COHORT RETENTION ANALYSIS SUMMARY

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Generated: 2025-12-12 01:40:36
Total Cohorts Analyzed: 6
Date Range: June 2025 to November 2025

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KEY METRICS

Month 1 Retention: 69.4% (avg across 6 cohorts)
Month 3 Retention: 20.0% (avg across 1 cohort)

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RETENTION TABLE

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COHORT PERFORMANCE ANALYSIS

Month 1 Retention Statistics:

• Mean: 69.4%
• Range: 33.3% - 100.0%

Best Performing Cohort: 2025-08

• Month 1 Retention: 100.0% (+30.6pp vs average)

Lowest Performing Cohort: 2025-11

• Month 1 Retention: 33.3% (-36.1pp vs average)

PRE vs POST PRODUCT UPDATE (August 2025):

• Pre-August cohorts (Jun-Jul): 45.8% avg Month 1 retention (2 cohorts)
• Post-August cohorts (Aug+): 81.2% avg Month 1 retention (4 cohorts)
• Change: +35.4pp

STRONG IMPROVEMENT - Product update appears to have significantly improved retention!

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INSIGHTS & RECOMMENDATIONS

Key Findings:

1. Product update in August shows dramatic impact: Month 1 retention jumped from 45.8% (pre-August) to 81.2% (post-August) - a +35.4 percentage point improvement.

2. August & September cohorts performing exceptionally well: Both achieved 100% Month 1 retention, suggesting the product changes are highly effective at keeping users engaged in their first month.

3. November cohort shows concerning decline: At 33.3% Month 1 retention, this represents the worst performance. Investigate if any changes were made in late October/November that may have degraded the user experience.

4. Long-term retention needs more data: Only June cohort has sufficient data for Month 3-4 tracking. While Month 3 shows 20% retention, more time is needed to evaluate long-term impact of the August update.

RECOMMENDED NEXT STEPS:

1. Deep-dive on August cohort: Document exactly what changed in the product update that drove 100% Month 1 retention
2. Compare power user behaviors: Analyze feature usage patterns between high-performing (Aug/Sep) vs low-performing (Jul/Nov) cohorts
3. Survey churned users: Contact users from June/July who churned to understand pre-update pain points
4. Track feature adoption: Identify which specific features August+ cohorts use differently than earlier cohorts
5. Investigate November decline: Urgently review any product/UX changes made in late October that may explain the sharp November drop
6. A/B test further improvements: With such strong post-update performance, test incremental onboarding enhancements to push Month 1 retention even higher
7. Monitor Month 2-3 retention for newer cohorts: Track August/September cohorts through Month 3-6 to validate long-term retention improvements

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OUTPUT FILES

CSV Table: /tmp/cohort_analysis/output/cohort_retention_table.csv
Full Summary: /tmp/cohort_analysis/output/analysis_summary.txt

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