Cohort Retention Table & Decay Builder
Model subscriber cohort decay over 12 months. Visualize retained accounts and calculate subscriber half-life with this free interactive SaaS tool.
Calculated Results
Subscriber Half-Life
Month 9
Month 6 Retained
606 (60.6%)
Month 12 Retained
368 (36.8%)
Remaining Customers(t) = Initial Cohort × (Monthly Retention Rate)^tProjected survival across 12 billing cycles under 92% monthly retention.
About this tool
A cohort retention table tracks how a specific group of subscribers acquired in the same period survives over subsequent months. Rather than looking at an aggregate company-wide churn rate that blends seasoned veterans with vulnerable newcomers, cohort analysis isolates decay curves by signup vintage. This visibility enables founders to diagnose whether recent onboarding revamps or pricing changes actually improved retention in newer cohorts compared to legacy groups. It also reveals your subscriber half-life: the exact month when 50% of an acquired cohort has churned out. Knowing when customer attrition stabilizes allows growth teams to model cash flow realistically and stop overspending on acquisitions that drop off before reaching payback.
How It Works
- 1Enter the initial cohort size (e.g. 500 customers acquired in Month 0).
- 2Enter your expected average monthly retention percentage (e.g. 92%).
- 3Select your projection timeline (up to 12 months).
- 4Inspect the generated monthly decay schedule and cohort half-life milestone.
Formula & Math
Transparent LogicRemaining Customers(t) = Initial Cohort × (Monthly Retention Rate)^t
Worked Practical Example
You acquire 1,000 customers in January with a steady 90% monthly retention rate (10% monthly churn).
Calculation / Steps
Month 1: 1,000 × 0.90 = 900; Month 6: 1,000 × (0.90)^6 = 531.
Expected Result
531 customers remain at Month 6; cohort half-life occurs at Month 7.
Frequently Asked Questions
What is subscriber half-life?+
Subscriber half-life is the number of months required for an initial cohort to decline to exactly half its original size under a given retention rate.
Why do retention curves usually flatten out over time?+
Early churn is typically driven by poor fit and onboarding friction. The customers who remain after 6 to 9 months are highly committed power users with much lower baseline churn.
How can I improve my cohort retention curve?+
Focus on first-week activation, automated setup assistance, and offering seamless pause alternatives before subscribers make final cancel decisions.
Automate retention on your cancel button
BoolBul flattens cohort decay curves by preventing impulsive cancellations during critical early and renewal billing cycles.
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