Customers are the bread and butter of your business. If you want your business to succeed, you need to understand your customers and the different behaviors that drive them. One of the ways you can do this is through cohort analysis.

How to Utilize Cohort Analysis for Deep Customer Insights

So, what is cohort analysis and how can you utilize it for deep customer insights? Let’s examine the term, its use and implementation in more detail.


Let’s start by dissecting the term ‘cohort analysis’. The word cohort is best explained in its simplicity by the cohort analysis website. According to the site, cohort is “a group of people who share a common characteristic over a certain period of time”. The analysis of the cohort is therefore simply “the study that focuses on the activities of a particular cohort”.

You are essentially looking at a group of individuals who share a similar trait and examining the behavior of the group. Since you can have different types of cohort, your cohort analysis can involve either a single cohort or be focused on comparing different cohorts.

An example cohort could be a group of people who bought a house for the first time in 2016. The commonality would be buying their first home in the year 2016. Cohort analysis could be looking at the group’s income growth within the next 10 years or how long it takes the group the pay back mortgage.

When you’d study these issues of the particular cohort, you’d be engaging in cohort analysis. As mentioned, your cohort analysis doesn’t have to rely on a single cohort. You could also conduct cohort analysis by comparing first-time homebuyers in 2016 with first-time homebuyers in 2014. This could help you identify differences in paying back the mortgage, for example.

If you want to say it in a fancier way, you can define cohort analysis as a subset of behavioral analytics, taking in collective data from a given dataset and then examining the chosen datasets or groups within a specific timeframe.

For a business, cohort analysis offers a tool to look at customers in more defined groups and to focus on a specific timeline. Instead of focusing on your customers as a single unit, you can look at groups such as moms, students, single people and so on. Furthermore, you can specify the timeframe for your analysis.

For instance, your business could use cohort analysis to examine what is the shopping behavior of recent graduates and how it might change as the graduate move further from their graduation date (i.e. might start working and grow their income).

Businesses can essentially examine two different types of cohorts.

First, you have the time-based cohorts, which refer to customer data that centers on a specific time frame. Examples would be cohorts based on cancellation date or the date the customer made first purchase.

The other general business cohort group is the segment-based cohort, which centers on a more specific characteristic within the dataset. These would include cohorts of customers signing with the annual contracts, or customers who opt for specific products.

As you can see, cohort analysis provides businesses with the option of examining their customers and identifying the different behaviors that influence their engagement with the business, its products and its services.

The analysis can therefore be a valuable way to gain customer insights and to use these for growing the business and developing the products/services further.


What about customer insights? These are essentially findings you can make about the person buying the specific product or service. Customer insights are about peering into the mind of the buyer and identifying the behaviors, feelings and actions controlling the buying decisions.

In essence, it’s about interpreting the different trends in behavior and using the findings to enhance a product or service’s appeal. Customer insights are about finding the answer to the question ‘why?’ Why do people buy a specific fizzy drink or watch a certain channel? What could change this or make them drink/watch more of the given product/service?

When you looked at the field of customer insights, you quickly notice the shift it has gone through in just a few decades. Jure Klepic, a digital strategist, wrote in the Huffington Post how the traditionally the field followed narrow departmental lines.

The research department looked at the numbers, while the marketing department just searched for new customers. The insights weren’t shared or analyzed widely within these different departments. But the segmentation doesn’t work anymore and customer insight has become its own field, acting as a bridge between these different departments.

It’s about providing each department the information they need to boost their performance – whether it is about customer numbers, retention of new customers, or developing the product or service further.

The benefits of customer insights are crucial for any type of business. In fact, without understanding your customers and knowing the ‘why’ behind their shopping decisions, you can’t sell products. Without sales, your business can’t grow, and without growth, you can’t continue to do what you are doing. It’s a rather convincing argument, right? To put it another way, customer insights matter because:

  • Growth is dependent on customers. A business can’t expand without having either more customers or selling more products/services to the existing customers.
  • Customer retention is more effective than acquisition of customers. Old customers have already gotten past the hurdle of buying for the first time. You have information about them and you’re able to reach out to them – their experience of the product will help them decide whether to buy again. Ensuring the experience is positive can be less costly than convincing new customers to buy (you need to find them, market to them, and convince them to try something new).
  • Customers can provide important market feedback and innovation regarding your product or service. Your customers can conduct market research for you, as they are unlikely to make shopping decisions without looking around. They can also provide great feedback in terms of the product or service and what could be improved and changed.

Therefore, a deeper understanding of your customers is necessary for the survival of the business. Your customers are essentially the reason for your business and you’d be fool to avoid understanding what makes them tick.

Here’s an example of how deep customer insight can boost sales:


Cohort analysis can promote your use of customer insights in two specific ways. It can either help you better understand customer behavior or boost your understanding of the product.

Helps understand customer behavior

One of the most basic reasons why a business becomes a success is because it understands its customers and can deliver them the product they want. Apple didn’t succeed with the iPhone because the phone was clearly better or more powerful than others on the market.

It succeeded because it sold people something they didn’t know they wanted. To succeed, you need to know your customers and why they make the shopping choices they make. Cohort analysis is extremely useful for this purpose.

You should start by focusing on two of the most important customer decisions: the decision to stay and the decision to leave. You want to learn from the customers who continue to buy your products – to understand what makes them love your products. You want cohort analysis to tell you answers to questions like:

  • How do the customers use the product?
  • When do they use the product?
  • Why do they use the product?

For example, you could divide your customers to different cohorts based on the timeframes they buy new things. Who are the customers that make a new shopping purchase the quickest? Focusing on these customer groups will ensure you who your most loyal customers are.

On the other hand, you should also focus on understanding the people who decide to stop using your product or service. You want to examine things like:

  • When do they leave?
  • Where do they go?
  • Would they come back?

If you use the cohort analysis to study the people you haven’t been able to retain or who hardly spend money, you can learn valuable lessons. You might be pointed out to studying your competition better, for example.

If your customers seem to switch from you to another retailer, you can use analytics to understand what the competition is doing better. Why them and not you?

You can also use cohort analysis to understand what are the things that might get someone to return. Perhaps they reached a financial limit and if you had priced the product differently, you could maintain a larger customer base.

Helps promote the product

The other key way to use cohort analysis for deep customer insight relates to product promotion. The above already reveals a lot about customer behavior and how it relates to the product. You can understand what makes your product special and therefore turn that as the focus point of your marketing.

Perhaps customer retention is high because your product helps save time. You can then use this information to market your product more as a timesaving thing, rather than focus on the price, for example.

Cohort analysis can help you understand the when, how and why of your product. This in turn will help you promote the product better and develop it further to meet the needs of customers. Cohort groups like loyalty of purchase, repurchase rates and others are crucial for boosting your product’s resale.

Let’s say you study when people are most likely to repurchase the product and the reasoning behind it. The answer (they repurchase every two months because they notice they’ve ran out of the product) can help you target customers with e-mails or calls right when the product is about to end and ensure people repurchase before they ran out and thus reduce the chances of them buying with someone else.


So, what does the process look like? If you want to study your customer behavior or improve your product, you can achieve these customer insights by following a similar method.

The route to cohort analysis only requires four stages and these are not dependent on the issues you are trying to solve or focus on. Essentially, you can use the below strategy whenever you want to utilize cohort analysis for deep customer insights.

Stage 1: Determine what questions you want answered

All analytical processes tend to start with a simple question: ‘why?” The ‘why’ can be about understanding the reason your customers stop buying your products or knowing why the sales increase on December 3rd every year.

Nonetheless, when you start analyzing data you always have a question you want answered. When it comes to cohort analysis, the questions are especially crucial and you should always begin by identifying the things you are looking to answer.

The point of cohort analysis is to obtain information that can result in a specific action, which aims to improve the business and its operations. You might want to look into customer insights to increase sales or improve user experience. The end doesn’t matter; what matters is that you determine the questions you are looking to answer to improve the business. When you are determining the questions, you need to carefully think what are the correct questions to ask in order to get the right analysis.

Consider an example of an app company. You might notice that user engagement continues to be high, but at the same time, your revenue starts to decline at a certain point. Your cohort analysis might therefore start with the question:

  • Why does revenue decline even when user engagement remains high?

You could, of course, continue with follow-up questions such as:

  • Why do customers stop using the paid services even when they want to engage with the product?
  • At what point do the payments stop? Is there a specific amount people spend first and then stop spending?

Stage 2: Define the metrics that help answer these questions

Once you have determined the question or questions you need answered, you need to move on to defining the metrics you think can answer these questions. This is about identifying the relevant data and cohorts to use as a basis of your analysis.

If you consider the example of declining revenue and customer engagement, your metrics could be things like:

  • The amount of money people have paid before revenue declines.
  • The times people spend using the product and the differences in the time when paying or not paying.
  • The customers who are the least likely to continue engaging with the product and not paying

My Customer published an informative post about picking metrics. While the article talks about metrics for customer relationship management (CRM), the points are valid whenever you are looking to identify metrics. You need to:

  • Understand and quantify your business goals.
  • Formulate the specific strategies and tactics to achieve the goals.
  • Establish the measures needed to achieve the goals.
  • Link the goals, strategies and metrics together.

Stage 3: Define which cohorts are relevant to the question

Aside from the metrics, you also need to consider the cohorts, which can help answer your question. For example, if you are looking for the people who stop spending, yet use the app, you wouldn’t want to include people who continue to spend money on the app to the cohort.

Essentially, when you are creating cohorts, you need to identify the specific groups of people that have the right relevance to your question at hand. Each cohort needs to be different in a manner that helps you find the answer to your question.

For instance, in the example of the app, you might create cohorts based on the time they use with the app or the amount of money they spend.

Stage 4: Perform analysis

Finally, you’ll perform the analysis of your findings. The analysis can often be performed using different software or data gathering systems, depending on your business and its needs.

For instance, you can find data in Google Analytics or Kissmetrics. The results can also be either visualized in a graph or organized on a spreadsheet, depending on your preferences.

Once you have the findings, you need to make a decision based on the findings and boost your customer retention, gains or product.


The process of conducting cohort analysis is straightforward, no matter what type of cohorts you are using or for what specific purpose. The above method of identifying the problem, collecting the necessary data, and looking for common trends will be the same whether you are interested in large or small cohorts or solving marketing or retention issues.

Nonetheless, you need to focus on a few practices to ensure you make the most out of the above process. So, before you begin your analysis, remember to:

Take small steps first and scale if necessary.

Although larder datasets tend to make it easier to create a more comprehensive and robust analysis, you don’t need to waste too much time figuring how to get all of the relevant data.

The more important part is to focus on the relevancy of the data, rather than the amount of data. Once you’ve identified your problem, you need to focus on the parameters and cohorts that you think are relevant.

You can begin analysis as soon as you begin identifying these data points and simply continue to add more data during the process, if you find it suitable for the context of your query.

Ensure your data is clean, relevant and reliable.

As I just mentioned, the key to good cohort analysis for deep customer insights is not just on the amount of data, but the relevance, reliability and correctness of the data.

If you use redundant or inaccurate data, then you will get the wrong answers to your questions. Let’s go back to my original example of comparing different first-time homebuyers. If your cohort of 2016 homebuyers includes people who are actually buying their second home or who are buy-to-let investors, you will have distortion in the outcome of mortgage repayments or income growth – you are essentially not looking at the groups you think you are studying.

Therefore, above anything else, you need to make sure the data you use is up-to-date, consistent and filtered to ensure accuracy, relevancy and reliability.

To achieve this, you need to clean, test and filter your data continuously. Don’t just rely on the data to be correct, but test it and make sure you are using the best and the most accurate datasets you can.

Focus your analysis on trends and anomalies.

As soon as you’ve gathered up all the relevant cohort data, you need to begin looking for the trends. You want to analyze the common behavior or the specific trends – for example, you comparison might show that first-time homebuyers tend to see an influx in income within the first two years of buying a home. These trends are important in learning about your customers and their behaviors.

Aside from focusing on trends, you also need to pay attention to anomalies in the data. This is crucial for spotting inconsistencies and irrelevancies in the data, essentially helping you perfect the cohorts you are using. So, let’s say you noticed sudden spikes in income for a specific time period in your first-time homebuyers data.

If these spikes were dramatic upward spikes, you would notice something is wrong and perhaps could spot that some of the people included are actually investors. Naturally, it might not always be a problem with the data, but in fact, a new trend you’ve picked up.

Nonetheless, when you study the trends, look for possible anomalies and if you find them, dive deeper into the data to see if the problem is with your data.

Use graphs and charts for clearer messaging.

When you are creating cohort analysis for deep customer insight, you definitely want to focus on conveying the output of your analysis in an easy-to-understand way. Graphs and charts are perfect for the purpose, because it boils the data down to its essence and quickly communicates the main points.

You can always delve deeper into the data with spreadsheets when needed, but cohort analysis is at its best quick information in accessible format. You can find great information about graph analytics from the below YouTube video:


Cohort analysis can be utilized for deep customers insight because it is another way of understanding customer groups and behaviors. It provides essential information on the different behaviors that drive customers to use your services or products, with the knowledge helping in ensuring you keep customers happy in the process.

You can predict their shopping behaviors, you can understand how to retain them better and you can promote your products more efficiently. Cohort analysis works because it can clearly demonstrate trends and pick up anomalies in behavior.

So, if you need to understand your customers, you definitely want to add cohort analysis to your toolbox.

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