# Cohort Retention and LTV Curve Calculator

URL: https://www.paydude.io/resources/tools/cohort-retention-calculator
Type: Free interactive calculator
Summary: Enter retention at months 1, 3, 6 and 12 to get an LTV built on the actual curve, and see how far the 1/churn formula is off.

The standard LTV formula divides revenue by churn, which assumes churn is constant. It is not — cohorts churn hardest in the first months and then flatten. This uses your real curve instead.

## Summary

Real retention curves are steep early and flat later. The 1/churn formula extrapolates your worst month forever. First-year value is the number to plan cash against. Needs a cohort with at least twelve months of history.

## Why the curve is not a straight line

Customers who leave in month one were never a good fit. Customers still there at month twelve have integrated the product into how they work. Those two groups do not churn at the same rate, and averaging them into a single figure loses the distinction entirely.

**A typical SaaS retention curve**

| Month | Still active | Implied monthly churn |
| --- | --- | --- |
| 1 | 85% | 15.0% |
| 3 | 72% | 6.3% |
| 6 | 64% | 3.2% |
| 12 | 58% | 1.6% |
| 24 | 50% | 1.2% |

Implied churn falls from 15% to under 2%. Applying the month-one rate to the whole lifetime would value this cohort at a fraction of its real worth — and applying the month-24 rate would wildly overvalue it.

## Where the curve flattens

Most SaaS cohorts stabilise somewhere between months 6 and 12. That flattening point is the most useful thing on the chart: it tells you when a customer has genuinely adopted the product, which is the moment onboarding should be aiming at.

> **What to do with this:** If your curve is steep to month three and then flat, the problem is onboarding, not the product. If it declines steadily forever, the product is not becoming more valuable with use — a much harder problem, and a different roadmap.

## Reading it against the simple formula

Compare the result here against the [LTV calculator](https://www.paydude.io/resources/tools/ltv-calculator). If the cohort figure is much higher, your early churn is masking genuinely durable long-term customers. If it is much lower, the flat tail you assumed does not exist.

**Retention starts with the payment going through** Failed cards churn customers who never chose to leave. — [See pricing](https://www.paydude.io/pricing)

## Frequently asked questions

### Where do I get retention curve data?

From a cohort that signed up at least twelve months ago. Take everyone who joined in a single month and count how many were still paying at months 1, 3, 6 and 12. Most billing platforms and analytics tools produce this directly.

### Why is cohort LTV different from the 1/churn formula?

Because 1/churn assumes a constant rate. Real cohorts churn hardest early and then flatten, so a single early rate applied forever understates the customers who survive — often substantially.

### What if my curve never flattens?

That is a meaningful signal. A curve declining steadily with no plateau suggests the product does not become more valuable with use, so customers eventually leave regardless of tenure. It is a product problem, not an onboarding one.

### How far should I extrapolate?

Five years is a reasonable ceiling and is what this uses. Beyond that the tail contributes little in discounted terms and the uncertainty is large. For planning cash, the first-year figure is far more useful than the lifetime total.