Cohort Analysis
Analyze user cohorts and retention
Overview
Generate comprehensive cohort retention analysis for SaaS subscriptions, mobile app engagement, and e-commerce repeat purchase patterns. Track how user groups behave over time, identify which acquisition channels produce sticky users, and spot revenue trends across monthly or quarterly cohorts.
This template produces retention curves, activation metrics, churn breakdowns, and segment comparisons - the core analysis you’d typically spend 2-3 hours building in SQL and Excel.
Use Cases
- Analyze SaaS trial-to-paid conversion rates across sign-up cohorts to optimize onboarding
- Track mobile app Day 1, Day 7, and Day 30 retention by acquisition channel for UA budget allocation
- Compare revenue per cohort for subscription products to forecast LTV and identify power users
- Measure e-commerce repeat purchase behavior by first purchase month during seasonal campaigns
- Identify feature adoption patterns across user tiers to inform product roadmap prioritization
- Benchmark retention curves before and after major product updates or pricing changes
Benefits
Save 2-3 hours per analysis - Get cohort breakdowns in 60 seconds instead of writing SQL queries and building pivot tables manually
Make faster decisions - See which cohorts are healthy and which need intervention without waiting on analysts
Compare apples to apples - Standardized analysis format makes it easy to track trends month-over-month
Catch problems early - Spot declining retention in recent cohorts before it impacts quarterly revenue
Align teams on metrics - Share consistent cohort definitions across product, growth, and finance teams
Template
Perform cohort analysis for:
Product/Feature: {{product}}
Cohort definition: {{cohortDefinition}}
Time period: {{timePeriod}}
Cohort size: {{cohortSize}}
Metrics to track: {{metrics}}
Include:
- Cohort retention curves
- Activation rates by cohort
- Revenue per cohort
- Churn analysis
- Behavioral patterns
- Cohort comparisons
- Insights and trends
- Recommendations for improvement
Segments to compare: {{segments}}
Properties
- product: Single-line Text (default:
Product or feature name) - cohortDefinition: Single Selection (default:
Sign-up month)- Options: Sign-up month, First purchase month, Feature adoption, Acquisition channel
- timePeriod: Single Selection (default:
Last 12 months)- Options: Last 3 months, Last 6 months, Last 12 months, Last 18 months, Last 24 months
- cohortSize: Single Selection (default:
Monthly cohorts)- Options: Weekly cohorts, Monthly cohorts, Quarterly cohorts
- metrics: Multiple Selection (default:
Retention rate, Revenue)- Options: Retention rate, Revenue, Engagement, Feature usage, Active days, and 2 more
- segments (optional): Multiple Selection
- Options: User tier (Free vs Paid), Geographic region, Device type, Acquisition channel, Company size
Example Output
Here’s what the template generates when analyzing a mobile banking app with monthly cohorts tracked over 12 months:
COHORT RETENTION ANALYSIS: Mobile Banking App
Period: January 2024 - December 2024 | Cohort Definition: Sign-up Month
═══════════════════════════════════════════════════════════
RETENTION CURVES BY COHORT
Month 0 (Sign-up) → Month 1 → Month 3 → Month 6 → Month 12
────────────────────────────────────────────────────────────
Jan 2024: 100% → 68% → 52% → 41% → 34%
Feb 2024: 100% → 71% → 55% → 43% → 36%
Mar 2024: 100% → 69% → 51% → 39% → -
Apr 2024: 100% → 73% → 58% → 45% → -
May 2024: 100% → 75% → 60% → - → -
Trend: Retention improving in recent cohorts (+7% Month 1 retention from Jan to May)
ACTIVATION RATES BY COHORT
First Transaction within 7 days:
Jan 2024: 42% | Feb 2024: 45% | Mar 2024: 48% | Apr 2024: 51% | May 2024: 54%
Feature Adoption (Bill Pay setup):
Jan 2024: 28% | Feb 2024: 31% | Mar 2024: 35% | Apr 2024: 38% | May 2024: 41%
REVENUE PER COHORT (Cumulative 6-month)
Jan 2024: $18,400 (412 users) = $44.66/user
Feb 2024: $21,200 (438 users) = $48.40/user
Mar 2024: $23,800 (451 users) = $52.77/user
Revenue per user trending up 18% from Jan to Mar cohorts
CHURN ANALYSIS
Highest churn periods:
- Days 3-7 post sign-up: 22% drop-off (onboarding friction)
- Days 28-35: 15% drop-off (end of first billing cycle)
Cohort with best retention: April 2024 (onboarding update launched)
Cohort with worst retention: January 2024 (pre-onboarding improvements)
BEHAVIORAL PATTERNS
High-retention users (>6 months active):
- Average 8.2 logins per month
- 73% use Bill Pay feature
- 61% have linked 2+ bank accounts
Low-retention users (churned <3 months):
- Average 2.1 logins per month
- 19% use Bill Pay feature
- 82% linked only 1 account
SEGMENT COMPARISON: Free vs Paid Users
Month 1 Retention:
Free: 64% | Paid: 89% (+39% higher retention)
Month 6 Retention:
Free: 31% | Paid: 78% (+152% higher retention)
Revenue Impact:
Paid users generate 4.2x more revenue per cohort at 6 months
SEGMENT COMPARISON: Acquisition Channel
Month 3 Retention by Channel:
Organic Search: 58% | Paid Social: 51% | App Store Browse: 62% | Referral: 71%
Best performing channel: Referrals (71% retention, $58/user LTV)
Worst performing channel: Paid Social (51% retention, $39/user LTV)
KEY INSIGHTS
1. Onboarding improvements in April drove 7% retention lift - continue iterating
2. Users who link 2+ accounts have 3.2x better retention - prompt this in onboarding
3. Bill Pay feature is strongest retention indicator - increase activation from 41% to 60%
4. Referral channel produces highest quality users - double referral program investment
5. Paid tier retention is 2.5x higher - focus free-to-paid conversion experiments
RECOMMENDATIONS
Immediate (This Quarter):
- Add second account linking prompt on Day 3 to boost multi-account adoption
- Create Bill Pay activation campaign for users who haven't set it up by Day 14
- A/B test referral bonus increase from $10 to $25 to scale best-performing channel
Medium-term (Next Quarter):
- Redesign Day 28-35 experience to reduce billing cycle churn (target 10% reduction)
- Build lookalike audiences based on referral user profiles for paid acquisition
- Implement win-back campaign for churned Jan-Feb cohorts using new onboarding flow
Long-term (6+ Months):
- Develop predictive churn model using login frequency + feature usage patterns
- Create premium tier specifically for high-engagement users (8+ logins/month segment)
- Build cohort monitoring dashboard to catch retention drops within 48 hours
Common Mistakes
Mixing time-based cohorts with behavior-based cohorts - Don’t compare “January sign-ups” with “feature adopters” in the same analysis. Pick one cohort definition and stick with it for clean comparisons.
Ignoring small sample sizes - Weekly cohorts with fewer than 100 users produce noisy data. Monthly or quarterly cohorts give more reliable trends for most products.
Looking at retention without activation - High Day 1 retention means nothing if users never complete setup. Always track both metrics together.
Comparing absolute numbers instead of percentages - A cohort with 1,000 users and 40% retention is healthier than 2,000 users at 25% retention, even though the latter has more active users.
Stopping at retention curves - The curve shows what’s happening but not why. Segment by user behavior, acquisition source, and tier to find actionable patterns.
Cherry-picking time periods - Analyzing only your best-performing months creates false optimism. Include full quarters or years to see real trends.
Forgetting to set retention definitions - “Active user” needs a clear definition (logged in? completed action? made purchase?) or your analysis becomes meaningless.
Frequently Used With
Get deeper insights by combining cohort analysis with these complementary templates:
- Funnel Optimization - Find where cohorts drop off during sign-up and onboarding flows
- Data Analysis Report - Turn cohort findings into executive presentations with visualizations
- Success Metrics - Define which cohort metrics matter most for your product stage
- KPI Dashboard - Build ongoing tracking for cohort health across retention and revenue
- Trend Analysis - Identify patterns in cohort performance over time and forecast future behavior