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Updated April 20, 2026 · SaaS & Subscription · Educational use only ·

Customer Cohort Analysis Calculator

Customer cohort retention value.

Calculate cohort retention metrics and 12-month cohort revenue from monthly retention rates and the cohort's starting MRR.

What this tool does

Cohort retention curves show how a starting group of customers thins over months. This calculator takes a cohort's opening size and its retention rates at months 1, 3, 6, and 12, then estimates the average number of retained customers across that period and projects total revenue over 12 months based on monthly recurring revenue per customer. The output illustrates how much revenue a cohort is likely to generate across its first year, helping you model the financial trajectory of customer groups acquired at different times. The calculation assumes linear interpolation between data points and does not account for expansion revenue, churn acceleration, or changes in pricing. Results are for educational modelling and illustrate trends rather than precise forecasts.


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Formula Used
Avg retained
MRR/customer

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Disclaimer

Results are estimates for educational purposes only. They do not constitute financial advice. Consult a qualified professional before making financial decisions.

Cohort analysis tracks how a specific group of customers (usually signup month) behaves over time. Month 1 retention, month 3, month 6, month 12 show the retention curve. Strong products show mild decay (month 12 at 60-80% of month 1); weak products show steep decay (month 12 at 10-20%). Revenue cohorts add MRR to this to reveal cohort lifetime value.

1,000 customer cohort: 95% M1 (950), 80% M3 (800), 65% M6 (650), 50% M12 (500). 80 avg MRR per retained × 12 months × avg retention ≈ 672,000 cohort year-1 revenue. The decay pattern reveals product stickiness: if M6 is 65% but M12 is 50%, year 2 may land around 35% - project forward to see full cohort value.

Cohort analysis matters because aggregate metrics lie. A business reporting 'overall retention 70%' might have month-1 cohorts retaining 90% but stale cohorts retaining 40%. Breaking apart by cohort reveals whether product is improving (newer cohorts retaining better) or declining (newer cohorts retaining worse) - the leading indicator aggregate metrics completely miss.

A worked example

Try the defaults: cohort size of 1,000, month 1 retained of 95%, month 3 retained of 80%, month 6 retained of 65%. The tool returns 696,000.00. You can adjust any input and the result updates as you type — no submit button, no reload. That's the real power here: seeing how sensitive the output is to one or two assumptions.

What moves the number most

The result responds to Cohort Size, Month 1 Retained %, Month 3 Retained %, Month 6 Retained %, and Month 12 Retained %. Not every input has equal weight. Adjusting one input at a time toward extreme values shows which ones move the result most.

The formula behind this

Retention at each milestone = cohort size × retention %. Avg retained = (M1 + M3 + M6 + M12) ÷ 4. 12-month revenue = avg retained × MRR × 12. Everything the calculator does is shown in the formula box below, so you can check the math against your own spreadsheet if you want.

Using this as a check-in

Re-run this every three months. A single reading tells you where you stand; four readings tell you whether things are improving. The trend matters more than any individual snapshot.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.

Example Scenario

1,000 cohort × 95/80/65/50% × ££80 = 696,000.00.

Inputs

Cohort Size:1,000
Month 1 Retained %:95
Month 3 Retained %:80
Month 6 Retained %:65
Month 12 Retained %:50
Avg MRR per Customer:£80
Expected Result696,000.00

This example uses typical values for illustration. Adjust the inputs above to match a specific situation and see how the result changes.

Sources & Methodology

Methodology

Retention at each milestone = cohort size × retention %. Avg retained = (M1 + M3 + M6 + M12) ÷ 4. 12-month revenue = avg retained × MRR × 12.

Frequently Asked Questions

What retention is healthy?
SaaS mid-market: M1 95%, M12 70-80%. SMB SaaS: M1 90%, M12 50-70%. Consumer subscriptions: M1 85%, M12 30-50%. Dating/gaming apps: M1 60-70%, M12 10-20%. Compare within category; cross-category comparison misleads.
Why M1 vs M0?
M0 is the signup month - everyone is 100% retained by definition. M1 is the first interesting number (did they come back?). M1 drops reveal onboarding problems; M3-M12 drops reveal product/value problems.
Can I compare cohorts?
Yes, this is the main power of cohort analysis. Q1 2023 cohort at M6 = 50%. Q1 2024 cohort at M6 = 65%. Product is improving retention meaningfully. Aggregate retention wouldn't reveal this because it mixes all ages of cohorts together.
Does this project full LTV?
No, this just calculates year-1 cohort revenue. Full LTV projects further - typical pattern: year 2 retention 10-20 points below year 1, year 3 another 5-10 points. Apply these decay rates to project LTV from year-1 cohort data.

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