meaning of sales analysis in research

Close more deals with the latest sales trends and tips from Salesblazers.

What Is Sales Analysis and How Does It Help You Close More Deals?

Illustration of a magnifying glass and sales report on a teal background

Don't rely on instinct to hit your sales goals. Dig into your data to find a clear path to success.

meaning of sales analysis in research

Jeffrey Steen

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Data and intuition walk into a bar. Data orders a gin martini — strong, reliable, and no-nonsense. Intuition, on the other hand, asks for something off the menu.

Data knows what it wants and orders based on experience, evidence, and time-honored tradition. Intuition isn’t interested in those things. It wants to push the envelope, get creative, and try something new.

Your team might be filled with intuitive sellers. But to hit your sales goals, there’s nothing stronger than data. That’s why sales analysis is so important. When you pair your instincts with real data, your reps can make evidence-based decisions and steer clear of guesswork.

Research from McKinsey shows that companies using data analytics to drive their sales processes have seen lead conversion rates soar up to 30%. We’ll break down how you can achieve the same success.

What you’ll learn :

What is sales analysis, the benefits of sales analysis, key sales metrics to watch, what to look for in a sales analysis tool, hit key kpis with real-time pipeline insights.

What could you do with relevant insights at your fingertips? Sell smarter, take action, and hit your forecasts. That’s how Sales Analytics works.

meaning of sales analysis in research

Sales analysis is the process of gathering and “dissecting” sales data to uncover trends and patterns in your sales team’s performance and your customer behaviors. For example, you might see patterns in product performance, customer purchasing patterns, or the effectiveness of your sales channels. This meticulous attention to detail helps sales leaders identify areas where they can enhance forecasting accuracy, boost team performance, and speed up internal processes.

For example, recognizing inefficiencies in the sales process could lead to targeted interventions, such as refining your approach to lead qualification or improving follow-up tactics, directly impacting sales effectiveness.

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Sales analysis is key to honing your sales strategy , providing a clear picture of where your efforts are paying off and where they’re not. It’s about increasing your team’s productivity by identifying the most efficient sales practices, and highlighting areas that aren’t working and need improvement. On the customer side of the equation, sales analysis can help you prioritize prospects most likely to close, ensuring resources are allocated effectively.

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Depending on your goals, there are different ways to slice and dice the sales data you can analyze for actionable insights. Below are the most common sales metrics and KPIs that sales teams analyze to understand performance and opportunity:

Pipeline metrics

These metrics, like how quickly a lead moves through the pipeline and how many leads are in the pipeline at any given time, offer a view of how successfully a rep is selling. It can reveal obstacles that cause deals to stall and help you manage rep workload to ensure they have enough — but not too much — to focus on.

Conversion rates

A critical measure for both sales reps and managers, conversion rates show you the rate at which leads are moved from one stage in the sales process to another. By analyzing these rates, sales reps can refine their approaches to improve actions in specific stages. For example, a rep may choose to add follow-up emails after the sales call stage to ensure prospective buyers remain engaged.

Average deal size

This metric provides insight into the typical value generated per sale. Ultimately, this guides sales managers in strategic planning, setting revenue targets , and identifying lucrative market segments for focused engagement.

Average sales cycle length

Used to assess the efficiency of the sales process, this metric helps in identifying bottlenecks that lengthen the cycle. Shortening the sales cycle through faster lead qualification and engagement tactics in various sales process stages can significantly impact overall sales performance.

Won and lost deals

Comparing won and lost deals reveals patterns and areas for improvement in sales strategies. This analysis is invaluable for enhancing onboarding and training programs and refining sales tactics to increase future success rates.

Customer churn

This metric pinpoints when and why customers leave your business and opens the door for conversations and ideas about how to improve retention rates. Are there common times when customers leave, or triggers that seem to push them out the door? How can you fix these?

Sales call feedback

Sales reps primarily use this metric to assess their call performance. Managers can also use it to coach reps on effective communication techniques and call strategies. Additionally, they can find insights to praise or discourage certain techniques during performance review conversations.

Lead response time

Your lead response time is the amount of time it takes for reps to follow up with potential clients after they first contact your company.

Forbes cited the average lead response time as 47 hours and notes that those who reach out within five minutes are 100+ times more likely to see success than those who wait even 30 minutes. Ultimately, understanding this metric can help sales reps become more proactive about reaching out to leads.

Annual revenue

A comprehensive metric that reflects the overall financial health of the business, annual revenue is crucial for strategic planning and growth projections. It provides a broad view of the company’s success and areas for future expansion.

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meaning of sales analysis in research

Sales analysis tools help you turn raw data into actionable insights. They not only simplify the process of gathering and analyzing sales information, but they also give you a complete view of your sales performance. To improve your sales analysis, look for tools with this functionality:

Easy-to-use customer relationship management (CRM) capabilities

CRMs manage sales data of all kinds in one place: customer interaction, sales activities, and overall performance. For example, without a CRM, you might miss key information like customer behavior patterns, such as when potential customers typically drop out of the sales cycle or which channels engage customers the most effectively.

A CRM that balances advanced features (like AI and automation) with an intuitive interface empowers your team to excel without unnecessary complexity. This ease of use is crucial for ensuring that the tool becomes an integral part of your daily operations. For maximum impact, it should facilitate seamless management of customer relationships and sales data analysis.

AI-powered insights

Many advanced CRM systems now incorporate AI for a deeper, more nuanced analysis. For example, AI can use data to identify which potential customers are most likely to purchase based on analysis of past deal behaviors and customer trends, so teams can create more targeted sales strategies.

Here’s an example: AI surfaces an insight showing female customers from the Midwest between the ages of 18 and 35 prefer a particular brand and flavor of soda. Sales teams can use this information to focus their efforts on this region with tailored promotions and personalized communication to this demographic via social media.

Visualization

Embedded in CRM systems, dashboard creation tools provide visual summaries of sales metrics, often in real-time. They highlight vital data like leads in pipe, conversion rates, and customer engagement levels. They can also flag problem areas. For example, a dashboard might reveal that many deals are stalled at the negotiation stage. This allows you to address the issue by refining negotiation tactics or adjusting pricing to start moving more deals forward.

Automation capabilities

Now often found built into CRMs, automation helps simplify tasks like record updates, report generation, and other admin duties. Automation not only brings consistency and efficiency to routine processes, but it frees up reps’ time to focus on relationship-building.

Predictive analytics

Using historical sales data, predictive analytics can forecast future trends . These forecasts are instrumental in preparing for market shifts or changes in customer behavior. For example, if analytics reveal that certain customers tend to upgrade their services at the end of the fiscal year, sales reps can proactively time their outreach. Then, they can tailor their conversations to offer upgrades or additional features around that period.

Take action with insights from sales analysis

Sales analysis digs into data for insights and strategies. It’s about finding what works based on solid evidence. When blended with a seller’s instinct and human intuition, sales teams can make informed decisions while testing new strategies and recipes to drive sales success.

Use AI to hit your forecast every time

Spot and address pipeline gaps that threaten your forecast. Discover how with Sales Analytics from Sales Cloud.

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As a business and marketing writer for 15 years, Jeffrey specializes in skill-up content at Salesforce. His work touches on everything from sales fundamentals to employee coaching, leadership best practices, and growth strategies.

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How to Perform a Sales Analysis (Step-by-Step): Methods & Metrics

Want to achieve your sales goals? Then you have to kiss guesswork and intuition goodbye. Instead, get cozy with regular sales analysis to generate cold, hard data for your team.

Of course, wanting to go steady with data and actually making it happen are two different things. To make it a reality, you have to know what sales analysis is, why it's so beneficial to sales teams like yours, and how to analyze sales metrics and KPIs for your sales strategy .

Keep reading to learn everything you need to know about sales data analysis. That way you can boost performance —for you and your team—and capture that elusive jena se qua that will turn your competitors green with envy. Let's do this!

What Is Sales Analysis?

It's pretty simple: sales analysis is what happens when sales professionals monitor sales data, in order to evaluate sales team performance. Doing so can uncover insights about:

  • Top-performing products/services
  • Underperforming products/services
  • Customer behavior and retention
  • New sales and market opportunities
  • The future outlook of your sales team

When done right, sales analysis can help you run a more efficient and effective sales department now and in the future .

Curious about total contract value ? Hey there, our article holds the answers.

How Often Should You Perform a Sales Analysis?

Worried you'll come off too strong? You definitely don't want to look desperate. How much is too much? Luckily, the quickest way to sales analysis' heart is to spend quality time with it.

In other words, check in on a regular basis. How regular? It depends on the sales metrics you need to track, overall performance, and the type of sales reports you're analyzing.

Your sales goals can also impact how often you should perform sales analysis.

In general, expect to track overarching metrics like net sales and/or deal size on a monthly basis . More specific metrics, like calls or emails sent, should be tracked on a shorter-term basis. Whatever your cadence, remember to monitor seasonal changes and YoY metrics, too.

What is Included in a Sales Analysis?

Better said: what isn't included in a sales analysis?

In the end, what you decide to include in your sales analysis report will depend on your goals. Here are some ideas:

  • Sales activity volume
  • Ratio of new leads to qualified leads
  • Information about your pricing structure
  • Data on your social media campaigns
  • Current sales trends
  • Revenue and costs for a specific period

Along with these things, a clear sales data analytics report will show you want to do with the information. Specific action steps are a key piece of sales analysis, meaning you can do more with the information you've gathered. Moreover, explore Google sheets alternatives that may better suit your sales analytics needs, providing enhanced functionalities for improved data management.

What is Sales Analysis Useful For? 4 Irresistible Benefits

Why should sales managers get serious about sales analysis? Two words: the benefits!

Seriously, if you want to see how your team performs against its sales goals—throughout the entire sales cycle—you need to monitor the specific metrics that pertain to them.

They may have unmet demands that will streamline your business processes and benefit you in the long run, including outsourcing needs that cover 3PL warehouse management , manufacturing, offshore or onshore, etc. This is one of the reasons why those metrics must be monitored.

Once you do, you'll be able to make better decisions, understand market trends, boost company profits, and improve customer satisfaction. Let's take a closer look:

  • Make better decisions: Sales analysis will reveal the real-time success of your sales plan . You can use this information to build a better, data-driven approach.
  • Understand market trends: It doesn't matter what you're doing—launching a new product, planning inventory , etc. A sales analysis report will help you uncover hot market opportunities and must-know trends to make the most of your efforts.
  • Boost company profits: Top sales reps spend more time talking to high-quality leads. Sales analysis will help you identify the best prospects so that your team can close more deals. It will also reveal information regarding your non-customers, which can be used to sharpen your sales pitch and personalize future marketing strategies .
  • Improve customer satisfaction: Finally, sales analysis will help you understand what customers want and why they buy. These details can be used to forge deeper bonds with your target audience that lead to more upsell and cross-sell opportunities.

Does the idea of sales analysis have you hot and bothered? Great! Now I'll show you a proven, four-step process you can use to analyze the metrics and KPIs that matter to you.

How to Perform Sales Analysis: A 4-Step Process

You're ready to take the plunge and generate your sales analysis report—but how? Follow this four-step process, and you'll have sales analysis wrapped around your finger in no time!

Step 1: Choose the Right Sales Analysis Method

Different sales analysis methods will allow you to generate different kinds of reports. So, before you do anything else, choose a method that aligns with your sales goals.

Here are seven specific sales analysis reports you need to know about:

  • Sales trend analysis: This type of sales analysis looks for patterns in sales data. Use it to track your team's progress towards its sales goals, while simultaneously understanding sales patterns in specific products, customers, and/or geographies.
  • Sales performance analysis: Sales performance analysis is crucial for effective sales performance management . This type of analysis will help you gauge your sales team's performance and evaluate the overall effectiveness of your sales strategy. Utilize it to compare actual results to expected outcomes, and then make necessary adjustments. Implementing these changes can lead to faster closing times, increased win rates, and a significant boost in revenue growth. (Dive into the world of CRM and its pivotal role in driving revenue growth .)
  • Predictive sales analysis: This type of sales analysis is designed to help you predict future risks and opportunities. Use it to create accurate sales forecasts.
  • Sales pipeline analysis: This type of sales analysis will help you discover common sales activities prospects go through before they convert. As such, it will give your sales team the context it needs to shorten sales cycles and close more deals.

How to Perform Sales Analysis - Choose the Right Sales Analysis Method

  • Product sales analysis: This type of sales analysis is perfect for large companies and/or companies with extensive product offerings. Why? Because it helps them determine which products actually affect their bottom lines. Use it to better understand your company's demographics, pinpoint popular products, and the like.
  • Prescriptive analysis: This type of sales analysis will empower your sales reps with knowledge, helping them determine which opportunities to pursue and which to dump like radioactive waste. Use it to increase rep success and team-wide win rates.
  • Market research: This type of sales analysis may seem old-fashioned, but it's never gone out of style. To use this technique, simply survey your customers, research your competitors through web scraping (a technique that automates the process of extracting data from a website ) using curl proxy for greater efficiency and reliability, and read general sales statistics. Once you do, you'll have a much better understanding of your customer's needs , thereby improving your sales effectiveness.

Step 2: Identify the Specific Information You Need

You've chosen the perfect sales analysis method. It just seems to get you and the sales goals you want to achieve. Congratulations! But your work is far from over…

Now you need to identify the specific bits of information that you need. For example, you might want to measure the impact of your sales training efforts. Or find the top-selling product from a recent marketing campaign. Or determine similarities between repeat customers.

When you know what information you need, you can choose metrics and KPIs that will help you acquire, track, and measure it. We'll talk about this a bit more in the next section.

Before we get there, though, we need to talk about timing—as in, what time frames should you collect data for? The answer to that question will depend on the metrics you're tracking, but weekly, monthly, quarterly, and yearly time periods are pretty common.

Just remember, consistency is essential, regardless of which metrics you decide to monitor. With that in mind, plan to conduct analysis more frequently during special promotions.

Step 3: Choose a Sales Analysis Tool and Analyze Your Data

Your sales analysis efforts are going strong! To keep them that way, invest in an analytics tool to help you get the most out of every metric you decide to track. Here are a few ideas:

  • Spreadsheets: You gotta love the classics, right? A spreadsheet tool like Microsoft Excel can help you analyze and interpret your sales data. Just make sure that you have sufficient quantity and quality of data before you get started. If you don't, you won't be able to make informed decisions that propel your company forward. (Note: sidle on up to these report templates to make spreadsheet reporting way easier!)
  • CRM software: Every sales organization needs a CRM. How else will you store contact information, automate email sequences, and view sales pipelines from one sales dashboard? Newsflash: your CRM tool can also be used for sales analysis. If you use Close , for example, you can easily generate reports for any metric or KPI, including detailed pipeline and funnel reports, which will help you with sales forecasting .
  • Sales analytics apps: Some tools are completely dedicated to sales analysis. Chorus.ai , for example, will help you analyze sales calls and pinpoint areas of improvement. Gong.io will help you report on customer interactions and forecast future sales. And Seismic will help you calculate the effect of your sales enablement efforts.

At the end of the day, choose the sales analysis tool to help you accomplish your goals. Look for substance, not style. We all know you'll make better business decisions with the right data analytics tools in your toolbox.

Step 4: Share Your Results with Relevant Stakeholders

Last but not least, you need to present your sales data analysis to key stakeholders.

Unless you’re asked to share the process by which you arrived at your results, only show the main findings. You can use graphs and visuals to help your audience interpret the data. Additionally, employing tools like the revenue growth calculator can be instrumental in visualizing and comprehending complex sales data effectively.

For example: If you lead a sales team and want to share information regarding sales team performance with your CEO, you might want to include charts around your sales goals, your best-selling products, and the revenue and expenses of your team.

Overall, your sales analysis presentations should share actionable insights and be easy to understand. End with recommendations to help accomplish this goal.

How to Perform Sales Analysis - Share Your Results with Relevant Stakeholders

Seeking sales excellence? Discover the power of challenger selling strategies .

Choosing the Sales Analysis Metrics and KPIs That Matter

At this point, you know exactly how to perform an in-depth sales analysis—just follow the four-step process above. Now you need to choose a few KPIs to monitor.

Here are 10 metrics you'll probably end up tracking at some point. This is definitely not an exhaustive list of KPIs. If you want that, check out this article when you're done with this one.

1. Monthly Sales Growth

This metric will give you the juicy deets on your overall sales revenue. Is it going up, going down, or holding steady? When you know, you can better optimize your sales processes.

2. Sales Opportunities

This KPI will tell you about the opportunities your sales reps create. It can be used to determine good and bad-fit prospects, which makes it useful for sales prospecting.

3. Lead Conversion Rate

This metric will help you understand why and how leads are converted . This information can then be used to design a foolproof customer acquisition plan for your company.

4. Average Conversion Time

This KPI is all about productivity. Track it to determine how long it takes for leads to convert into paying customers. You can also combine it with other metrics, like lead conversion rate and total sales opportunities, for a handy bird’s eye view of your company's sales pipeline.

5. Monthly Onboarding and Demo Calls Booked

This metric will help you understand the health of your sales funnel. Why? Because prospects that make it to the demo and/or onboarding stages of your funnel are likely to convert.

6. Pipeline Value

This KPI will tell you the amount of revenue you can expect to generate from the sales opportunities in your department's pipeline, within a specific time frame.

7. Sales Targets

This metric will share historical data regarding team performance. Want to know the amount of revenue generated or the number of product subscriptions sold? This metric will help.

8. Customer Lifetime Value (CLV)

This is an extremely popular KPI —with good reason. Track it to learn how much revenue the average customer generates for your company during their lifetime, based on the average deal size and how long your customers stay with you. Then use it to predict future revenue, make informed decisions about customer acquisition , etc..

9. Calls and Emails Per Rep

This metric will tell you how many calls and emails your sales team makes every day, week, and month. It can be used to evaluate productivity and to identify broken sales funnels.

Want to amplify your sales results? Dive into our comprehensive guide on the best sales productivity tools available.

10. New and Expansion Monthly Recurring Revenue (MRR)

These are important metrics for SaaS companies because they tell them how much revenue they generated this month, compared to last month. Brands can then use this information to determine the effectiveness of their sales and marketing teams, and help minimize churn.

How to calculate these two metrics:

Fall in Love with Sales Analysis

Sales analysis reports create accountability, reveal insights about one's customer base , the specific traits top-performing sales reps have… Honestly, they have the power to revolutionize your entire sales and decision-making processes, which is why they deserve your unending love and devotion.

The question is, which tool will you use to generate said reports? Here's my advice: choose Close. Our top-rated CRM platform has all the tools you need to create custom reports and monitor specific KPIs. Even better, you can try before you buy with this 14-day free trial .

That's right, I'm not asking you to put a ring on our hand just yet. Take us out on a date, see what we have to offer, then decide if you want to spend the rest of your life with us. (Or at least the rest of your sales career .) Something tells me we're a match made in heaven!

I want to try Close for free for 14 days . (Just say YES!!!)

START YOUR FREE TRIAL→

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meaning of sales analysis in research

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meaning of sales analysis in research

In this beginner’s guide to sales analysis, we will walk through the definition, benefits, types, techniques, and methods of sales analysis. Also, we will provide some examples of sales trend analytics, sales pipeline analysis, and so on. If you’re interested, just go and check it out!

1. What is a Sales Analysis?

2.benefits of sales analysis, 3. 5 types of sales analysis methods and techniques, 4.1 indicators monitoring , 4.2 regularity analysis of indicators, 4.3 comparative analysis of indicators, 5. recommended sales analysis tools, 6. sales analysis examples.

Let’s learn the definition of sales analysis from scratch. Sales analysis is the process of integrating, analyzing, and understanding various data related to sales activities such as sales, customers, and transaction data.

meaning of sales analysis in research

It allows managers to look at sales from many aspects and decide what is working and what is not working.

It also provides information and reporting so sales managers and executives can ascertain what region is achieving its goals and which areas are not achieving goals, which salespeople are doing well and which are not, etc. 

A useful sales analysis has the following three benefits:

Understanding customer needs

One of the critical reasons for poor product sales is that we don’t accurately capture customer needs.

Through analyzing sales performance, you can find the needs of your most profitable customers and potential customers, then formulate sales measures to improve business performance.

Sharing sales skills and know-how 

Traditional sales activities rely on the intuition and experience of the person in charge, so the useful skills and knowledge to sell products have not been systematized.

Through objective sales analysis, it is intuitive to discover the causes of failure and success, and share sophisticated sales know-how with your team.

Being aware of the market trend

Based on past data, sales analytics allows you to identify market opportunities, grasp trends in sales performance, and predict “how much your company’s products will sell in a certain period in the future.” 

How to analyze sales data? There are various data analysis methods, such as cross-tabulation, association, and decision tree. But for beginners, none of them are applied to sales analysis immediately. Here are five easy and practical ways to analyze sales.

3.1 Factorization

First of all, the method of sales analysis that beginners need to manipulate is factorization.

By factoring sales into various aspects, you will understand the factors behind the decrease and increase in sales.

Let’s analyze sales on an EC site as an example.

(1) Product sales = sales volume x unit price.  If sales decline, is it due to low sales volume or low unit price?

(2) Sales volume = Sales volume of sales channel A + Sales volume of sales channel B + Sales volume of sales channel C.  Analyze the sales volume for each sales channel to see which one is lower.

(3) Sales volume of sales channel = number of clicks x turnover ratio.  If the sales volume of sales channel A is low, is it due to the low number of clicks or low turnover ratio? If the turnover ratio is low, you have to double-check whether the target customer of the channel matches the target customer of the product.

(4) The number of clicks = number of times displayed × click rate.  Is an insufficient number of impressions or a low number of clicks cause a low number of clicks? If your clicks are low, why not improve your ad content?

In this way, sales can be factored, and through an in-depth analysis of data, you can find the flow from the process to the results and the essential factors that cause the decrease.

3.2 Association analysis

If you know data analysis, you may know that association analysis is often used.

Association analysis is an analysis method that analyzes the accumulated transaction data for each customer and finds the law that “X% of people who buy product A also buy product B.”

The most famous example of association analysis is “diapers and beer”. It has shown that many men who come to their supermarket to buy diapers also buy beer.

The results of association analysis are useful for understanding which products sell and which do not, and for conducting effective sales promotion activities to increase sales.

3.3 Regression analysis

The multiple regression analysis is to analyze the factors (explaining variables) related to the results (objective variables), which factors affect the results and the extent to which the future is a statistical method for predicting.

When used for sales analysis, it predicts future sales by regression analysis of what influences sales among multiple factors. These include the number of employees, number of products sold, product price, and so on.

3.4 RFM analysis

RFM analysis is an effective method for finding profitable customers in sales analysis.

Customers are ranked by three indicators: Recency (last purchase date), Frequency (cumulative number of purchases), and Monetary (cumulative purchase price). 

RFM score=RS*100+FS*10+MS*1. In this way, you can identify profitable customers and customized marketing services to provide strong support for more marketing decisions.

3.5 ABC analysis

ABC analysis is an analysis of a range of items that have different levels of significance and should be handled or controlled differently.

It is an application of Pareto analysis (80:20’s law). In other words, 80% of sales volume is generated by 20% of all products.

The products are grouped into three categories (A, B, and C) in order of their estimated importance. ‘A’ items are essential, ‘B’ things are important, ‘C’ items are marginally valuable.

You can use ABC analysis to find out “selling products” and “dead products” and use them for product ordering, inventory management, sales management, etc.

4. How to Perform Sales Analysis ?A Three-step Process

Practically, you can perform a 3-step sales analysis process in the following way.

  • Real-time & cumulative indicator monitoring
  • Regularity analysis of indicators
  • Comparative analysis of indicators

It is common to monitor the indicators. The traditional way is mail reporting. The modern way is real-time monitoring from the dashboard on the large screen.

Now, many companies have realized the automation of indicator monitoring, multi-platform integration and tracking on the mobile apps

Here is an example of a sales report built with FineReport:

sales analysis

As a sales manager, you never want to miss any progress and activity of your sales team.

Now you can easily monitor the real-time sales information by product, region, customer, and more with this dashboard.

You will have at-a-glance details that show the progress of the sales, and gain an insight into how this sort of real-time sales monitoring can augment your operational management.

It is difficult to find any abnormalities when looking at things independently. But when you expand the time dimension, there will be many discoveries.

For example, you can expand the time dimension by year

sales anlysis by year

Or by month. In this way, you can know which periods of time your sales are doing well and which periods of time your marketing strategy are useful.

sales analysis by month

Or by order data. Maybe the distinct sales performance is due to holidays such as Christmas.

sales analysis by order data

For example, from the regional dimension, comparing the differences between regions from multiple angles, the data is used to give invisible pressure to the relevant teams, remind each team of abnormal situations and help them promptly deal with the issues.

In the above figure, the maps are used to display the sales situation in various regions visually, and you can choose different comparison standards to display.

The two charts on the right form a linkage with the map, show the product and customer information related to each region. For more detailed sales information, you can drill through the chart to get it. 

It is also valuable to compare the value contribution degree of different products from the commodity dimension, which will provide a reference for brand managers reference to adjust the commodity strategy.

For example, in the pie chart above, it is clear to see the profit contribution of each product category. The following detailed list provides you with more detailed sub-products, so you can find which product affects the sales performance of categories.

meaning of sales analysis in research

Current sales data and information are scattered across various in-house systems such as Point-of-Sale, CRM and inventory management. Outputting data from each system and processing it in Excel is the traditional way of sales analysis. In addition to the labor and time required for manual work, we cannot guarantee the accuracy of the analysis results. 

Therefore, I would like to recommend to use BI tools for sales analysis.

BI tools are tools for gathering and analyzing vast amounts of data to help make quick decisions. The ”  FineReport  ” has a full range of functions for sales analysis.

  • Extract, aggregate and analyze sales data from different data sources
  • Can create schedules and sales analysis reports by dragging and dropping
  • Real-time data update

Also, please feel free to make an appointment for a live demo with our product experts. We will be more clear about your needs and see how FineReport can help you and your organization to transform data into value.

Book a Free Demo

Sales Pipeline Analysis

Typically, sales pipeline analysis follows the basic inbound funnel, in which buyers move from the beginning awareness to the end purchase decision-making. By researching the sales pipeline analysis, you can understand how many leads are converted into end customers, so as to analyze patterns of potentially qualified leads and where and how to get these qualified leads.

Sales pipeline analysis template

Product Sales Analysis

Product sales analysis is a judgment on the market performance of a product. For each product sold by your business, it is recommended that you perform a product sales analysis to compare the profit contribution of different products. If certain products do not perform well in generating profits, you can consider whether to stop or reduce the investment in that product.

Sales Trends Analysis

This type of sales analytics is looking for trends in sales data over time. The trend can be upward, downward, steady, or periodic fluctuations.

For example, the figure below is a simple sales trend analysis showing that a company’s product revenue are increasing over time.

sales trend analytics example

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Sales Analysis: Elements, Process, Principles, Problems

  • Post author: Anuj Kumar
  • Post published: 10 July 2023
  • Post category: Sales Management
  • Post comments: 0 Comments

Table of Contents

  • 1 What is Sales Analysis?
  • 2.1 Allocation of Sales Efforts
  • 2.2 Data for Sales Analysis
  • 2.3 Objectives of Sales Analysis
  • 3.1 Purpose of Evaluation
  • 3.2 Comparison Standards
  • 3.3 Reporting and Control System
  • 3.4 Hierarchical Sales Analysis
  • 4.1 Determining Source of Sales Information
  • 4.2 Collection of Sales Data
  • 4.3 Processing of Sales Data
  • 4.4 Studying the Results
  • 5.1 Total Sales Volume
  • 5.2 Sales by Territories
  • 5.3 Sales by Products
  • 5.4 Sales by Customer Classifications
  • 6.1 Iceberg Principle
  • 6.2 80-20 Principle
  • 6.3 Cross Classification
  • 7 Problems in Sales Analysis
  • 8.1 What are the elements of sales analysis?
  • 8.2 What is the process of sales analysis?
  • 8.3 What are the principles of sales analysis?

What is Sales Analysis?

Sales analysis is the process of examining sales data to gain insights and understand various aspects of a company’s sales performance. It involves analyzing sales trends, patterns, and metrics to evaluate the effectiveness of sales strategies and make informed business decisions.

Allocation of Sales Efforts

Data for sales analysis, objectives of sales analysis, purpose of evaluation, comparison standards, reporting and control system, hierarchical sales analysis, determining source of sales information, collection of sales data, processing of sales data, studying the results, total sales volume, sales by territories, sales by products, sales by customer classifications, iceberg principle, 80-20 principle, cross classification, problems in sales analysis, faqs about the sales analysis.

Through sales analyses, management seeks insights on strong and weak territories, high-volume and low-volume products, and the types of customers providing satisfactory and unsatisfactory sales volume. Sales analysis uncovers details that otherwise lie hidden in the sales records. It provides information that management needs to allocate sales efforts effectively. These aspects are discussed below considerably:

A small percentage of the territories, customers, products, or orders bring in a high percentage of the sales in many businesses and vice-versa. In most of the companies, eighty percent of the customers accounted for only fifteen percent of the sales. Comparable situations exist in most companies.

This is an example of the ‘iceberg principle’; only a small part of the total situation is above the surface and known while the submerged part is less than the surface and unknown. Sales analysis detects such situations, alerting management to opportunities for improving the operations in the organization.

Sales efforts and selling expenses ordinarily are divided on the basis of customers, territories, orders, and so forth, rather than on the basis of sales potentials or actual sales. It usually costs as much to maintain sales personnel in poor territories as in good ones, and almost as much to promote a slow-selling product as one that sells.

It costs as much to have sales personnel call on customers who give small orders as on those who place large orders. Normally, a large proportion of the total spending for personal-selling efforts brings in a small proportion of the total sales and profits. Sales analysis detects these situations.

Data availability for sales analysis varies in all companies. At one extreme, some have no data other than the accounting system records as sales are made, and, of course, copies of sales invoices. On the other hand, some maintain detailed sales records and have data readily available for use in making all types of analyses.

The original sources of data for sales analysis are the sales invoices. In a company with a good information system , detailed data from sales invoices are transferred to computer tapes or data-processing cards.

The information on each transaction identifies the customer in terms of name, geographical location, and so on; the salesperson in terms of name, territory, etc.; and includes such sales data as order date, products sold and quantities, price per unit, total dollar sales per product, and total order amount. With information stored by the sales organization , sales analyses are performed quickly and at a low cost.

The sales analyses portray the strengths and weaknesses of a sales organization in terms of sales, and each type of sales analysis glimpses different aspects. The sales territories analysis depicts about that the particular product where it can be sold. Analysis of sales by products answers how much of what is being sold.

Analysis of sales by customers’ answers about who is buying how much: All sales analyses relate to how much is being sold, but each answers the question in a different way. Sales analyses identify different aspects of sales strengths and weaknesses, but they cannot explain why strengths and weaknesses exist. In addition to the above, sales analysis answered four questions of the sales manager :

  • It revealed the sales territories with good and poor performances.
  • It showed that whom so ever salespersons are above; at par and below the quota given to them.
  • It indicated that Edwards’ performance improved as accounts got smaller, but was unsatisfactory with all sizes of accounts.
  • Where sales were weak and strong, which salespersons were performing above or below quota, which classes of accounts were buying, and which products were being sold.

Elements of Sales Analysis

A typical sales analysis involves comparing the sales of the company at two different time periods or comparing the sales with external data to exercise better control over the performance of the sales function. The key elements of sales analysis process are described below:

Elements of Sales Analysis

A sales manager must determine the purpose of the evaluation before starting the analysis. A basic sales analysis lists current sales variables and their values, while a comparative analysis compares sales performances across territories or time periods.

The manager also decides on the required information, such as total sales volume, territory-wise sales, product line sales, and sales personnel performance.

Identifying information needs can be challenging as strengths and weaknesses are revealed through sales activity analysis. Different organizational levels have varying information requirements, and the sales manager selects the data source and report type.

A simple sales analysis simply states facts whereas a comparative sales analysis compares the sales figures with some standards. Standards are the yardsticks to evaluate the effectiveness of a system. There are different standards that sales managers can use to determine the efficiency of the sales function in the organization.

The effectiveness of the sales function can be measured in an absolute or relative sense. An absolute measure is an expected or an ideal measure. The performance of the sales function can also be measured in relative terms. The average sales volume is an example of an average or relative standard of measurement.

Companies utilize sales information systems to store and process data, generating reports that depict trends, seasonal patterns, regression analysis, etc. These reports serve both for evaluating the sales force and forecasting sales.

Sales managers are concerned about the report type and its contents, finding reports focused on exceptions, such as significantly high or low sales figures, to be valuable. Additionally, managers must decide on the necessary source inputs and processing methods for report generation.

The sales invoice is commonly used as a source, along with cash register receipts, salesperson call reports, expense reports, financial records, warranties, etc. Aggregating sales variables is another critical decision in sales analysis to avoid analyzing individual transactions or solely focusing on aggregate sales, as neither serves the purpose of sales analysis.

It involves studying the sales performance at a micro level by investigating and analyzing its components. This helps sales managers to pinpoint any weakness and the cause for it.

This will help identify if there are any fundamental reasons adversely affecting the performance of sale personnel in a particular area, such as poor economic conditions, high unemployment, fierce competition, low sales morale, etc.

Process of Sales Analysis

Having decided on the purpose of sales analysis and the information that is needed from it, a sales manager can perform a sales analysis. These are the steps of the process of sales analysis :

Process of Sales Analysis

The most critical element in sales analysis is sales information. There are many sources of sales information that include data from the marketing information system, company records, customers, sales personnel, field visits, and insights of the manager as well as external sources such as newspaper and magazine reports, trade, journals, etc.

The sales data is collected from sales invoices, historical records of sales volume, customer complaints, bills of sale, cash registers, etc.

The sales invoice identifies the amount and type of products that customers have bought. This source document should capture that data in a format that can be easily read and processed.

Most firms use an information system to capture, store and process sales data. Typical sales information systems provide more functionality than just supporting sales analysis. Sales managers can also use the information system to help them in other sales activities like sales planning , forecasting, etc.

Sales analysis only indicates what additional investigation is required; it does not offer a solution. The result of sales analysis should be carefully studied to identify the facts and acquire a lead for further analysis. It does not indicate the reasons for good or bad performance.

Bases for Analysing Sales Volume

Following are a few bases for analyzing sales volume :

Bases for Analysing Sales Volume

It is a combined sale of all products in all territories for all customers. The study of total sales volume requires the following data:

  • The annual sales figures for the company over the past several years,
  • The annual industry sales in the geographic market are covered by the firm. From these figures, the company’s share of the market can be determined.

Analysis of sales volume by territories helps management to identify which territories are strong and which are weak in relation to sales potential.

A few products may bring most of the sales volume (80-20 rule). There is no relation between volume and profit. Products having a high volume of sales may not contribute a high percentage of net profits.

Types of sales volume analysis by-products that may be helpful to sales management are:

  • A summary of present and past total sales divided into individual products or groups of products, which helps sales managers to study the sales trend for each individual product/group of products.
  • Sales of each product line in each territory can be used to determine the geographical market in which each product is strong or weak.

As a part of sales volume analysis by products, sales managers must decide what to do about low-volume products and products that did not meet their goals.

80-20 principles are applicable to sale analysis by customers. A small percentage of customers account for a major share of total sales volume. In addition, a firm may sell to many accounts on a marginal or even unprofitable basis. Sales volume by customer groups is analyzed in the following ways:

  • Accounts on an industry basis
  • By channel of distribution
  • On the basis of accounts (e.g., key accounts)
  • Combination basis

Any of these customer classifications usually should be analyzed for each territory and for each line of products. A sales volume analysis alone usually does not furnish enough information to the sales department. The rupee sales volume does not provide any data regarding gross margin.

Hence, a marketing profitability analysis is ideal. Therefore, sales performance measures include marketing–cost analysis and profitability analysis in addition to sales volume analysis. When the sales volume analysis includes the cost of merchandise sold, the sales manager can do a gross margin analysis by territories, products, or customer groups.

Principles of Sales Analysis

The sales cost and profitability analyses are based on the principle that not every factor affecting the sales and the marketing function is revealed completely unless the details are probed. Let’s discuss the principles of sales analysis :

Principles of Sales Analysis

The iceberg principle suggests that aggregating the total sales figures of a firm and comparing them with past performance may reveal a positive picture of sales even though there may be a larger problem concealed. When individual sales figures are aggregated into totals, values that are too high or too low offset each other and so lose their significance.

All strengths and weaknesses may not be revealed when aggregates are used for analysis. The same rule applies to marketing costs as well. Total costs may not reveal all aspects of the costs incurred or all the details that are necessary for efficient cost control.

This is like the visible portion of an iceberg is only a minute fraction of what is underneath and thus the term ‘iceberg principle’. Therefore, in order to obtain accurate and complete information about the sales figures, the sales data should be broken down into individual sales segments.

The 80-20 principle, is also called the Pareto principle. The 80-20 rule states that 20% of the elements are responsible for 80% of the results. This rule can be applied to all areas of business and helps management to focus on the real problem or issues.

In sales analysis, this principle states that 80% of a firm’s sales volume comes from 20% of its customers. Thus, not all business units of a firm contribute equally to profitability. Firms facing the 80/20 situation can adopt certain strategies to alter the ratio and increase their profits.

This rule has a similar implication for the costs and expenses as well. Cost analysis of firms shows that about 15% of expense categories account for about 80 % of all expenses. Therefore, management should focus its attention on designing measures that address the 15% category of costs that contribute to the bulk of expenses of the firm.

It can be used when the sales data has to be analyzed on the basis of more than one category. If sales managers require information on both customer and product categories, either they can opt for two separate analyses, one by-product and the other by the customer, or they can go for one analysis, by customer cross-classified by product.

It helps the sales managers to arrive at the same information but with the product-customer detail added. It involves more than two categories that complicate the information.

The sales analysis is dependent on accounting records for gross sales and sales returns. Therefore, if there are flaws in the accounting system, the sales analysis is affected and it might not give a true picture of the strengths and weaknesses of the salesmen’s selling efforts.

Though a sales analysis identifies the problems and their causes, it does not really reflect the performance of the company in relation to the industry or its competitors. It also does not emphasize sales profitability.

A sales analysis only talks about sales volume and does not give any indication of whether the sales were profitable for the company. To analyze the profitability of sales, a distribution or marketing cost analysis has to be done.

What are the elements of sales analysis?

Here the elements of sales analysis are: 1. Purpose of Evaluation 2. Comparison Standards 3. Reporting and Control System 4. Hierarchical Sales Analysis.

What is the process of sales analysis?

Having decided on the purpose of sales analysis and the information that is needed from it, a sales manager can perform a sales analysis by: 1. Determining the Source of Sales Information 2. Collection of Sales Data 3. Processing of Sales Data 4. Studying the Results.

What are the principles of sales analysis?

Iceberg Principle, 80-20 Principle, and Cross Classification are three major principles of sales analysis.

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What is Sales Analysis?

Table of Contents

What is a Sale

A sale is an activity that generates profit or revenue for every business which consequently covers all the costs and expenses. Sale is very important for every organization. However, there are many ways to achieve the sale, easy and difficult both, they vary from business to business.

What is a Sales Analysis?

As the name implies sales analysis means analyzing the company’s sales over time. Different companies create sales analysis reports at different times; it might be on a daily basis, weekly basis, monthly basis, quarterly basis or annual basis. The purpose of sale analysis is to check the company’s performance and how it can be improved.

Importance of Sales analysis

Opportunities to expand your reach.

By analyzing the sales data helps us to see the opportunities that we have missed or couldn’t claim it in the past and how we can achieve it in the future. It also helps us to make better decisions like which product to keep (continue) and which to discontinue. Or rearrange market activities, change in the manufacturing process, inventory management and which scheme or offer to be launched or not.

Customer Analysis

It would be absolutely right if we say that sales analysis equals to customer analysis because it tells us the buying and shopping of our targeted customer and how he reacts to our product or service.

Product Mix Analysis

Whenever a company plans to launch a new product; it is actually based on the sales analysis which tells us market trends and customer’s buying pattern. Sale analysis also tells the timing of the product to be launched, seasonally or off seasonal, holidays or festivals, because customer’s buying pattern changes depending upon the timing and situation.

Decision Making

All the top management decisions are based on the sales analysis, for instance, if a product isn’t selling then the company will decide to discontinue the product. Back in 2005-2006, Nokia button pad phones were at their peak but the management of Nokia ignored sales analysis reports and growth of upcoming touchpad phones at the time. What happened? 10 years later, Samsung captured the whole market of touchpad phones which were once belonged to Nokia’s button pad phones. Why? Nokia’s management refused to make a decision based on the sales analysis report.

Types of Sales Analysis

Different companies use different types of sales analysis depending upon their requirements. Here are some such as;

Descriptive Analysis

It describes the product, link or channel of distribution isn’t selling well and how it could be improved, or it should discontinue.

Predictive Analysis

As the name implies, it foretells the future sales based on the current and past sales analysis reports and timing.

Product Analysis

The sale of different products is different at different times of the year. For instance, winter’s clothes are only sold in the winter, but not in summer. Summer clothes are only sold in the summer, not in the winter.

Sales Analysis Examples and Templates

The purpose of sales analysis is to get the exact results of profit by sales. Now, the question is how to present your data into some understandable format, here’s a link to some templates;

Template.Net

Template.net is a good place where you can find sales analysis templates in different formats like Word, PDF, and excel. These sales templates can help you to collect and analyze sales data.

Sampletemplates.Net

This website is another good example of marketing and sales templates. It is not possible to set targets for the upcoming period without a detailed analysis. This is a place where you can find desired templates to analyze your sales reports, revenue, loss or any other expense incurred.

How to Create Your Sales Analysis Report

Sales analysis report can help you to discover very valuable information; it could be whether to change the product, price or even launch some new product or service.

The difference between the financial statement and sales analysis report is that the financial statement only provides you sales records and numbers. Sales analysis report, on the other hand, also provides you the new opportunities to grow your business and how it can be improved along with the financial statement.

What You Want to Track

Sales analysis report provides you an opportunity to dig deeper through the surface of certain product, service or department. If you’re planning to create a sales analysis report, then your objectives must be outlined, such as;

  • Repetitive sales to your targeted customers at one location
  • Newly acquired customer in a given time period
  • Frequently mentioning the purchases during the campaign.

How Frequently You Need Sales Analysis

Your company must decide how often they want to track their sales whether it is daily, weekly, monthly, quarterly, and so on because it gives you an overview of your previous sale. There must be more than one sales analysis reports with multiple variables.

How frequently your sales analysis reports should be; it depends on the nature and category of sale. As Gandy explains it that you want more frequent reports during the period of marketing promotions and in the season rather than in an ordinary typical month.

Set the Important Variable

You must know your key variables and how they are going to provide you the necessary data to achieve your objectives. Gandy suggests that one should drill down from top to bottom for more detailed variables like;

  • Amount of sale

If you want to be more precise about your product line, service, and customer trends; then add some more detailed variables such as;

  • Product number
  • Gross margins of the product
  • Category of the product
  • Customer data like phone number, email address, and name
  • Metrics for your sale campaign

Collect all the data (sales) from the sales point to customer management and put it into a spreadsheet.

Tabular and Graphically Representation

Once you have your required sales data in an excel spreadsheet. Now convert it into line graph or bar chart, it’s all automatic, just select the option and excel will the rest. This graphical representation will tell you that which of your sales variables are decreasing, growing, and maintaining a steady level. You can also compare your sales performance in a certain time period like a holiday, off-seasons, festivals, and etc. To check your sale performance from the historical perspective, you can track certain incrementing variables over the life of your product, service or business.

Analyze Your Results

Once your data is present in charts and graphs; then the final step is to analyze your sales report and look for market emerging patterns and trends and asks questions like;

  • What mix (product & service) is bought by the customers collectively?
  • When certain items of a mix (product or service) are being sold mostly?
  • Does the buyer follow any seasonal trends? Or any pattern emerges?
  • Repetitive customers and first-time customers are buying the same items of certain or different products?

Most importantly, notice the changes in your sales over a period of time. If some product or service isn’t performing, find out why and how it could be improved.

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Ahsan Ali Shaw

LeadSquared

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  • What is Sales Analysis?

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By finding out where you are and projecting what will happen in the future, you’ll be able to make educated decisions on how to make changes for the better. This is where sales analysis comes into the picture.

What sales reports show and how to analyze them

At the most basic level, a sales report shows you how much sales (in numbers of sales or income from those sales) were made over a period of time. If it were to show sales of $1 in January, $2 in February, and $4 in March, you could project sales of $8 in April as the figures have doubled each month. You can assume there is little that needs to change, and things seem to be going well.

This information can be split in various different ways to give you more and more detail on how these sales are happening.

Imagine that the whole of your sales information is an onion, and you are the scientist who is about to investigate the onion. You can see it growing in the ground, and by measuring the size of it every week, you can predict how the onion will grow in the coming weeks – that’s your sales forecast .

sales_analysis_sales_report_analyze

If you pull the onion out of the ground, you can slice it in half and you’ll see there are many layers inside. These layers could represent geographic locations where your sales team operates. If you could non-destructively measure these layers, you could predict how each layer (location) would grow over time, and see which layers might need a little more nutrition.

But you’re a scientist, so you’re always on a quest for more information. You take out one of these onion layers and place it under a microscope. Lo and behold, there are cells visible inside the onion now!

The cells could represent individual members of your sales team. If you measured them and saw how each cell grew compared to the other cells, you’d most likely find that some grew faster than others. You’d know which cells needed a little bit more attention to grow properly.

Of course, when it comes to onions, there’s only so much you can do to enhance their growth, but with employees within your company, you can respond to their needs accordingly.

Your onion might be growing faster on one side, rather than the other. This could be due to that side of the onion receiving more sunshine than the other side. Maybe particular locations convert more easily in your business, giving you this unbalanced onion shape.

Knowing this information – that sales are growing faster in one area than another – allows you to more accurately predict the future sales levels. When the sales have gone $1, $2, and $4, the next month may be $7 instead of $8 – the increase wasn’t due to doubling overall, but the sales in one area first increased by $1 (from $1 to $2 total) and then by $2 (from $2 to $4) and will now increase by $3 – 1, 2, 3 is the logical progression.

Who works hard and who works well?

sales_analysis_sales_report

But you’ll only be able to do this if you have access to the data in the first instance.

Tracking Leads

The success of your sales team depends partially on the availability of leads to work with. Information relating to how many people are in each stage of your sales process will identify the areas in which you need to make the improvement.

sales_analysis_tracking__leads

If your leads are perennially stuck in the first stage of your sales process, then something needs to be done to kickstart the entire sales system – the offer or appeal clearly isn’t strong enough to draw the crowd in.

Alternatively, if every lead is stuck at the penultimate step and not buying, your sales team will need to improve their closing skills.

When Analysis Cannot Occur

There is only one occasion when sales analysis cannot occur – when there is no data to look at. The reasons that cause you to have no data can vary.

Perhaps you are a startup, with no track record – that’s fine. Get working, results will come.

Perhaps you haven’t made any sales – that’s OK, but not great. Put some more work in, data is coming.

Perhaps you don’t bother to record this information – that’s really not OK. You cannot expect to build and grow a successful business without knowing where your business is right now.

sales_analysis_when_analysis_cannot_occur

By recording your sales data with LeadSquared you open up a whole world of reports and analysis. Not only is the information saved and presented to you as you want it, but it can also be segmented and rearranged exactly as you need it.

It’s like having a GPS, a book of maps, and a multitude of signposts to guide the way. Wouldn’t you feel better if you knew where your business was headed?

So how do you start analyzing your sales results? By having a good sales analytics tool that helps you to know what is happening at every single stage of your sales funnel. It should also give you detailed insights into the performance of your marketing campaigns and your sales team in terms of efficiency and revenue.

And that’s why LeadSquared is your best bet. Don’t believe me? Take a quick 15-day trial to check it out yourself.

meaning of sales analysis in research

Rajat is VP - Sales & Marketing at LeadSquared. A designer at heart, he is a truss bridge for sales and marketing teams. You can reach out to him on LinkedIn or write to him at [email protected].

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Everything About Sales Analytics: Meaning, Types and Benefits

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Every firm depends on sales income to stay afloat and run its operations. That is why a sales study is so important to business. Teams can use it to find new opportunities and recognize and fix issues in their sales process.

The effectiveness of key performance indicators (KPIs), the efficiency of the sales process, and the efficient use of resources are all elements that are taken into account by a sales analysis.

For the majority of businesses, when the business grows and sales pick up, it becomes more challenging (and urgent). Sales managers and teams frequently don’t know where to start or what a well-developed analysis process looks like.

In this article, we’ll define a sales analysis, go through its advantages and significance, and look at some of the most typical varieties. By the time it’s all said and done, you’ll know more about how it can help your business and how to go about putting your own process in place.

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Why is Sales Analytics Important?

sales analytics

Here are some of the reasons why it’s important:

  • Data-driven decision-making: It can help you make informed decisions about your sales strategy by providing insights into your sales performance, customer behavior, and market trends. For example, you can use sales analytics to determine which products are selling well, which customers are most likely to buy from you, and how you can improve your sales process.
  • Identifying trends and patterns: It helps you identify trends and patterns in your sales data. This information can be used to anticipate future sales trends and make proactive adjustments to your sales strategies. For example, if you see that sales are declining in a particular region, you can take steps to address the issue.
  • Improving sales performance: Sales analytics can help you evaluate the performance of your sales team and individual sales representatives. This information can be used to identify areas for improvement and provide targeted training and coaching to your sales team. For example, if you see that a particular sales representative is struggling to close deals, you can provide them with additional training or coaching. Analytics aid salespeople in reducing breaches in the sales funnel in addition to a 20% average boost in sales productivity.
  • Optimizing marketing efforts : It helps you measure the effectiveness of your marketing campaigns. This information can be used to determine which marketing initiatives are driving the most sales and allocate your resources accordingly. For example, if you see that a particular marketing campaign is not generating leads, you can stop running the campaign and focus on other initiatives. More than 44% of companies utilize customer analytics to attract new clients.
  • Forecasting and planning: Sales analytics can provide insights that can be used for sales forecasting and planning. This information can be used to allocate resources, plan inventory levels, and make strategic business decisions. For example, if you see that sales are likely to increase in the coming quarter, you can increase your inventory levels to meet demand.
  • Competitive advantage: By leveraging analytics sales data, you can gain a competitive advantage in the market. Understanding your sales data and customer behavior better than your competitors allows you to identify untapped opportunities, tailor your offerings to meet customer needs, and stay ahead of market trends. This helps you differentiate yourself from competitors and drive business growth.

Overall, sales data analytics is an essential tool for any business that wants to improve its sales performance. By collecting, analyzing, and interpreting sales data, you can gain insights that can be used to make informed decisions, identify trends, improve sales performance, optimize marketing efforts, forecast and plan for the future, and gain a competitive advantage.

Important Types of Sales Analytics

  • Market Research: Information about consumer preferences, behaviors, and needs is gathered through market research. This aids in the creation of content that is correctly targeted at a company’s target audience, as well as the production of goods and services that best serve customers.
  • Sales Trends: Sales trends can be used to predict revenue and inform marketing departments about which approaches work best with specific demographics in a company’s target market.
  • Predictive Sales: Predictive sales is a method of forecasting sales based on marketing information from the past and present. This facilitates the management of the marketing budget as well as the planning of resources.
  • Sales Pipeline: The sales pipeline analysis examines the entire sales process, including market research, customer acquisition, sales pitches, and closing sales. It helps to improve a business’s sales strategy, by looking at the sales pipeline and each stage of the sales process.
  • Product Sales: A product sales analysis studies all the company’s products available in the market. It’s essential to keep track of each product and emphasize those that are performing the best.

What are the Benefits of Sales Analytics?

sales analytics

  • It can help a lot to optimize the sales funnel. It provides a closer and more precise review of each stage of the sales funnel and helps in optimizing each part of the process by making the required changes.
  • Sales analytics like the team performance helps in identifying the strengths and weaknesses of your sales team. Further, it can also help in determining what practices work best for making sales.
  • It increases sales effectiveness, it is a metric that can detect patterns in lead generation, and what type of content customers are engaging with. It further assists in the optimization of the sales funnel. It further helps in the enhancement of the sales process, resulting in increased efficiency.

Sales analytics is a potent instrument that offers valuable insights into market trends, customer behavior, and sales effectiveness. Businesses can use these insights to optimize sales strategies, make data-driven decisions, and gain a market advantage. It is a crucial tool for any company wanting to succeed in today’s competitive marketplace due to its capacity to increase sales performance, make accurate forecasts, and spur corporate growth.

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Aparna is an enthralling and compelling storyteller with deep knowledge and experience in creating analytical, research-depth content. She is a passionate content creator who focuses on B2B content that simplifies and resonates with readers across sectors including automotive, marketing, technology, and more. She understands the importance of researching and tailoring content that connects with the audience.

If not writing, she can be found in the cracks of novels and crime series, plotting the next word scrupulously.

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The Ultimate Guide to Sales Analysis Reports

meaning of sales analysis in research

Table of contents

If you were to ask the most successful CEOs in the world to create a list of the most important areas in their companies, they will all inevitably have the same thing at the top of the list – Sales.

This isn’t surprising. After all, sales drive revenue, and improving the sales process should always be the number one priority in any business, no matter its size.

However, analyzing and then optimizing your sales process can hardly be done without the help of additional tools. And that’s where Sales analysis reports come in.

Sales analysis reports provide you with an overview of all the significant data and metrics related to your sales process. They also make sales process optimization a lot easier since you will be able to identify all the strengths and weaknesses much quicker.

In this article, we are going to walk you through what sales analysis reports are, how to create them, explain their benefits, and show you how to analyze the data you acquire.

What Is a Sales Analysis Report?

How are sales analysis reports helpful, what should be included in a sales analysis report, types of sales analysis reports, how do you analyze sales results, how to write a sales analysis report in 6 steps, improve sales analysis and forecasting with databox.

HubSpot CRM – Sales Analytics Overview Template

A sales analysis report is a document that includes all of the most important data of your business’s sales process and provides you with a complete overview of your sales trends, volume, and overall sales activities.

Some of the metrics included in sales analysis reports are:

  • Sales trends
  • Lead conversion rate
  • Number of leads in the sales pipeline
  • Historic sales data
  • Sales team performance
  • Product assessment

The main goal of this report is to inform you whether there is an increase or reduction in sales. Once you finish analyzing the data in a sales analysis report, you will be able to create better strategies, avoid unnecessary costs, and identify which areas in the sales process need to be optimized.

From new startups to huge corporations, sales analysis reports are an indispensable practice for all companies. During a fiscal year, sales managers will often turn to sales analysis reports in order to come up with efficient strategies going forward.

Now that you understand what sales analysis reports are, you might be wondering about the exact benefits that they bring to the table.

We will break them down one by one so you understand just how crucial these reports are to your company.

Show Actual and Projected Sales

Evaluate product demand, assess market prices, provide customer analysis, improve sales strategies.

It is also crucial to know the main differences between analytics vs. reporting . By doing to, you can ensure that you use the appropriate methods and techniques to meet your reporting goals and provide actionable insights to stakeholders.

Sales analysis reports provide you with an insight into the actual sales that occurred in a specific time period. You can filter this either quarterly, yearly, weekly, or even daily.

Different companies have different data sets contained in the report. For example, big companies mainly focus on subsidiary, division, or regional data sets, while smaller businesses tend to be more interested in data categorized by location or product. When it comes to specialized businesses, they usually incorporate general sales data sets.

One of the main reasons why managers use sales analysis reports is to find new market opportunities that they can exploit and improve sales volume.

Identifying these new opportunities can be a lot easier once you figure out the peak periods of your business and compare actual sales to projected sales.

Related : Sales Report Templates For Daily, Weekly Monthly, Quarterly and Yearly Statements (Sourced from 40+ Sales Pros)

Another thing that a sales analysis report can provide you with is an evaluation of your business’ product demand and whether there are any issues with it.

For instance, if there is a lasting sales decline for a particular product, there is most likely some type of problem with it.

One of the reasons may be that your competition has a better offer for the same product. However, if this is the case with most of your products, it may be time to completely reconstruct your brand.

In situations where the decline is occurring due to a change in customers’ needs, repackaging the product for a new purpose or finding a new target market could be the potential solutions.

Some companies use sales analysis reports to create market price forecasts. For example, the market value of a specific product could be determined by its features and depending on how much money were customers willing to pay for it in the past.

This is especially the case in the real estate industry where specific characteristics of a house affect its overall value. These characteristics could be anything from the number of rooms, interior, location, swimming pool, square footage, and others.

In other cases, it could be the material of a product, its reputation, and its brand name.

Drawing new customers to your business is never easy, especially if you are a new company. The process consists of creating efficient marketing strategies and spending money on different sales consultations.

Sales analysis reports can make this process a bit easier. By analyzing the data in the report, you will have an easier time understanding your customers’ needs and coming up with targeted solutions.

In summary, a good sales analysis report will help you recognize your customers’ patterns and allow you to analyze their behavior.

Additionally, during this customer analysis, you will also gain an insight into which customers are generating the most revenue. You can use this information to create special discounts for them to keep them coming back for more.

Related : 7 Ways to Use Customer Data for More Efficient Marketing

Once we combine all of these previous benefits, we realize that sales analysis reports ultimately help you improve and optimize your future sales strategies.

If you properly analyze your product’s performance, customers, and the overall market, creating the perfect sales strategy will be a piece of cake.

Related : How to Boost Revenue with a Data-Driven Sales Enablement Strategy

Depending on which type of business you run, sales analysis reports tend to include different things.

This is because not every company focuses on the same areas of a sales process, some need insight into specific metrics more than others.

However, there are some universal things that all sales analysis reports should contain, such as.

  • Significant sales KPIs and metrics (turnover, net margin, quantity sold, etc.)
  • Total sales volume
  • Net sales (different from sales volume since it is displayed through dollar figures)
  • Gross sales
  • KPI percentage comparisons between historic and current reports

While this list is rather short, it’s because sales analysis reports don’t have to include an abundance of data. In most cases, you will want to focus only on the most significant areas of your sales process in order to optimize it properly. Going through dozens of pages will only overwhelm you and your team.

But keep in mind, your goal isn’t only to display the numbers – you should also explain the story behind them.

PRO TIP: How to Set SMART Goals for Your Sales Team’s Performance

To decide which goals meet the SMART criteria, sales managers need to look at sales analytics for their teams and monitor sales KPIs, for example:

  • Average Time to Close Deal
  • New Deals Amount
  • Number of Customers
  • Average Revenue per New Customer

Based on these metrics, and in light of other revenue-based and activity-based goals, you can identify and set desired goals for future performance, but how to get this information?

Now you can benefit from the experience of our sales experts, who have put together a great Databox template showing an overview of your sales team’s performance. It’s simple to implement and start using as a standalone dashboard or in sales reports, and best of all, it’s free!

HubSpot CRM – Sales Analytics Overview - featured section

You can easily set it up in just a few clicks – no coding required.

To set up this Sales Analytics Overview Dashboard , follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot account with Databox. 

Step 3: Watch your dashboard populate in seconds.

For sales managers to acquire a clearer overview of the sales process, they incorporate different types of sales analysis reports.

This helps with data categorization and allows you to focus on specific areas of the sales process.

Here are some of the most common types of sales analysis reports that companies use nowadays.

Pipeline Report

Typical conversion rates report, average deal size report, average sales cycle length report, marketing collateral usage report, won and lost deals analysis report, churned customers report, sales call report, lead response time report, revenue report.

Pipeline reports are one of the best ways to create accurate estimates of your business’s health. By analyzing your sales pipeline , you will know which deals are the most successful, which are failing, and how each deal affects the overall pipeline individually.

But remember, coming up with accurate forecasts can only be done if your sales team does their due diligence. Make sure the representatives you pick for the job are well-qualified to create a realistic pipeline.

A conversion rate report showcases prospects to lead and lead to customer conversions. This report is mostly used for gaining insight into the efficiency of your sales strategies. It’s a great way to identify its strengths and weaknesses.

For example, if your strategy has proven successful in converting leads to opportunities, you should continue using it or even upgrade it. But, if opportunities for customer conversions aren’t working out, you will know which areas need to be optimized.

For forecasting revenue and tracking sales pipeline effectiveness, we use the average deal size report. For instance, if your quarterly revenue target is $100,000 and an average deal size is $10,000, you will naturally need ten deals to hit the target.

Of course, this is fairly obvious, but it’s useful to track these metrics through an individual report just to make sure you don’t get caught up in the numbers.

Additionally, this report can be a great way to set expectations and milestones for your sales team.

Related : 12 Tried and Tested Tips for Increasing your SaaS Average Deal Size

This report tracks the exact amount of time that it takes for a sales representative to close out a sale. We can use the average sales cycle length report to also analyze individual sales rep performances and how efficient the sales process is overall and what is the sales closing rate .

Before you start using this report, you should set an appropriate timeframe that can be considered as a benchmark. By using the benchmark, you will be able to estimate the amount of time an individual sales rep needs to close a sale.

In case they are struggling to meet the standards, you can help them realize which areas they need to work on. However, if your whole sales team is struggling, then you will have to re-evaluate your operations and approach.

PRO TIP: Are you struggling to track close rates by sales rep? Here are a few different ways on how to easily track and visualize close rates by sales reps from HubSpot CRM, no manual workarounds included – with Databox.

Marketing collateral is frequently used by sales reps to efficiently allocate prospects through the sales process. However, you should make sure that they are using the full advantage of marketing collateral and this report can help you with that.

You can use the marketing collateral usage report to check out which marketing campaigns have been the most successful and which failed to attract new prospects. Later, you can communicate your findings to the marketing team so they can have a better idea of what to improve.

While deals-in-progress are immensely important, you shouldn’t overlook won and lost deals statistics. The won and lost deals analysis report help you track these metrics.

Although winning deals is always one of the top priorities, it’s equally important to analyze lost deals and what caused them. Try to find patterns between the two so you can acquire meaningful insights into your product’s advantages and disadvantages.

Related : 19 Tried-and-True Lead Nurturing Tips for Closing More Deals

The churned customers report helps you figure out the exact reasons why users depart from your customer base.

This report can capture the problematic areas in your sales process, so you will have a better idea of what to improve.

Related : Save Your Business From Churn: 9 Churn Risk Factors to Identify

One more underestimated aspect of a sales process is sales calls. The sales call report allows you to monitor the number of calls that your sales representatives make to prospects. This metric directly impacts your team’s close rate as well.

Naturally, a successful sales rep will have a decent number of won deals compared to the number of prospects they contacted.

Additionally, once you know which sales reps are closing the most deals, you can ask them to share their tactics with the rest of the team and help out those that are struggling in this area.

Related : 11 Successful Plays for Running Great Sales Calls

The lead response time report, as the name suggests, tracks your sales reps’ response time for converting leads into opportunities.

Studies have established that if you contact your prospects in the first five minutes when they become a lead, you are much more likely to also convert them into an opportunity. Make sure your sales team knows this so they can act quickly when new leads appear.

PRO TIP: Struggling to reduce your average response time? Find out how Databox reduced median first response time , and which measures have been implemented to ensure this success is long-term and sustainable.

The revenue report provides you with insight into how the work of your sales representatives affects the overall sales process.

You can use it to monitor which representatives contributed to the business and renewals and how much. It’s recommended that you establish sales and revenue goals beforehand.

Related : How ProfitWell Grew Revenue Per Customer by 400 Percent in 12 Months

Once you have all of your important sales metrics and KPIs in one report, your next step is to start analyzing them.

This process may seem tedious at first, but with the right practices, you will be able to do it in no time. Here are three steps you can follow to efficiently analyze your sales results.

Identify the Data You Want to Track

Choose a sales analysis tool and analyze your data, share your results with relevant stakeholders.

We already mentioned that in sales analysis reports it’s best to separate the data that is significant to your business, to prevent getting caught up and overwhelmed with all the other metrics.

This way, you will only be analyzing the sales data that is the most important and you will have an easier time generating relevant insights.

If you aren’t sure how to separate the useful data, start by figuring out which products or departments in your business are top-performers and which need to be improved.

After you categorize your primary metrics, you should identify the different data sources, objective-related variables, and the performance metrics that you most commonly turn to.

Lastly, you should pick an appropriate time frame for data collection. This can be either on a daily, weekly, monthly, quarterly, or annual basis.

To acquire sufficient results from your analysis, you will need a sales analytics tool at your disposal. Analyzing your data can be done manually, but it’s simply not worth it – you will end up losing both your time and nerves.

A lot of companies incorporate Microsoft Excel nowadays since it’s one of the most straightforward data analysis tools.

However, if you want a robust tool that offers more advanced features, you can try out Databox .

With Databox, you can connect all of your most important sales data into one comprehensive report, making the analysis process far easier. Additionally, you will also be able to visualize the sales metrics through the various visualization tools and transform them into meaningful charts and graphs.

After wrapping up the analysis, your last step is to present your findings to the highest-ranking members of the company.

Make sure to only include the key points of the analysis in your presentation, unless you are asked to do otherwise. Throwing in graphs and visuals can also go a long way in making the data more understandable to the stakeholders.

In summary, you should leave out the guess factor in your analysis and make everything as simple as possible – your stakeholders should be able to quickly comprehend the data and use it to create future strategies.

Related : How to Present Qualitative Data in a Business Report? A Step-By-Step Guide

If you don’t already have some experience writing sales analysis reports, the process may seem a bit too complex.

But don’t worry, we prepared a step-by-step guide that breaks down all the important parts of the process.

Follow these steps to create a great sales analysis report in no time.

Step 1: Make an Outline

Step 2: know your audience, step 3: create an overview of previous and current trends, step 4: compile the data, step 5: organize and present the information accordingly, step 6: proofread the report.

Create a plan on how your sales report should be organized. Remember, only throwing in a bunch of numbers won’t cut it, you will need to provide thorough explanations of those numbers.

Also, the report shouldn’t be an eyesore and the readers should be able to go through it with ease.  

In most cases, your report will vary in terms of included metrics, depending on your audience. For instance, if you are part of the sales team and you are preparing the report for your head of sales, you should focus on including as many significant KPIs as you can.

However, if you present the report to executives and stakeholders, they probably don’t want to be bothered by an abundance of details, so only include the key findings.

Related : Reporting Strategy for Multiple Audiences: 6 Tips for Getting Started

Depending on the size of your business and how much sales data you receive, you will have to determine an appropriate view for conveying information. This can be done either daily, weekly, monthly, quarterly, or yearly.

Once enough time has gone by, you can compare the information between equivalent periods. This helps you stay on top of previous and current trends and allows you to determine the best tactics going forward.

After making an outline and setting an appropriate data collection period, it’s time to start assembling the sales data.

This usually includes pulling out data from the CRM software your company incorporates and then compiling it in one place.

As we said, sales analysis reports shouldn’t consist solely of numbers. Including graphs, charts, and even images can go a long way in making the data more comprehendible to the readers.

In some cases, you can even include recommendations for the next steps that should be taken in order to optimize the sales process.

Even if you have finished writing the report, it’s still not time to relax. It’s very important that you go over the report once or twice more and double-check everything that you included.

A good practice is to also ask your fellow colleagues or even a friend to go over the report as well. This provides you with an extra set of eyes.

Incorporating sales analysis reports is one of the best ways to stay on top of your sales data and optimize the overall process.

However, creating a great sales analysis report isn’t exactly the easiest task in the world. It requires gathering a massive amount of data, putting it together, analyzing it, and then presenting it to your internal stakeholders and executives.

This whole process can become a lot quicker and easier if you use an advanced reporting tool such as Databox.

Databox provides you with pre-built and customizable dashboards that you can use to gather all of the most important KPIs and sales-related metrics in one comprehensive place. You can connect data from any type of sales channel and then compile it into a meaningful report.

This will take a load off the analysis process as well. With all the numbers you need in one place, you won’t have to struggle with opening dozens of tabs during the analysis.

Additionally, with the help of our advanced visualization tools, you will be able to transform the numbers into insightful graphs and charts that will inevitably impress your shareholders during the presentation.

Do you want to optimize your sales analysis reporting process? Sign up for a free trial and experience the magic yourself.

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What is Sales Analytics? A Complete Guide

meaning of sales analysis in research

Sales analytics is a key component to your sales team’s success. It helps teams monitor and predict customer behaviors, spot market trends, and iterate on sales strategies.

Sales analytics ultimately outlines where your strengths and weaknesses are in terms of your sales process and each individual rep, and does so in three key areas: productivity, proficiency, and performance.

In this blog post, we will delve into sales analytics and how it relates to sales enablement .

What Is Sales Analytics?

  • Benefits of Sales Analytics

Challenges With Sales Analytics

How sales analytics empowers businesses, top 10 sales analytics metrics to track, what to look for in a sales analytics tool, highspot analytics drive business optimization.

Sales analytics is the process of collecting, analyzing, and interpreting sales data to gain valuable insights into various aspects of sales activities. This approach helps organizations make more effective data-driven decisions, identify opportunities for sales growth, and enhance overall performance. Sales analytics involves leveraging advanced technologies and statistical models to transform raw data into actionable information that drives revenue growth and customer satisfaction.

Analytics gives you key insights into more than just sales performance but also how your sales enablement strategy is going. Robust sales analytics provides the data and insights needed to drive effective sales enablement strategies, while sales enablement leverages those insights to equip the sales team with the right tools and knowledge to maximize their performance and drive revenue growth. The combination of both leads to more efficient and successful sales processes.

Key Components Of Sales Analytics

Data collection.

The foundation of sales analytics lies in the collection of accurate and relevant data. This data can encompass a wide range of information, including sales figures, customer demographics, lead sources, customer interactions, and market trends.

Sales Data Analysis

After collecting the data, it undergoes rigorous analysis. Sales analysts employ various statistical techniques and algorithms to identify patterns, trends, and correlations within the data.

Visualization

To make data more accessible and easily understandable, sales analytics often employs data visualization techniques like charts, graphs, and sales dashboards. These visually appealing representations of sales reports enable stakeholders to comprehend complex data effortlessly.

Predictive Analytics

One of the most powerful aspects of sales analytics is its ability to forecast future sales trends and outcomes. Predictive analytics uses historical data to anticipate potential customer behavior and sales patterns, helping businesses make proactive decisions.

Benefits Of Sales Analytics

Sales analytics simplifies the complex in your end-to-end process. It makes it easier for your team to make sense of the challenges of the sales process and find the best ways to overcome them. Leveraging sales analytics provides several benefits, such as:

  • Improved Sales Performance: Sales analytics empowers organizations to pinpoint their best-performing sales tactics and allocate resources more efficiently. By identifying successful sales strategies , teams can focus on high-impact activities that drive revenue.
  • Enhanced Customer Understanding: Understanding customer behavior is crucial for any business. Sales analytics provides valuable insights into customer preferences, pain points, and buying habits, allowing organizations to tailor their sales approach and messaging.
  • Optimized Sales Funnel: With sales analytics, businesses can optimize their sales pipeline by identifying bottlenecks and areas for improvement. This leads to better lead conversion rates and a more streamlined sales process.
  • Real-Time Decision Making: Sales analytics enables real-time data monitoring, allowing businesses to react promptly to market changes, customer demands, and emerging opportunities. These actionable insights are essential for staying ahead in a competitive landscape.

Like with any valuable resource, sales analytics comes with its fair share of challenges. It is crucial to understand these roadblocks as it allows sales teams to navigate them effectively and get the most out of your data.

Data Quality and Integration

Poor data quality can significantly impact the accuracy of sales analytics. Sales data may come from various sources like CRM systems, POS systems, spreadsheets, and external data providers. Integrating and cleaning these diverse data sources can be difficult and time-consuming.

Data Volume and Velocity

Businesses generate large volumes of sales data every day. Handling and processing this massive data influx in real-time can be daunting. Traditional analytics tools might struggle to keep up with the velocity of data generated, leading to delays in generating insights.

Scalability

As businesses grow, the volume of sales data also increases. Ensuring that the sales analytics system can scale up to handle larger datasets and demands is crucial.

Integration with Sales Process

For sales analytics to be effective, it needs to integrate seamlessly with the sales team’s workflow. Sales professionals must be able to access and interpret the insights easily, making them actionable in their day-to-day activities.

Aligning Data with Business Objectives

Sales analytics should be aligned with the overall business and sales goals. However, it can be complicated to determine the relevant KPIs and metrics that truly measure success and progress toward these objectives.

Real-time Analytics

In fast-paced business environments, real-time insights are highly valuable. Implementing real-time sales analytics requires advanced technology and infrastructure, which can be costly and resource-intensive.

With the right strategies and tools, sales analytics becomes a game-changer for businesses. It uncovers untapped potential by providing a bird’s eye view on sales performance and customer behavior. Here are the different ways it can help your organization excel and stay ahead of the game:

  • Informed Strategy Formulation: Sales analytics empowers businesses to make strategic decisions based on data rather than gut feelings or assumptions. This data-driven approach minimizes risks and enhances the chances of success.
  • Effective Sales Forecasting: With predictive analytics, businesses can forecast metrics with greater accuracy, enabling better inventory management and resource allocation.
  • Sales Team Performance Optimization: CRMs and sales enablement platforms with built-in analytics helps identify top-performing sales representatives and provides insights into their strategies. This information can then be used to train and motivate the rest of the sales team.
  • Competitive Edge: Embracing sales analytics gives businesses a competitive edge by leveraging data analytics to identify market trends, customer needs, and potential gaps in the competition.

Resource: Check out Sales Enablement PRO’s 2023 Sales Enablement Analytics Report to find out what to measure and track to help improve your sales enablement strategy.

Sales analytics metrics provide sales teams with a benchmark to assess their productivity, performance, and proficiency. The metrics listed below give a health check on whether or not sales reps are meeting their goals and moving the organization forward.

Total sales generated over a specific period, indicating the overall financial success of the sales efforts.

Total Contract Value / Contract Duration

2. Sales Growth

Percentage increase in sales revenue over a given period, comparing it to a previous period.

Current Period Sales – Prior Period Sales / Prior Period Sales * 100

3. Customer Acquisition Cost (CAC)

The average cost incurred to acquire a new customer, including marketing, sales, and other related expenses.

Total Sales + Marketing Expenses / # of New Customers

4. Customer Lifetime Value (CLV)

The total revenue a customer is expected to generate over their entire relationship with the company, helping to assess the long-term value of customers.

Average ACV * Retention Period (Years)

5. Sales Conversion Rate

The percentage of leads or prospects that convert into paying customers, providing insights into the effectiveness of the sales process.

Total # of Sales / Total # of Qualified Leads * 100

6. Sales Pipeline Value

The total value of all deals in the sales pipeline, indicating the potential revenue that can be generated in the future.

# of Deals in Pipeline * Average Deal Size

7. Win Rate

The percentage of opportunities or deals that result in a successful sale, indicating the efficiency of the sales team.

Closed Won Deals / Total Opportunities

8. Average Deal Size

The average value of a closed deal, helping to understand the typical value of each sale.

Sum of Deals in Period / # of Deals

9. Sales Cycle Length

The average time it takes to close a deal, from the initial contact with a prospect to the final sale.

Total # of Days to Close Deal / Total # of Closed Deals

10. Lead Response Time

The average time taken by sales representatives to respond to leads or inquiries from potential customers.

Time/Date of New Lead – Response Time / Total # of Leads Responded to

While this list is by no means comprehensive, it’s a great starting point for teams. You can take this one step further by integrating sales enablement metrics and strategies with sales analytics to determine productivity gaps, or measure the success of your sales process.

It can be difficult to decide on what sales analytics tool is best for your company. Do you need a robust analytics tool like a CRM, or is the business in a stage where processes can be done more manually? Depending on what you need, it’s important to use a tool that provides visibility across workflows, bridges the gap between sales and marketing, and empowers your reps to engage customers effectively. If you’re in the beginning stages of exploration, here’s what to look for in terms of functionality:

Data Visualization

Look for tools that offer powerful data visualization capabilities. Visual representations like charts, graphs, and heatmaps make it easier to understand complex data and identify patterns and trends quickly.

Sales Forecasting

A good sales analytics tool should have forecasting features that leverage historical data and trends to project future sales performance accurately. This helps with resource planning and setting realistic targets.

Customizable Metrics and KPIs

Different businesses have unique key performance indicators (KPIs) that align with their goals and strategies. Make sure the tool allows you to create and track custom metrics specific to your organization’s needs.

Advanced Analytics and AI capabilities

Some sales analytics tools incorporate advanced analytics techniques, such as machine learning algorithms, to provide deeper insights and predictive analysis for sales performance management .

Partners and Integrations

When evaluating a sales analytics tool, keep in mind the depth of partners and integrations in the tool’s ecosystem. Does the tool integrate with your current stack or will this create more work for your reps? Make sure that your tool seamlessly integrates with your current tools to increase adoption.

Types of Integrations to Consider: CRM, CMS, Social Selling, Training and Coaching, Marketing Automation, Mobile Apps, Email and Workflow Tools, Web Conferencing, Productivity, File Storage, Digital Asset Management Tools, SSO, Sales Engagement Platforms

With a sales analytics tool, it’s crucial to track the impact of the resources your sales team uses. You should be able to see how buyers engage with content and how it influences revenue.

Integrating sales enablement strategies with sales analytics allow organizations to create a powerful synergy that drives sales growth, enhances customer satisfaction, and boosts overall business performance.

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Data-driven decision making via sales analytics: introduction to the special issue

  • Published: 26 July 2020
  • Volume 8 , pages 125–126, ( 2020 )

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  • J. Ricky Fergurson 1  

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Sales powers business throughout the world. While sales (and marketing) literature has spent much time exploring analytics and measurement, there seems to be a revitalized interest in sales and especially sales data and analytics. Marketing analytics powers the current wave of data-driven decision making, and leveraging strategic data remains a source of building a sustainable competitive advantage. In Volume 1, edition 1 of Journal of Marketing Analytics , Breur ( 2013 , p. 1) proclaimed in the journal’s first editorial,

We’re drowning in data. Structured data, unstructured data, ‘Big Data,’ in an increasingly digital world, we create even more data. According to an IDC report, the global growth in data volumes amounts to about 60 percent per year. That means it will grow tenfold every 5 years!.

As we stand here seven years later, the available amount of data has grown exponentially due to the increased connectivity and data availability made possible by technology increasingly permeating the sales profession. Salespeople, sales managers, and executives must quickly make sense of oceans of sales-related data. With this influx of sales data, organizations need to develop actionable insights for their sales teams and their clients. The Sales Education Foundation ( 2020 ) notes that sales-specific research is necessary for bridging the gap between academia and industry. In reinforcing this need, the Sales Education Foundation has provided more than $125,000 in grants to promote high-quality sales research since 2011. (Sales Education Foundation 2020 ).

Additionally, more universities are beginning to add sales analytics to their available sales courses. Given this increased availability of sales data and information and the apparent growing demand for sales research, I feel that reaching a better understanding of sales analytics is paramount in academic research. That is why I felt honored to be invited to edit this special issue on sales analytics.

The business world changes rapidly, and organizations must be able to help there sales teams adapt to these changes. Sales managers need the availability to quickly access published research to gain insights into best practices and solid methodology to deal with their daily challenges (Sales Education Foundation 2020 ). With the recent upheaval of industry due to the COVID-19 pandemic, the potential rise or fall of some corporations hinges on their ability to leverage sales data assets quickly and effectively. The goal of the Journal of Marketing Analytics has always been to incorporate rigorous research methods with real-world cases so that academics and industry professionals can stay on top of the latest trends and cutting-edge analytics. Measurement has always played a pivotal role in connecting theoretical concepts, and the conclusions reached about these concepts in academic research. As noted by Krishen and Petrescu ( 2018 , p. 117), “Metrics and data are empty shells without proper theories and interpretations behind them.” Hall and Lee ( 2019 ) reinforced this insight in the Journal of Personal Selling & Sales Management’s special edition on “Measurement in Sales Research” by accentuating the strong links between theories, empirical data, and research conclusions.

As I set out to consider the many submissions for this special issue and extend invitations to reviewers, it was inspiring to see the high commitment level by sales scholars. The overarching goal was to meld strong theoretical and empirical analytics research in sales and sales management. The articles published in this special issue accomplish that goal and offer insightful views into each of their chosen topics. In each article, the authors’ insights and perspectives lay a foundation that should be considered for future academic research. The first two articles provide a common theme in regard to using CRM.

First, Hoyle et al. dissect how sales managers and salespeople are using the modern-day tools at their disposal to achieve accurate sales forecasting and the resulting impacts. In doing so, this article examines factors that influence the type of forecasting used and potential explanations for why. While recognizing the importance of data-driven decisions and predictive analytics in organizational success and the ability to improve day-to-day efficiencies, Hoyle et al.’s research demonstrates that there is a lack of action and follow through on these ideals among both sales managers and salespeople. This research offers several managerial contributions relating to the process of sales forecasting. It also puts forth a call for further research on forecasting, including the role of varied CRM and ERP systems, sales force automation, and other technologies to identify the diverse impacts on forecasting, planning, and goal setting.

Second, Rodriguez and Boyer integrate Technology Acceptance Model (TAM) and IS success model to explore the influence (Mobile CRM) mCRM has on sales performance. This article applies an adaptation of mCRM to salespeople in a business-to-business context. This research also helps elucidate the role mCRM plays in traditional CRM adoption.

In the other two articles, Merkle et al. use a unified theory of brand equity to explore the decline of Major League Baseball (MLB) ticket sales and game attendance within the framework of MLB brand equity. Additionally, this research examines the mediating role of attendance and local television and the moderating role of Twitter followers in the relationships between MLB marketing assets and the financial performance of the teams using secondary data from multiple sources. Additionally, Said looks at a bibliometric analysis of salesforce research from 1912–2019 to put forth a four-step procedure to merge SCOPUS and Web of Science databases when performing a bibliometric analysis. This research demonstrates that doing separate bibliometric analyses of each database does not prove a complete picture of the state of knowledge and tendencies in a field.

It was my pleasure to work with the reviewers and authors to put this special issue together. As I feel that critical research into sales analytics is still in its infancy, I hope that this special issue lays a foundation for the academic community to conduct further sales analytics research.

Breur, T. 2013. Editorial. Journal of Marketing Analytics 1: 1–2.

Article   Google Scholar  

Hall, Z.R., and N. Lee. 2019. Taking the measure of measurement in sales research: Introduction to the special issue. Journal of Personal Selling & Sales Management 39 (3): 201–206.

Krishen, A.S., and M. Petrescu. 2018. Marketing analytics: Delineating the field while welcoming crossover. Journal of Marketing Analytics 6: 117–119.

Sales Education Foundation. 2020. Elevating sales research . https://salesfoundation.org/elevating-sales/sales-research/ . Accessed 14 June 2020.

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Fergurson, J.R. Data-driven decision making via sales analytics: introduction to the special issue. J Market Anal 8 , 125–126 (2020). https://doi.org/10.1057/s41270-020-00088-2

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Published : 26 July 2020

Issue Date : September 2020

DOI : https://doi.org/10.1057/s41270-020-00088-2

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Sales Analysis

meaning of sales analysis in research

Table of Contents

What is sales analysis.

Sales analysis is reviewing your sales data to identify trends and patterns. Sales data can help you make better decisions about your product, pricing, promotions, inventory, customer needs other aspects of your business.

Sales analysis can be as simple as reviewing your sales figures regularly. But it can also involve more complex statistical methods. Either way, the goal is to gain insights that will help you boost sales and improve your bottom line.

There are many ways to approach sales analysis. Some businesses use software that automatically crunches the numbers and produces charts and graphs. Others prefer to do things manually, using Excel or another spreadsheet program.

The most important thing is to review your sales data regularly and look for opportunities to improve your business. With sales analysis, you can make informed decisions that will help you grow your business and achieve your sales goals.

  • Sales analytics
  • Sales data analysis
  • Sales revenue analysis

The Importance of Sales Analysis 

Sales analysis is a critical tool for businesses of all sizes. By understanding revenue-driving metrics , companies can make informed decisions, from pricing and product development to sales strategies and target markets. Essential metrics to analyze are sales volume, growth, mix, and trends.

Sales volume is the total number of sales made over a specific time. This metric can help assess whether a business is growing or declining.

Sales growth is the percentage change in sales volume from one period to the next, which can help determine whether a business is growing at a healthy rate.

The sales mix is the ratio of different products and services a business sells. This metric can assess whether a company sells a diversified product and service mix.

Sales trends are changes in sales volume over time. This metric can assess whether a business is experiencing seasonal or long-term sales patterns.

Sales analysis is a critical tool for businesses of all sizes. Companies can make informed decisions about growing revenue and increasing profits by understanding these sales metrics. 

Types of Sales Analysis

Sales analysis is a process that helps you to measure and manage your organization’s sales performance . The three most common types of sales analysis are:

1. Sales Forecasting

2. Sales Management

3. Sales Reporting

Sales Forecasting

Sales forecasting is the process of predicting future sales. This type of analysis is usually done internally by companies. For example, if a company wants to know how much revenue they expect to generate this year, they may forecast sales based on historical data. They might then compare those predictions with actual results to see how well they did. If their forecasts were off, they would have to take action to correct any problems in their sales processes or implement new sales enablement tools.

Sales Management

Sales management is the process of managing existing sales. Companies use sales management to ensure that they are meeting their sales targets. They also use it to identify areas where they need to improve the customer journey and accelerate the sales cycle length. For example, if they find that they are not growing revenue as expected, they could adjust prices, add new products., or enable their sales team with tools and strategies to optimize the sales process.

Sales Reporting

Sales reporting is the process of summarizing information about sales. Companies often use sales reporting to track sales progress and to communicate with investors or executives. For example, they may report monthly sales figures to shareholders so that investors can better understand how their investments are performing.

How to Use Sales Analysis Data

Sales data provides companies with a valuable source of data to make informed decisions about their sales operations and strategies. 

Sales data can be used in several ways, including:

  • Analyzing customer needs and preferences
  • Understanding buying patterns
  • Tracking competitor activity
  • Analyzing the customer journey and sales cycle
  • Measuring sales team performance
  • Improving marketing efforts
  • Targeting new customers

Top Sales Analysis Metrics & KPIs

Sales analysis is a vital part of any business. It helps you understand what’s working and what isn’t so that you can make changes to improve your sales process. Here are the top KPIs for analyzing sales performance:

The most critical metric in sales is revenue, which is the money your company makes from its products or services. To calculate revenue, multiply the number of units sold by the price per unit.

Net Profit Margin

Net profit margin measures how profitable your company is. The net profit margin, also known as net margin, indicates how much net income or profit is generated as a percentage of revenue. It’s the proportion of total profits to revenue for a firm or sector.

Gross Profit Margin

Gross profit margin , also known as gross margin, measures your company’s efficiency at turning orders into revenue. Gross profit margin is a financial ratio that calculates the percentage of revenue that exceeds the cost of goods sold. The gross profit margin ratio is important because it allows investors and analysts to see how well a company performs relative to its costs.

Customer Lifetime Value (LTV)

Customer lifetime value measures how valuable your current customers are to your company. LTV is calculated by multiplying the average order size by the customer’s retention rate. The longer customers stay with your company, the more valuable they become.

The churn rate measures how often customers cancel their accounts. Churn rate is calculated by dividing the number of active users who have canceled their accounts by the total number of active users.

Retention Rate

The retention rate measures how long customers stay with your company after signing up. The retention rate is calculated by dividing active users by new users.

It’s also vital to analyze the sales pipeline to pinpoint areas where leads are not moving from one stage to the next. By tracking the correct data, you can better measure your sales pipeline performance and identify areas of improvement. Sales pipeline data to analyze include the number of leads generated, the number of qualified leads, conversion rates, average sales cycle length , average deal size , and win rates.

How Sales Analysis Reports Help 

Analyzing sales reports provides valuable insights into the business’s current state and helps organizations make informed decisions about strategies to improve sales performance. Ways to use sales analysis reports include:

  • Evaluate sales deal data to make informed decisions about growing revenue and improving sales performance.
  • Assess overall sales trends and determine whether growth is occurring.
  • Understand what customer needs are being met and where opportunities for new product development may occur.
  • Determine whether prices are realistic and align with customer demand.
  • Examine which channels are performing well and where there may be opportunities for improvement.
  • Assess the effectiveness of marketing campaigns.
  • Identify areas of waste or inefficiency in the sales process.
  • Benchmark performance against competitors.
  • Generate reports to share with key stakeholders.

Sales analysis is a critical part of running a successful business. By reviewing sales deal data regularly, companies can gain valuable insights into their performance and make informed decisions to improve sales results.

Sales Analysis Tools

Sales analysis tools come in many different shapes and sizes. Some are designed to give you a general overview of your sales data, while others focus on specific aspects or types of sales data. The most common types of sales analysis tools include:

Sales reports: These provide a high-level overview of your sales data, typically including information such as total sales, average order size, and top-selling products or services.

Sales dashboards: These provide a more detailed view of your sales data, typically including information such as customer types, geographical regions, and sales by channel.

Sales performance analysis: This type of tool is designed to help you track and improve your sales performance, typically by providing information such as win/loss ratios and conversion rates.

Sales pipeline analysis: This type of tool is designed to help you manage your sales pipeline , typically by providing information such as lead conversion rates and deal size.

Customer profile analysis: This type of tool is designed to help you understand your customers better, typically by providing information such as customer types, buying habits, and demographic information.

The right sales analysis tool for your business will depend on a number of factors, including the types of data you need to track, the level of detail you need, and your budget. Some sales tools cover many of the use-cases above, such as CRM and CPQ. 

CPQ (configure price quote) software can help with sales analysis by providing accurate pricing and product configuration data. This data can be used to understand how customers are buying your products and how prices impact demand. Additionally, CPQ data can be used to evaluate the effectiveness of marketing campaigns and optimize future efforts.

Sales analysis is an important part of any sales organization. CPQ software can help make this process more efficient and accurate, resulting in better decision-making and improved sales results.

People Also Ask

Why do we do sales analysis.

There are several ways to do sales analysis, but the basic goal is always the same: to better understand your company’s sales so that you can make better decisions about how to grow your business. Sales analysis involves looking at which products are selling well, which markets are most profitable, and where you might be losing sales. In addition, it can help you make informed decisions about pricing, product development, marketing, sales operations , and other areas of your business by identifying opportunities and threats.

How do you analyze sales growth?

Sales growth can be analyzed by looking at overall sales figures, comparing sales figures to previous periods, and analyzing customer acquisition and retention rates. By understanding how sales grow, businesses can make more informed decisions about where to invest their resources. Overall sales figures can give you a broad overview of your business’s performance. You can track total revenue, average order value, and other key metrics to see how they trend over time. This can be helpful in spotting overall trends in your business. Comparing sales figures to previous periods can help you understand whether your business is growing or declining. By looking at year-over-year sales growth, month-over-month sales growth, or even day-over-day sales growth, you will see how your sales are trending, and you can make more informed decisions about how to grow your business Analyzing customer acquisition and retention rates can give insights into how well your business attracts and retains customers. For example, you can track how many new customers you acquire each period and how many existing customers you lose. This data helps identify whether your customer acquisition efforts are working and whether you’re at risk of losing customers.

What should sales analysis include?

Sales analysis evaluates sales data to make informed decisions to help sales reps win more deals , and help the company grow revenue. Some key factors to consider are: Sales volume: This is the total number of units sold over time which helps assess overall sales trends and growth. Sales mix: This refers to the types of products or services sold. This information can be useful in understanding customer needs and opportunities for new product development. Sales price: The average sale price can give insights into whether prices are realistic and align with the perceived value of the product or service. Sales expenses: This includes all costs associated with generating sales, such as advertising, commissions, and travel. Tracking sales expenses can help to identify areas where costs may be too high or where there may be opportunities for cost savings. Sales pipeline: This is a list of all potential sales that are in the process of being closed. The sales pipeline can help forecast future revenue and assess the health of the sales department.

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The two disciplines are distant relatives in the world of marketing – they seldom interact. The one (MR) deals more with end-users, consumers, insights-generation; the other (sales) with customers, very often B2B, and is highly operationally focused.

All the more reason to grab the opportunity to explore the interface when the chance arises!

Pascal Schöling is currently a working student with Happy Thinking People’s Berlin Office. He’s been with us for the past five months as part of his studies programme at the Business & Law School Berlin, so he has had time to experience first-hand the realities of MR project work.

Before this, Pascal worked for almost two years as a sales executive for a specialist oil company in Bremen.

We caught up with him to learn more about his view on research - from a sales perspective.

Pascal – you’ve been with H/T/P for a while now. How’s it been?

Really good. I’ve learned a lot. It’s been an extremely interesting time. I’ve got to work on a huge variety of topics and categories. Last week fintech – a couple of weeks ago vacuum cleaners. And I got the opportunity to moderate communities on my own, help develop discussion guides, do expert interviews. All interesting stuff. Pascal Schöling

Quite a contrast to your sales experience, I imagine. Can you tell us more about that?

Sure. Sales roles are very different according to the type of company you’re working for. In a start-up, you’ll be maybe cold-calling for lead generation. My role was with a specialist oil company, looking after existing major accounts. It was very much about having a 360 degree take on the customers’ issues. I was responsible for monitoring changes in the market, developments in the prices of raw ingredients, keeping tabs on stocking situations, that sort of stuff. It was a very different environment from the one I’ve experienced with Happy Thinking People. We had a pretty fixed working routing – customer timelines were key, lab analyses organised, customer specifications checked, orders entered into the system by a certain time to meet a delivery deadline. Once an order was finalised, all checks completed, then you passed over the responsibility to the logistics department - job done. So manageable – and more 9 to 5 hours. With market research, it’s different. Outputs are more customised. There are multiple details that need to be right going into each and every full report, and that requires a high degree of attention to detail. Pascal Schöling

It sounds pretty responsible, maybe even a bit more relaxed than the type of work we have in research. Or how do you see it?

Very different. With research, a client comes to H/T/P with a problem, and that’s the focus: specific, in-depth, clearly delineated. That’s what you deliver on – the briefs are to-the-point, outputs need to be fit-for-purpose. It’s actually more stressful. Pascal Schöling
A deadline is a deadline. And everything needs to be spot-on, analytically rigorous, with no mistakes. That seems to be the norm for all projects—high precision work. I’d say it’s a high-performance environment. But over time – you need to manage the work-life balance. Pascal Schöling

It sounds like research is actually more stressful than sales! Fascinating. I’d have thought it was the other way around 😉

In sales, you need to be on top of things, but with ongoing major clients, it’s about managing relationships, keeping an eye on the whole picture, anticipating pricing impacts delivery situations, keeping in touch with departments like purchasing. Not so ad hoc stressful, I’d say. And more structured in the sense of when tasks need to be performed. Pascal Schöling

Coming back to research, any suggestions for de-stressing? Artificial Intelligence, maybe?

Well, I’m not the person to talk to about what AI can do – but I doubt if it can help much when it comes to people understanding, to be honest. People are complicated. One thing maybe – what I’d guess you’d call division of labour. So, specialists for say visualisation, analysis, or discussion guides. Would speed things up. Pascal Schöling

I’m not Adam Smith but wouldn’t that lead to an even less holistic perspective for people working in MR?

laughs… Sure, maybe – and it requires a certain resource effort, a size of company. So maybe not practical for smaller SME companies. Pascal Schöling

Tell us about the skills you think a market researcher needs.

Empathy, for sure. Analytical ability. And the ability to read between the lines. Pascal Schöling

Anything else?

The ability to go the extra mile over and over again. That’s passion, I guess. And then seeing how things hang together, that’s important. Pascal Schöling

You mention passion – again, a surprising word in a market research context. Can you expand?

As I said, it’s a high-performance environment. You need to be passionate about it to really handle that over the months, I think, to really believe in it, see the value again and again. Pascal Schöling

OK – again, a surprising word in the context of market research, where legacy adjectives were more “dusty” and “number-crunching”. But moving on: culture has a role to play in shaping work environments. How have you found it at Happy Thinking People?

Young, creative, but above all, incredibly free. I’ve never experienced such a free, self-determining environment – you know you have to deliver, but it’s your choice where and when you do that. So, you can say leave at 15.00h but work later on in the week at a time that suits, even if it’s midnight. Your choice. It’s really cool. Pascal Schöling

Fantastic. Moving on quickly and wrapping up: thinking about the image of market research – do you think the industry does a good job at selling itself?

I don’t think many students, or people generally, really know about the sort of work that Happy Thinking People do. My friends, or ex-colleagues, if you’d ask them about market research, they’d likely think about a few questions over the phone, something like that. Pascal Schöling

Would you think that needs correcting?

Yes and no. Maybe it’s cool that there’s a certain mystique that remains. I mean, if you see a cool Nike film, you don’t really want to know how it’s made, do you? Pascal Schöling

😂 No, probably not. Pascal, thanks for your time.

Pascal’s sales-informed perspective on the world of MR reveals a topsy-turvy worldview – market research can actually be more stressful than some sales jobs! The role Pascal described has clear structures, defined going home times, orderly processes. Research with Happy Thinking People at least allows high degrees of freedom, with massive variety, but can be demanding.

It’s food for thought.

Tomorrow’s generation often talks about not replicating the work-until-you-drop mindset of their parents. Maybe research needs to look for ways to play to the positives, but ensuring a work-life balance is realistic.

Edward Appleton

Edward Appleton is Chief Marketing Officer with Happy Thinking People in Berlin. Edward has worked for over 20 years in market research on both Agency and client side. Prior to his current role, Edward was Senior Insights Manager with Coca-Cola in Berlin; before that he was European Insights Manager at Avery Dennison. He started his career with Mass Observation UK, which he left to join the Insights team at Nestle UK. He blogs regularly at  https://researchandreflect.blogspot.com . Edward is bi-cultural English/ German, speaks fluent French.

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

  • Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
  • Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
  • Null hypothesis: Parental income and GPA have no relationship with each other in college students.
  • Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

  • In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
  • In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
  • In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

  • In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
  • In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
  • In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
  • Experimental
  • Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

  • Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
  • Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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Population vs sample

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

  • Probability sampling: every member of the population has a chance of being selected for the study through random selection.
  • Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

  • your sample is representative of the population you’re generalizing your findings to.
  • your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

  • Will you have resources to advertise your study widely, including outside of your university setting?
  • Will you have the means to recruit a diverse sample that represents a broad population?
  • Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

  • Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
  • Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
  • Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
  • Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

  • Organizing data from each variable in frequency distribution tables .
  • Displaying data from a key variable in a bar chart to view the distribution of responses.
  • Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

  • Mode : the most popular response or value in the data set.
  • Median : the value in the exact middle of the data set when ordered from low to high.
  • Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

  • Range : the highest value minus the lowest value of the data set.
  • Interquartile range : the range of the middle half of the data set.
  • Standard deviation : the average distance between each value in your data set and the mean.
  • Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

  • Estimation: calculating population parameters based on sample statistics.
  • Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

  • A point estimate : a value that represents your best guess of the exact parameter.
  • An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

  • A test statistic tells you how much your data differs from the null hypothesis of the test.
  • A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

  • Comparison tests assess group differences in outcomes.
  • Regression tests assess cause-and-effect relationships between variables.
  • Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

  • A simple linear regression includes one predictor variable and one outcome variable.
  • A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

  • A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
  • A z test is for exactly 1 or 2 groups when the sample is large.
  • An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

  • If you have only one sample that you want to compare to a population mean, use a one-sample test .
  • If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
  • If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
  • If you expect a difference between groups in a specific direction, use a one-tailed test .
  • If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

  • a t value (test statistic) of 3.00
  • a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

  • a t value of 3.08
  • a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval

Methodology

  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hostile attribution bias
  • Affect heuristic

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Blog 4 Fascinating Sales Research Studies You Should Know By Heart

4 Fascinating Sales Research Studies You Should Know By Heart

Keep reading to check out some of these sales research studies that will help you gain some insights into what makes a successful sales team.

RELATED: Only 28% Of Business Deals Are Forecasted Accurately, Shows New Research

In this article:

What They Did

What they discovered, key research sales takeaway, key sales and market research takeaway, key takeaway, getting sales down to a science with sales lead research, sales analysis | using data to help teams sell more and sell smarter, creating sales best practices based on sales data.

Want to know what makes a high-performing sales team? Science offers valuable insights for sales leaders at companies of all sizes.

Using data as a starting point, sales managers can create easy to use guides for sales best practices. Aside from that, they can also help their teams sell more and sell smarter.

Here are the key findings from four fascinating sales research papers, covering:

  • Sales compensation and bonuses
  • Personality traits of top performers
  • Key performance indicators
  • Performance goals vs. learning goals

1. Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans

You want to improve sales, and you want your team to crush it. At the same time, your team also wants to crush it, but only if they’re compensated appropriately.

But what sort of carrot should you dangle in front of your sales reps to bring out their inner sales superstar?

Do bonuses actually inspire salespeople to go above and beyond, or do they make no difference in the long run?

And is it better to offer smaller, quarterly bonuses, or one big bonus at the end of every year?

Business scientists from Yale and Harvard tackled this critical question in their  Do Bonuses Enhance Sales Productivity? study.

The researchers examined 348 salespeople at a Fortune 500 company from 1999 to 2001.

The company sells a wide range of equipment, from cheap machines purchased by small businesses to massively complex instruments that cost six figures.

They tracked the performance of each salesperson, then they compared that information against the compensation structure.

The main discovery during their sales analysis was that the promise of a bonus does actually motivate salespeople and increase revenue. Specifically, when used with a compensation plan that included a base salary, commissions.

Additionally, there should also be bonuses for sales reps. These bonuses are the quarterly bonuses, annual bonuses, and overachievement bonuses that help create inspired salespeople.

In fact, it inspired them so much that they were able to raise revenue 17.9% over salespeople who were commission-only.

Additionally, the sales analysis report also found that quarterly bonuses are more effective than annual bonuses.

“In the absence of quarterly bonuses, failure in the early periods to meet targets cause agents to fall behind more often than in the presence of quarterly bonuses. Thus, the quarterly bonus serves as a valuable sub-goal that helps the sales force stay on track in achieving its overall goal; such incentives are especially valuable to low performers,” the paper explains.

They also uncovered a fascinating fact about the behavior of salespeople. When they’re far away from a quota, they tend to give up.

However, when they burst through a quota, they rarely stop there. Especially if your team is offered bonuses for overachievement.

If you want your salespeople to reach for the stars, you’ll need to build a bonus structure to help launch them there. Preferably one that rewards them every quarter.

Although informal rewards are acceptable every once in a while, they aren’t a replacement for monetary rewards like bonuses.

Aside from that, your comp plan should include accelerators that encourage and reward overachievement.

2. Rethinking the Extraverted Ideal: The Ambivert Advantage

Everyone knows how salespeople appear in movies and in the imaginations of people who don’t talk to actual salespeople that often.

They’re slick hucksters with boundless confidence who can work a room with ease and persuade anyone to do anything. They’re, in a word, extroverts.

What is an extrovert?  An extrovert is someone who takes energy from the presence of other people.

Basically, people believe that if you want to thrive as a sales professional, you have to be a bit of an extrovert.

But is that too simplistic?

A report published in the journal Psychological Science suggests so.

Despite a lot of research into the topic, no one has been able to find a strong correlation between high extroversion and sales performance.

Rethinking The Extraverted Ideal: The Ambivert Advantage  proposes that the business world has been thinking about sales personalities the wrong way.

The author of the study, Adam M. Grant, sent a personality questionnaire to outbound call center representatives. He examined the answers from the 340 employees who filled out the survey in full.

From the answers, the employees were assigned a level of extroversion on a scale of 1.0 to 7.0. In the scale, 1 will be the highest level of introversion, and 7 will be the highest level of extroversion.

sales research data | Fascinating Sales Research Studies You Should Know by Heart | sales analysis | market research sales

The introverts in this study earned an average hourly revenue of $120.10 . The extroverts earned an average of $125.19 per hour.

However, the “ambiverts,” those who scored between 3.75 and 5.50 on the personality test, blew both groups out of the water. Data analysis shows that they were earning an average of $154.77 .

Additionally, those who scored a “perfect” ambivert score of 4.0 earned an average of $208.34 .

Grant observes that, while sales require socializing and assertiveness, it also requires the salesperson to consider the “needs, interests, and values of customers.”

In this case, ambiverts have the most advantage. This is because ambiverts have enough extroversion to seek out prospects and get their attention.

On the other hand, they have enough introversion to consider how their behavior and words affect others. This one-two combo makes them the ideal sales performer.

Stereotypes about salespeople may be all wrong. Trend analysis shows that star performers aren’t always the brashest, loudest people on the sales floor.

Sales data shows that they’re often people who love socializing but are reflective enough to sympathize with the needs of clients. Thus, a balance between extroversion and introversion are valuable traits for sales operations.

RELATED: Case Study: Fortune 500 Company Uses AI To Boost Revenue 28% In 3 Months

3. Drivers Of Sales Performance: A Contemporary Meta-Analysis. Have Salespeople Become Knowledge Brokers?

What factors influence sales performance most of all? Is it a salesperson’s mindset, or is it their selling ability?

“Selling” is often categorized as a skill or a talent, but really it’s a collection of skills.

However, which selling-related skills are most vital for performance? Also, which skills are overrated?

In this sales trend analysis and report, you’ll find which set of skills matter the most.

In  Drivers Of Sales Performance , the researchers examined a broad swath of published research from 1982 to 2008. For the sales data analysis and report, they looked at the effect of around 20 performance indicators.

Then, they identified the performance indicators that actually have the most significant impact on sales success.

Based on the analysis report, they found that five factors significantly correspond to sales performance.

1. Selling-Related Knowledge

According to sales analytics, having a set of knowledge of sales had the most significant impact. The researchers discovered that the relationship between sales performance and selling-related knowledge, when measured as a standardized coefficient, is .28.

Selling-related knowledge simply refers to a salesperson’s ability to size up a sales situation. Someone with a high amount of selling experience can answer critical questions.

For example: Who are the best prospects? Who are the real decision-makers?

What solutions are the prospects really looking for?

Having the answers to these questions makes a salesperson better at constructing strategy. This makes them better overall at selling.

2. Degree Of Adaptiveness

The relationship between the degree of adaptiveness and sales performance is .27.

Does the salesperson make the identical pitch every time? On the other hand, do they adapt their pitch to the prospect?

Salespeople who have displayed a high degree of adaptiveness were found to be more successful. The reason being that they show responsiveness towards each client that they deal with.

Adaptiveness makes an experience with a salesperson feel more personal. Thus, this may be contributing to their successes with their potential customer.

3. Role Ambiguity

Role ambiguity is a lack of clarity about your role. This was the only factor in the top five to show a negative correlation with sales performance. The significance was calculated at -.25.

When salespeople don’t know what they ought to do, they (understandably) perform much worse. After all, this significantly hinders their ability to adapt.

Aside from that, ambiguity isn’t directly related to selling-knowledge at all. Thus, the sales rep’s performance and sales strategy will suffer when they have role ambiguity.

4. Cognitive Aptitude

Cognitive aptitude is raw brainpower. How well can the salesperson think, use words, and understand numbers?

In this case, the significance here was measured at .23. This proved to be necessary, but not the most essential factor for successful salespeople.

5. Work Engagement

sales factor | Fascinating Sales Research Studies You Should Know by Heart | sales analysis | sales research group

How enthusiastic and motivated is the salesperson? Not surprisingly, salespeople who threw themselves into their job performed much better.

Additionally, this factor was found to have an identical amount of significance as cognitive aptitude.

The researchers state that this model can account for 32% of the variance in sales performance.

The ideal salesperson understands the sales process inside and out, is flexible, knows exactly what is expected of them, is smart, and loves what they’re doing.

Data from the analysis has shown that knowledge is a potent tool in sales. Thus, you should try and research as much as possible about your target market.

4. The Influence of Goal Orientation and Self-Regulation Tactics on Sales Performance

No one knows the importance of goals better than salespeople. However, what kinds of sales goals really drive revenue?

The Influence of Goal Orientation and Self-Regulation Tactics on Sales Performance  tested two kinds of goal orientations against each other.

The first is performance goals, or goals that achieve specific outcomes. These are outer goals.

People who value performance goals want to hit big numbers that impress their managers and colleagues.

The second is learning goals, or goals to achieve a certain level of skill or knowledge. These are inner goals.

The study examined salespeople from a medical supplies distributor in the Southwest. During a quarterly meeting, the sales team completed a questionnaire that asked them about their goal orientation and self-regulation tactics.

The researchers then compared their answers to how the salespeople actually performed by the end of the quarter. They also tested how well the salespeople performed on three key self-regulation tactics: goal setting, effort, and planning.

strategy | Fascinating Sales Research Studies You Should Know by Heart | sales analysis | sales lead research

Surprisingly, a focus on performance goals was not positively related to sales performance and self-regulation.

However, those who focused on learning goals performed much better.

In other words, those who were dedicated to growing their talents outshined those who merely wanted to put up big numbers.

According to the abstract, “a focus on skill development, even for a veteran workforce, is likely to be associated with high performance.”

The learning goal orientation was positively related to the level of goal setting, with a standardized coefficient of .30. However, performance goal orientation was not, with a standardized coefficient of .11.

The metrics also show that people who valued learning goals also put in more effort.

Those with a performance orientation fared a little better when it came to territory and account planning. The researchers actually found that it had a positive relationship in these areas.

Territory planning had a standardized coefficient of .17, and for account planning, it was .20.

However, the relationship was much stronger between learning orientation and planning. The standardized coefficient for the relationship between learning and territory planning was calculated at .44.

On the other hand, for account planning, it is .37.

Why is this?

According to the researchers, “Individuals with performance goal orientations view a challenging task as a threat because there is the risk of failure that would demonstrate their inadequate ability.”

On the other hand, “Individuals with learning goal orientations … view a challenging task as an opportunity for growth and development.”

If you want your team to do their best, it’s not enough to give them ambitious quotas and praise their success. You should also provide training opportunities and give them opportunities to grow professionally and intellectually.

Your most valuable team members are the ones who are humble enough to know that they don’t know everything. However, they still need to be ambitious enough to stretch themselves at every opportunity.

If a team member relies too much on external recognition, it may stifle their ambitiousness. Fear of a challenge may deter them from taking on more complicated tasks.

Thus, they must be encouraged to reach their sales target because they want to grow and improve. Not just to get the recognition and praise of others.

Sales leaders usually draw from three sources to make decisions for their teams: their experience, their gut, and hard data.

The next time you’re unsure of how to guide your team, you can turn to the data in these four valuable sales research studies for answers.

After all, acting on a gut instinct may be okay sometimes. However, it might more productive and less risky to act based on sales strategies that have been proven by science and data to work.

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Which of the sales strategies mentioned in these studies will you use for your sales team? Let us know why and how you think this will impact your team’s performance in the comments section below. 

  • Top Sales & Marketing Priorities For 2019: AI And Big Data, Revealed By Survey
  • Artificial Intelligence Solves Sales Challenges, But Not Widely Accessible, Study Shows
  • How Chatbots Are Changing The Way We Sell W/ Billy Bateman @Chatfunnels

4 Fascinating Sales Research Studies You Should Know By Heart https://www.insidesales.com/blog/inside-sales-best-practices/4-sales-research-studies/

Editor’s Note: This post was originally published on October 26, 2015, and has been updated for quality and relevancy.

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9 sales tools to increase productivity, how to sell well while you work from home, outreach sales: how to outreach better.

Next-gen B2B sales: How three game changers grabbed the opportunity

Driven by digitalized operating models, B2B sales have seen sweeping changes over the recent period amid rising customer demand for more seamless and transparent services. 1 “ The multiplier effect: How B2B winners grow ,” McKinsey, April 13, 2023. However, many industrial companies are failing to keep pace with their more commercially focused peers and, as a result, are becoming less competitive in terms of performance and customer services.

The most successful B2B players employ five key tactics to sharpen their sales capabilities: omnichannel sales teams; advanced sales technology and automation; data analytics and hyperpersonalization; tailored strategies on third-party marketplaces; and e-commerce excellence across the full marketing and sales funnel. 2 “ The multiplier effect: How B2B winners grow ,” McKinsey, April 13, 2023.

Companies using all of these tactics are twice as likely to see more than 10 percent market share growth than companies focusing on just one. 3 “ The multiplier effect: How B2B winners grow ,” McKinsey, April 13, 2023. However, implementation is not as simple, requiring a strategic vision, a full commitment, and the right capabilities to drive change throughout the organization. Various leading European industrial companies—part of McKinsey’s Industrial Gamechangers on Go-to-Market disruption in Europe—have achieved success by implementing the first three of these five sales tactics.

Omnichannel sales teams

The clearest rationale for accelerating the transition to omnichannel go-to-market is that industry players demand it. In 2017, only about 20 percent of industrial companies said they preferred digital interactions and purchases. 4 Global B2B Pulse Survey, McKinsey, April 30, 2023. Currently, that proportion is around 67 percent. In 2016, B2B companies had an average of five distinct channels; by 2021, that figure had risen to ten (Exhibit 1).

Excelling in omnichannel means enabling customers to move easily between channels without losing context or needing to repeat information. Companies that achieve these service levels report increased customer satisfaction and loyalty, faster growth rates, lower costs, and easier tracking and analysis of customer data. Across most of these metrics, the contrast with analogue approaches is striking. For example, B2B companies that successfully embed omnichannel show EBIT growth of 13.5 percent, compared to the 1.8 percent achieved by less digitally enabled peers. Next to purely digital channels, inside sales and hybrid sales are the most important channels to deliver an omnichannel experience.

Differentiating inside versus hybrid sales

Best-in-class B2B sellers have achieved up to 20 percent revenue gains by redefining go-to-market through inside and hybrid sales. The inside sales model cannot be defined as customer service, nor is it a call center or a sales support role—rather, it is a customer facing, quota bearing, remote sales function. It relies on qualified account managers and leverages data analytics and digital solutions to optimize sales strategy and outreach through a range of channels (Exhibit 2).

The adoption of inside sales is often an advantageous move, especially in terms of productivity. In fact, inside sales reps can typically cover four times the prospects at 50 percent of the cost of a traditional field rep, allowing the team to serve many customers without sacrificing quality of service. 5 McKinsey analysis. Top performing B2B companies are 50 percent more likely to leverage inside sales.

Up to 80 percent of a company’s accounts—often smaller and medium-sized customers, accounting for about half of revenues—can be covered by inside sales teams. 6 Industry expert interviews; McKinsey analysis. The remaining 20 percent often require in-person interactions, triggering the need for hybrid sales. This pertains to highly attractive leads as well.

Hybrid sales is an innovative model combining inside sales with traditional in-person interactions. Some 85 percent of companies expect hybrid sales will be the most common job role within three years. 7 Global B2B Pulse Survey, McKinsey, December 2022. Hybrid is often optimal for bigger accounts, as it is flexible in utilizing a combination of channels, serving customers where they prefer to buy. It is scalable, thanks to the use of remote and online sales, and it is effective because of the multiplier effect of numerous potential interactions. Of companies that grew more than 10 percent in 2022, 57 percent had adopted a hybrid sales model. 8 Global B2B Pulse, April 2023.

How an industrial automation solution player implemented game-changing inside sales

In 2019, amid soaring digital demand, a global leader in industrial digital and automation solutions saw an opportunity to deliver a cutting-edge approach to sales engagement.

As a starting point, the company took time to clearly define the focus and role of the inside sales team, based on product range, customer needs, and touchpoints. For simple products, where limited customer interaction was required, inside sales was the preferred go-to-market model. For more complex products that still did not require many physical touchpoints, the company paired inside sales teams with technical sales people, and the inside sales group supported fields reps. Where product complexity was high and customers preferred many touch points, the inside sales team adopted an orchestration role, bringing technical functions and field sales together (Exhibit 3).

The company laid the foundations in four key areas. First, it took time to sketch out the model, as well as to set targets and ensure the team was on board. As in any change program, there was some early resistance. The antidote was to hire external talent to help shape the program and highlight the benefits. To foster buy-in, the company also spent time creating visualizations. Once the team was up and running, early signs of success created a snowball effect, fostering enthusiasm among both inside sales teams and field reps.

Second, the company adopted a mantra: inside sales should not—and could not—be cost saving from day one. Instead, a significant part of the budget was allocated to build a tech stack and implement the tools to manage client relationships. One of the company’s leaders said, “As inside sales is all about using tech to obtain better outcomes, this was a vital step.”

The third foundational element was talent. The company realized that inside sales is not easy and is not for everyone—so finding the right people was imperative. As a result, it put in place a career development plan and recognized that many inside sales reps would see the job as a stepping stone in their careers. Demonstrating this understanding provided a great source of motivation for employees.

Finally, finding the right mix of incentives was key. The company chose a system based on compensation and KPI leading and lagging indicators. Individual incentives were a function of whether individuals were more involved with closing deals or supporting others, so a mix of KPIs was employed. The result was a more motivated salesforce and productive cooperation across the organization.

Advanced sales technology and automation

Automation is a key area of advanced sales technology, as it is critical to optimizing non-value adding activities that currently account for about two-thirds of sales teams’ time. More than 30 percent of sales tasks and processes are estimated to be partially automatable, from sales planning through lead management, quotation, order management, and post-sales activities. Indeed, automation leaders not only boost revenues and reduce cost to serve—both by as much as 20 percent—but also foster customer and employee satisfaction. (Exhibit 4). Not surprisingly, nine out of ten industrial companies have embarked on go-to-market automation journeys. Still, only a third say the effort has achieved the anticipated impact. 9 McKinsey analysis.

Leading companies have shown that effective automation focuses on four areas:

  • Lead management: Advanced analytics helps teams prioritize leads, while AI-powered chatbots contact prospective customers via text or email and schedule follow-up calls at promising times—for example, at the beginning or end of the working day.
  • Contract drafting: AI tools automate responses to request for proposal (RFP) inquiries, based on a predefined content set.
  • Invoice generation: Companies use robotic process automation to process and generate invoices, as well as update databases.
  • Sales commission planning: Machine learning algorithms provide structural support, for example, to optimize sales commission forecasting, leading up to a 50 percent decline in time spent on compensation planning.

How GEA seized the automation opportunity

GEA is one of the world’s most advanced suppliers of processing machinery for food, beverages, and pharmaceuticals. To provide customers with tailored quotes and services, the company launched a dedicated configure, price, quote (CPQ) system. The aim of the system was to enable automated quote creation that would free up frontline sales teams to operate independently from their back office colleagues. This, in turn, would boost customer interaction and take customer care to the next level.

The work began with a bottom-up review of the company’s configuration protocols, ensuring there was sufficient standardization for the new system to operate effectively. GEA also needed to ensure price consistency—especially important during the recent supply chain volatility. For quotations, the right template with the correct conditions and legal terms needed to be created, a change that eventually allowed the company to cut its quotation times by about 50 percent, as well as boost cross-selling activities.

The company combined the tools with a guided selling approach, in which sales teams focused on the customers’ goals. The teams then leveraged the tools to find the most appropriate product and pricing, leading to a quote that could be enhanced with add-ons, such as service agreements or digital offerings. Once the quote was sent and agreed upon, the data automatically would be transferred from customer relationship management to enterprise resource planning to create the order. In this way, duplication was completely eliminated. The company found that the sales teams welcomed the new approach, as it reduced the time to quote (Exhibit 5).

Data analytics and hyperpersonalization

Data are vital enablers of any go-to-market transformation, informing KPIs and decision making across operations and the customer journey. Key application areas include:

  • lead acquisition, including identification and prioritization
  • share of wallet development, including upselling and cross-selling, assortment optimization, and microsegmentation
  • pricing optimization, including market driven and tailored pricing, deal scoring, and contract optimization
  • churn prediction and prevention
  • sales effectiveness, so that sales rep time allocations (both in-person and virtual) are optimized, while training time is reduced

How Hilti uses machine data to drive sales

Hilti is a globally leading provider of power tools, services, and software to the construction industry. The company wanted to understand its customers better and forge closer relationships with them. Its Nuron battery platform, which harvests usage data from tools to transform the customer experience and create customer-specific insights, provided the solution.

One in three of Hilti’s frontline staff is in daily contact with the company’s customers, offering advice and support to ensure the best and most efficient use of equipment. The company broke new ground with its intelligent battery charging platform. As tool batteries are recharged, they transfer data to the platform and then to the Hilti cloud, where the data are analyzed to produce actionable insights on usage, pricing, add-ons, consumables, and maintenance. The system will be able to analyze at least 58 million data points every day.

Armed with this type of data, Hilti provides customers with advanced services, offering unique insights so that companies can optimize their tool parks, ensuring that the best tools are available and redundant tools are returned. In the meantime, sales teams use the same information to create deep insights—for example, suggesting that companies rent rather than buy tools, change the composition of tool parks, or upgrade.

To achieve its analytics-based approach, Hilti went on a multiyear journey, moving from unstructured analysis to a fully digitized approach. Still, one of the biggest learnings from its experience was that analytics tools are most effective when backed by human interactions on job sites. The last mile, comprising customer behavior, cannot be second guessed (Exhibit 6).

In the background, the company worked hard to put the right foundations in place. That meant cleaning its data (for example, at the start there were 370 different ways of measuring “run time”) and ensuring that measures were standardized. It developed the ability to understand which use cases were most important to customers, realizing that it was better to focus on a few impactful ones and thus create a convincing offering that was simple to use and effective.

A key element of the rollout was to ensure that employees received sufficient training— which often meant weeks of engagement, rather than just a few hours. The work paid off, with account managers now routinely supported by insights that enrich their interactions with customers. Again, optimization was key, ensuring the information they had at their fingertips was truly useful.

Levers for a successful transformation

The three company examples highlighted here illustrate how embracing omnichannel, sales technology, and data analytics create market leading B2B sales operations. However, the success of any initiative will be contingent on managing change. Our experience in working with leading industrial companies shows that the most successful digital sales and analytics transformations are built on three elements:

  • Strategy: As a first step, companies develop strategies starting from deep customer insights. With these, they can better understand their customers’ problems and identify what customers truly value. Advanced analytics can support the process, informing insights around factors such as propensity to buy and churn. These can enrich the company’s understanding of how it wants its go-to-market model to evolve.
  • Tailored solutions: Customers appreciate offerings tailored to their needs. 10 “ The multiplier effect: How B2B winners grow ,” McKinsey, April 13, 2023. This starts with offerings and services, extends to pricing structures and schemes, and ways of serving and servicing. For example, dynamic pricing engines that model willingness to pay (by segment, type of deal, and route to market) may better meet the exact customer demand, while serving a customer completely remotely might better suit their interaction needs, and not contacting them too frequently might prevent churn more than frequent outreaches. Analytics on data gained across all channels serves to uncover these needs and become hyperpersonalized.
  • Single source of truth: Best-in-class data and analytics capabilities leverage a variety of internal and external data types and sources (transaction data, customer data, product data, and external data) and technical approaches. To ensure a consistent output, companies can establish a central data repository as a “single source of truth.” This can facilitate easy access to multiple users and systems, thereby boosting efficiency and collaboration. A central repository also supports easier backup, as well as data management and maintenance. The chances of data errors are reduced and security is tightened.

Many companies think they need perfect data to get started. However, to make productive progress, a use case based approach is needed. That means selecting the most promising use cases and then scaling data across those cases through speedy testing.

And with talent, leading companies start with small but highly skilled analytics teams, rather than amassing talent too early—this can allow them to create an agile culture of continual improvement and cost efficiency.

As shown by the three companies discussed in this article, most successful B2B players employ various strategies to sharpen their sales capabilities, including omnichannel sales teams; advanced sales technology and automation; and data analytics and hyperpersonalization. A strategic vision, a full commitment, and the right capabilities can help B2B companies deploy these strategies successfully.

Paolo Cencioni is a consultant in McKinsey’s Brussels office, where Jacopo Gibertini is also a consultant; David Sprengel is a partner in the Munich office; and Martina Yanni is an associate partner in the Frankfurt office.

The authors wish to thank Christopher Beisecker, Kate Piwonski, Alexander Schult, Lucas Willcke, and the B2B Pulse team for their contributions to this article.

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Oil Market Report - March 2024

03 March

About this report

The IEA Oil Market Report (OMR) is one of the world's most authoritative and timely sources of data, forecasts and analysis on the global oil market – including detailed statistics and commentary on oil supply, demand, inventories, prices and refining activity, as well as oil trade for IEA and selected non-IEA countries.

  • Global oil demand is forecast to rise by a higher-than-expected 1.7 mb/d in 1Q24 on an improved outlook for the United States and increased bunkering. While 2024 growth has been revised up by 110 kb/d from last month’s Report, the pace of expansion is on track to slow from 2.3 mb/d in 2023 to 1.3 mb/d, as demand growth returns to its historical trend while efficiency gains and EVs reduce use.
  • World oil production is projected to fall by 870 kb/d in 1Q24 vs 4Q23 due to heavy weather-related shut-ins and new curbs from the OPEC+ bloc. From the second quarter, non-OPEC+ is set to dominate gains after some OPEC+ members announced they would extend extra voluntary cuts to support market stability. Global supply for 2024 is forecast to increase 800 kb/d to 102.9 mb/d, including a downward adjustment to OPEC+ output.
  • Refinery crude runs are forecast to rise from a February-low of 81.4 mb/d to a summer peak of 85.6 mb/d in August. For the year as a whole, throughputs are projected to increase by 1.2 mb/d to average 83.5 mb/d, driven by the Middle East, Africa and Asia. Refining margins improved through mid-February before receding, with the US Midcontinent and Gulf Coast as well as Europe leading the gains.
  • Global observed oil inventories surged by 47.1 mb in February. Offshore stocks dominated gains as seaborne exports reached an all-time high and shipping disruptions through the Red Sea tied up significant volumes of oil on water while onshore inventories declined. Global stocks plunged by 48.1 mb in January, with OECD industry stocks at a 16-month low.
  • ICE Brent futures rose by $2/bbl during February as ongoing Houthi shipping attacks in the Red Sea kept a firm bid under crude prices. With oil tankers taking the longer route around Africa more oil was kept on water, further tightening the Atlantic Basin market and sending crude’s forward price structure deeper into backwardation. At the time of writing, Brent was trading at $83/bbl.

Oil on water

Benchmark crude oil prices were range bound in early March, as the market had already priced in the announced extension of OPEC+ voluntary production cuts through 2Q24. North Sea Dated rose by $2.13/bbl to $84.66/bbl during February as continued tanker attacks in the Red Sea lengthened supply routes and global on-land oil inventories fell for a seventh consecutive month to reach their lowest level since at least 2016.

Global onshore oil stocks fell a further 38 mb last month, taking the draw down since July to 180 mb, according to preliminary data. Over the same period, oil on water surged. Trade dislocations from the rerouting of Russian barrels and more recently due to unrest in the Middle East, have boosted oil on water by 115 mb. In February alone, oil on water surged by 85 mb as repeated tanker attacks in the Red Sea diverted more cargoes around the Cape of Good Hope. At nearly 1.9 billion barrels as of end-February, oil on water hit its second highest level since the height of the Covid-19 pandemic.

Trade flow disruptions also boosted bunker fuel use. Longer shipping routes and faster vessel speeds saw Singapore bunkering reach all-time highs. That, along with surging US ethane demand for its petrochemical sector underpins a slight upward revision to our global oil demand expectations for this year by 110 kb/d compared with last month’s Report. World oil demand growth is now forecast at 1.3 mb/d in 2024, down sharply from last year’s 2.3 mb/d expansion.

The slowdown in growth, already apparent in recent data, means that oil consumption reverts towards its historical trend after several years of volatility from the post-pandemic rebound. A weaker economic outlook further tempers oil use, as do efficiency improvements and surging electric vehicle sales. Growth will continue to be heavily skewed towards non-OECD countries, even as China’s dominance gradually fades. The latter’s oil demand growth slows from 1.7 mb/d in 2023 to 620 kb/d in 2024, or from roughly three-quarters to half of the global total, under the gathering weight of a challenging economic environment and slower expansion in its petrochemical sector.

As in 2023, non-OPEC+ oil supply growth will eclipse the oil demand expansion by some margin. Led by the United States, non-OPEC+ production is forecast to rise by 1.6 mb/d in 2024 compared to 2.4 mb/d last year when global oil output climbed by 2 mb/d to 102 mb/d. Substantial gains will also come from Guyana, Brazil and Canada, all forecast to pump at record-highs this year. Together, the non-OPEC+ Americas quartet is set to add 1.3 mb/d of new oil production in 2024.

Iran, which last year ranked as the world’s second largest source of supply growth after the United States, is expected to increase production by a further 280 kb/d this year. Output policy for the remainder of the OPEC+ bloc will be revisited when ministers meet in Vienna on 1 June to review market conditions. In this Report, we are now holding OPEC+ voluntary cuts in place through 2024 – unwinding them only when such a move is confirmed by the producer alliance (see OPEC+ cuts extended). On that basis, our balance for the year shifts from a surplus to a slight deficit, but oil tanks may get some relief as the massive volumes of oil on water reach their final destination.

1. Includes extra voluntary curbs where announced. 2. Capacity levels can be reached within 90 days and sustained for an extended period. 3. Excludes shut in Iranian, Russian crude. 4. Angola left OPEC effective 1 Jan 2024. 5. Iran, Libya, Venezuela exempt from cuts. 6. Mexico excluded from OPEC+ compliance. 7. Bahrain, Brunei, Malaysia, Sudan and South Sudan.

Definitions of key terms used in the OMR, access the OMR Glossary here.

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