Have you ever dreamed of having a fiercely loyal customer base that keeps returning for more? 

More purchases, more orders, more money?

You can wake up now, as this dream is more obtainable than ever.

Enter cohort retention analysis: the key to unlocking hidden retention patterns and insights that can skyrocket your business growth.

Today’s article dives into cohort retention analysis: a powerful tool that goes beyond traditional metrics to reveal the true drivers of customer loyalty. 

Let’s ride!

What Is Cohort Retention Analysis?

Cohort retention analysis represents a method used in business and data analytics to understand and measure the behavior and retention of groups of customers (or users) over time. 

cohort refers to a specific group of customers who share a common experience within a defined time frame – for example, customers who made their first purchase in a specific month.

In eComm & Retail specifically, cohort retention analysis focuses on analyzing customer and user retention and uncovering patterns within a specified time frame.

Cohort analysis allows businesses to answer questions such as: 

  • How good are we at retaining customers after the first purchase?
  • How do customer retention rates vary across different cohorts?
  • What factors contribute to improving customer retention in specific cohorts?
  • Are particular customer segments or cohorts exhibiting higher or lower retention rates?
  • How do customer behavior and retention change over time within each cohort?

The goal of cohort retention analysis is to track how well you retain your customer cohorts over time, identify behavioral patterns & trends, and uncover issues affecting the churn rate. 

Types of Customer Cohorts

Evidently, things can’t be so simple. 

You could use several types of cohorts analysis and criteriums to classify and divide customers into your cohorts.

However, to keep this guide to cohort analysis short and to the point, let’s discuss the most common ones.

  • Time-based cohorts

As the name suggests, these customers are split into cohorts based on the acquisition time. 

For example, you can create cohorts based on busy shopping seasons (such as Christmas or Black Friday) and analyze how retention differs across cohorts over time.

You might be surprised to uncover some truths about customer acquisition. 

One of these revelations is that you have a lower retention rate for people who purchased close to a Holiday. This could signal these customers are bargain hunters and don’t love your brand as much as they love your discounts.

  • Acquisition cohorts

With acquisition cohorts, you’re mostly focused on grouping people based on the source through which customers were acquired.

Such cohorts might include customers acquired through organic search, paid advertising, social media, email marketing, or referral programs. 

Comparing the retention rates of these cohorts reveals how effective all your channels are in bringing in quality customers who stay with you for the long haul. 

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  • Product-specific cohorts

This type of cohort is extremely useful in highlighting toxic products that may cause customer churn – or bestsellers who steal customers’ hearts. 

Since these cohorts are based on the specific products customers have purchased, you can analyze them to understand how different customer segments engage with and retain an interest in specific products.

  • Customer segmentation cohorts

You can also group customers based on common characteristics (such as demographics, behavior, preferences, or purchase history.) 

The RFM Segmentation is an example of a customer segmentation cohort, where people are grouped based on their Frequency, Recency, and Monetary Value. 

This segmentation is ideal for uncovering high-valued customers and prioritizing their needs, as well as ensuring you aren’t wasting resources on customers who don’t support your brand.

  • Behavioral cohorts

Another cohort is based on specific actions or behaviors customers have exhibited. 

For instance, you can create cohorts for cart abandoners, customers who have made multiple purchases, or customers who have left product reviews. 

Behavioral cohorts analysis helps you identify behavioral patterns and develop targeted retention and prevention strategies. 

  • Loyalty Program Cohorts

Supposing you’re supporting customer retention with loyalty programs, it would be ideal if you observed how customers interact with these programs. 

This is where loyalty program cohort analysis is handy, as it helps isolate customers participating in your programs. 

Analyzing these cohorts helps you truly measure the impact of loyalty programs on customer retention, engagement, and purchasing behavior

Sending customer surveys based on your performed cohort analysis on loyalty programs gives even more insights, as it shows you exactly what customers expect from you, giving you full control over their retention.

Metrics to Focus on when Using Cohort Retention Analysis

We can’t speak data analysis without discussing retention metrics

After all, numbers without order are just chaos.


Here are some essential metrics to follow in the context of cohort retention analysis in eComm & Retail:

  • Repeat Purchase Rate

This metric provides insights into customer loyalty and the effectiveness of your retention efforts. 

Tracking the repeat purchase rate will highlight cohorts with high repeat purchase behavior – allowing you to target and personalize marketing efforts for these segments.

The AOV will help you understand variations in customer spending behavior and identify cohorts that generate higher revenue. 

(and those with lower spending)

This insight helps you make sure you’re not spamming high spenders while still sending incentives to low-spenders and getting to buy more products. This will reduce churn in time as you take a more personalized approach.

Truth be told, we’re strong advocates for turning CLV into a North-Star metric. It’s the only way organizations can hold themselves accountable for becoming truly customer-centric. 

Now, let’s talk about this specific context. 

Tracking CLV within cohorts reveals cohorts with higher long-term value, allowing you to focus on retaining and engaging customers in those cohorts. 

A retention strategy that started with cohort analysis, focused on CLV, is one where you can rest assured that all your resources are effectively allocated and prioritized.

  • Purchase Frequency

This metric follows the same logic as the AOV: track it and identify both high and low purchase frequency in your cohorts. 

Then, use the info to develop targeted retention strategies, personalized recommendations, loyalty programs, or reactivation campaigns.

  • Average Customer Lifespan

Moving on, we have a metric helping you understand the average duration of the customer lifecycle within each cohort. 

In cohort analysis examples, tracking the average customer lifespan helps you evaluate the effectiveness of your acquisition campaigns. 

For example, suppose you’re tracking the lifespan of customers acquired in this year’s Halloween campaign vs. customers acquired without special promos. 

If those acquired in the campaign quickly churned, it’s a sign the products promoted didn’t deliver on the promise or you acquired low-valued customers. 

This insight alone could increase your profits x3 in the next campaigns.

  • Customer Engagement Metrics

Finally, we have the engagement metrics, such as website visits, time spent per session, open email rates, or click-through rates.

Analyzing them within each cohort helps:

  • Uncover customer behavior patterns 
  • Identify highly engaged cohorts 
  • Find cohorts that require additional efforts to improve engagement
  • Understand your users on a deeper level 
  • Improve retention rates and grow sustainably

How to Measure Cohort Retention

So, how do you perform a proper cohort analysis which unveils valuable insights into customer engagement and loyalty?

As with all things marketing, cohort retention analysis is a step-by-step reiterative process requiring monitoring, research, and data analysis. 

But we’re getting ahead of ourselves. 

The first step in a cohort retention analysis process is defining the period you want to track cohort retention. 

We could talk for weeks, months, or even years – depending on your business and the nature of your customers. 

Whichever the length, selecting an appropriate period ensures that you capture meaningful retention trends and patterns.

Your next step would be to create the cohorts

Use the abovementioned types (or create your own if they don’t match your goals) to divide customers into specific groups. 

Your cohorts should be large enough to provide statistically significant insights while being granular enough to capture meaningful differences in behavior.

It’s a fine line to walk, so consider the context of your business and the specific research question or objective you are addressing before you look at the cohort size. 

Should you realize that you need either smaller or larger cohorts, don’t be afraid to return to this step and adjust. 

Following this step, you will want to decide on the key metric to measure retention within each cohort. 

If you want to measure recommendations, track the NPS. If you want to know about retention, track the retention rate.

Again, choose a metric that aligns with your business goals and accurately reflects the desired customer behavior.

Now that all is set in motion, it’s time to start the party: tracking cohort activity. 

Monitor the behavior of each cohort over time by collecting data on their interactions with your business. 

This data could come from sources such as transaction records, return forms, or customer engagement metrics.

Ensure you have robust data collection mechanisms to track cohort activity and engagement accurately.

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The next step is to calculate the retention rates

This one is pretty self-explanatory.

This step involves dividing the number of active customers by the total number of customers or users in the cohort. 

For example, if you measure monthly retention, you would calculate the % of customers still active each subsequent month after their initial interaction. 

This calculation quantitatively measures how well cohorts retain customers or users over time.

To make your job easier, you could use a tool that helps you visualize the data with line graphs, bar charts, or heat maps. 

Visual representations make it easier to compare the retention rates of different cohorts over time. More than that, they will make any trends or patterns visually recognizable. 

Lastly, visualizations also enable you to effectively communicate the insights from your cohort retention analysis.

Next, you must analyze the data across cohorts to identify variations or insights. 

Look for factors or events that may have influenced retention, such as changes in product pricing, marketing campaigns, or customer support initiatives. 

Identify cohorts with higher or lower retention rates and analyze the potential reasons behind these differences.

(take advantage of customer surveys and interviews for this step)

Lastly, you must conclude the cohort retention analysis and derive actionable insights. 

Identify cohorts who exhibit higher or lower retention rates. Explore potential reasons behind the differences. Reveal the truth behind the numbers.

This information will guide decision-making and help orchestrate a strategy that improves retention.

How to Build a Retention Strategy Using Cohort Retention Analysis

Now it’s time to get tactical and move on to orchestrate a customer retention strategy based on cohort retention analysis. 

A mouthful? Yes. 

An incredibly useful tool empowering you to develop powerful initiatives for improved retention? 

Also yes. 

So, here’s your step-by-step guide to building an effective retention strategy:

Firstly, analyze the cohort retention data to identify cohorts that exhibit high or low retention rates.

Identifying trends, patterns, or anomalies within different cohorts is the first step when beginning to conduct cohort analysis.

You can either focus on cohorts representing significant portions of your customer base or those showing potential for a higher value.

It’s your choice and should match your business goal.  

Next, dive deeper into the characteristics of high-retention and low-retention cohorts

Analyze factors such as customer demographics, purchase behavior, product usage, engagement patterns, or any other relevant data points. 

This step will aid in figuring out factors that drive retention within each cohort.

With a clear understanding of cohort characteristics, identify the key factors or actions contributing to higher retention rates within successful cohorts. 

This could include:

  • product features
  • customer support
  • personalized experiences
  • communication channels
  • any other aspects that positively influence customer retention 

You can tailor your strategies by pinpointing the drivers specific to each cohort.

Repeat the process for the factors contributing to low retention rates and address these gaps. 

Develop strategies and initiatives to improve customers’ issues and provide a more meaningful and relevant customer experience

These initiatives can span from product improvements to personalized messaging, onboarding enhancements to loyalty programs, or any other actions that can positively impact retention.

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Leverage the insights from cohort analysis to tailor marketing campaigns, communication strategies, or customer support initiatives that match the preferences of each cohort. 

As you implement your retention strategies and initiatives, closely monitor their impact. 

Use A/B testing or controlled experiments to measure the effectiveness of different approaches. Remember to iterate and refine your strategies based on customer feedback continuously. 

Remember that retention strategies should extend beyond the initial acquisition or onboarding phase. 

Continuously provide value, nurture customer relationships, and maintain customer communication to improve user engagement and drive customer loyalty.  

Monitor cohort retention over the long term to ensure sustained success.

Wrap Up

Go ahead, our cohort champion! 

You’re now armed with the power of cohort retention analysis, an insightful and sometimes surprising look into your customer base.

As you embark on your journey of crunching data and innovating retention, remember there is no specific destination in sight.

Keep measuring, analyzing, and refining your cohort retention strategies. Stay agile, nimble, and always on the lookout for new insights and opportunities. 

Good luck!

Frequently Asked Questions about Cohort Analysis

How do you calculate cohort retention?

Cohort retention is calculated by dividing the number of customers who remain active over a specific period by the total number of customers.

The formula is as follows: (Number of retained customers or users / Total number of customers or users in the cohort) x 100.

The retention rate is often measured over subsequent time intervals, such as monthly or quarterly, to track customer behavior over time.

What is the cohort analysis method?

The cohort analysis method studies and compares the behaviour and characteristics of groups of customers who share a common characteristic within a defined time period.

It involves creating cohorts based on specific criteria, such as the month of first purchase or the week of sign-up, and then tracking and analyzing their behavior over time.

Cohort analysis helps identify trends, patterns, and insights that can guide business strategies, improve customer retention, and optimize decision-making.

What is an example of a cohort analysis?

An example of a cohort analysis could be studying the retention rates of customers who made their first purchase in different months.

By creating cohorts based on the month of first purchase, you can track how these cohorts behave over subsequent months.

For instance, you may discover that customers who made their first purchase in January have higher retention rates compared to those who made their first purchase in March.

This insight can help you understand the impact of customer acquisition timing and tailor retention strategies accordingly.