Cohort analysis is a method of analyzing user or customer behavior by grouping individuals into “cohorts” based on shared characteristics or experiences within a defined time period. This technique helps organizations understand how behavior and performance metrics change over time within specific user segments.
Instead of analyzing all users as a single group, cohort analysis provides more meaningful insights by focusing on how different segments behave making it a powerful tool for retention, marketing, product usage, and lifecycle analytics.
What Is a Cohort?
A cohort is a group of people who share a common trait or event in a specific timeframe. Common cohort types include:
- Acquisition cohort: Users who signed up or made a purchase in the same week/month
- Behavioral cohort: Users who performed a specific action (e.g., downloaded an app, subscribed to a plan)
- Geographic cohort: Users from the same location or market segment
Once the cohorts are defined, you can analyze how they behave over time — such as how long they remain active, how often they purchase, or when they drop off.
Why Is Cohort Analysis Important?
Cohort analysis is essential for businesses that want to:
- Understand user retention: Track how long users stay active after joining
- Measure product performance: Identify when users disengage or churn
- Compare marketing effectiveness: See how different acquisition channels impact long-term engagement
- Improve customer lifetime value (CLV): Focus on high-performing segments
Key Metrics in Cohort Analysis
- Retention rate: Percentage of users from a cohort still active over time
- Churn rate: Percentage of users who stop using the product
- Repeat purchase rate: Percentage of users who make more than one purchase
- Session frequency: How often users return to the app or platform
Example of a Cohort Analysis Table
Below is a simplified cohort analysis showing user retention over several weeks:
| Cohort (Signup Week) | Week 0 | Week 1 | Week 2 | Week 3 |
|---|---|---|---|---|
| Week of Jan 1 | 1,000 | 800 (80%) | 600 (60%) | 400 (40%) |
| Week of Jan 8 | 900 | 720 (80%) | 540 (60%) | 360 (40%) |
Example of a Cohort Analysis Visualization
Let’s take another example that shows how leads from January, February, and March cohorts move from MQL to SQL to Customer over a period of 6 months.
This visualization, a grouped bar chart, looks at the January cohort and shows the number of MQLs, SQLs, and Customers for each month after they entered the funnel.
Let’s take Month 3 (3 months after becoming MQL):
- MQLs: 80. These are leads still qualified by marketing and not yet moved forward.
- SQLs: 6. These are leads the sales team has accepted and are actively engaging with.
- Customers: 30. These leads have successfully converted into paying customers.
When to Use Cohort Analysis?
Use cohort analysis when you want to:
- Identify the root cause of retention issues
- Compare the long-term effects of A/B tests
- Measure the impact of product changes over time
- Segment marketing efforts by acquisition date or channel
Major Benefits of Cohort Analysis
- More actionable insights: Breaks down aggregated data into clear behavioral patterns
- Better retention strategies: Pinpoints exactly when and where users churn
- Improved personalization: Target cohorts with tailored messaging or features
- Stronger product decisions: Track how updates affect different user groups
How ClicData Supports Cohort Analysis
ClicData lets you create powerful cohort dashboards by blending data from multiple sources and applying time-based filters, groupings, and custom calculations. With ClicData, you can:
- Automatically group users into time-based or behavioral cohorts
- Visualize retention curves, churn rates, and user activity by cohort
- Apply filters and drill-downs for deeper analysis
- Embed dashboards and share reports with internal or external users
Whether you’re measuring customer retention, feature adoption, or campaign performance, ClicData gives you the tools to explore cohort behavior and make smarter, data-informed decisions.
Cohort Analysis FAQ
What’s the difference between cohort analysis and segmentation?
While both break users into groups, segmentation is usually static and based on attributes (like age or country), whereas cohort analysis is time-based, tracking how users who share a starting point behave over time. Think of segmentation as a snapshot and cohort analysis as a timeline.
But you can read a more detailed explanation in this article.
Why don’t the values in each stage add up to the original cohort size over time?
In cohort analysis, the values shown at each stage often represent different types of metrics—some are current counts, others are cumulative totals. That’s why they don’t necessarily add up to the original cohort size.
For example:
- One stage might show how many users are still active,
- Another shows how many have completed a key action over time,
- And another tracks how many have dropped off or converted.
These categories can overlap. A single user might be counted in multiple stages across time—like being active one month and converted the next. Unless the chart uses mutually exclusive stages (e.g., active, churned, converted), the totals won’t match the original cohort size.
Always check whether the values are:
- Cumulative or point-in-time
- Exclusive or overlapping
- Measured as counts or percentages
Understanding that helps avoid misinterpretation and gives you a more accurate view of user or lead behavior over time.
Can users belong to more than one cohort?
In most use cases, no. A user is assigned to one cohort based on a specific event, like signup date or first purchase. But in more advanced behavioral cohorting, users might appear in multiple cohorts if the event repeats (e.g., weekly purchase behavior). Be careful not to double-count in these cases.
Should I track cumulative values or point-in-time metrics?
It depends on your goal:
- Use point-in-time metrics (e.g., “active in Week 3”) to understand retention and drop-off.
- Use cumulative metrics (e.g., “converted by Week 3”) to see funnel progression or lifecycle impact.
Combining both gives the richest insight.
How many time periods should I include in a cohort analysis?
That depends on the behavior you’re tracking:
- For apps: Weekly for 8–12 weeks is common
- For subscription models: Monthly for 6–12 months
- For ecommerce: Focus on purchase cycles or holidays
Watch for when the retention curve flattens. Extra time periods beyond that might add noise rather than insight.
Can I combine cohort analysis with A/B testing?
Yes, and you should! Use cohorts to track the long-term impact of A/B variants on retention, conversion, or revenue. Segment users by test variant, then analyze cohort behavior side by side. It helps avoid short-term bias and reveals how changes perform over time.
What tools are best for cohort analysis?
You can build cohort analysis in:
- Excel or Google Sheets (great for small teams or quick tests)
- BI platforms like ClicData, which automate cohort creation, filtering, and visualization
- Product analytics tools (e.g., Mixpanel, Amplitude) for in-app behavior tracking
Choose the one that best fits your data stack and how often you’ll analyze cohorts.