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How to Design an AI Marketing Strategy

• Thomas H. Davenport,
• Abhijit Guha,
• Dhruv Grewal

In order to realize AI’s giant potential, CMOs need to have a good grasp of the various kinds of applications available and how they may evolve. This article guides marketing executives through the current state of AI and presents a framework that will help them classify their existing projects and plan the effective rollout of future ones. It categorizes AI along two dimensions: intelligence level and whether it stands alone or is part of a broader platform. Simple stand-alone task-automation apps are a good place to start. But advanced, integrated apps that incorporate machine learning have the greatest potential to create value, so as firms build their capabilities, they should move toward those technologies.

What the technology can do today—and what’s next

Idea in Brief

The challenge.

At many firms, the marketing function is rapidly embracing artificial intelligence. But in order to fully realize the technology’s enormous potential, chief marketing officers must understand the various types of applications—and how they might evolve.

The Framework

Classifying AI by its intelligence level (whether it is simple task automation or uses advanced machine learning) and structure (whether it is a stand-alone application or is integrated into larger platforms) can help firms plan which technologies to pursue and when.

Implementation

Companies should take a stepped approach, starting with rule-based, stand-alone applications that help employees make better decisions, and over time deploying more-sophisticated and integrated AI systems in customer-facing situations.

Of all a company’s functions, marketing has perhaps the most to gain from artificial intelligence. Marketing’s core activities are understanding customer needs, matching them to products and services, and persuading people to buy—capabilities that AI can dramatically enhance. No wonder a 2018 McKinsey analysis of more than 400 advanced use cases showed that marketing was the domain where AI would contribute the greatest value.

Chances are, you haven’t asked the right questions.

• Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).
• AG Abhijit Guha is an associate professor of marketing at the Darla Moore School of Business, University of South Carolina.
• DG Dhruv Grewal holds the Toyota Chair in Commerce and Electronic Business and is a professor of marketing at Babson College. He is a coauthor of Marketing and Retailing Management and the author of Retail Marketing Management: The 5 Es of Retailing .

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24 Machine Learning in Marketing Examples

The promise of using machine learning in marketing is this: Data, properly harnessed, can help marketers and advertisers better understand customers and automate finely tuned, targeted brand campaigns with unsurpassed consumer personalization.

Machine Learning in Marketing

To give you a better sense of how this promise is being kept, we rounded up examples of machine learning in marketing.

Uses of Machine Learning in Marketing

• Analyzing customer data. 
• Recording and transcribing sales calls. 
• Recommending products and services to customers.
• Mapping the social networks of leads.
• Mining and analyzing public social media posts.

Klaviyo’s Marketing Automation Platform

Klaviyo makes a marketing automation platform for businesses in multiple industries, including retail and e-commerce. Powered by AI and machine learning, its software informs and drives email and SMS marketing. The technology uses customer data to support automated marketing campaigns that engage customers with personalized messaging across channels.

JPMorgan Chase’s AI Copywriter

JPMorgan Chase equipped its marketing team with an AI copywriter that produces more customer-friendly content. The financial giant showcases a partnership with Persado , which uses machine learning and AI technology to write ad copy that catches consumers’ attention. By enlisting the capabilities of this software startup, JPMorgan Chase has enjoyed higher click rates with its revamped copywriting.

Gong ’s Revenue Intelligence Platform

Gong employs artificial intelligence (including machine learning) to help B2B sales teams close more deals by automatically recording, transcribing and analyzing the content of all sales-oriented calls, web conferences and emails. Its revenue intelligence technology integrates well with platforms like Salesforce, Office 365 and Slack, creating a seamless experience for sales teams looking to refine their operations.

Netflix’s Recommender System 

Netflix uses machine learning in its recommender system , as well as for research efforts and A/B testing . The company also uses machine learning to inform its decisions on which content to add to its streaming platform and to optimize production of original TV series and movies through its film studio.

IBM ’s AI Assistant Watson 

IBM offers a suite of AI tools that helps businesses streamline their marketing strategies and customer interactions. Its AI assistant Watson taps into the possibilities of machine learning to conduct audience analytics, personalize one-on-one conversations and connect with audiences through their preferred channels.

Bluecore ’s One-on-One Campaign Tool 

E-commerce companies can stay one step ahead of customers with Bluecore ’s platform, which personalizes interactions for online shoppers. AI and machine learning guide one-on-one conversations and recommend products to customers across a range of channels. Predictive automated intelligence also gathers info on the ideal ways to reach out to customers, so teams can adapt marketing activities to audiences’ preferences.

Aerosolve From Airbnb 

Airbnb hosts several open-source products, including Aerosolve , a machine learning library. The library is ideal for running algorithms with easily interpretable features like searching for keywords and filters for vacation rentals. Aerosolve is the same library that Airbnb uses to predict booking probability and suggest pricing to its hosts.

AI-Powered Customer Service Chat From Ada  

Ada gives companies the ability to deliver consistent, high-quality customer service with a brand interaction platform that displays conversational AI features. Machine learning models enable Ada’s platform to analyze text in over 100 languages and then predict the needs of customers. With this proactive technology, businesses can reduce the time it takes to resolve issues and provide customers with the answers they’re looking for.

More on Marketing 20 Big Data Companies Redefining Marketing

GumGum ’s Contextual Intelligence Platform

As part of its partnership with Appen , GumGum has leveraged AI and machine learning to determine ideal web pages and digital spaces for posting ads. Verity serves as the company’s contextual intelligence platform, which scans videos, audio clips, images, text and other online elements. With this tool, businesses can place ads on web pages and platforms without accidentally associating their brands with irrelevant or controversial content.

Strong Analytics ’s Platform for Building Machine Learning Applications 

Strong Analytics makes it easier for marketers to develop personalized content and campaigns with a machine learning platform. A combination of AI, analytics and machine learning tools enables teams to compile data on customer behavior, predict future needs and tailor marketing efforts to meet those needs. In addition, marketers can rely on Strong Analytics’ platform to help them quickly deploy machine learning applications and streamline their operations.

Sales Automation Tools From People.ai

People.ai ’s sales automation tools use machine learning algorithms , freeing up time for the sales and marketing professionals who use them. With the company’s data platform, teams are able to compile information from various interactions to determine who needs to be contacted to increase revenue streams moving forward. The company’s platform also sends out timely AI-driven alerts, so teams know which accounts to focus on to close deals.

Applecart ’s Social Graph Platform

Applecart ’s marketing platform locates promising leads — and their connections — leading to more efficient marketing campaigns. After collecting data from public sources, the company’s Social Graph Platform uses machine learning algorithms to determine professional and personal relationships for each individual. Companies can quickly map out the social networks of leads and tailor their content to these audiences.

Brandfolder ’s Brand Intelligence Tool 

Working behind the scenes, Brandfolder makes it easier for teams to find marketing assets with its Brand Intelligence platform. This platform uses AI, natural language processing and machine learning algorithms to tag creative assets, understand how they’re organized and recommend ways for classifying them. Teams and stakeholders can then save time and energy locating assets within a cleaner marketing ecosystem.

Real Estate Marketing Tools From Ylopo

A digital marketing technology platform for real estate agents, Ylopo incorporates a variety of ingredients — including social media marketing , targeted demographic and psychographic advertising, big data and AI — into its Total Digital Marketing Solution product. Its AI agent RAIYA blends natural language processing and machine learning to carry conversations with customers and guide them along their property search.

Drift ’s Conversational Cloud Platform

Drift helps businesses forge bonds with customers and prospects through its AI-based platform. Equipped with machine learning, Drift’s Conversational Cloud platform engages with site visitors and potential buyers to answer questions, close deals and encourage future visits. Chatbots also record insights during each conversation, so companies can understand customer needs and better serve returning visitors.

Acquia ’s Marketing Cloud Data Platform

Acquia provides marketers with the data they need to better understand audiences and tailor content to specific target groups. Machine learning features build models and classify customers based on certain behaviors, so marketing teams can form accurate insights and buyer personas. As a result, businesses can adapt their products to customers’ preferences and create unique marketing experiences.

Recommender Tools From Dynamic Yield

Dynamic Yield employs advanced machine learning to help marketers increase revenue through single-platform personalization, recommendations, automatic optimization and one-on-one messaging. Once businesses link their marketing campaigns with Dynamic Yield’s platform, they can sort through rich data sets and select content that caters to target audiences’ interests.

Affinitiv ’s Atlas DX Platform

Affinitiv helps car dealerships and OEMs improve customer service and long-term loyalty with digital services. The company’s Atlas DX platform features rich demographic and behavioral data, so dealerships can match their marketing content to the interests of prospective and current customers. As a result, teams can determine the best channels through which to contact audiences and convert them into buyers.

Machine Learning Media Buying From Bliss Point Media

To optimize marketing campaigns, Bliss Point Media has designed an application that reveals where marketers should focus their attention. Machine learning tools compile real-time data, analyze it and determine sources that deliver the most revenue. Marketers can shape strategies around their target audiences while still diversifying their channels and outreach methods.

Generative AI Chatbots From Conversica

Built on an AI platform that blends machine learning with natural language processing and natural language generation , Conversica ’s AI assistant automatically contacts, engages, qualifies and follows up with leads using natural two-way communication. It also fills in lead contact information, keeping a company’s CRM up to date.

Emotional Analysis Tools From Swayable

Swayable allows companies to gauge audience opinions through emotional analyses. A mix of machine learning and computer vision technology captures consumers’ reactions to products and tracks wider opinions after gathering data. Businesses use Swayable’s causal AI to sharpen their marketing campaigns right from the beginning.

More on Machine Learning 16 Machine Learning Examples You Should Know

Instapage’s AI-Generated Landing Page Copy

Instapage ’s landing page builder helps marketers design their website with conversion in mind. It uses AI and machine learning to analyze ad campaigns and match the language to landing pages for more successful conversion rates. This AI-generated landing page content saves marketers time and money that would otherwise be spent on rewriting and A/B testing.

Dstillery ’s Custom AI Audiences Tool

An applied data science company, Dstillery employs machine learning to produce actionable customer insights from its sprawling database of constantly updated online and offline behavioral profiles. Marketers use Dstillery’s Custom AI Audiences tool to build customer profiles with first-party data, pinpointing relevant leads.

DoorDash ’s Historical Marketing Campaign Data

DoorDash is coming up with smarter ways to spend its marketing budget by employing machine learning to study previous campaigns. Machine learning algorithms review historical data to separate successful campaigns from less successful ones. With a clearer picture of what works and what doesn’t, DoorDash can adjust its marketing strategies and invest in content that resonates more with customers’ needs and interests.

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• Technologies

Machine learning in marketing:
 10 use cases and implementation tips

September 19, 2023

Machine learning

Machine learning consulting

• Artificial intelligence services
• Predictive analytics consulting
• Agriculture
• Manufacturing
• Anomaly detection
• Stock market prediction

From ecommerce to banking to healthcare, organizations in various sectors are using machine learning for their marketing efforts. So let’s explore why companies turn to machine learning app development to enable marketing automation, enhance marketing campaigns, and improve customer experience.

Table of contents

• Market overview
• Use cases & examples

The state of ML and AI in marketing

of marketers have a fully defined AI strategy

of marketers say their customer engagement is data-driven

say AI is very important to their marketing success over the next 12 months

• Drift and Marketing AI Institute

will intelligently automate over 25% of their tasks in the next 5 years

Top 10 ML examples and use cases in marketing

Let’s delve deep into the most popular machine learning use cases in marketing and explore their real-life examples.

Marketing analytics

Example: affectiva.

Affectiva, a company that specializes in building machine learning models that understand human emotions and cognitive states, provides its Media Analytics solution to 28% of the Fortune Global 500 companies including Mars, Kellogg's, and CBS. Emotion recognition could be a valuable ML-based technology in retail , however, given very strict regulations around the storage and analysis of biometric information, companies can use Affectiva’s tech only on volunteers. As viewers watch advertisements, Media Analytics captures their facial emotions, analyzes them, and presents findings in an easy-to-use dashboard. It can also correlate facial emotions with crucial marketing indicators like a person's purchase intent or brand recall.

Image title: Affectiva Media Analytics platform in action Data source: affectiva.com — Affectiva Media Analytics for Ad Testing

Personalization

Example: commonwealth and appier.

CommonWealth, one of the biggest media outlets in Taiwan, turned to Appier, a company that provides an AI-powered platform to brands to help increase customer engagement. Appier’s custom machine learning model was able to identify reader profiles in detail by analyzing how customers interact with the mobile app and website as well as interpreting data from a CRM system. This allows CommonWealth to identify reader profiles in real-time and dynamically deliver personalized ads. For example, only by changing keywords, Appian’s ML model managed to increase an ad’s CTR sixfold.

Image title: Appier’s platform in action Data source: appier.com — Aixon. Connect your data & predict user actions

Content creation

Example: vanguard and persado.

Vanguard is one of the largest investment organizations in the world. One of its divisions struggled to personalize messages to customers in search of a retirement plan sponsor. Given that this sector is highly regulated, the company has a very limited number of channels to reach potential customers. In fact, LinkedIn was the only social media platform the company used to reach prospects. To stand out from the competition, the company turned to Persado to make marketing messages more personalized at scale. Persado’s platform was used to craft the exact phrases, choose the right formatting, and deliver a meaningful CTA to individual customers. As a result, Vanguard has managed to increase the conversion rates of its division by 15%.

Image title: Persado Motivation AI framework 
 Data source: persado.com — Persado Motivation AI Platform

Marketing automation

Weather-triggered marketing, example: walgreens, clinch, and ibm.

Walgreens, the second-largest pharmacy store chain in the US, joined forces with an AI-powered ad personalization platform Clinch to drive traffic during allergy season. Given that the weather and pollen counts are one of the main triggers of allergies, Walgreens wanted to advertise its products when customers needed them the most. Based on users’ locations and weather data from IBM, consumers in a specific area were targeted with a dynamic ad and provided a coupon for allergy medicine. Based on local time, weather, user data, and other parameters, Clinch’s machine learning-powered platform personalized 160 creatives by location. As a result, Walgreens saw a 276% increase in CTR and a 64% decrease in cost per click.

Image title: Weather-triggered advertising for Walgreens Data source: clinch.co — Walgreens Provides Allergy Solutions Through Data-Driven Campaign

Contextual advertising

Example: mini and gumgum, identification of marketing opportunities.

While we often attribute machine learning's value to improving the speed and accuracy of the data-driven decision-making process , its disruptive potential also lies in its capability to process loads of unstructured data. As much as relying on well-known metrics to craft marketing campaigns is instrumental, marketers also try to dig deeper and understand what societal trends and cultural nuances drive consumer demand. Books, movies, music, and a myriad of other media sources influence our thinking and, consequently, our buying behaviors.

Recommendation systems

Nowadays, recommendation systems are the backbone of successful ecommerce stores, helping customers to navigate enormous online catalogs and find items they need. Machine learning-powered recommendation engines play an invaluable role in increasing customer satisfaction and engagement and improving a company’s bottom line.

Marketing budget optimization

Lead conversion, related technologies and services to pair with ml in marketing.

Machine learning is not a standalone technology actively used to improve marketing operations in various businesses and industry domains. Itransition has vast experience in other emerging technologies that could be beneficial for marketing purposes: 

Robotic process automation (RPA)

RPA in marketing departments can generate reports, automate email campaigns, and manage multiple communication channels. It can also automatically adjust pay-per-click (PPC) bids in ad campaigns to improve their performance. You can set an ad schedule for your Google Ads and increase bids when your target audience is more likely to make purchases.

Augmented reality (AR)

The use of augmented reality is growing in marketing strategies. By overlaying audio, visual, and other sensory information on real-world scenarios, AR enables brands to offer immersive user experiences, attract new customers, and increase their interest and engagement.

Virtual reality (VR)

Virtual reality creates a realistic simulation of the environment, which enables companies to showcase their products and services, giving customers a 360° view of their offering. Companies from various domains, from retail, to real estate, to entertainment, leverage VR to let users interact with their services or products, increasing retention and brand awareness.

Predictive analytics

ML-powered analytics solutions are widely used for marketing purposes, enabling companies to forecast consumer demand and identify market trends, predict customer behavior and automate decision-making. Predictive analytics can help brands improve their marketing strategies, enhance conversion rates, and maximize ROI.

Augment your marketing with machine learning

Benefits of machine learning in marketing, decreased costs.

With ML-enabled marketing automation, companies can reduce operational costs and free marketers’ time for more value-added tasks.

Hyper-personalization

Machine learning allows companies to better understand consumer profiles, create personalized offerings at scale, and increase customer lifetime.

Content optimization

With advancements in natural language processing and generation, ML enables companies to deliver content that increases customer engagement.

Improved segmentation

With the help of ML in digital marketing, organizations can automate customer segmentation, predict their lifetime value, and discover new, more potent customer groups.

Accelerated revenue growth

Accurately predicting consumer behavior and needs, marketers can generate greater ROI for their marketing campaigns.

Scheme title: Outcome of using AI in marketing Data source: drift.com — 2022 State of marketing and sales AI report

Challenges of ML adoption in marketing

Despite multiple use cases and successfully delivered projects, ML adoption in marketing can encounter several barriers.

Potential solution

Data quality and accessibility

Machine learning models require vast amounts of data for training. Therefore, it could be a challenge to ensure that data from different sources, including sales, customer interactions, and social media, is accurate and easily accessible for analysis.

• To effectively deal with loads of data, you can implement several best practices:
• Start gathering data from all your sales and marketing activities, customer journeys, as well as their demographics, preferences, and churn rates
• Plan which workflows you want to revamp with an ML tool
• Get a consultation from ML experts on how to guarantee your data accuracy and consistency, perform data cleaning, and set up data warehouses to build accurate forecasting models

Lack of expertise

Leveraging machine learning for marketing purposes requires skilled data science professionals. However, not every company has enough resources to hire in-house ML experts.

• Mitigating the shortage of required expertise will require investments for:
• Hiring in-house data scientists with marketing experience
• Training in-house marketing specialists to upskill them in machine learning and data science
• Outsourcing your ML adoption project to external AI/ML service providers (will also require additional investments, as you still will need an internal point of contact involved in the project)

Black box problem

Getting more sophisticated, ML models can generate accurate but hard-to-interpret results. An unexplainable logic behind the solution’s reasoning can impede trust and further technology acceptance.

• To ensure interpretability and transparency of the results, provided by your ML solution, you can:
• Pick the right metrics to evaluate the result. This won't allow you to move AI into the white-box field, but will give you a better understanding of how the system reacts to any changes and get direction for its improvement. 
• Divide the pipeline into definitive steps and establish human oversight over the decision-making and the final outcome
• Regularly assess the ML model’s performance and fine-tune it to achieve accurate results

Make your marketing smart with ML

Marketing has always been about establishing meaningful connections with customers. While banking on technology to establish a more personalized approach to every customer may sound counterintuitive, machine learning can be exceptionally good at deciphering human emotions, needs, wants, and intentions by analyzing mountains of data that companies have at hand.  Whether it’s marketing automation or analytics, machine learning is ripe for marketers to use, and it’s high time companies augmented their marketing departments with it. Turn to Itransition’s engineers to harness the power of machine learning and increase customer engagement, attract more leads, and improve your company’s bottom line.

Explore our range of machine learning consulting services, along with related technologies, use cases, implementation roadmap, and payoffs.

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99 machine learning case studies from 91 enterprises by 2024.

• 99 use cases in 17 industries
• 14 business processes in 14 business functions
• Implementations in 91 companies in 20 countries
• 10 benefits
• Growth over 6 years
• 9 vendors which created these case studies

Which industries leverage machine learning?

The most common use case of machine learning is Financial services which is mentioned in 19% of case studies.

The most common industries using machine learning are:

• Financial services

Which business functions leverage machine learning?

The most common business function of machine learning is Analytics which is mentioned in 14 case studies.

Most common business functions using machine learning are:

Which processes leverage machine learning?

The top process reported in machine learning case studies is Credit appraisal.

Most common business processes using machine learning are:

• Credit appraisal
• Financial planning & analysis
• Marketing analytics
• Data quality management
• Innovation management
• Product development
• Shipping / transportation management
• Financial risk management
• Customer journey mapping
• Incident management
• Performance management
• Sales forecasting
• Campaign management
• Data governance

What is the geographical distribution of machine learning case studies?

Click on the countries with links to explore how that country’s machine learning market is structured including top vendors, case studies etc.

Countries that use machine learning most commonly are listed below.

• United States of America
• United Kingdom

What are machine learning’s use cases?

The most common use case of machine learning is customer segmentation which is mentioned in 27% of case studies.

What are machine learning’s benefits?

The most common benefit of machine learning is time saving which is mentioned in 24% of case studies.

How are machine learning case studies growing?

Growth by vendor.

Leading vendors in terms of case study contributions to machine learning are:

• CognitiveScale

Growth over time

Years in which the;

• The first case study in our DB was published: 2016
• Most machine learning case studies have been published: 2019
• The highest increase in the number of case studies was reported vs the previous year: 2018
• The largest decrease in the number of case studies was reported vs the previous year: 2020

Comprehensive list of machine learning case studies

AIMultiple identified 99 case studies in machine learning covering 10 benefits and 99 use cases. You can learn more about these case studies in table below:

Our research on machine learning software

If you want to learn more about machine learning software, you can also check our related research articles that can assist you in your decision:

• Social Media
• Account-Based Marketing
• Affiliate Marketing
• Digital Marketing

AI in Marketing: 4 Real-World Examples and Case Studies

Artificial intelligence (AI) is rapidly transforming the marketing field, offering businesses new ways to personalize their messaging, analyze customer data, and create more effective marketing campaigns.

By using machine learning algorithms and predictive analytics, companies can better understand customer behaviors , preferences, and needs, and tailor their marketing efforts accordingly.

In this blog post, we’ll explore some real-world examples of how businesses are using AI to improve their marketing efforts.

By examining these case studies, we’ll gain a better understanding of the potential benefits of AI in marketing, as well as the challenges and opportunities that this technology presents.

Whether you’re a small business owner or a marketing professional, understanding how AI is being used in marketing can help you stay ahead of the curve and make more informed decisions about your marketing strategy.

So let’s dive in and explore some of the most compelling case studies of AI in marketing .

1. How Netflix Uses AI to Deliver a More Personalized Customer Experience

Netflix is perhaps one of the best-known examples of how AI is being used in marketing.

The streaming giant has long relied on data analysis and machine learning to recommend content to its users. This personalized recommendation system has been a major driver of the company’s success.

Netflix’s AI-powered recommendation system analyzes a wide variety of data points, including a user’s viewing history, search history, and ratings, as well as information about the content itself, such as its genre and director.

Using this data, the system can generate personalized recommendations that are tailored to each user.

The benefits of AI in marketing have been significant

According to Netflix, its recommendation system is responsible for 80% of the content that users watch on the platform.

This has helped to improve customer retention and engagement, as well as drive new sign-ups. The company estimates that its recommendation system has saved it over $1 billion in customer retention costs.

But the benefits of AI in marketing go beyond just recommendations

Netflix is also using AI to optimize its content creation process.

By analyzing viewer data and identifying patterns in what users are watching, the company can make more informed decisions about what types of content to produce, and how to market that content to its users.

Overall, Netflix is a powerful example of how AI can transform marketing.

By using machine learning to understand its users better, the company has improved customer engagement, driven sales, and stayed ahead of the competition.

2. How Sephora Uses AI to Transform the Beauty Industry

Sephora, the cosmetics retailer, is another excellent example of how AI can improve customer experience.

The company has implemented a chatbot on its website and mobile app that uses machine learning algorithms to personalize the customer experience and provide support and recommendations to shoppers.

The chatbot called the Sephora Virtual Artist, uses facial recognition technology to help customers try on different makeup products and experiment with different looks.

Users can upload a photo of themselves to the app, and the chatbot will use machine learning algorithms to analyze their features and provide personalized makeup recommendations.

In addition to the Virtual Artist, Sephora has also implemented an AI-powered chatbot that can answer customer questions and help shoppers find the products they’re looking for.

The chatbot is designed to mimic the experience of talking to a real sales associate and uses natural language processing to understand customer inquiries and respond appropriately.

The benefits of these AI-powered tools have been significant

By providing personalized recommendations and support, Sephora has been able to improve customer satisfaction and drive sales.

The company reports that customers who engage with the Virtual Artist are more likely to make a purchase and that the chatbot has helped to reduce the number of support inquiries that its customer service team receives.

Overall, Sephora’s use of AI in marketing demonstrates the potential for these technologies to transform the customer experience.

By providing personalized recommendations and support, companies can improve customer satisfaction, drive sales, and build long-term customer loyalty.

Suggested Reading: 7 Free Online AI Chatbots to Improve Your Customer Service

3. How Coca-Cola Uses AI to Deliver More Personalized Marketing Campaigns

Coca-Cola is another company that has embraced the power of AI in its marketing efforts.

One notable example is the company’s use of machine learning to optimize its product packaging and distribution.

Coca-Cola worked with a data analytics firm to create an AI system that would analyze sales data and identify patterns in customer preferences.

Using this data, the system was able to recommend specific product packaging and distribution strategies for different markets and demographics.

The results of this approach were impressive.

Coca-Cola was able to improve its sales and distribution efficiencies by up to 30%, resulting in significant cost savings and increased profits.

The company was also able to tailor its marketing efforts more effectively, using data-driven insights to develop more targeted campaigns and messaging.

In addition to its work on product packaging and distribution, Coca-Cola has also used AI to develop a virtual assistant that can help customers find the products they’re looking for and answer common questions.

The virtual assistant is available on the company’s website and mobile app and uses natural language processing to understand customer inquiries and respond appropriately.

By embracing AI in its marketing efforts, Coca-Cola has been able to stay ahead of the competition and drive significant business results.

By using machine learning to better understand customer preferences and optimize its operations, the company has been able to improve its sales, reduce costs, and build a stronger brand.

4. How Unilever Uses AI to Build Brands and Drive Sales

Unilever is a global consumer goods company that has also been at the forefront of using AI in its marketing efforts.

The company has implemented several AI-powered initiatives to improve its customer engagement and marketing ROI.

One of the most notable examples is the company’s use of machine learning to optimize its social media advertising.

Unilever worked with a digital marketing firm to create an AI system that could analyze data from social media platforms and identify the most effective advertising strategies for different demographics and markets.

Using this approach, Unilever was able to improve its advertising performance significantly, with some campaigns achieving up to a 50% increase in engagement and click-through rates.

The company was also able to optimize its advertising spend, reducing costs while improving results.

Unilever has also used AI to develop a personalized shopping assistant for its customers

The assistant, called “Peggy,” uses natural language processing to understand customer inquiries and provide personalized product recommendations and support.

By leveraging AI in its marketing efforts, Unilever has been able to improve its customer engagement, increase sales, and build stronger relationships with its customers.

By optimizing its advertising and providing personalized support, the company has been able to drive significant business results while improving the customer experience.

AI in Marketing: Key Takeaways from Real-World Case Studies

These case studies demonstrate the significant impact that AI can have on marketing efforts.

By leveraging data-driven insights and machine learning algorithms, companies like Netflix, Sephora, Coca-Cola, and Unilever have been able to optimize their marketing strategies and drive significant business results.

However, it’s important to note that AI is not a magic bullet for marketing success.

These companies were able to achieve significant results because they invested time and resources into building and implementing effective AI-powered systems.

It takes careful planning, dedicated resources, and ongoing monitoring and analysis to ensure that an AI system is delivering the desired results.

That being said, the potential benefits of AI in marketing are clear.

By providing more personalized, targeted experiences for customers and optimizing advertising and distribution strategies, companies can improve their ROI and build stronger relationships with their customers.

As AI technology continues to develop and evolve, we can expect to see even more innovative applications in the world of marketing.

By staying up to date on the latest trends and best practices, companies can stay ahead of the competition and drive continued success.

Ali is a digital marketing blogger and author who uses the power of words to inspire and impact others. He has written for leading publications like Business2Community, Inc. Magazine, and Marketing Profs. When not writing, he enjoys spending time with his family.

Harnessing AI in Marketing: 16 Powerful Case Studies

In the dynamic and ever-changing world of digital marketing, businesses are perpetually on the hunt for innovative methods to engage their target audience. One groundbreaking technology that’s causing a stir in this sphere is Artificial Intelligence (AI).

AI in marketing is a revolutionary force, offering a plethora of opportunities for businesses to amplify their strategies and enrich customer journeys.

Demystifying AI in Marketing

AI in marketing is a multifaceted concept that involves leveraging customer data and AI technologies, such as machine learning, to predict customer behavior and enhance their journey.

This technology is not a futuristic fantasy; it’s a present reality that’s revolutionising the way businesses attract, nurture, and convert prospects.

AI in marketing is about understanding your customers better than ever before. It’s about using data to predict their needs, wants, and behaviours, and then using that insight to deliver personalised, relevant experiences that drive engagement and loyalty.

It’s about automating routine tasks, freeing up your team to focus on strategic, high-value activities. And it’s about using advanced analytics to measure performance, optimise campaigns, and continually improve results.

The Profound Impact of AI in Marketing

According to Salesforce, the adoption of AI in marketing has seen a meteoric rise, surging from 29% in 2018 to a whopping 84% in 2020 .

This trend is set to continue, with the global value of AI marketing projected to skyrocket from $12 billion in 2020 to an astounding $108 billion in 2028 .

These figures underscore the growing importance of AI in marketing. Also highlighting the pressing need for businesses to embrace this transformative technology.

The impact of AI in marketing is multi-dimensional. It’s not just about improving efficiency or boosting performance – although AI certainly delivers on those fronts. It’s also about enabling new ways of working, new ways of engaging customers, and new ways of driving growth.

It’s about empowering marketers to be more creative, more strategic, and more customer-centric. And it’s about equipping businesses with the tools and insights they need to compete – and win – in an increasingly digital, data-driven world.

AI is changing the game in marketing, and businesses that fail to adapt risk being left behind.

But those that embrace AI stand to reap significant benefits, from improved customer engagement and loyalty, to increased efficiency and effectiveness, to a stronger bottom line.

The Role of AI in Ecommerce: A New Era for Business

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A Complete Guide to Machine Learning in Marketing

Learn how your digital marketing strategies can benefit from machine learning and AI.

Machine learning and artificial intelligence technologies have come a long way in the past few years.

From self-driving cars that allow you to go hands-free to robo-advisors that make investments on your behalf, machine learning has applications that impact all types of industries. Machine learning can even help you build better data-driven digital marketing strategies by quickly analyzing relevant data sets and automating repetitive tasks.

If you run a business, you’re always on the lookout for technologies that can give your company a competitive edge and make your job easier. By using machine learning in marketing, you can build stronger campaigns, personalize your marketing materials, and save yourself time and energy in the process.

While machine learning may sound like a difficult concept to wrap your head around, it can actually be a very useful tool for business owners and marketers seeking to automate certain aspects of their campaigns and leverage valuable data.

Machine learning in digital marketing is poised to revolutionize the industry, making everything from digital ad campaigns to content to personalized recommendations easier to create and manage.

In this article, we explain what machine learning is and how machine learning in marketing can prove useful. Additionally, we share some suggestions for how you might use machine learning for marketing so that you can build better data-driven campaigns.

Read the article from start to finish for our complete guide to machine learning in marketing.

What is machine learning?

Machine learning falls under the umbrella of artificial intelligence. Essentially, machine learning describes systems and software applications that are designed to “learn” based on analyzing data sets.

Or, in other words, machine learning consists of technology that receives input, identifies patterns, and independently adapts to new data in order to form solutions and solve problems.

This is a high-level definition of machine learning. In practice, there is a vast array of machine learning technologies that exist, many of them designed for highly specialized purposes. Machine learning is a concept that can be applied to virtually every industry you can think of, from healthcare to retail to shipping logistics to marketing.

Machine learning in marketing tends to involve software programs and other technologies that allow marketers to quickly analyze and extract valuable insights from large data sets. Machine learning software can also help automate many different processes, streamlining certain marketing tasks and saving marketers precious time and energy.

Many repetitive, mundane tasks that marketers would typically have to do themselves—such as combing through analytics and reports, optimizing content, and segmenting audiences—can be achieved more quickly and efficiently with machine learning.

How has machine learning impacted the way businesses operate?

While machine learning is a relatively new technology, it is quickly evolving and changing the ways in which many businesses operate. This is in large part because so many businesses rely on large and often complex sets of data to make decisions and create short- and long-term strategies. Machine learning models can help businesses organize and manage their data and—perhaps even more importantly—interpret the raw data in order to produce valuable insights.

In this day and age, machine learning can also help your business remain competitive in a crowded industry. Being able to collect and leverage data related to your customers, your business, and your industry can provide you with a competitive edge.

By combining accurate data with sophisticated machine learning technology, you can stay relevant in an era where the strategies of industry leaders are often dictated by data and algorithms.

Machine learning has had a particularly large impact on the digital marketing industry, which is constantly adopting new technologies and innovative methods to more effectively reach customers and build campaigns.

When used properly, machine learning models can prove extremely valuable to marketers and business owners. By leveraging machine learning algorithms to increase marketing automation and optimize marketing campaigns, you can actually do less work while increasing your bottom line.

In the next section, we go into even more detail about how machine learning algorithms can be used to take your marketing efforts to the next level.

How can machine learning be used in marketing?

Machine learning has many potential applications when it comes to marketing. Whether you’re trying to boost customer engagement, optimize marketing campaigns, or provide better customer service, there are ways for machine learning to help you achieve your goals.

So how, exactly, can machine learning be used in marketing? Below, we touch on five key areas where machine learning can help your marketing efforts and improve your customer experience.

Customer segmentation

Customer segmentation describes the process of breaking your customers up into different groups based on common characteristics.

By grouping customers together based on specific buying characteristics, you can more precisely target these groups and personalize your marketing campaigns. For instance, you might segment customers based on factors such as age, income level, geographic location, education level, purchasing habits, and so on.

With machine learning software, you can automate the customer segmentation process, making it more accurate and efficient. This is crucial for a few reasons.

First of all, large companies with vast amounts of customer data will likely find it tedious, time-consuming, and difficult to manually sort through data sets and segment customers. In addition to being an ineffective use of time, manually creating customer segments tends to be much more error-prone than using machine learning software.

Additionally, machine learning algorithms can help you identify patterns and segments that you never noticed before. With the right machine learning model, you can engage in hyper-segmentation, which allows you to break down broad customer segments into even smaller, more specific segments.

This allows you to create highly relevant and personalized content for your audience, which in turn can contribute to better customer relationships and more effective marketing campaigns.

As digital marketing software becomes more sophisticated, brands now have more data at their disposal than ever before.

In general, this is a good thing—with more data on hand, you can better understand your customers, your competition, and your industry at large. However, collecting large amounts of data can also lead to problems.

When dealing with huge amounts of data points, one of the main problems is that the data becomes difficult to manage and analyze. A large data set can prove to be overwhelming or unmanageable for any human being. Yet, a machine learning program can easily process and organize massive amounts of data in a short amount of time.

Whether you’re parsing through sales data or engaging in reputation management on the web, machine learning can help expedite tasks that may otherwise take hours or even days to complete. Additionally, machine learning models can identify complex patterns that would be hard for a human to recognize and even perform predictive analytics in order to predict probable outcomes.

Optimizing marketing campaigns

Using machine learning technologies, you can optimize marketing campaigns by making data-driven decisions. While in the past, marketers might launch a campaign with blind hope that it proves successful, this is no longer the case. Modern digital marketing methods leverage data so that you can reach the right customers at the right time, and get the most bang for your buck.

When working with a limited campaign budget, you want to get the highest return on investment (ROI) that you can. Machine learning models can help point you in the right direction by using existing data to show you which marketing channels might best be utilized to achieve your goals.

Machine learning software can even automate marketing campaigns for you, thereby saving you time and money. For instance, machine learning can help when running digital ad campaigns by automatically targeting relevant audiences and tailoring content to suit the goals of your particular campaign.

Put the power of AI to work for you

In this report conducted by Forrester, explore how AI can help you save time and create better marketing.

Customer service

If you’ve had a customer service interaction recently, you may have engaged with machine learning technology yourself. That’s because so many brands are now using machine learning models to create automated chatbots and facilitate positive customer service experiences.

Providing excellent customer service is important for virtually any business. This is because excellent customer service typically leads to high customer satisfaction, which in turn can lead to a high customer retention rate. Essentially, when you make your customers happy and quickly resolve their problems, this leads to happier customers who are more likely to make future purchases with your company.

Machine learning can help improve customer service by enabling you to create chatbots and digital assistants that can automatically address customer needs. You can train machine-learning chatbots to use natural language processing and handle a wide variety of customer issues, from shipping concerns to website navigation.

While chatbots may not be suited to handle every customer service task, they can be a helpful asset when customers want to ask a quick question or troubleshoot a common problem. Programming chatbots and virtual assistants can also free up more time for your customer service team, allowing them to provide higher quality service, quickly address customer issues, and avoid burnout.

Forecasting

When running a business, forecasting can often be one of the most complex and painstaking tasks you have to deal with. Whether you’re forecasting demand or calculating customer lifetime value (CLV), it can be difficult to come up with accurate figures when you’re dealing with large data sets and shifting market conditions.

Forecasting is especially important because erroneous figures can lead to real-world problems. For instance, demand forecasting has a direct impact on both pricing and supply.

Incorrectly forecasting demand can lead to you having a shortage, an excess of inventory, or prices that undercut your potential profits. Using machine learning tools, you can quickly identify patterns in data and more accurately forecast future events.

In addition to helping you forecast demand, machine learning models can help you forecast consumer purchases, customer lifetime value, customer retention rates, churn rates, and a number of other business metrics. With accurate business forecasts, you can better understand market conditions and strategically position yourself in your industry.

How can using machine learning in marketing benefit your business?

Through the use of machine learning solutions, you can benefit your business in more ways than one. Both basic and advanced machine learning models present opportunities for you to bolster your marketing campaigns, improve customer experience, save time and money, and make more data-driven decisions.

Curious about how machine learning can help your business? Below, we list four ways in which machine learning can help your company’s marketing efforts.

Lower costs

Given the fact that machine learning solutions are so sophisticated and capable of so much, you might assume they are expensive—however, this is not the case. In fact, these days, machine learning tools are often built into existing marketing software.

For instance, you can use Mailchimp’s Content Optimizer to build better email marketing campaigns with the help of artificial intelligence. Or you can use our dynamic content tool to automatically create more personalized marketing emails that resonate with your audience.

On top of being surprisingly affordable, machine learning tools can cut down on costs in other areas as well. You can save money by automating manual processes that may have otherwise taken hours and use accurate insights to make business decisions that lower costs for your organization.

Higher-quality data analysis

Perhaps one of the best features that machine learning has to offer is its ability to quickly and accurately analyze large sets of data. While it’s possible to conduct data analysis manually, this causes several problems.

First of all, manually conducting data analysis on a large scale can be extremely time-consuming. A job that a machine learning model can complete in seconds or minutes may take human beings hours or days.

Manual data analysis is also more prone to error than data analysis conducted by machine learning tools. Machine learning programs can quickly process large batches of information and highlight patterns that would often be unrecognizable to the human eye.

With high-quality data analysis, you can create highly specialized customer segments, accurately track campaign performance, and gain key insights that can help guide business decisions moving forward.

Automation of processes

You can save your team time, money, and energy by leveraging automation in your business and marketing processes. Automation can be a great tool to streamline time-consuming, repetitive tasks. Whether you’re targeting specific audiences or building out the customer journey, automation can help you more effectively engage with customers while saving you time.

Automation facilitated by machine learning can prove useful in several aspects of your business.

For instance, you can use automation to quickly respond to customer inquiries and improve customer service. Machine learning also allows you to automate different parts of your marketing campaigns. With Mailchimp, you can set up automation to send abandoned cart emails, set up retargeting campaigns, collect customer feedback, and much more.

Customer satisfaction

Customer satisfaction measures how well you satisfy customer expectations and keep consumers happy. Virtually every business owner strives to maximize customer satisfaction since satisfied customers tend to be more engaged, loyal, and likely to make purchases in the future.

By incorporating machine learning tools into your business and customer service processes, you can increase customer satisfaction and establish a loyal customer base for your brand. That’s because machine learning enables you to build chatbots that quickly address customer problems and concerns.

Additionally, by enabling automation and using data to optimize the customer journey, you can increase the number of positive interactions that consumers have with your brand.

Implement machine learning models in your business operations

Machine learning presents marketers and business owners with a number of opportunities to optimize digital marketing campaigns and improve business processes. Using machine learning, you can more effectively analyze data, improve the customer experience, and automate all sorts of tasks. Overall, this can help you better serve your customers while improving your bottom line.

Mailchimp’s all-in-one marketing platform incorporates machine learning and other innovative technologies to help your business run as smoothly as possible. From AI design features to smart recommendations , Mailchimp’s software can help you create better content and run data-driven marketing campaigns that generate results.

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Artificial Intelligence Case Studies: Two companies that boosted brand awareness with AI and another marketer that used humans instead

This article was published in the MarketingSherpa email newsletter .

“Apply to join our new research cohort where we will help you test and build an AI-calibrated MECLABS SuperFunnel,” Flint McGlaughlin offered in Landing Page Conversion: 4 powerful ways to develop a cohesive, effective strategy .

It’s part of our effort (MECLABS Institute is the parent organization of MarketingSherpa) to work with marketers and entrepreneurs to determine how to best use current and emerging AI-driven technologies to build an effective funnel.

But rest assured, to serve you, this reporter has to remain a skeptic of artificial intelligence, machine learning, and really, every other “next best thing.” In much the same way I’m guessing a magician can’t muster up the suspension of disbelief necessary to watch someone else’s magic show, after a lifetime in the marketing and advertising industry I just can’t hop on a bandwagon without a healthy dose of skepticism.

So, to help you determine when to use (and avoid) AI, today we bring you two stories of when brands leveraged AI…but also a story about a brand that avoided using the technology.

First up, a television network that used artificial intelligence and machine learning to get more traffic from organic distribution. Then, how SAP used AI to avoid cookies and increase brand awareness with a paid campaign. And finally, an adventure travel marketplace that invested in humans instead of AI to increase sales from organic traffic.

Quick Case Study #1: How artificial intelligence and machine learning helped television network increase pageviews 63% from Twitter

Sistema Brasileiro de Televisão (SBT) is a free-to-air Brazilian broadcaster with 114 television stations and over 6,000 employees. The network’s website attracts 11 million unique viewers and more than 99 million pageviews per month. The TV network as 12 million followers on its main Facebook page.

Social media challenge

On an average day, SBT posts between 100 to 150 pieces of content to Facebook alone.

“We do not have a centralized social media team, so every content production team ends up posting independently,” says Rodrigo Hornhardt, Journalism Integration and Planning Manager, Sistema Brasileiro de Televisão . In total, he estimates that around 20 people throughout the company post regularly to SBT’s social media accounts, rendering the post scheduling process time-consuming and convoluted.

SBT posts content to a wide variety of social platforms, but Facebook is the most important. According to Hornhardt, “around 40% to 50% of our web traffic comes from Facebook.”

Social media content approach – leverage artificial intelligence and machine learning

To solve these challenges, SBT turned to marketing automation technology powered by artificial intelligence (AI) and machine learning.

Before, SBT’s staff had undertaken the laborious task of posting new content manually — a workflow that was as time consuming as it was inefficient, especially given the decentralized nature of SBT’s social media management. “With so many people posting so much content across the company on different platforms, AI is so important in keeping everything aligned,” notes Hornhardt.

SBT’s reasons for selecting an AI solution were manifold: “The possibility of having AI recommend the best content to post was important, but so, too, was the ease with which multiple people can manage posting for a unified output. The ability to automate workflows was also a key consideration,” he said.

To increase the reach, visibility, and engagement on posts, the team makes regular use of real-time platform trend data alongside SBT’s own audience data to determine the best time to share content. This not only reflects audience habit — when do SBT’s readers most often engage with posts? — but also the ever-changing factors of Facebook’s Feed algorithm. Hornhardt notes that SBT News in particular uses AI to understand the optimal time to publish and maximize the impact of the multiple news items it posts each hour.

AI technology determines optimal share timing by constantly analyzing and computing the predicted performance of shares to maximize their inclusion in the Feed. Using advanced machine learning, it becomes possible to continuously reverse engineer the workings of Facebook’s algorithm, producing a sophisticated and accurate picture of the best time to post to drive engagement.

When posts are shared, AI automatically continues to update each post’s optimal time based on the latest data, pushing certain posts back if new, higher-potential posts are added, or bringing posts forward if their short-term potential goes up. This is just one example of how using AI technology to augment SBT’s social sharing has meant, in Hornhardt’s word’s, “increased reach and organic and substantial growth.”

Social media results

By incorporating artificial intelligence into its social media strategy, SBT saw strong improvements in performance on social media. Within four months of adopting this AI technology, SBT’s social media pages saw increases in daily clicks of 25%, whilst daily organic impressions rose by 61%. On Facebook, SBT saw a 52% increase in daily impressions, while the company also saw improvement gains on Twitter — pageviews from the platform increased by 63%.

In addition to these performance gains, SBT has increased workflow efficiency using automation, bringing time savings in the process. Within four months of adopting AI, the team shared almost 40,000 posts; making these shares via AI and using automation has saved the company 14 hours per day.

“Brands and content producers can greatly benefit from intelligent automation, but it must be tailored to their content and audience,” says Simran Cashyap, Head of Product & Design, Echobox (SBT’s content automation provider). “While each brand must experiment to determine the ideal level of automation for them, we tend to find that the more a brand automates, the bigger the performance gains, as the AI algorithms have more opportunities to continuously learn from the content and audiences.”

By relying on AI’s capability of calculating the optimal share time, the team has increased traffic and impressions whilst saving significant amounts of time. Daniela Nobre, Customer Success Representative, Echobox, explains AI and automation “completely overhaul a company's organic marketing strategy by digging into granular data, something little to no marketing teams could try to replicate.”

For Hornhardt, this is only the start of what could become a more specialized social strategy. “We need to dedicate more working time to planning and defining strategies for networks,” he told us. “One way would be to have specialists dedicated to this strategy with the possibility of creating an audience/networking team. We see AI supporting us in this endeavor.”

Quick Case Study #2: How SAP used AI-powered contextual intelligence to increase brand awareness 4% without cookies

The demise of the third-party cookie has left data-driven companies with a new focus: to build cookie-less alternatives into their marketing strategy. And that’s exactly what global software company SAP did this year.

The team has always had a robust marketing strategy in place, but with the end of the third-party cookies looming, it recognized the increasingly pressing need to focus on privacy-first, cookie-less campaigns.  

The contextual experiment to overcome the cookie-less challenge

The team chose to place its ads alongside contextually relevant content, targeting key business decision makers based on what they had chosen to look at in the moment.

They used contextual targeting technology that uses AI to see and analyze the full content of a page by scanning all of the main content data signals – from text and imagery to audio and video – for accurate and safe ad placement, without the need for third-party cookies or any personally identifiable information (PII).

The team used a wrap-style ad that wraps around the content the visitor is viewing. The ad had an entrance animation to attract the user’s attention. The right panel had a countdown to the advertised webcast. And the left panel had a message and CTA.

Creative Sample #1: Wrap ad for SAP

When the user scrolled, the background changed to another visual. The headline was repositioned onto the left panel, so it had a consistent presence.

Creative Sample #2: Scrolled state of wrap ad for SAP

“Throughout the campaign, the aim was to boost brand and product awareness,” commented Moritz Fisecker, Integrated Media Manager EMEA, SAP .

Advertising campaign measurement methodology

In order to successfully establish the impact of SAP’s campaign, the team analyzed the difference in results for a control sample (users who were not exposed to the high-impact ad creative) and the target audience (users who were exposed to the ad creative).

Finally, they used eye tracking to understand the level of consumer attention the ads garnered.

Advertising campaign results

“Overall, the campaign results proved that brands can reach consumers just as effectively – if not more effectively – using a completely cookie-less approach to digital marketing rather than relying on behavioral data,” Fisecker said.

More than half (61%) of users exposed to the campaign took or intended to take some sort of action; and the ads had a 93.2% viewability rate.

Others results included:

• 0.9% click-through rate
• 4% uplift in overall awareness as a direct result of the campaign
• 5% positive shift among the target audience
• 5% improvement in perceptions of SAP as offering the best business software and solutions
• 7% improvement in perceptions of SAP as a trusted brand 
• 1 in 3 users felt the ad was informative

In addition, the study concluded that the ad creative drove significantly more users to become “very interested” in finding out more after being exposed to the ad.

“In addition to awareness, Lumen Research and On Device Research (ODR) – SAP’s measurement partners for the campaign – confirmed that users who saw the ads had a heightened interest in finding out more. The results also affirm the role of contextual data in the future of mar tech. In this new cookie-less, privacy-first era, tapping into the customer’s mindset ‘in the moment’ will be the key to inspiring them, and ultimately, influencing their behavior,” said Peter Wallace, General Manager EMEA, GumGum (SAP’s contextual intelligence partner).

Quick Case Study #3: How adventure travel marketplace generated $1 million in sales from organic traffic by having humans (instead of machines) write content

10Adventures is an online marketplace that connects adventure travel enthusiasts with local guides around the world. In addition to allowing users to book adventure travel, it recently launched a subscription-based GPS-trail app that enables people to safely explore the outdoors.

“We launched 10Advnetures in 2019, with a WordPress MVP (minimum viable product) and great content,” said Richard Campbell, Founder & CEO, 10Adventures . The site currently has more than 1.5 million annual unique visitors.

Let’s take a look at the most – and the least – effective tactic the team used to grow website traffic.

Most Effective Tactic – Human-written content paired with SEO basics and site speed enhancements

“We have had an incredible journey to build our skills in ranking high in Google,” Campbell said. “There are literally dozens of small bits of work we have done to get our content to the top of Google and Bing.”

The team invested the time necessary to create engaging, factual, useful content that targeted the right keywords.

 “Users want quality content. The web is filled with superficial content, written by content farms with lots of mistakes. This is especially true in the outdoors market,” Campbell said. “In the world of content, the cost of efficiency usually comes at the expense of quality. Many businesses will turn to cheaper or faster solutions like AI writers or outsourcing to meet their goals but won’t see the desired performance if the quality isn’t there.”

The team’s most popular content is their free route guides, especially regional pages of the best hikes (or road bike rides, backpacking routes, cross-country skiing, etc.) in a geographic region.

Creative Sample #3: Route guide content on adventure travel marketplace website

The team then implemented the following elements of on-site SEO:

• URL structure
• Page titles
• Internal links

For external links, the team tried third-party backlink providers but found that doing it themselves was cheaper and ensured that all backlinks were organic.

The team felt a Google algorithm change punished the site for having slow site speed. “Algorithm changes are a constant reality, and Google places plenty of demands on websites to perform,” Campbell said. “Being listed first in Google is tough, and recently site speed has been an important metric that we felt was hindering our ability to be first in Google.”

Campbell continued, “Unfortunately, our site was based on WordPress, and we had done everything we could to improve site speed.” This included identifying performance bottlenecks and optimizing the databases and code behind them.

Ultimately, though, the team had to implement a new solution, and moved to a static site, rebuilding the entire front-end in a new language. This meant each page is rendered and doesn’t need database calls, creating a quicker user experience and greatly improved page speed performance. In addition, they now have edge servers around the world. In the month since this change, organic traffic is up 67% compared to last year. 

For this reason, the team has baked in regular content reviews to look for opportunities to rank higher, as well as evaluate the website’s performance.

Least Effective Tactic – Paid marketing

The team found that paid marketing was not only expensive when compared to what they were doing with organic, but it also resulted in a lower conversion rate – their paid advertising leads convert at 10% of their organic leads.

The team has created many permutations of different ads, but in general can’t replicate what worked pre-pandemic. In 2019 when they launched, customer acquisition cost was about $300. Since the end of last year and the first seven months of this year, it was around $1,000.

Thanks to the traffic generated by their organic tactics, they now have a registered user base of more than 50,000.

The previously mentioned increase in organic traffic has also led to growth in another key area of the business – sales. When looking at the 10Adventures marketplace and tour sales over the last 18 months, the team saw steady growth that has culminated in nearly $1 million of sales over that period, and almost 5x growth year-on-year.

Going forward, the team is working on experiments with their new site to understand their ability to influence rankings from on-page rewrites and other small-scale optimizations. They want to know if it’s easier to move content from third place to second, from eighth to third, or from 40th to eight, etc.  Does this ability vary based on the type of content, or the location the content describes? These discoveries will guide their work in the coming year to continue to grow their organic traffic.

Related Resources

High-Converting Landing Page: If you don’t ask this question you will never maximize conversion

Artificial Intelligence and Machine Learning in Marketing: What marketers (even those who don’t care about tech) should know about AI and ML

Marketing Funnel: 3 quick case studies to help you increase conversion

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Optimal planning with AI

How a leading US retailer used AI to improve marketing spend.

3-minute read

Call for change

Every year, a top American retailer sees some $14-15 billion in marketing-driven sales, which means decisions on how to allocate marketing dollars—and specifically, media spend—aren’t taken lightly.

But using historical data to decide where to spend among the dozens of channels available—from traditional TV to Tik Tok—isn’t easy. The data is often stale by the time it’s available to analyze, and the number of new channels and platforms grows all the time.

With so much money at stake and the difficulty in getting quick answers, increased speed and agility were at the top of the retailer’s wish list, and the company issued a challenge to Accenture: To get more specific, actionable insights faster.

When tech meets human ingenuity

Accenture partnered with the retailer to design an AI-powered solution that would enable faster and better data collection and more precise modeling to optimize media spend. The first task was speeding up the existing data flow process, then aggregating and processing all the data from media channels, sales and spend that fed the measurement model. By customizing AIP+ , Accenture’s pre-integrated AI services and capabilities, to do the data aggregation, we helped cut the existing process by 80% using automation to accelerate processing and validation.

With data flow addressed, the team looked next to alter the underlying model that produced the measurement. Previously, these models were hypothesis-driven, i.e., people would painstakingly hypothesize every possible interdependency between different channels. New machine learning was introduced to the process, helping to proactively identify those interdependencies between channels that potentially drive sales. With the new monthly cadence, the team could refresh the models every month, iterating from the previous month’s model instead of starting from scratch. By hosting deep-dive training sessions for employees on the modeling methodology, the team offered them transparency that earned buy-in and trust in the solution.

A valuable difference

The results were significant..

The solution shortened the lag between the measurement period and performance insights from five months to five weeks, opening up a 10 and a half month planning runway for the same period the following year. Also, going from one annual measurement (where performance was expressed as an average) to monthly measurements meant that insights were more nuanced, so the team could see how one channel or another might vary in performance throughout the year.

Even more concretely, the team estimates that $300 million in media buying opportunities and value creation was unlocked by implementing the new tool. This meant the team could spend the same amount on media and generate an additional $300 million in sales.

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Exploring Machine Learning in Digital Marketing with Examples

Vlada Malysheva ,  Creative Writer @ OWOX

The Ultimate Guide to Marketing Attribution Modeling: Everything You Need to Know

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A couple of decades ago, the first thing that came to mind when you heard the words “artificial intelligence” was likely the rise of machines and the Terminator with a sawed-off shotgun. Today, this term has rather positive associations. Almost everyone encounters machine learning in ordinary life. For example, you might communicate with a chatbot on a website, be shown promotional offers corresponding to your hobbies, or set up a spam filter in your email service.

For marketers, machine learning is an opportunity to make crucial decisions based on big data quickly. In this article, we’ll talk about what decisions you can make based on big data.

Note: This post was originally published in August 2020 and was completely updated in January 2024 for accuracy and comprehensiveness. 

Let’s start with a little terminology. According to Wikipedia, machine learning (ML) is a class of artificial intelligence methods characterized by their not providing direct solutions to problems but rather training systems to apply solutions.

There are many methods of machine learning, but they can roughly be divided into two groups: 

learning with a teacher 

learning without a teacher.

In the case of  learning with a teacher , a person supplies the machine with initial data in the form of  situation–solution pairs. The machine learning system then analyzes these pairs and learns to classify situations based on known solutions. For example, a system can learn when to mark incoming messages as spam.

In the case of  learning without a teacher , the machine receives unsorted information — situations — without solutions and learns to classify those situations based on similar or different signs without human guidance.

Digital Marketers use machine learning to find patterns in user activities on a website. This helps them predict the further behavior of users and quickly optimize advertising offers.

What is the potential of behavioral data of the users?

In psychology, a  pattern is a particular set of behavioral reactions or a common sequence of actions. Therefore, we can talk about patterns with regard to any area where people use templates (which is most areas of life).

Consider the example of a pattern used on websites. If the user isn’t interested in the offer in the pop-up window shown below, they can close this window by:

• clicking on the X sign
• clicking No thanks
• clicking anywhere on the site that’s outside the pop-up window.

In addition to these three actions the user can take, the pop-up window will close on its own after a certain period of time.

So we get four possible user actions:

• Click X — Can be true/false
• Click No thanks — Can be true/false
• Click past the pop-up — Can be true/false
• Pop-up viewing time is 5 seconds

When hundreds of such parameters are collected, the collected data gains value because it contains   behavior patterns and dependencies. It hides the enormous potential of behavioral data, allowing us to supplement user data with the missing parameters based on the data we already have for other users.

For example, the simplest way to define a target audience is by gender and age. But what if users fill out this data only in 10% of cases? How can you understand how many of your website users fall into your target audience? Patterns of behavior can help.

You can use gender and age data from 10% of users to determine patterns specific to a particular gender and age. Then you can use these patterns to predict the gender and age of the remaining 90% of users.

Having complete data about gender and age, you can now make personalized offers to all website visitors.

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The role of machine learning in marketing is to allow you to make decisions based on big data quickly.

The algorithm for the work of marketers is as follows: Marketers create hypotheses, test them, evaluate them, and analyze them. This work is long and labor-intensive, and sometimes the results are incorrect because information changes every second.

For example, a marketer will need about four hours to evaluate 20 advertising campaigns considering 10 behavioral parameters for five different segments. If such an analysis is carried out daily, the marketer will spend precisely half their time assessing the quality of campaigns. When machine learning in digital marketing is used, evaluation takes minutes, and the number of segments and behavior parameters is unlimited.

With machine learning, you can respond faster to changes in the quality of traffic brought by advertising campaigns. As a result, you can devote more time to creating hypotheses rather than to carrying out routine actions.

The value of your results depends on the relevance of the data on which the analysis was conducted. As data becomes obsolete, its value decreases. A person simply can’t process the volumes of information that are collected every minute by analytical systems. Machine learning systems can process hundreds of requests, organize them, and provide results in the form of a ready answer to a question.

Key benefits of machine learning in marketing:

• Improves the quality of data analysis
• Enables you to analyze more data in less time
• Adapts to changes and new data
• Allows you to automate marketing processes and avoid routine work
• Does all of the above quickly

Machine learning is transforming marketing through data-driven insights and automated decision-making, enhancing customer understanding and campaign effectiveness. In the following sections, we'll explore specific examples of machine learning in digital marketing:

1. Customer Lifetime Value Prediction

Essenc e: Estimating the total value a customer will bring to a company over the entire duration of their relationship.

What it predict s: The potential revenue a customer can generate in the future.

How this data is used: To prioritize marketing and sales efforts and tailor retention strategies for high-value customers.

Result: More efficient resource allocation and a focus on cultivating long-term customer relationships.

Common algorithms for this purpose: XGBoost , SVM , Random Forest , and Logistic Regression .

Examples: E-commerce platforms like Amazon use CLV prediction to identify high-value customers. By analyzing purchase history, browsing behavior, and product reviews, they can forecast future buying habits. Such insights allow them to provide these high-value customers with premium services, such as exclusive offers or early sales access, which encourages continued loyalty and spending.

2. Sales Forecasting

Essence: Estimating future sales.

What it predicts: Expected sales for a future period based on historical data.

How this data is used: To make inventory, staffing, and other business decisions.

Result: Optimized operations and better preparation for market demands.

Common algorithms for this purpose: ARIMA 

Examples: Walmart, in anticipation of major events like Black Friday, leverages sales forecasting to optimize inventory management. The company predicts upcoming product demand based on past sales data, current market dynamics, and existing inventory levels. This analytical approach allows retail giants, including others like Target, to be adequately stocked and prepared, ensuring they meet consumer demand during peak shopping seasons.

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3. Churn Rate Forecasting

Essence: Determining the likelihood of customers discontinuing their use of a product or service.

What it predicts: Which customers are at risk of leaving or not renewing their subscriptions?

How this data is used: To engage at-risk customers with retention strategies, special offers, or personalized communications.

Result: Reduced customer turnover, sustained revenue streams, and insights into potential areas of improvement.

Common algorithms for this purpose: Support Vector Machines , Logistic Regression

Examples: Verizon, a telecom giant, could predict churn by examining factors like call drop rates, customer service interactions, and changes in monthly bill amounts. By identifying customers who exhibit behaviors indicative of churn, they can proactively engage them with targeted promotions, personalized offers, or service improvements to retain their business

4. Customer Segmentation

Essence: Grouping customers based on shared characteristics, behaviors, or preferences.

What it predicts: Which segment does a particular customer belong to?

How this data is used: To tailor marketing messages, offers, and product recommendations for different segments.

Result: More targeted and personalized marketing, leading to better customer engagement and increased sales.

Common algorithms for this purpose: Hierarchical Clustering , and K-means clustering .

Examples: Netflix employs machine learning to categorize its audience according to their watch history, favorite genres, frequency of streaming, and device preferences. This enables Netflix to refine content suggestions, and craft targeted marketing strategies for diverse user groups.

5. Predictive Analytics

Essence: Anticipating future outcomes based on historical and current data.

What it predicts: Potential future events such as sales trends, customer behaviors, or market changes.

How this data is used: To make informed decisions, prepare for future demands, and optimize strategies.

Result: Proactive business decisions that align with anticipated market needs.

Common algorithms for this purpose: Linear Regression , Decision Trees .

Examples: Using predictive analysis, Walmart refines its store inventory planning approach. By scrutinizing historical sales patterns, factoring in seasonal changes, and integrating insights from local events and promotions, the retailer optimizes its stock levels, adeptly reducing excesses and safeguarding against product deficits.

6. Chatbots and Virtual Assistants

Essence: Automating customer interactions and service requests.

What it predicts: The intent and context behind a user's question or statement.

How this data is used: To provide relevant responses or actions.

Result: Quick, consistent, and scalable customer support or engagement.

Common algorithms for this purpose: Natural Language Processing (NLP) and Seq2Seq models .

Examples: Google Assistant doesn't just respond to queries; it uses machine learning to understand context, user preferences, and even nuances in voice tone. This continuous learning allows it to provide more accurate and personalized responses over time.

7. Email Marketing Optimization

Essence: Enhancing the effectiveness of email campaigns.

What it predicts: Best send times, optimal content, and target audience for email campaigns.

How this data is used: To send emails with higher open rates, click-through rates, and conversions.

Result: Improved engagement and ROI from email marketing campaigns.

Common algorithms for this purpose: Random Forest . 

Examples: Mailchimp doesn't only send emails. Analyzing metrics like open rates, click-through rates, and conversion rates can suggest optimal send times, content tweaks, or audience segmentation to ensure the best engagement for each campaign.

8. Ad Targeting

Essence: Displaying the most relevant advertisements to users.

What it predicts: Which ad a specific user is most likely to engage with?

How this data is used: To place ads in front of the most suitable audience.

Result: Higher ad engagement, click-through rates, and conversion rates.

C ommon algorithms for this purpose: Logistic Regression , and Decision Trees .

Examples: Nike collaborates with platforms like Facebook and Instagram to launch its new shoe. These platforms use machine learning to analyze user interests and behaviors, pinpointing long-distance runners. Nike then delivers personalized ads highlighting the shoe's benefits to this targeted group, resulting in increased engagement and efficient ad campaign performance.

9. Sentiment Analysis

Essence: Gauging public sentiment from text data.

What it predicts: Positive, negative, or neutral sentiment of a given piece of text.

How this data is used: To understand customer opinions, feedback, and overall brand perception.

Result: Informed strategies that address customer concerns and leverage positive feedback.

Common algorithms for this purpose: NLP .

Examples: Using machine learning, platforms like Yelp can automatically categorize user reviews based on their sentiment. By parsing the text, Yelp swiftly discerns whether a review leans positive, negative, or neutral. This approach not only streamlines the user experience by highlighting prevailing sentiments but also offers businesses a consolidated view of customer feedback, facilitating more effective responses.

10. Website Content Optimization

Essence: Dynamically adjusting website content based on user behavior.

What it predicts: Content or layout that a specific visitor is most likely to engage with.

How this data is used: To personalize the website experience for individual visitors.

Result: Higher engagement, lower bounce rates, and improved conversion rates.

Common algorithms for this purpose: Multi-Armed Bandit , and Reinforcement Learning .

Examples: Online platforms like The New York Times utilize machine learning to enhance user experience by tailoring content displays. The site can make informed decisions by tracking and analyzing user behavior, like their reading preferences and frequency. If a user frequently engages with tech articles, the algorithm ensures those topics are more prominently showcased on their homepage or suggested articles list. This results in a more personalized user experience, encouraging longer site visits, increased engagement, and greater loyalty to the platform.

11. Attribution Modeling

Essence: Determining the impact of different marketing touchpoints.

What it predicts: The contribution of each touchpoint to a conversion.

How this data is used: To allocate budget and focus on the most impactful marketing channels.

Result: Optimized marketing spending and strategy.

Common algorithms for this purpose: Logistic Regression , Random Forests , and Shapley Value .

Examples: Airbnb aims to understand the effectiveness of its diverse marketing channels in driving bookings. Through this method, they can trace the entire customer journey, from an initial touchpoint, such as a Facebook ad, a Google search result, or an email campaign, to the final booking. This insight helps them optimize their marketing budget by focusing on the most impactful channels. Tools like Google Analytics assist businesses in comprehending the customer's path by showcasing the influence of various touchpoints, whether they're search ads, display ads, or other campaigns, in achieving desired conversions.

Don't just guess.

Attribution modeling in OWOX BI brings clarity to your marketing

Why is machine learning in digital marketing needed, and how does it help you solve the attribution problem ? This is a topic for a separate article (which we’re already preparing).

In this article, let’s figure out at what level decisions are made using attribution. We’ll compare these levels based on several criteria:

The level itself

Key decision-makers

Types of decisions made

Attribution models most often employed

Levels at which attribution-based decisions are made:

1. Vision Level

Who: A company’s vision is formed by its board of directors, CEO, and marketing director. 

When: Quarterly, yearly

What: Vision-level decisions are associated with investments in the brand and balancing budgets between online and offline. 

How: The tools used to make such decisions are market research and consultants. 

Model:   There’s rarely a place for classical data-based attribution models since the data on which decisions are based is not digitized enough.

2. Strategic Level

Who: Strategic decisions are made monthly by marketing and e-commerce directors.

When: Monthly

What: These decisions are devoted to allocating the budget between channels and defining the top-level KPI. 

How: The tools that help in making strategic decisions are Visual IQ, OWOX BI, or casual models. 

Model:  Here, the business uses data-driven attribution by GA4 , variations on the theme of the Shapley value and Markov chains, or machine-learning funnel-based attribution. At this level, it’s important to understand the mutual influence of channels and make strategic decisions on their development.

Examine the advantages and disadvantages of popular attribution models , ranging from standard ones to Markov chains, Conversion Lift, and Machine-Learning Funnel Based models.

3. Tactical level

Who: Usually, tactical decisions are made weekly or even more frequently by the manager of paid traffic acquisition. 

When: Weekly

What: Budget allocation occurs between campaigns and ad sets, and decisions are aimed at clarifying KPI and campaign goals.

How: For making tactical decisions, you can use Google Sheets or OWOX BI. 

Model:   Often at this level, specialists use standard attribution models

The peculiarity of this stage is that the budget for a channel has already been allocated. So at this point, it’s important to understand what campaigns to spend it on, control the results, and quickly turn off inefficient campaigns.

4. Execution Level

Who: Bid manager

W hen: Nearly in real-time

What: This is when the decision to evaluate the contribution of a particular announcement or keyword occurs in close to real-time.

How: Such decisions are typically made inside advertising services (Google Ads, Facebook Ads). 

Model:   In fact, the customer doesn’t care what optimization mechanisms are used here, as they look at the results of each service separately.

As you can see, machine learning in digital marketing is most useful for strategic and tactical tasks. Sometimes, it’s also applied at the execution level, but the general trend is that advertising systems develop fast and have a lot of data. The internal algorithms used in these systems to manage advertising campaigns produce better results than an external model based on machine learning.

The reason is that to apply machine learning in marketing, it’s necessary to export large amounts of data from the advertising service quickly and then quickly import results back. Technically, this is a difficult task to solve on an industrial scale. Therefore, at the execution level, marketers tend to rely on internal algorithms for optimizing advertising services.

To use machine learning to solve tactical and strategic problems, you need to ensure the completeness of your data. You can do this with OWOX BI. OWOX combines data from your website, advertising services, and CRM so you can automate your marketing reporting and build the attribution model you trust to grow your brand’s ROI.

Machine learning segmentation

OWOX BI has developed our ML-based solution , which calculates the probability of purchasing considering the purchased orders for each website user. Based on this calculation, you can create audiences, and use them to target advertising campaigns and 2X ROI , as one of our customers did .

Features of ML segmentation with OWOX BI

The OWOX BI model can be trained on data from different sources: CRM, websites, and mobile applications. Our solution allows selecting any targeted action: transactions, purchased goods, phone calls, adding goods to the cart, etc.

You can also set any period relevant to your business as a conversion window, depending on the timing of the purchase decision.

You can use the calculation results in different advertising services: Google Ads, Facebook, Instagram, etc.

You can use conversion prediction results to:

Adjustment bids in search or display ads.

Improve targeting in media campaigns.

Set expectations in media campaigns.

Increase incoming traffic, buy more widely and cheaper, and adjust the ads after user assessment.

Book a demo to learn more about ML segmentation opportunities for your business.

Conversion Lift attribution with OWOX BI

The Conversion Lift attribution model is based on ML and shows the incremental contribution of each channel and campaign to the sale. That is why you can immediately analyze any launched campaign without waiting months to get a full overview, even if the sale would take place only in the future.

For example, you have launched campaigns that work on the top or middle of the funnel. And you need to get quick feedback on the impact of these campaigns on sales over the past week. 

This assessment can be carried out with Conversion Lift as early as the first week after the launch. You purchased traffic today. You don’t have sales yet, but you can already estimate the contribution to future sales.

In OWOX BI, you can connect any standard attribution model to your reporting. Also, our analysts can configure any algorithmic model based on conversion forecasting or a custom model for your rules and sales funnel.

You can analyze your digital campaigns from different angles, compare the results of calculations on several attribution models, and choose the one that is designed to suit your business goals.

Sign up for a demo and we’ll show you how OWOX BI attribution works. Our expert team will show you real-world examples of how to select the right attribution model for your business and how you can benefit from that.

Machine learning helps marketers use data to make better decisions, improve campaigns, and understand customers. It allows for accurate sales forecasts, targeted ads, and personalized marketing strategies, boosting business performance and customer satisfaction.

To use machine learning to solve tactical and strategic problems, you need to ensure the completeness of your data. You can do this with OWOX BI. OWOX combines your data from your website, advertising services, and CRM so you can create a funnel that takes into account the peculiarities and efforts of your business and is aimed at attracting customers and growing sales.

What kind of positive impact will machine learning have on the advertising industry?

What impact has machine learning made on the marketing industry, why is machine learning effective in marketing.

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3 years ago Data Science for Marketing Optimization – Case Studies from Airbnb, Lyft, DoorDash

Originally published in blogboard journal , jan 7, 2021..

In this article we’ll look at several case studies of data science being used to optimize marketing efforts at companies like Lyft, Airbnb, Netflix, Doordash, Wolt, Rovio Entertainment.

In the first quarter of 2019, Airbnb spent $367 million on sales and marketing. When you think about this from a technical standpoint, two obvious problems come to mind:

• How do you scale your marketing processes to be able to spend $300+ million per quarter on ads?
• Once you have systems in place to spend huge ad budgets, what’s an optimal way to allocate the money?

Summarizing articles from official blogs of these companies, we’ll get a high level overview of marketing automation and then zoom in on the parts where data science and machine learning play their role.

If you read on, you’ll find these three sections:

• Marketing automation systems  – what are they, what subsystems they comprise, where in the process is data science usually applied
• Performance estimation  – why estimating the performance of your campaigns is the fundamental problem in marketing analytics and what is the data science tool set used for this
• Optimizing bidding and budget allocation  – once your marketing efforts are at the scale of hundreds or thousands of concurrent campaigns, it’s impossible to allocate you budget manually in an optimal way. We look at two simple algorithms for budget allocation, shared by DoorDash and Lyft engineers.

Marketing Automation Systems

In large and analytically mature organizations, the  optimization  piece usually comes as a part of a larger  marketing automation  system, but as we’ll see it’s not always the case. Allocating budgets manually but aided by data science can be hugely profitable and might be a good first step towards a fully automated workflow.

To continue reading this article, click here .

Recommended

• This new forecasting model is better than machine learning, researchers say

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Machine Learning Case Studies with Powerful Insights

Explore the potential of machine learning through these practical machine learning case studies and success stories in various industries. | ProjectPro

Machine learning is revolutionizing how different industries function, from healthcare to finance to transportation. If you're curious about how this technology is applied in real-world scenarios, look no further. In this blog, we'll explore some exciting machine learning case studies that showcase the potential of this powerful emerging technology.

Machine-learning-based applications have quickly transformed work methods in the technological world. It is changing the way we work, live, and interact with the world around us. Machine learning is revolutionizing industries, from personalized recommendations on streaming platforms to self-driving cars.

But while the technology of artificial intelligence and machine learning may seem abstract or daunting to some, its applications are incredibly tangible and impactful. Data Scientists use machine learning algorithms to predict equipment failures in manufacturing, improve cancer diagnoses in healthcare , and even detect fraudulent activity in 5 . If you're interested in learning more about how machine learning is applied in real-world scenarios, you are on the right page. This blog will explore in depth how machine learning applications are used for solving real-world problems.

We'll start with a few case studies from GitHub that examine how machine learning is being used by businesses to retain their customers and improve customer satisfaction. We'll also look at how machine learning is being used with the help of Python programming language to detect and prevent fraud in the financial sector and how it can save companies millions of dollars in losses. Next, we will examine how top companies use machine learning to solve various business problems. Additionally, we'll explore how machine learning is used in the healthcare industry, and how this technology can improve patient outcomes and save lives.

By going through these case studies, you will better understand how machine learning is transforming work across different industries. So, let's get started!

Table of Contents

Machine learning case studies on github, machine learning case studies in python, company-specific machine learning case studies, machine learning case studies in biology and healthcare, aws machine learning case studies , azure machine learning case studies, how to prepare for machine learning case studies interview.

This section has machine learning case studies along with their GitHub repository that contains the sample code.

1. Customer Churn Prediction

Predicting customer churn is essential for businesses interested in retaining customers and maximizing their profits. By leveraging historical customer data, machine learning algorithms can identify patterns and factors that are correlated with churn, enabling businesses to take proactive steps to prevent it.

In this case study, you will study how a telecom company uses machine learning for customer churn prediction. The available data contains information about the services each customer signed up for, their contact information, monthly charges, and their demographics. The goal is to first analyze the data at hand with the help of methods used in Exploratory Data Analysis . It will assist in picking a suitable machine-learning algorithm. The five machine learning models used in this case-study are AdaBoost, Gradient Boost, Random Forest, Support Vector Machines, and K-Nearest Neighbors. These models are used to determine which customers are at risk of churn. 

By using machine learning for churn prediction, businesses can better understand customer behavior, identify areas for improvement, and implement targeted retention strategies. It can result in increased customer loyalty, higher revenue, and a better understanding of customer needs and preferences. This case study example will help you understand how machine learning is a valuable tool for any business looking to improve customer retention and stay ahead of the competition.

GitHub Repository: https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction  

2. Market Basket Analysis

Market basket analysis is a common application of machine learning in retail and e-commerce, where it is used to identify patterns and relationships between products that are frequently purchased together. By leveraging this information, businesses can make informed decisions about product placement, promotions, and pricing strategies.

In this case study, you will utilize the EDA methods to carefully analyze the relationships among different variables in the data. Next, you will study how to use the Apriori algorithm to identify frequent itemsets and association rules, which describe the likelihood of a product being purchased given the presence of another product. These rules can generate recommendations, optimize product placement, and increase sales, and they can also be used for customer segmentation.  

Using machine learning for market basket analysis allows businesses to understand customer behavior better, identify cross-selling opportunities, and increase customer satisfaction. It has the potential to result in increased revenue, improved customer loyalty, and a better understanding of customer needs and preferences. 

GitHub Repository: https://github.com/kkrusere/Market-Basket-Analysis-on-the-Online-Retail-Data

3. Predicting Prices for Airbnb

Airbnb is a tech company that enables hosts to rent out their homes, apartments, or rooms to guests interested in temporary lodging. One of the key challenges hosts face is optimizing the rent prices for the customers. With the help of machine learning, hosts can have rough estimates of the rental costs based on various factors such as location, property type, amenities, and availability.

The first step, in this case study, is to clean the dataset to handle missing values, duplicates, and outliers. In the same step, the data is transformed, and the data is prepared for modeling with the help of feature engineering methods. The next step is to perform EDA to understand how the rental listings are spread across different cities in the US. Next, you will learn how to visualize how prices change over time, looking at trends for different seasons, months, days of the week, and times of the day.

The final step involves implementing ML models like linear regression (ridge and lasso), Naive Bayes, and Random Forests to produce price estimates for listings. You will learn how to compare the outcome of these models and evaluate their performance.

GitHub Repository: https://github.com/samuelklam/airbnb-pricing-prediction  

New Projects

4. Titanic Disaster Analysis

The Titanic Machine Learning Case Study is a classic example in the field of data science and machine learning. The study is based on the dataset of passengers aboard the Titanic when it sank in 1912. The study's goal is to predict whether a passenger survived or not based on their demographic and other information.

The dataset contains information on 891 passengers, including their age, gender, ticket class, fare paid, as well as whether or not they survived the disaster. The first step in the analysis is to explore the dataset and identify any missing values or outliers. Once this is done, the data is preprocessed to prepare it for modeling.

The next step is to build a predictive model using various machine learning algorithms, such as logistic regression, decision trees, and random forests. These models are trained on a subset of the data and evaluated on another subset to ensure they can generalize well to new data.

Finally, the model is used to make predictions on a test dataset, and the model performance is measured using various metrics such as accuracy, precision, and recall. The study results can be used to improve safety protocols and inform future disaster response efforts.

GitHub Repository: https://github.com/ashishpatel26/Titanic-Machine-Learning-from-Disaster  

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Not sure what you are looking for?

If you are looking for a sample of machine learning case study in python, then keep reading this space.

5. Loan Application Classification

Financial institutions receive tons of requests for lending money by borrowers and making decisions for each request is a crucial task. Manually processing these requests can be a time-consuming and error-prone process, so there is an increasing demand for machine learning to improve this process by automation.

You can work on this Loan Dataset on Kaggle to get started on this one of the most real-world case studies in the financial industry. The dataset contains 614 unique values for 13 columns: Follow the below-mentioned steps to get started on this case study.

Analyze the dataset and explore how various factors such as gender, marital status, and employment affect the loan amount and status of the loan application .

Select the features to automate the process of classification of loan applications.

Apply machine learning models such as logistic regression, decision trees, and random forests to the features and compare their performance using statistical metrics.

This case study falls under the umbrella of supervised learning problems in machine learning and demonstrates how ML models are used to automate tasks in the financial industry.

With these Data Science Projects in Python , your career is bound to reach new heights. Start working on them today!

6. Computer Price Estimation

Whenever one thinks of buying a new computer, the first thing that comes to mind is to curate a list of hardware specifications that best suit their needs. The next step is browsing different websites and looking for the cheapest option available. Performing all these processes can be time-consuming and require a lot of effort. But you don’t have to worry as machine learning can help you build a system that can estimate the price of a computer system by taking into account its various features.

This sample basic computer dataset on Kaggle can help you develop a price estimation model that can analyze historical data and identify patterns and trends in the relationship between computer specifications and prices. By training a machine learning model on this data, the model can learn to make accurate predictions of prices for new or unseen computer components. Machine learning algorithms such as K-Nearest Neighbours, Decision Trees, Random Forests, ADA Boost and XGBoost can effectively capture complex relationships between features and prices, leading to more accurate price estimates. 

Besides saving time and effort compared to manual estimation methods, this project also has a business use case as it can provide stakeholders with valuable insights into market trends and consumer preferences.

7. House Price Prediction

Here is a machine learning case study that aims to predict the median value of owner-occupied homes in Boston suburbs based on various features such as crime rate, number of rooms, and pupil-teacher ratio.

Start working on this study by collecting the data from the publicly available UCI Machine Learning Repository, which contains information about 506 neighborhoods in the Boston area. The dataset includes 13 features such as per capita crime rate, average number of rooms per dwelling, and the proportion of owner-occupied units built before 1940. You can gain more insights into this data by using EDA techniques. Then prepare the dataset for implementing ML models by handling missing values, converting categorical features to numerical ones, and scaling the data.

Use machine learning algorithms such as Linear Regression, Lasso Regression, and Random Forest to predict house prices for different neighborhoods in the Boston area. Select the best model by comparing the performance of each one using metrics such as mean squared error, mean absolute error, and R-squared.

This section has machine learning case studies of different firms across various industries.

8. Machine Learning Case Study on Dell

Dell Technologies is a multinational technology company that designs, develops, and sells computers, servers, data storage devices, network switches, software, and other technology products and services. Dell is one of the world's most prominent PC vendors and serves customers in over 180 countries. As Data is an integral component of Dell's hard drive, the marketing team of Dell required a data-focused solution that would improve response rates and demonstrate why some words and phrases are more effective than others.

Dell contacted Persado and partnered with the firm that utilizes AI to create marketing content. Persado helped Dell revamp the email marketing strategy and leverage the data analytics to garner their audiences' attention. The statistics revealed that the partnership resulted in a noticeable increase in customer engagement as the page visits by 22% on average and a 50% average increase in CTR.

Dell currently relies on ML methods to improve their marketing strategy for emails, banners, direct mail, Facebook ads, and radio content.

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9. Machine Learning Case Study on Harley Davidson

In the current environment, it is challenging to overcome traditional marketing. An artificial intelligence powered robot, Albert is appealing for a business like Harley Davidson. Robots are now directing traffic, creating news stories, working in hotels, and even running McDonald's, thanks to machine learning and artificial intelligence.

There are many marketing channels that Albert can be applied to, including Email and social media.It automatically prepares customized creative copies and forecasts which customers will most likely convert.

The only company to make use of Albert is Harley Davidson. The business examined customer data to ascertain the activities of past clients who successfully made purchases and invested more time than usual across different pages on the website. With this knowledge, Albert divided the customer base into groups and adjusted the scale of test campaigns accordingly.

Results reveal that using Albert increased Harley Davidson's sales by 40%. The brand also saw a 2,930% spike in leads, 50% of which came from very effective "lookalikes" found by machine learning and artificial intelligence.

10. Machine Learning Case Study on Zomato

Zomato is a popular online platform that provides restaurant search and discovery services, online ordering and delivery, and customer reviews and ratings. Founded in India in 2008, the company has expanded to over 24 countries and serves millions of users globally. Over the years, it has become a popular choice for consumers to browse the ratings of different restaurants in their area. 

To provide the best restaurant options to their customers, Zomato ensures to hand-pick the ones likely to perform well in the future. Machine Learning can help zomato in making such decisions by considering the different restaurant features. You can work on this sample Zomato Restaurants Data and experiment with how machine learning can be useful to Zomato. The dataset has the details of 9551 restaurants. The first step should involve careful analysis of the data and identifying outliers and missing values in the dataset. Treat them using statistical methods and then use regression models to predict the rating of different restaurants.

The Zomato Case study is one of the most popular machine learning startup case studies among data science enthusiasts.

11. Machine Learning Case Study on Tesla

Tesla, Inc. is an American electric vehicle and clean energy company founded in 2003 by Elon Musk. The company designs, manufactures, and sells electric cars, battery storage systems, and solar products. Tesla has pioneered the electric vehicle industry and has popularized high-capacity lithium-ion batteries and regenerative braking systems. The company strongly focuses on innovation, sustainability, and reducing the world's dependence on fossil fuels.

Tesla uses machine learning in various ways to enhance the performance and features of its electric vehicles. One of the most notable applications of machine learning at Tesla is in its Autopilot system, which uses a combination of cameras, sensors, and machine learning algorithms to enable advanced driver assistance features such as lane centering, adaptive cruise control, and automatic emergency braking.

Tesla's Autopilot system uses deep neural networks to process large amounts of real-world driving data and accurately predict driving behavior and potential hazards. It enables the system to learn and adapt over time, improving its accuracy and responsiveness.

Additionally, Tesla also uses machine learning in its battery management systems to optimize the performance and longevity of its batteries. Machine learning algorithms are used to model and predict the behavior of the batteries under different conditions, enabling Tesla to optimize charging rates, temperature control, and other factors to maximize the lifespan and performance of its batteries.

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12. Machine Learning Case Study on Amazon

Amazon Prime Video uses machine learning to ensure high video quality for its users. The company has developed a system that analyzes video content and applies various techniques to enhance the viewing experience.

The system uses machine learning algorithms to automatically detect and correct issues such as unexpected black frames, blocky frames, and audio noise. For detecting block corruption, residual neural networks are used. After training the algorithm on the large dataset, a threshold of 0.07 was set for the corrupted-area ratio to mark the areas of the frame that have block corruption. For detecting unwanted noise in the audio, a model based on a pre-trained audio neural network is used to classify a one-second audio sample into one of these classes: audio hum, audio distortion, audio diss, audio clicks, and no defect. The lip sync is handled using the SynNet architecture.

By using machine learning to optimize video quality, Amazon can deliver a consistent and high-quality viewing experience to its users, regardless of the device or network conditions they are using. It helps maintain customer satisfaction and loyalty and ensures that Amazon remains a competitive video streaming market leader.

Machine Learning applications are not only limited to financial and tech use cases. It also finds its use in the Healthcare industry. So, here are a few machine learning case studies that showcase the use of this technology in the Biology and Healthcare domain.

13. Microbiome Therapeutics Development

The development of microbiome therapeutics involves the study of the interactions between the human microbiome and various diseases and identifying specific microbial strains or compositions that can be used to treat or prevent these diseases. Machine learning plays a crucial role in this process by enabling the analysis of large, complex datasets and identifying patterns and correlations that would be difficult or impossible to detect through traditional methods.

Machine learning algorithms can analyze microbiome data at various levels, including taxonomic composition, functional pathways, and gene expression profiles. These algorithms can identify specific microbial strains or communities associated with different diseases or conditions and can be used to develop targeted therapies.

Besides that, machine learning can be used to optimize the design and delivery of microbiome therapeutics. For example, machine learning algorithms can be used to predict the efficacy of different microbial strains or compositions and optimize these therapies' dosage and delivery mechanisms.

14. Mental Illness Diagnosis

Machine learning is increasingly being used to develop predictive models for diagnosing and managing mental illness. One of the critical advantages of machine learning in this context is its ability to analyze large, complex datasets and identify patterns and correlations that would be difficult for human experts to detect.

Machine learning algorithms can be trained on various data sources, including clinical assessments, self-reported symptoms, and physiological measures such as brain imaging or heart rate variability. These algorithms can then be used to develop predictive models to identify individuals at high risk of developing a mental illness or who are likely to experience a particular symptom or condition.

One example of machine learning being used to predict mental illness is in the development of suicide risk assessment tools. These tools use machine learning algorithms to analyze various risk factors, such as demographic information, medical history, and social media activity, to identify individuals at risk of suicide. These tools can be used to guide early intervention and support for individuals struggling with mental health issues.

One can also a build a Chatbot using Machine learning and Natural Lanaguage Processing that can analyze the responses of the user and recommend them the necessary steps that they can immediately take.

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15. 3D Bioprinting

Another popular subject in the biotechnology industry is Bioprinting. Based on a computerized blueprint, the printer prints biological tissues like skin, organs, blood arteries, and bones layer by layer using cells and biomaterials, also known as bioinks.

They can be made in printers more ethically and economically than by relying on organ donations. Additionally, synthetic construct tissue is used for drug testing instead of testing on animals or people. Due to its tremendous complexity, the entire technology is still in its early stages of maturity. Data science is one of the most essential components to handle this complexity of printing.

The qualities of the bioinks, which have inherent variability, or the many printing parameters, are just a couple of the many variables that affect the printing process and quality. For instance, Bayesian optimization improves the likelihood of producing useable output and optimizes the printing process.

A crucial element of the procedure is the printing speed. To estimate the optimal speed, siamese network models are used. Convolutional neural networks are applied to photographs of the layer-by-layer tissue to detect material, or tissue abnormalities.

In this section, you will find a list of machine learning case studies that have utilized Amazon Web Services to create machine learning based solutions.

16. Machine Learning Case Study on AutoDesk

Autodesk is a US-based software company that provides solutions for 3D design, engineering, and entertainment industries. The company offers a wide range of software products and services, including computer-aided design (CAD) software, 3D animation software, and other tools used in architecture, construction, engineering, manufacturing, media and entertainment industries.

Autodesk utilizes machine learning (ML) models that are constructed on Amazon SageMaker, a managed ML service provided by Amazon Web Services (AWS), to assist designers in categorizing and sifting through a multitude of versions created by generative design procedures and selecting the most optimal design.  ML techniques built with Amazon SageMaker help Autodesk progress from intuitive design to exploring the boundaries of generative design for their customers to produce innovative products that can even be life-changing. As an example, Edera Safety, a design studio located in Austria, created a superior and more effective spine protector by utilizing Autodesk's generative design process constructed on AWS.

17. Machine Learning Case Study on Capital One

Capital One is a financial services company in the United States that offers a range of financial products and services to consumers, small businesses, and commercial clients. The company provides credit cards, loans, savings and checking accounts, investment services, and other financial products and services.

Capital One leverages AWS to transform data into valuable insights using machine learning, enabling the company to innovate rapidly on behalf of its customers.  To power its machine-learning innovation, Capital One utilizes a range of AWS services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), and AWS Lambda. AWS is enabling Capital One to implement flexible DevOps processes, enabling the company to introduce new products and features to the market in just a few weeks instead of several months or years. Additionally, AWS assists Capital One in providing data to and facilitating the training of sophisticated machine-learning analysis and customer-service solutions. The company also integrates its contact centers with its CRM and other critical systems, while simultaneously attracting promising entry-level and mid-career developers and engineers with the opportunity to gain knowledge and innovate with the most up-to-date cloud technologies.

18. Machine Learning Case Study on BuildFax

In 2008, BuildFax began by collecting widely scattered building permit data from different parts of the United States and distributing it to various businesses, including building inspectors, insurance companies, and economic analysts. Today, it offers custom-made solutions to these professions and several other services. These services comprise indices that monitor trends like commercial construction, and housing remodels.

Source: aws.amazon.com/solutions/case-studies

The primary customer base of BuildFax is insurance companies that splurge billion dollars on rood losses. BuildFax assists its customers in developing policies and premiums by evaluating the roof losses for them. Initially, it relied on general data and ZIP codes for building predictive models but they did not prove to be useful as they were not accurate and were slightly complex in nature. It thus required a way out of building a solution that could support more accurate results for property-specific estimates. It thus chose Amazon Machine Learning for predictive modeling. By employing Amazon Machine Learning, it is possible for the company to offer insurance companies and builders personalized estimations of roof-age and job-cost, which are specific to a particular property and it does not have to depend on more generalized estimates based on ZIP codes.  It now utilizes customers' data and data from public sources to create predictive models.

What makes Python one of the best programming languages for ML Projects? The answer lies in these solved and end-to-end Machine Learning Projects in Python . Check them out now!

This section will present you with a list of machine learning case studies that showcase how companies have leveraged Microsoft Azure Services for completing machine learning tasks in their firm.

19. Machine Learning Case Study for an Enterprise Company

Consider a company (Azure customer) in the Electronic Design Automation industry that provides software, hardware, and IP for electronic systems and semiconductor companies. Their finance team was struggling to manage account receivables efficiently, so they wanted to use machine learning to predict payment outcomes and reduce outstanding receivables. The team faced a major challenge with managing change data capture using Azure Data Factory . A3S provided a solution by automating data migration from SAP ECC to Azure Synapse and offering fully automated analytics as a service, which helped the company streamline their account receivables management. It was able to achieve the entire scenario from data ingestion to analytics within a week, and they plan to use A3S for other analytics initiatives.

20. Machine Learning Case Study on Shell

Royal Dutch Shell, a global company managing oil wells to retail petrol stations, is using computer vision technology to automate safety checks at its service stations. In partnership with Microsoft, it has developed the project called Video Analytics for Downstream Retail (VADR) that uses machine vision and image processing to detect dangerous behavior and alert the servicemen. It uses OpenCV and Azure Databricks in the background highlighting how Azure can be used for personalised applications. Once the projects shows decent results in the countries where it has been deployed (Thailand and Singapore), Shell plans to expand the project further by going global with the VADR project. 

21. Machine Learning Case Study on TransLink

TransLink, a transportation company in Vancouver, deployed 18,000 different sets of machine learning models using Azure Machine Learning to predict bus departure times and determine bus crowdedness. The models take into account factors such as traffic, bad weather and at-capacity buses. The deployment led to an improvement in predicted bus departure times of 74%. The company also created a mobile app that allows people to plan their trips based on how at-capacity a bus might be at different times of day.

22. Machine Learning Case Study on XBox

Microsoft Azure Personaliser is a cloud-based service that uses reinforcement learning to select the best content for customers based on up-to-date information about them, the context, and the application. Custom recommender services can also be created using Azure Machine Learning. The Xbox One group used Cognitive Services Personaliser to find content suited to each user, which resulted in a 40% increase in user engagement compared to a random personalisation policy on the Xbox platform.

All the mentioned case studies in this blog will help you explore the application of machine learning in solving real problems across different industries. But you must not stop after working on them if you are preparing for an interview and intend to showcase that you have mastered the art of implementing ML algorithms, and you must practice more such caste studies in machine learning.

And if you have decided to dive deeper into machine learning, data science, and big data, be sure to check out ProjectPro , which offers a repository of solved projects in data science and big data. With a wide range of projects, you can explore different techniques and approaches and build your machine learning and data science skills . Our repository has a project for each one of you, irrespective of your academic and professional background. The customer-specific learning path is likely to help you find your way to making a mark in this newly emerging field. So why wait? Start exploring today and see what you can accomplish with big data and data science ! 

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1. What is a case study in machine learning?

A case study in machine learning is an in-depth analysis of a real-world problem or scenario, where machine learning techniques are applied to solve the problem or provide insights. Case studies can provide valuable insights into the application of machine learning and can be used as a basis for further research or development.

2. What is a good use case for machine learning?

A good use case for machine learning is any scenario with a large and complex dataset and where there is a need to identify patterns, predict outcomes, or automate decision-making based on that data. It could include fraud detection, predictive maintenance, recommendation systems, and image or speech recognition, among others.

3. What are the 3 basic types of machine learning problems?

The three basic types of machine learning problems are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data. In unsupervised learning, the algorithm seeks to identify patterns in unstructured data. In reinforcement learning, the algorithm learns through trial and error based on feedback from the environment.

4. What are the 4 basics of machine learning?

The four basics of machine learning are data preparation, model selection, model training, and model evaluation. Data preparation involves collecting, cleaning, and preparing data for use in training models. Model selection involves choosing the appropriate algorithm for a given task. Model training involves optimizing the chosen algorithm to achieve the desired outcome. Model evaluation consists of assessing the performance of the trained model on new data.

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Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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Machine learning is one of the most exciting and rapidly growing fields in the world today, with countless real-world applications that are transforming industries and improving people’s lives. In this blog, we will look at some case studies and success stories that demonstrate the power of machine learning in the real world.

Healthcare:

Machine learning has the potential to revolutionize healthcare by enabling more accurate diagnosis, personalized treatment plans, and better patient outcomes. Here are a few examples:

• Cancer diagnosis: Researchers at Stanford University developed an algorithm that can diagnose skin cancer with an accuracy level that matches that of experienced dermatologists. This algorithm has the potential to save countless lives by detecting cancer in its early stages.
• Disease management: Google’s DeepMind has developed an AI system that can predict the progression of kidney disease in patients, allowing doctors to intervene earlier and prevent complications.
• Patient monitoring: Hospitals are using machine learning algorithms to monitor patients in real-time, detecting changes in vital signs and alerting doctors to potential problems before they become critical.

Finance: 

Machine learning is being used extensively in the finance industry to identify fraud, make investment decisions, and improve customer experience. Some examples include:

• Fraud detection: Credit card companies use machine learning algorithms to detect fraudulent transactions, saving millions of dollars each year.
• Trading: Hedge funds and investment banks use machine learning algorithms to analyze market trends and make more accurate investment decisions.
• Customer service: Chatbots powered by machine learning algorithms are becoming increasingly common in the finance industry, allowing customers to receive instant support and reducing the burden on customer service staff.

Transportation:

Machine learning is playing an important role in the transportation industry, enabling autonomous vehicles and improving traffic flow. Here are a few examples:

• Autonomous vehicles: Companies such as Tesla, Waymo, and Uber are using machine learning algorithms to develop self-driving cars that can navigate roads safely and efficiently.
• Traffic management: Cities around the world are using machine learning algorithms to optimize traffic flow, reducing congestion and improving air quality.
• Predictive maintenance: Airlines are using machine learning algorithms to predict when aircraft components will fail, allowing for maintenance to be performed before a problem occurs.

Entertainment: 

Machine learning is also having an impact on the entertainment industry, enabling more personalized recommendations and better content creation. Here are a few examples:

• Recommendation engines: Streaming services such as Netflix and Amazon Prime use machine learning algorithms to recommend content to users based on their viewing history and preferences.
• Content creation: Machine learning algorithms are being used to generate more realistic special effects in movies and to create music and art that is indistinguishable from human-created content.
• Gaming: Machine learning algorithms are being used to create more intelligent non-player characters (NPCs) in video games, making gameplay more challenging and engaging.

Agriculture:  

Machine learning is being used in agriculture to optimize crop yields, monitor plant health, and predict weather patterns. For example, John Deere’s See and Spray technology uses machine learning algorithms to identify and selectively spray weeds, reducing the need for herbicides.

Manufacturing:  

Machine learning is being used to improve efficiency and reduce waste in manufacturing. For example, General Electric has developed a machine learning algorithm that can predict when equipment is likely to fail, allowing for preventive maintenance to be performed.

Energy:  

Machine learning is being used in the energy sector to optimize power generation and reduce costs. For example, Google’s DeepMind has developed an AI system that can reduce the energy used for cooling data centers by up to 40%.

Retail: 

Machine learning is being used in the retail industry to optimize inventory management, personalize marketing, and improve the customer experience. For example, Amazon uses machine learning algorithms to make personalized product recommendations to customers based on their browsing and purchase history.

Education:  

Machine learning is being used in education to personalize learning, identify at-risk students, and improve outcomes. For example, Carnegie Learning has developed an adaptive learning platform that uses machine learning algorithms to customize lessons for each student based on their strengths and weaknesses.

Conclusion:

Machine learning is a powerful tool with a wide range of real-world applications. From healthcare and finance to transportation and entertainment, the impact of machine learning can be seen in almost every industry. As the technology continues to evolve, we can expect even more exciting developments and success stories in the years to come.

If you’re looking to take your Machine Learning skills to the next level, consider taking an online course on LearnTube . LearnTube is a safe and reliable platform. At LearnTube, students are taught using various tools such as the LearnTube app and a WhatsApp bot. The platform offers a wide range of Machine Learning courses, from beginner-level courses to advanced certification courses. Click here to explore LearnTube’s Machine Learning course offerings and take your ML skills to the next level.

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How C Programming Enhances Machine Learning

Dive into C Programming for Machine Learning: setting up environments, key concepts, and practical algorithm implementation for developers.

C programming isn't typically the first language that comes to mind when thinking about machine learning, but it holds unique advantages. Its speed and efficiency, coupled with a rich library ecosystem, make it a practical choice for certain machine learning applications. Let's explore how C can be effectively utilized in the realm of machine learning, breaking down concepts and techniques in a way that enhances your coding toolkit.

Understanding C Programming In Machine Learning

Setting up the c programming environment for ml, basic machine learning concepts with c, implementing machine learning algorithms in c, optimizing performance and efficiency, case studies: machine learning projects using c, frequently asked questions, why use c for machine learning, basic ml concepts in c, handling data in c, optimizing algorithms.

C Programming has long been a cornerstone in the field of software development, known for its efficiency and control over system resources. In the context of Machine Learning (ML) , C can be particularly powerful for performance-critical applications.

Despite the rise of higher-level languages, C's speed and low-level memory access offer significant advantages. It's particularly useful in scenarios where control over hardware resources is crucial, such as in embedded systems or when processing large data sets.

Implementing ML concepts in C requires a solid understanding of data structures and algorithms. For instance, a simple linear regression can be implemented using arrays and functions. Consider the following syntax and example:

Data handling is a critical aspect of ML. In C, this often means working with files and standard I/O to process data sets. For example:

Optimization is key in ML. In C, this often involves memory management and efficient algorithm implementation. For example, using pointers can significantly speed up array manipulations.

Choosing A Compiler

Installing libraries, setting up an ide, configuring the build environment, testing the setup.

Setting up the C Programming Environment for machine learning involves a few key steps. Ensuring your environment is correctly configured is crucial for efficient ML development.

The first step is selecting a C Compiler . Popular choices include GCC (GNU Compiler Collection) for Linux and Clang for macOS. Windows users often opt for MinGW or Microsoft's Visual C++.

For ML tasks, you'll need specific Libraries like BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra Package) for numerical operations. Install these libraries to handle complex mathematical computations efficiently.

While not mandatory, using an Integrated Development Environment (IDE) like Code::Blocks or Eclipse CDT can simplify your coding process. They provide useful features like code completion and debugging tools.

Configuring your build environment is essential. For most C projects, this involves setting up a Makefile . A Makefile dictates how your program is compiled and linked.

Finally, test your setup with a simple Hello World program to ensure everything is working correctly.

Data Structures For ML

Algorithms in c, performance considerations, debugging and validation.

Understanding Basic Machine Learning Concepts with C involves grasping how traditional programming paradigms apply in the context of ML. C, with its procedural nature, offers a different approach from more commonly used ML languages like Python.

Effective ML in C requires adept use of Data Structures . Arrays, for instance, are fundamental for handling numerical data, such as feature sets in machine learning models.

Implementing ML Algorithms in C can be challenging but rewarding. For example, a simple linear regression algorithm can be written from scratch to understand the underlying mechanics.

When dealing with ML, Performance is key. C's ability to manage low-level memory and processing operations allows for optimizations that can significantly speed up ML algorithms.

Debugging and Validation are crucial in ensuring the accuracy of your ML models. Tools like GDB for debugging and unit testing frameworks can be invaluable in this process.

Linear Regression Example

K-means clustering, decision trees, neural networks in c.

Implementing Machine Learning Algorithms in C can be a meticulous yet insightful endeavor. It provides a deeper understanding of the algorithms' inner workings, often obscured in higher-level languages.

A fundamental ML algorithm is Linear Regression . It's a great starting point due to its simplicity and widespread application in predictive modeling.

Another popular algorithm is K-Means Clustering , used in unsupervised learning to group data into k number of clusters.

Decision Trees are versatile and can be used for both classification and regression tasks. Implementing them in C, however, requires a good grasp of tree-based data structures.

While more complex, implementing Neural Networks in C can be highly educational. It involves understanding and coding the network architecture, activation functions, and backpropagation algorithm.

Memory Management

Algorithm optimization, parallel computing, profiling and debugging.

In C Programming for Machine Learning , optimizing performance and efficiency is crucial. This involves fine-tuning both the code and the way algorithms handle data.

Effective Memory Management is key. Using dynamic memory allocation wisely helps in managing large datasets more efficiently. For instance, using pointers and dynamic memory can optimize space usage.

Algorithm Optimization is another crucial aspect. Optimizing the algorithm can lead to significant improvements in execution time. This can be achieved by reducing the complexity of the algorithm or by optimizing its implementation.

Leveraging Parallel Computing can dramatically speed up ML tasks. This is particularly effective in tasks like training models or processing large datasets.

Regular Profiling and Debugging are essential to identify bottlenecks in your code. Tools like Valgrind and GDB can help in finding memory leaks and performance issues.

Image Recognition System

Natural language processing, robotics control systems, financial modeling.

Exploring Case Studies of machine learning projects using C can provide valuable insights into practical applications and the effectiveness of C in this field.

One notable project is an Image Recognition System . Such systems, often foundational in AI, can be built using C for handling image processing and pattern recognition efficiently.

Another application is in Natural Language Processing (NLP) . C can be used to build efficient text processing systems, crucial for tasks like sentiment analysis or language translation.

C is extensively used in Robotics Control Systems . Its ability to interact closely with hardware makes it ideal for programming microcontrollers and sensors in robots.

C's speed and efficiency make it suitable for Financial Modeling . Algorithms written in C can process large datasets for stock market prediction, risk assessment, and algorithmic trading.

Can C Language Be Used For Machine Learning?

Yes , C can be used for machine learning, especially in scenarios where resource management and performance are critical. Its efficiency in memory and processing makes it suitable for ML algorithms, though it might require more effort compared to higher-level languages.

Is C Better Than Python For Machine Learning?

It Depends . Python is generally preferred for machine learning due to its simplicity, extensive libraries, and community support. However, C offers better performance and is more suitable for scenarios requiring close hardware interaction or when running on systems with limited resources.

How Do I Start With Machine Learning In C?

Start by learning the basics of C programming, focusing on data structures and algorithms. Then, move on to understanding machine learning concepts and how they can be implemented in C. Practical projects and case studies can be very helpful.

Are There Any Machine Learning Libraries Available In C?

Yes , there are libraries such as libsvm, Shark, and FANN (Fast Artificial Neural Network) that can be used for machine learning in C. These libraries offer various functionalities typical in ML workflows but might not be as comprehensive as those in Python.

Let's test you

Which Library Is Not Typically Used In Setting Up The C Programming Environment For ML?

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Permutation flow shop scheduling with makespan objective and truncated learning effects

• Original Research
• Published: 17 April 2024

Cite this article

• Ming-Hui Li 1 ,
• Dan-Yang Lv 1 ,
• Li-Han Zhang 1 &
• Ji-Bo Wang   ORCID: orcid.org/0000-0003-2271-6459 1  

In this paper, we study the m -machine ( \(m\ge 3\) ) permutation flow shop scheduling in which the processing time of a job depends not only on the sum of actual processing times of previous jobs but also on its job-dependent truncation parameter, i.e., the general truncated learning effects. The goal is to find a sequence so as to minimize the makespan, we prove that this problem is NP-hard. Two simple heuristics are proposed and their worst-case bounds are presented. We also propose a branch-and-bound algorithm and some heuristics to solve this problem. Further, computational experiments are conducted to determine the efficiency of the proposed algorithms.

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Data Availability Statement

The data used to support the findings of this paper are available from the corresponding author upon request.

Wu, C.-C., Lee, W.-C.: A note on the total completion time problem in a permutation flowshop with a learning effect. Eur. J. Oper. Res. 192 (1), 343–347 (2009)

Article   Google Scholar  

Wu, C.-C., Hsu, P.-H., Chen, J.-C., Wang, N.-S.: Genetic algorithm for minimizing the total weighted completion time scheduling problem with learning and release times. Comput. Oper. Res. 38 , 1025–1034 (2011)

Article   MathSciNet   Google Scholar  

Yin, Y., Wu, C.-C., Wu, W.-H., Cheng, S.-R.: The single-machine total weighted tardiness scheduling problem with position-based learning effects. Comput. Oper. Res. 39 (5), 1109–1116 (2012)

Yin, Y., Wu, W.-H., Wu, W.-H., Wu, C.-C.: A branch-and-bound algorithm for a single machine sequencing to minimize the total tardiness with arbitrary release dates and position-dependent learning effects. Inf. Sci. 256 , 91–108 (2014)

Azzouz, A., Ennigrou, M., Said, L.B.: Scheduling problems under learning effects: classification and cartography. Int. J. Prod. Res. 56 (4), 1642–1661 (2018)

Wang, J.-B., Gao, M., Wang, J.-J., Liu, L., He, H.: Scheduling with a position-weighted learning effect and job release dates. Eng. Optim. 52 (9), 1475–1493 (2020)

Zhao, S.: Scheduling jobs with general truncated learning effects including proportional setup times. Comput. Appl. Math. 41 , 146 (2022)

Ren, N., Wang, J.-B., Wang, E.: Research on delivery times scheduling with truncated learning effects. Comput. Appl. Math. 42 , 243 (2023)

Wang, S.-H., Lv, D.-Y., Wang, J.-B.: Research on position-dependent weights scheduling with delivery times and truncated sum-of-processing-times-based learning effect. J. Indus. Manag. Optim. 19 (4), 2824–2837 (2023)

Wang, Y.-C., Wang, J.-B.: Study on convex resource allocation scheduling with a time-dependent learning effect. Mathematics 11 (14), 3179 (2023)

Rad, S.F., Ruiz, R., Boroojerdian, N.: New high performing heuristics for minimizing makespan in permutation flowshops. Omega 37 , 331–345 (2009)

Pan, Q.-K., Ruiz, R.: A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime. Comput. Oper. Res. 40 (1), 117–128 (2013)

Bai, D., Liang, J., Liu, B., Tang, M., Zhang, Z.-H.: Permutation flow shop scheduling problem to minimize nonlinear objective function with release dates. Comput. Indus. Eng. 112 , 336–347 (2017)

Liu, F., Wang, S., Hong, Y., Yue, X.: On the robust and stable flowshop scheduling under stochastic and dynamic disruptions. IEEE Trans. Eng. Manage. 4 (64), 539–553 (2017)

Yu, T.S., Pinedo, M.: Flow shops with reentry: Reversibility properties and makespan optimal schedules. Eur. J. Oper. Res. 282 (2), 478–490 (2020)

Lv, D.-Y., Wang, J.-B.: Study on proportionate flowshop scheduling with due-date assignment and position-dependent weights. Optim. Lett. 15 (6), 2311–2319 (2021)

Qian, J., Han, H.: Improved algorithms for proportionate flow shop scheduling with due-window assignment. Ann. Oper. Res. 309 , 249–258 (2022)

Lee, W.-C., Chung, Y.-H.: Permutation flowshop scheduling to minimize the total tardiness with learning effects. Int. J. Prod. Econ. 141 (1), 327–334 (2013)

Wang, J.-J., Zhang, B.-H.: Permutation flowshop problems with bi-criterion makespan and total completion time objective and position-weighted learning effects. Comput. Oper. Res. 58 , 24–31 (2015)

Bai, D., Tang, M., Zhang, Z.-H., Santibanez-Gonzalez, E.D.R.: Flow shop learning effect scheduling problem with release dates. Omega 78 , 21–38 (2018)

Liang, X.-X., Zhang, B., Wang, J.-B., Yin, N., Huang, X.: Study on flow shop scheduling with sum-of-logarithm-processing-times-based learning effects. J. Appl. Math. Comput. 61 , 373–388 (2019)

Geng, X.-N., Wang, J.-B., Bai, D.: Common due date assignment scheduling for a no-wait flowshop with convex resource allocation and learning effect. Eng. Optim. 51 (8), 1301–1323 (2019)

Liu, W.-W., Jiang, C.: Flow shop resource allocation scheduling with due date assignment, learning effect and position-dependent weights. Asia-Pac. J. Oper. Res. 37 (3), 2050014 (2020)

Zhao, S.: Resource allocation flowshop scheduling with learning effect and slack due window assignment. J. Indus. Manag. Optim. 17 (5), 2817–2835 (2021)

Sun, X., Geng, X.-N., Liu, F.: Flow shop scheduling with general position weighted learning effects to minimise total weighted completion time. J. Oper. Res. Soc. 72 (12), 2674–2689 (2021)

Jiang, Z., Chen, F., Zhang, X.: Single-machine scheduling problems with general truncated sum-of-actual-processing-time-based learning effect. J. Comb. Optim. 43 , 116–139 (2022)

Wang, J.-B., Zhang, L.-H., Lv, Z.-G., Lv, D.-Y., Geng, X.-N., Sun, X.: Heuristic and exact algorithms for single-machine scheduling problems with general truncated learning effects. Comput. Appl. Math. 41 , 417 (2022)

Wu, W.-H., Wu, W.-H., Chen, J.-C., Lind, W.-C., Wu, J., Wu, C.-C.: A heuristic-based genetic algorithm for the two-machine flowshop scheduling with learning consideration. J. Manuf. Syst. 35 , 223–233 (2015)

Wang, J.-B., Liu, F., Wang, J.-J.: Research on \(m\) -machine flow shop scheduling with truncated learning effects. Int. Trans. Oper. Res. 26 (3), 1135–1151 (2019)

Bai, D., Liu, T., Zhang, Y., Ren, T., Zhang, Z.-H., Dong, Z.: Scheduling non-permutation flowshop with finite buffers and two competitive agents. Comput. Indus. Eng. 177 , 108939 (2023)

Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1 (2), 117–129 (1976)

Nawaz, M., Enscore, E.E., Jr., Ham, I.: A heuristic algorithm for the \(m\) -machine, \(n\) -job flow-shop sequencing problem. Omega 11 , 91–95 (1983)

Lai, K., Hsu, P.-H., Ting, P.-H., Wu, C.-C.: A truncated sum of processing-times-based learning model for a two-machine flowshop scheduling problem. Human Factors Ergon. Manuf. Serv. Indus. 24 (2), 152–160 (2014)

Lv, D.-Y., Xue, J., Wang, J.-B.: Minmax common due-window assignment scheduling with deteriorating jobs. J. Oper. Res. Soc. China (2023). https://doi.org/10.1007/s40305-023-00511-2

Wu, W., Lv, D.-Y., Wang, J.-B.: Two due-date assignment scheduling with location-dependent weights and a deteriorating maintenance activity. Systems 11 , 150 (2023)

Wang, J.-B., Bao, H., Wang, C.: Research on multiple slack due-date assignments scheduling with position-dependent weights. Asia-Pac. J. Oper. Res. (2024). https://doi.org/10.1142/10.1142/S0217595923500392

Miao, C.X., Kong, F.Y., Zou, J., Ma, R., Huo, Y.J.: Parallel-machine scheduling with step-deteriorating jobs to minimize the total (weighted) completion time. Asia-Pac. J. Oper. Res. 40 (1), 2240011 (2023)

Sun, X., Liu, T., Geng, X.-N., Hu, Y., Xu, J.-X.: Optimization of scheduling problems with deterioration effects and an optional maintenance activity. J. Sched. 26 , 251–266 (2023)

Wang, J.-B., Wang, Y.-C., Wan, C., Lv, D.-Y., Zhang, L.: Controllable processing time scheduling with total weighted completion time objective and deteriorating jobs. Asia-Pac. J. Oper. Res. (2024). https://doi.org/10.1142/S0217595923500264

Zhang, L.-H., Geng, X.-N., Xue, J., Wang, J.-B.: Single machine slack due window assignment and deteriorating jobs. J. Indus. Manag. Optim. 20 , 1593–1614 (2024)

Lv, Z.-G., Zhang, L.-H., Wang, X.-Y., Wang, J.-B.: Single machine scheduling proportionally deteriorating jobs with ready times subject to the total weighted completion time minimization. Mathematics 12 , 610 (2024)

Lv, D.-Y., Wang, J.-B.: No-idle flow shop scheduling with deteriorating jobs and common due date under dominating machines. Asia-Pac. J. Oper. Res. (2024). https://doi.org/10.1142/S0217595924500039

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This work was supported by the Science Research Foundation of Educational Department of Liaoning Province (JYTMS20230278).

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Ming-Hui Li, Dan-Yang Lv, Li-Han Zhang & Ji-Bo Wang

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Li, MH., Lv, DY., Zhang, LH. et al. Permutation flow shop scheduling with makespan objective and truncated learning effects. J. Appl. Math. Comput. (2024). https://doi.org/10.1007/s12190-024-02080-w

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Received : 22 February 2024

Revised : 25 March 2024

Accepted : 31 March 2024

Published : 17 April 2024

DOI : https://doi.org/10.1007/s12190-024-02080-w

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