignoring User Feedback: Not incorporating feedback from potential users can lead to missing crucial usability improvements.
• Underestimating Costs: Failing to consider the cost implications of design choices can result in budget overruns.
• Skipping Iterations: Moving too quickly to a high-fidelity product prototype design without sufficient low-fidelity testing can overlook fundamental design flaws.
• Overlooking Manufacturing Constraints: Designing without regard to manufacturing realities can lead to prototypes that are impossible or too costly to produce.

Prototyping is an essential phase in the product development process, providing a tangible means to explore, test, and refine ideas before committing to full-scale production. 

By understanding what a product prototype design is, its purpose, and how to effectively develop and use it, designers and engineers can significantly enhance the likelihood of product success. 

Avoiding common mistakes and applying best practices in prototyping can lead to more innovative, user-friendly, and cost-effective products. Through iterative design and feedback integration, product prototypes in entrepreneurship or companies or our own products bridge the gap between concept and reality, turning visions into viable products.

Frequently Asked Questions

Yes, a well-developed product prototype design can demonstrate a product's potential to investors and partners, making it easier to secure funding or strategic partnerships by showcasing the concept's feasibility, market appeal, and technological viability.

In UX design, prototyping is crucial for testing and refining user interfaces and interactions. It allows designers to evaluate usability, improve the user journey, and ensure that the product meets the target audience's needs and expectations.

Managing prototyping for complex products involves a phased approach, starting with simple models to test basic concepts and gradually increasing fidelity. It requires close collaboration among cross-functional teams, including designers, engineers, and technical experts, to address technical challenges and integrate necessary features progressively.

Vikram Gupta

A Senior Software Engineer (Backend) working for Integral Ad Science and a CS graduate, having 5+ years of experience. 

I like to solve complex problems and work in a challenging, fast-paced, and competitive environment.  

I have experience in analyzing and improving the efficiency, scalability, and stability of various system resources and have worked on building large-scale infrastructure applications. While developing the software applications, I emphasize System Design, Clean Architecture, and Well-Tested Code. 

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April 3, 2024

SK hynix announces semiconductor advanced packaging investment in Purdue Research Park

SK hynix announced Wednesday (April 3) semiconductor advanced packaging investment in Purdue Research Park. From left to right: Indiana Gov. Eric Holcomb; Kwak Noh-jung, SK hynix president and CEO; Woojin Choi, SK hynix executive vice president; Arati Prabhakar, director, White House Office of Science and Technology Policy, and assistant to the president for science and technology; Mung Chiang, Purdue University president (speaking); Arun Venkataraman, U.S. Department of Commerce assistant secretary; U.S. Sen. Todd Young; Hyundong Cho, ambassador of the Republic of Korea to the United States; David Rosenberg, Indiana secretary of commerce; Mitch Daniels, Purdue Research Foundation chairman. (Purdue University/Kelsey Lefever)

The company's facility for AI memory chips marks the largest single economic development in the history of the state

WEST LAFAYETTE, Ind. — SK hynix Inc. announced Wednesday (April 3) that it plans to invest close to $4 billion to build an advanced packaging fabrication and R&D facility for AI products in the Purdue Research Park. The development of a critical link in the U.S. semiconductor supply chain in West Lafayette marks a giant leap forward in the industry and the state. 

“We are excited to build a state-of-the-art advanced packaging facility in Indiana,” said SK hynix CEO Kwak Noh-Jung. “We believe this project will lay the foundation for a new Silicon Heartland, a semiconductor ecosystem centered in the Midwest Triangle. This facility will create local, high-paying jobs and produce AI memory chips with unmatched capabilities, so that America can onshore more of its critical chip supply chain. We are grateful for the support of Gov. Holcomb and the state of Indiana, of President Chiang at Purdue University, and of the broader community involved, and we look forward to expanding our partnership in the long run.”

SK hynix joins Bayer, imec, MediaTek, Rolls-Royce, Saab and many more national and international companies bringing innovation to America's heartland. The new facility — home to an advanced semiconductor packaging production line that will mass-produce next-generation high-bandwidth memory, or HBM, chips, the critical component of graphic processing units that train AI systems such as ChatGPT — is expected to provide more than a thousand new employment opportunities in the Greater Lafayette community. The company plans to begin mass production in the second half of 2028.

The project marks SK hynix’s intention for long-term investment and partnership in Greater Lafayette. The company’s decision-making framework prioritizes both profit and social responsibility while promoting ethical actions and accountability. From infrastructure developments that make accessing amenities easier to community empowerment projects such as skill development and mentorship, the SK hynix advanced packaging fabrication marks a new era of collaborative growth.

“Indiana is a global leader in innovating and producing the products that will power our future economy, and today’s news is proof positive of that fact,” said Indiana Gov. Eric Holcomb. “I’m so proud to officially welcome SK hynix to Indiana, and we’re confident this new partnership will enhance the Lafayette-West Lafayette region, Purdue University and the state of Indiana for the long term. This new semiconductor innovation and packaging plant not only reaffirms the state’s role in the hard-tech sector, but is also another tremendous step forward in advancing U.S. innovation and national security, putting Hoosiers at the forefront of national and global advancements.” 

Investment in the Midwest and Indiana was spurred by Purdue’s excellence in discovery and innovation and its track record of exceptional R&D and talent development through collaboration. Partnerships among Purdue, the corporate sector, and the state and federal government are essential to advancing the U.S. semiconductor industry and establishing the region as the Silicon Heartland.

“SK hynix is the global pioneer and dominant market leader in memory chips for AI,” Purdue President Mung Chiang said. “This transformational investment reflects our state and university’s tremendous strength in semiconductors, hardware AI and development of the Hard-Tech Corridor. It is also a monumental moment for completing the supply chain of the digital economy in our country through the advanced packaging of chips. Located at Purdue Research Park, the largest facility of its kind at a U.S. university will grow and succeed through innovation.”

In 1990 the U.S. was producing nearly 40% of the world’s semiconductors. However, as manufacturing moved to Southeast Asia and China, the U.S. global output of semiconductor manufacturing has fallen to closer to 12%.

“SK hynix will soon be a household name in Indiana,” said U.S. Sen. Todd Young. “This incredible investment demonstrates their confidence in Hoosier workers, and I’m excited to welcome them to our state. The CHIPS and Science Act opened a door that Indiana has been able to sprint through, and companies like SK hynix are helping to build our high-tech future.” 

To aid in bringing semiconductor manufacturing closer to home and shoring up global supply chains, the U.S. Congress introduced the Creating Helpful Incentives to Produce Semiconductors for America Act, or CHIPS and Science Act, on June 11, 2020. Signed by President Joe Biden on Aug. 9, 2022, it funds holistic development of the semiconductor industry to the tune of $280 billion. It supports the nation's research and development, manufacturing, and supply chain security of semiconductors.

“When President Biden signed the bipartisan CHIPS and Science Act, he put a stake in the ground and sent a signal to the world that the United States cares about semiconductor manufacturing,” said Arati Prabhakar, President Biden’s chief science and technology advisor and director of the White House Office of Science and Technology Policy. “Today’s announcement will strengthen the economy and national security, and it will create good jobs that support families. This is how we do big things in America.”

Purdue Research Park, one of the largest university-affiliated incubation complexes in the country, unites discovery and delivery with easy access to Purdue faculty experts in the semiconductor field, highly sought-after graduates prepared to work in the industry, and vast Purdue research resources. The park also offers convenient accessibility for workforce and semitruck traffic, with access to I-65 just minutes away.

This historic announcement is the next step in Purdue University’s persistent pursuit of semiconductor excellence as part of the Purdue Computes initiative. Recent announcements include these

• Purdue University Comprehensive Semiconductors and Microelectronics Program
• A strategic partnership with Dassault Systèmes to improve, accelerate and transform semiconductor workforce development
• European technology leader imec opens innovation hub at Purdue
• The nation’s first comprehensive Semiconductor Degrees Program
• Purdue continues to create unique lab-to-fab ecosystem for the state and country
• Green2Gold, a collaboration between Ivy Tech Community College and Purdue University to grow Indiana’s engineering workforce

What they’re saying

• “This decision by a world-renowned, best-in-class company represents a dramatic fulfillment of Purdue’s duty to serve the state as not only its premier academic institution but also its No. 1 economic asset. It’s also a gratifying validation of our Discovery Park District initiative to bring new opportunities to our students, faculty and Greater Lafayette neighbors. Today marks the Purdue ecosystem’s latest and greatest, but assuredly not its last, contribution to a more prosperous Indiana and a stronger America.” — Mitch Daniels, chairman of the board, Purdue Research Foundation
• “On behalf of my fellow trustees, we are pleased to welcome SK hynix Inc. to the Purdue Research Park. Their arrival will significantly strengthen Purdue University’s dual commitments to educating the next generation of workforce leaders in semiconductors and supporting the national security of our nation.” — Michael Berghoff, chair, Purdue Board of Trustees
• “The impact of SK hynix is more than the creation of high-paying careers for Hoosiers. Undergraduates will have opportunities for internships, co-op and full-time employment when they graduate. Graduate students and faculty will work closely with SK hynix researchers, not only on basic research, but also to accelerate the transition of research into pilot production and manufacturing. This is just the beginning. As other companies see what’s happening here in the heart of the heartland, they’ll come too, and a significant new cluster of semiconductor manufacturing and research will emerge.” — Mark Lundstrom, chief semiconductor officer, Purdue University
• “West Lafayette is thrilled to join our national efforts to bring the semiconductor industry to the United States through President Biden’s CHIPS and Science Act. This partnership will leverage Purdue University’s science and research expertise with SK hynix’s innovation in semiconductor technology. The impact on West Lafayette will enable us to continue to provide the high level of service our community expects and to increase our quality-of-life amenities for the region so we can attract and retain the excellent graduates of Purdue University. In addition, SK hynix’s global dedication to net zero carbon emissions by 2050, water process reduction and recycling, and zero-waste-to-landfill programs aligns with our community’s commitment to environmental stewardship. We are grateful for SK hynix’s investment and commitment to West Lafayette and for our partners Purdue University, Purdue Research Foundation, the city of Lafayette, Tippecanoe County and the Greater Lafayette region.” — Erin Easter, mayor of West Lafayette
• “The pandemic disruption has shown the reliance on semiconductors, with production concentrated in limited regions around the world. Greater Lafayette has worked continuously and cooperatively for years to position ourselves for an opportunity of this magnitude, and we look forward to the long-term economic impact this will have on our communities. The collaborative efforts between cities and county governments, Purdue University, the state of Indiana and Sen. Todd Young’s office is a testament to these efforts. Our joint investments in infrastructure, innovation, along with quality-of-life initiatives, have contributed to this venture becoming a reality. We look forward to working with and welcoming SK hynix to Greater Lafayette!” — Tony Roswarski, mayor of Lafayette
• “Ivy Tech, as Indiana’s largest postsecondary institution, is focused on building Indiana talent pipelines aligned to employers and emerging industries which strengthen Indiana’s economy. The microelectronics industry will play a key role in Indiana’s success, which is why we are pleased to work with SK hynix and Purdue to provide training, credentials and degrees designed for the semiconductor industry. SK hynix’s commitment to Indiana reinforces that we all win when we address complex issues through strong partnerships." — Sue Ellspermann, president, Ivy Tech Community College
• “Semiconductors and microelectronics are at the forefront of focus for Purdue Research Foundation. I am pleased to welcome SK hynix to Indiana and start the hard work of ensuring this is the best business decision that SK hynix has ever made.” — Brian Edelman, president, Purdue Research Foundation
• “The Alliances team is thrilled to welcome SK hynix to the Purdue ecosystem, and we look forward to empowering them to thrive here in Indiana with all the immense assets Purdue and Greater Lafayette offer. We look forward to forging a strong relationship with mutual value for SK hynix, Purdue Research Foundation and the broader Greater Lafayette community for many years to come.” — Gregory Deason, senior vice president of alliances and placemaking, Purdue Research Foundation
• “During my time at Purdue Research Foundation, we have consistently been successful in assisting our partners like Saab in developing complex builds well ahead of schedule and within budget. I look forward to building on our excellent track record with SK hynix to help them in creating a state-of-the-art facility which best meets their unique needs.” — Richard Michal, senior vice president of capital projects and facilities, Purdue Research Foundation

About SK hynix Inc.

SK hynix Inc., headquartered in Korea, is the world’s top-tier semiconductor supplier offering Dynamic Random Access Memory chips (“DRAM”), flash memory chips (“NAND   flash”)   and CMOS Image Sensors (“CIS”) for a wide range of distinguished customers globally. The Company’s shares are traded on the Korea Exchange, and the Global Depository shares are listed on the Luxembourg Stock Exchange. Further information about SK hynix is available at   www.skhynix.com ,   news.skhynix.com .  

About Purdue Research Foundation

Purdue Research Foundation is a private, nonprofit foundation created to advance the mission of Purdue University. Established in 1930, the foundation accepts gifts, administers trusts, funds scholarships and grants, acquires and sells property, protects and licenses Purdue's intellectual property, and supports creating Purdue-connected startups on behalf of Purdue. The foundation operates Purdue Innovates, which includes the Office of Technology Commercialization, Incubator and Ventures. The foundation manages the Purdue Research Park, Discovery Park District, Purdue Technology Centers and Purdue for Life Foundation.

For more information on licensing a Purdue innovation, contact the Office of Technology Commercialization at [email protected] . For more information about involvement and investment opportunities in startups based on a Purdue innovation, contact Purdue Innovates at [email protected] .

About Purdue University

Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue’s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap — including its first comprehensive urban campus in Indianapolis, the new Mitchell E. Daniels, Jr. School of Business, and Purdue Computes — at https://www.purdue.edu/president/strategic-initiatives . 

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Note to journalists:   Photo, b-roll and sound bites from this announcement will be available for media use on   Google Drive .

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