Cohort analysis is a behavioural analytics technique that groups users sharing specific characteristics or experiences (cohorts) and tracks how these groups behave over time. By comparing cohorts, organisations identify trends, assess the impact of changes, understand user lifecycle patterns, and determine whether improvements actually benefit users.
Cohort Concept
Cohorts are user groupings with shared attributes:
- Acquisition cohort - Users acquiring in same period (e.g., January 2024 signups)
- Behavioural cohort - Users sharing behaviours (e.g., feature users)
- Demographic cohort - Users sharing characteristics (e.g., geographic location)
- Custom cohort - Any shared attribute or experience
Tracking cohorts over time reveals patterns and changes.
Common Cohort Types
Different cohort approaches serve different purposes:
Acquisition Cohorts
- By signup date - Monthly cohorts of new users
- By source - Users from specific traffic sources
- By campaign - Users from specific marketing campaigns
- By device - Users acquiring via specific devices
- By geography - Users from specific regions
Acquisition cohorts reveal if newer users differ from earlier users.
Behavioural Cohorts
- Feature users - Users who adopted specific features
- Engagement level - High, medium, low engagement users
- Purchase frequency - Regular vs. occasional purchasers
- Activity level - Active vs. inactive users
- Spend level - Heavy vs. light spenders
Behavioural cohorts compare different user types.
Time-Based Cohorts
- Monthly cohorts - Users from specific months
- Quarterly cohorts - Users from specific quarters
- Seasonal cohorts - Users from specific seasons
- Event-based cohorts - Users around specific events
Time-based cohorts reveal temporal patterns.
Cohort Retention Analysis
Measuring user persistence:
- Retention rate - Percentage of cohort returning
- Churn rate - Percentage leaving
- Day 1 retention - Percentage active after 1 day
- Day 7 retention - Percentage active after 7 days
- Day 30 retention - Percentage active after 30 days
- Retention curves - How retention changes over time
Retention patterns reveal user stickiness.
Cohort Engagement Analysis
Measuring user activity:
- Feature adoption - Percentage using features
- Session frequency - How often cohort uses application
- Session duration - Time spent per session
- Pages/screens viewed - Content consumption
- Actions taken - Feature usage patterns
- Event frequency - How often specific events occur
Engagement metrics reveal how different cohorts behave.
Cohort Revenue Analysis
Measuring financial contribution:
- Average order value - Revenue per transaction
- Purchase frequency - How often cohort purchases
- Lifetime value - Total revenue per user
- Revenue per user - Average revenue across cohort
- Customer acquisition cost - Cost to acquire cohort
- Return on investment - Revenue vs. acquisition cost
Revenue analysis reveals financial impact of different cohorts.
Creating Cohorts
Practical cohort definition:
Step 1: Identify Characteristic
- What user attribute or experience defines the cohort?
- Is it acquisition-based, behavioural, or demographic?
- Is it quantifiable and trackable?
Step 2: Define Membership
- What exact condition defines membership?
- What is the cohort size?
- How representative is it?
Step 3: Track Over Time
- What metric will you track?
- What time period?
- What is the granularity (daily, weekly, monthly)?
Step 4: Analyse and Interpret
- How does this cohort compare to others?
- What patterns emerge?
- What insights can you draw?
Cohort Analysis Tools
Platforms supporting cohort analysis:
- Mixpanel - Native cohort analysis
- Amplitude - Cohort builder and analysis
- Google Analytics - Cohort analysis reports
- Fullstory - User cohort analysis
- Data warehouses - Custom SQL cohort analysis
- Custom dashboards - Bespoke cohort tracking
Tool selection depends on tracking needs and platform preference.
Interpreting Cohort Results
Understanding cohort data:
Retention Curves
- Steep decline - Users quickly leaving
- Shallow decline - Users persist long-term
- Flat pattern - Consistent retention across time
- Early improvement - Better retention in later cohorts
- Early decline - Declining cohort quality
Retention curves show user lifecycle patterns.
Cohort Comparison
- Older vs. newer - Are newer cohorts better/worse?
- Source comparison - Do acquisition sources differ?
- Feature comparison - Do feature users differ?
- Segment comparison - Do segments perform differently?
Comparison reveals which factors impact performance.
Cohort Analysis and Product Changes
Measuring impact:
- Pre-change cohort - Users before product change
- Post-change cohort - Users after product change
- Control cohort - Unaffected comparison group
- Treatment cohort - Affected by change
- Confidence interval - Statistical reliability
Cohort comparison reveals whether changes actually improve outcomes.
PixelForce Cohort Analysis
At PixelForce, cohort analysis is integral to understanding user lifecycles and product impact. Whether tracking fitness app user retention by acquisition month, marketplace vendor cohort behaviour, or enterprise system adoption patterns, cohort analysis reveals trends driving business decisions. Our experience across 100+ projects provides insights into what drives user retention and success.
Survival Curves
Advanced cohort analysis:
- Kaplan-Meier curves - Accounting for users lost to follow-up
- Hazard rates - Probability of churn at each time point
- Median lifetime - When 50% of cohort churn
- Statistical tests - Comparing survival between cohorts
Advanced techniques provide deeper insights.
Cohort Analysis Challenges
Common obstacles:
- Sample sizes - Smaller cohorts have unreliable results
- Selection bias - Early adopters different from later users
- External factors - Business environment affects all cohorts
- Time lag - Takes time to gather meaningful data
- Data quality - Incomplete or inaccurate tracking
- Interpretation - Distinguishing signals from noise
- Privacy - Tracking users across time periods
Addressing challenges improves analysis quality.
Cohort Communication
Sharing insights:
- Clear visualisations - Charts and graphs
- Trend lines - Showing direction of change
- Comparison highlights - Emphasising differences
- Context - Explaining what cohorts mean
- Actionable insights - What should we do?
- Caveats - Important limitations
Effective communication ensures stakeholder understanding.
Linking Cohorts to Action
Translating analysis to improvements:
- Identify problems - What is the cohort experiencing?
- Root cause analysis - Why is this happening?
- Propose solutions - What changes might help?
- Test improvements - Validate solutions with A/B tests
- Measure impact - Does improvement actually help?
- Scale success - Roll out improvements widely
Analytics-driven improvement ensures efforts focus on real issues.
Real-World Cohort Example
Fitness app scenario:
Comparing monthly signup cohorts reveals:
- January cohort: 40% still active at 30 days
- February cohort: 45% still active at 30 days
- March cohort: 52% still active at 30 days
Investigation shows March cohort received improved onboarding. Improved onboarding became standard, increasing overall retention.
Conclusion
Cohort analysis is essential for understanding user lifecycles and measuring product impact. By grouping users and tracking how different cohorts behave over time, organisations identify trends, assess changes, and make informed decisions about product improvements and strategy.