Data is king. Understanding customer behavior is the key to a successful ecommerce business.

A customer cohort is a group of customers who share common characteristics or experiences within a defined time frame, and analyzing these cohorts is essential for understanding patterns in customer behavior, retention, and revenue over time.

But how you convert data into useful information is everything — which is why cohort analysis is a must-have tool for your Shopify store. Businesses analyze cohorts to identify trends, assess the impact of new features or interventions, and make data-driven decisions to enhance the customer experience. To make the most of your data, it's important to apply cohort analysis to business metrics like churn, net revenue retention (NRR), and customer lifetime value (LTV) for strategic decision-making. Effective cohort analysis relies on high-quality data points and access to historical data, allowing you to uncover actionable insights and long-term trends. Cohort analysis also helps map the user journey, making it easier to identify points of friction or drop-off and improve overall retention.

So what is a cohort?

This refers to a group of people who share a similar trait over a specific time frame, often grouped by users who perform an action within the same time period.

A cohort analysis involves studying the behavior of a specific group of people. It may also incorporate one cohort or many different cohorts.

There are two main types of cohorts.

One is time-based cohorts. This component considers customer data focused on a specific time, such as the acquisition date, which is the specific date when users first sign up or make a purchase. Time-based cohort analysis often involves analyzing user behavior over a specific period to identify trends and patterns. For example, when a customer first buys a product.

The other type is segment-based cohorts. This element focuses on specific characteristics like customers that have annual contracts. You can also create cohorts from specific user groups based on behaviors or events.

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Why is Cohort Analysis important?

Cohorts allow you to dig deeper into which factors are really driving revenue for your business a given date range. Cohort analysis can also help increase retention by identifying opportunities to improve user engagement and reduce churn.

For example, let’s say you are a marketer for a growing ecommerce brand. You ran an Adwords campaign a month ago, so your metrics went up. At first glance, you might think to yourself, “Great, we’re seeing growth in our numbers, so Adwords is a great investment and marketing channel for our company.”

But reviewing metrics through cohort analysis, lets you dig much deeper, and may provide a different conclusion for this marketing campaign. Analyzing engagement patterns within cohorts can reveal which strategies are most effective at driving long-term user activity. A cohort will answer the question, “Of all the customers who purchased your product last month, how many people are still engaged after 1 month, 2 months, 3 months, and so on?” By examining these cohorts, you can also identify successful behaviors that lead to higher retention and increased customer lifetime value.

This data set can inform further actions to try and improve repurchase rate (more on this below) and other key metrics. You can use cohort analysis to understand the related groups of customers and their behaviors, which is invaluable for your business’s growth. Retention data and cohort data are essential for making informed business decisions and optimizing your strategies for long-term success.

Curious about some of Peel’s Cohort analysis options?

Let’s dive in.‍

Types of Cohorts

Cohort analysis is all about grouping your users or customers into cohorts based on shared characteristics or behaviors, allowing you to uncover valuable insights into customer behavior and user retention. There are several types of cohorts you can use to analyze user engagement and retention rates, each offering a unique perspective on how different groups interact with your business:

• Acquisition cohorts: These cohorts are based on when users first interact with your product or service—such as the date they sign up or make their first purchase. By analyzing acquisition cohorts, you can see how different marketing channels or campaigns impact user retention and user behavior over time. This helps you understand which acquisition strategies are most effective at bringing in loyal customers.
• Behavioral cohorts: Here, users are grouped by specific actions they take, like completing a tutorial, making a purchase, or engaging with a feature. Behavioral cohorts help you analyze how certain behaviors influence retention and revenue, revealing which user actions are linked to higher engagement and long-term value.
• Time-based cohorts: These cohorts are organized by when users perform a specific action, such as during a holiday sale or a limited-time promotion. Time-based cohorts are especially useful for evaluating how external events or seasonal trends affect user behavior and retention rates.
• Segment-based cohorts: With segment-based cohorts, you group users by demographic or firmographic data—like location, industry, or company size. This approach enables you to tailor your marketing efforts and personalize user experiences for different segments, leading to more effective engagement and higher conversion rates.
• Size-based cohorts: These cohorts are defined by the scale of user interaction, such as the number of purchases made or frequency of logins. Size-based cohorts help you spot behavioral patterns among users with similar levels of activity, which can inform strategies for nurturing high-value customers or re-engaging less active ones.

By leveraging these different types of cohorts in your cohort analysis, you gain a deeper understanding of how various user segments respond to your marketing efforts, how retention changes across different groups, and where to focus your resources for maximum impact. This approach empowers you to refine your strategies, boost user engagement, and drive sustainable growth.

How to Conduct a Cohort Analysis

Conducting a cohort analysis is a powerful way to analyze customer behavior, track user retention, and uncover actionable insights that can transform your business. Here’s a step-by-step guide to help you get started:

1. Define the goal: Start by clarifying what you want to achieve with your cohort analysis. Are you looking to improve user retention, increase revenue, or better understand user engagement? Setting a clear objective will guide your entire process.
2. Choose the cohort type: Select the type of cohort that best aligns with your goal. For example, use acquisition cohorts to evaluate the effectiveness of different marketing channels, or behavioral cohorts to see how specific actions impact retention.
3. Collect data: Gather the relevant customer data you need, such as demographics, user actions, transaction history, and engagement metrics. The quality and depth of your data will directly impact the insights you can draw.
4. Create a cohort table: Organize your data into a cohort table, where each row represents a cohort (such as users who signed up in the same month), and each column represents a time period or key metric (like retention rate or revenue over time).
5. Analyze the data: Examine your cohort table to identify patterns and trends. Look for correlations between user behavior and retention, and see how different cohorts perform over various time periods.
6. Visualize the data: Use data visualization tools to create cohort charts or graphs. Visual representations make it easier to spot trends, compare different cohorts, and communicate your findings to stakeholders.
7. Interpret the results: Draw conclusions from your analysis. Identify which cohorts have the highest retention rates, which marketing efforts are most effective, and where users drop off in the customer lifecycle.
8. Refine the strategy: Use the actionable insights from your cohort analysis to optimize your marketing efforts, enhance user experience, and increase revenue. Adjust your campaigns, product offerings, or engagement strategies based on what you’ve learned.

By following these steps, you can use cohort analysis to gain a deeper understanding of your customer base, track how retention changes over time, and make data-driven decisions that drive growth. Whether you’re analyzing user segments, evaluating the impact of marketing campaigns, or seeking to retain customers more effectively, cohort analysis is an essential tool for any business looking to thrive in today’s competitive landscape.

1. Customer Retention Rate By Cohorts

This metric applies mainly to subscription businesses. Every subscription based business model wants to cut down churn and increase the lifecycle of better user retention. Tracking customer retention rate is a key metric for understanding and improving long-term business performance.

On the Peel dashboard, it’s not always the first thing you see—don’t be alarmed. You can also monitor active users to assess ongoing engagement and retention trends.

It’s more expensive to acquire new customers than to retain old ones. This is why customer retention is essential.

Different customer cohorts may have different retention rates.

For example, you may observe a higher retention rate on some products and not others. By analyzing how many users remain engaged over time, you can gain deeper insights into user behavior. This can influence business decisions like what products to market or even drop. Those decisions are the driving factors that can help drive down your customer churn from month to month.‍

2. Repurchase Rate by Cohort

The repurchase rate helps you figure out how many customers bought your product more than once. This is another of our behavioral cohorts that gives you a window into your customers’ common characteristics. Using acquisition cohort analysis, you can see how the timing of user acquisition affects repurchase behavior and identify trends in retention and early churn.

This metric is crucial if you don’t have a subscription business (where you’d be more interested in preventing churn rate specifically). How often are customers coming back to your brand and making another purchase? Tracking new users and their repurchase rates can reveal onboarding issues and help improve retention strategies.

Repurchase rate by cohorts helps you pinpoint the specific groups of customers that like your product. Analyzing a behavioral cohort, such as those who make repeat purchases, helps identify engagement patterns and optimize targeted retention strategies.

Knowing which cohorts buy your products frequently can positively influence your marketing plan while detecting weak elements. Thinking about what you can do to increase that repurchase rate - send SMS or more email campaigns, maybe even offer special pricing promotions for a limited time - might increase the repurchase rate for future cohorts.

It’s an invaluable practice for you to understand the period of time that transpires between first time a customer buys your product and the next time they return and convert for a subsequent sale.‍Like all of Peel’s metrics, you can segment the repurchase rate by cohort metric by-products, vendors, SKU’s, discount codes, locations. Doing this can help you determine the success of using a specific discount on a campaign, or whether a specific product in your offering initiates better loyalty and repurchase.

Thinking about the segment values of your business and looking at the analysis can help you better plan for growth opportunities.

3. Cohort Customers per Order Count

This metric shows you how many customers made more than one order.

A retention curve can be used to visualize how repeat orders change over time for each cohort. You can also create a cohort chart to track and compare order counts across different cohorts.

Essentially, how many customers came back for a particular order. Do 500 of your customers from that cohort purchase 4 times? Number of orders per month

This metric is relevant for businesses that may sell subscriptions in an irregular frequency, such as every two weeks, seven weeks, or three months. This helps those businesses focus in on the offerings that have the best conversion rate based on the cohort size.

4. LTV Per Customer

LTV is total profit divided by the number of customers.

The lifetime value measures how much profit you make per customer over time. This indicator is usually an ever-increasing number, especially if you’re retaining customers. Retention data is essential for accurately calculating LTV, as it reveals how long customers stay active and continue generating revenue.

This is how much revenue you can expect per customer from a cohort. Leveraging behavioral analytics helps identify patterns and actions that drive higher LTV, providing actionable insights for improving customer engagement and retention. Time based cohort analysis can be used to track changes in LTV across different acquisition periods, such as during marketing campaigns or product updates. It is very insightful to measure customer LTV in relation to the cost of customer acquisition (CAC). Peel’s LTV: CAC ratio tells you how long it takes to make back the investment of acquiring that customer - hence profitability.

5. Segmentation and Cohort Analysis

Segmentation can help you understand the lifetime value of your customer better.

By conducting customer cohort analyses, you can uncover the key drivers of LTV and identify patterns in customer behavior over time.

You can look at the LTV while segmenting by different products or attribution channels.

Generating a cohort analysis report allows you to visualize and share insights from segmentation, making it easier to collaborate and make strategic decisions.

This metric can also help you understand the critical drivers of LTV, such as higher-priced products or specific attribution channels - Is Facebook more valuable than Google, etc?.

*Check out our latest product update that adds new avenues for custom segmentation to Peel’s automated analytics.

6. Cohort LTV

You can also analyze the Lifetime Value of a customer by cohort. It tells you how much, in particular, is that cohort bringing in - was there a specific campaign that you ran that made that month’s cohort more valuable than others? Tracking the LTV of existing customers is crucial for maximizing long-term value and improving retention strategies.

Knowing these numbers and pinpointing your marketing activities will help you define what works and what doesn’t work for your business. Leveraging historical data allows you to identify trends across cohorts and inform future marketing strategies.

This element helps you determine how much money a particular cohort brought you in profits. The goal of LTV is to tell you how much money you’re making over time.

The more costs you have and the more input you have, the more accurate the LTV is.

But wait! There’s more to Cohort Analysis!

Want to be fully equipped to grow your ecommerce store with cohort analytics?

Be sure to watch the video above to learn how to navigate these elements. If you want to get started with our automated analytics tool that brings your cohort analysis reports to life with data visualization, visit the Shopify App Store. You're 1 click away from a free 7 day trial that will open the door to better understanding your user behavior through detailed cohort analysis.