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COHORT RETENTION ANALYSIS

Cohort Retention Table

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KEY RETENTION METRICS

Week 1 Retention: 94.9% (avg across 8 cohorts)
Week 4 Retention: 11.6% (avg across 8 cohorts)
Week 8 Retention: 0.0% (avg across 8 cohorts)

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COHORT PERFORMANCE ANALYSIS

Week 1 Retention Statistics:

• Mean: 94.9%
• Range: 85.7% - 100.0%

Best Performing Cohort: 2025-07-28

• Week 1 Retention: 100.0% (+5.1pp vs average)

Lowest Performing Cohort: 2025-08-11

• Week 1 Retention: 85.7% (-9.2pp vs average)

Trend Detected: Retention is improving

• Early cohorts avg: 89.7%
• Recent cohorts avg: 100.0%
• Change: +10.3pp

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INSIGHTS & RECOMMENDATIONS

Findings:

1. GOOD: Week 1 retention at 94.9% shows solid initial engagement.

   • Nearly all users who sign up return within the first week
   • This indicates strong initial value delivery

2. CRITICAL CONCERN: Large drop-off from Week 1 to Week 4 (83.3pp)

   • Only 11.6% of users remain active by Week 4
   • This represents the primary retention challenge for your SaaS product
   • Action Required: Identify Week 2-4 engagement drivers, implement re-engagement campaigns

3. SEVERE: Week 8 retention is 0.0%

   • No cohorts in this dataset show activity beyond 5 weeks
   • This indicates a fundamental long-term retention problem
   • Action Required: Investigate why users churn before establishing habit formation

4. POSITIVE TREND: Recent cohorts showing improvement

   • Week 1 retention has improved +10.3pp from early to recent cohorts
   • Recent cohorts (Sept 2025) achieve 100% Week 1 retention
   • This suggests recent product improvements or better user targeting

Recommended Next Steps:

1. Immediate Actions:

   • Implement automated email campaigns at Days 7, 14, and 21 to prevent Week 2-4 drop-off
   • Survey users who churned between Week 1-4 to understand friction points
   • Add in-app value reinforcement during Week 2-3 (the critical drop-off period)

2. Investigation & Analysis:

   • Segment cohorts by acquisition channel to identify high-quality sources
   • Analyze feature usage patterns among retained vs churned users
   • Track “aha moments” and correlate with retention success
   • Identify if there’s a paywall, feature limit, or onboarding gap causing Week 4 cliff

3. Product Improvements:

   • Strengthen Week 2-4 engagement hooks (notifications, reminders, social proof)
   • Consider implementing a milestone-based progression system
   • Test retention interventions on new cohorts and measure impact

4. Long-term Strategy:

   • Run user interviews with any users who reach Week 4+ to understand success patterns
   • Build retention prediction model to identify at-risk users early
   • Establish Week 4 retention as a North Star metric for product team

Output Files:

• Retention table saved to: /tmp/cohort_retention_table.csv