Cohort retention analysis tracks how well you keep customers over time by grouping them based on shared characteristics, like their first purchase date.
The payoff of returning customers is significant. According to research from retention platform Growave, customers who engage with loyalty programs generate 115% more revenue per person than non-participants, yet many brands struggle to pinpoint the friction points in their repeat purchase cycle.
Divy Ojha, founder of produce delivery service Odd Bunch, found that tracking specific cohorts was the only way to validate the success of the brand’s referral program. When the company analyzed one acquisition cohort, it found that customers who’d referred at least one person had a dramatically higher six-month retention rate than those who hadn’t.
This guide will walk you through the mechanics of cohort retention analysis, five steps to performing your own, how to use Shopify’s built-in tools, and how to turn cohort insights into actionable retention strategies.
What is cohort retention analysis?
Cohort retention analysis groups customers by shared characteristics, like their first purchase date, and tracks their behavior over a defined period. Instead of looking at your entire user base as one group, you break it into behavioral cohorts to see how engagement evolves. Where standard metrics like total revenue or store-wide retention offer a broad snapshot, cohort analysis offers a chronological arc of the customer relationship.
In Shopify’s customer cohort analysis report, the default grouping is by the month of a customer’s first purchase. By following each group through the customer life cycle, you can see how long they remain active users and pinpoint where they drop off. This helps you identify which segments have the highest long-term value and what drives loyalty. These insights power retention strategies that reduce churn rate, identify power users, and increase customer lifetime value (LTV).
Cohort-based analysis vs. blended averages
A blended retention average is a single percentage that represents the retention rate for your whole store. Relying solely on a blended retention average can be misleading. A store might report a stable 30% retention rate, but a retention cohort analysis could reveal that customers acquired two years ago retain at 70% while new users acquired last month retain at 90%.
Blended averages can be deceptive, averaging loyal long-timers with high-churn newcomers into a single number. Cohort analysis breaks the data into distinct groups, acting as an early warning system if a recent campaign or product change is driving customers away.
Common cohort types
While time-based cohorts are the standard for data analysis, successful brands also use acquisition channels, behavior, and products as grouping mechanisms.
Acquisition cohorts
This method groups customers by the month, channel, or campaign of their first purchase. Comparing the “July 2025 TikTok Ads” cohort against the “July 2025 Organic Search” cohort could reveal whether social media hype leads to long-term fans or one-and-done shoppers.
Behavioral cohorts
Behavioral cohorts group users by specific actions, like joining a loyalty program, using a specific hook, or referring a friend. For example, comparing a “loyalty program sign-up” cohort against a “guest checkout” cohort might reveal that the loyalty group has 40% higher retention after three months, confirming that your rewards program is successfully locking in customers.
Product cohorts
You can also group users by the first product they bought, which often dictates their future needs. A skin care brand might compare a “Cleanser First-Timer” cohort to a “Sunscreen First-Timer” cohort. If cleanser buyers return twice as often, the brand could focus its top-of-funnel marketing on that cleanser.
Sean Frank, CEO of the accessory brand Ridge, uses product cohorts to address low repeat purchase rates. He found that “ring customers have the highest LTV out of any cohort in our business,” he says on Shopify Masters. Since a customer rarely buys a second metal wallet, which is often a once-in-a-decade purchase, Ridge shifted toward acquiring users through rings and then cross-selling wallets to enhance retention.
How to perform a cohort retention analysis
- Define the active user and time parameters
- Group acquisition cohorts and track repeat activity
- Calculate the retention rate
- Compare approach results
- Make data-driven decisions
Using raw retention data and a repeatable process, you can calculate cohort retention and gain valuable insights to inform future campaigns.
1. Define the active user and time parameters
Determine what constitutes an engaged user for your business. In ecommerce, this is usually a repeat purchase. Then, set your time periods. Monthly cohorts are standard, though high-frequency brands like Odd Bunch use weekly increments to identify patterns in grocery consumption. Divy explains that they engineered their product so customers “cannot live without it every week,” making the weekly cohort the most relevant measure of customer stickiness.
2. Group acquisition cohorts and track repeat activity
List every customer with their acquisition date and group them by “birth month.” For example, the January 2026 cohort includes all the users who made their first purchase that month. This creates a baseline without the data being skewed by older, more loyal customers.
With cohorts established, record how many members from each group return to perform the target action in each subsequent period (for instance, Month 1, Month 2, and so on). This data serves as the foundation for visualizing the user journey and identifying exactly when interest began to fade.
3. Calculate the retention rate
To measure retention, calculate the percentage of the original cohort still active in each period. The period can be day, month, or year, depending on your business model, and the original cohort is the total number of users who signed up or made their purchase during a specific time frame (e.g., January). Here’s the formula:
Retention rate = (Active users in period / Original cohort size) x 100
For example, say 1,000 users signed up in January 2026:
| Period | Active users | Retention calculation | Retention rate |
| Month 0 (January) | 1,000 | (1,000 / 1,000) x 100 | 100% |
| Month 1 (February) | 800 | (800 / 1,000) x 100 | 80% |
| Month 2 (March) | 600 | (600 / 1,000) x 100 | 60% |
By calculating this for every starting month, you can understand if your product is getting better at retaining customers over time. If your January cohort had 60% retention in Month 2 (March) but your March cohort hits 75% two months later (May), it’s a clear sign your recent improvements are working.
4. Compare approach results
Plotting a retention curve helps you visualize where users drop off. By comparing curves across different cohorts, you can see whether changes to your onboarding process, product mix, or the customer experience are flattening the curve.
Odd Bunch saw this firsthand when it pivoted from its original Food Fund model, where customers chose every item, to the mystery box format. Divy says that the brand’s old graphs were “looking down into the left,” indicating high churn. The mystery box model significantly improved retention patterns by removing the chore of weekly selection.
5. Make data-driven decisions
Analyze your customers’ journey to identify where they’re dropping off, then use these cohort insights to fix specific friction points. If your cohort table data shows a sharp drop at month three, for example, trigger a win-back campaign at the two-and-a-half-month mark for that cohort.
Useful cohort retention analysis tools
You don’t need manual spreadsheets to analyze cohorts. Shopify’s internal tools and third-party platforms can provide enterprise-level visibility into your business.
Shopify customer cohort analysis report
Found in the reports section of your Shopify Analytics dashboard, the customer cohort analysis report automatically groups users by first order date and tracks their retention rates over time. You can slice this data into actionable segments across a few core areas:
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Customer metrics. Toggle between retention rate, gross sales, and amount spent per customer to see which cohorts are the most profitable.
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Average order value (AOV) analysis. Check if customers acquired during sales like Black Friday have a lower customer lifetime value than those acquired organically.
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Pattern identification. A built-in heatmap to help you identify patterns in spending and loyalty at a glance.
Shopify’s Analytics dashboard provides retention curve visualizations for your entire user base, letting you track how site-wide retention measures impact long-term loyalty.
Shopify customer segmentation
Once you identify high-performing power users through cohort analysis, use Shopify’s customer segmentation tools to act on those insights. Create a segment of only those users from a successful cohort, like Ridge’s high-LTV ring buyers, and target them with exclusive offers or use them to build lookalike audiences for new customers.
Recency, frequency, monetary (RFM) analysis categorizes customers into 11 groups, like “champions” or “at risk.” Combining RFM with cohort retention analysis lets you see the current status of customers within specific acquisition cohorts. For example, track how many users from your top-performing “June Cohort” have moved into the “champions” segment.
External tools
For advanced product analytics, some brands and investors use third-party platforms, including:
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Reveal by Omniconvert. Use it to map user behavior and forecast revenue.
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Google Analytics.Use its cohort exploration report to connect website user engagement to retention patterns.
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Mixpanel. Use it to track behavioral cohorts based on specific on-site actions, such as users who interacted with a “surprise item” feature.
Cohort retention analysis FAQ
How is cohort analysis different from retention analysis?
Retention analysis is the broad study of whether customers return. Cohort retention analysis is a specific technique that breaks customers into user groups based on a shared characteristic (like acquisition data) to see how their behavior differs over the same period.
What is an example of a cohort?
A common example is a “first purchase month” cohort. You might group all the users who bought for the first time in December 2025. By tracking this retention cohort through 2026, you can see if they became loyal customers or were simply “one-hit wonders” attracted by a holiday discount.
How can ecommerce businesses improve retention using cohort analysis?
Cohort analysis helps you identify the exact month users drop off. You can use these insights to implement retention strategies such as targeted email flows or loyalty rewards at specific friction points to enhance retention and increase customer lifetime value.
