App User Retention Calculator

Turn your Day 1, 7 and 30 retention into a projected curve out to a year - and see if your own numbers even add up.

Everyone tracks Day 1, Day 7 and Day 30 retention. Almost nobody uses those three numbers to answer the question that actually matters: how many of today's users will still be around in 90 days, or a year from now?

This calculator fits a decay curve through your Day 1 and Day 30 numbers and projects it forward - and checks the fit against the Day 7 you actually measured, so you can see whether your retention is decaying smoothly or something happened mid-month.

Everything runs in your browser. Nothing is uploaded, nothing is stored.

Runs entirely in your browser. Nothing is uploaded, nothing is stored, and there is no email box on this page. Close the tab and it is gone.
01 Cohort
02 Day 1 retention
%
03 Day 7 retention
%

Not used to build the curve - kept aside to check the curve fits.

04 Day 30 retention
%

What this deliberately does not model

  • A permanent floor above zero - most real apps flatten out once the casual users who were never going to stay are gone. Treat Day 90 and beyond as a lower bound, not a prediction.
  • Anything that changes retention mid-flight: a product update, a marketing push, seasonal swings, or a shift in the kind of user you are acquiring.
  • Reactivation - users who come back after lapsing are not in this model at all.
  • Category norms - retention that is weak for a game can be strong for a utility app, and this tool has no idea which one you are building.

Watch: how to use this tool

How to use it

  1. Enter the size of the cohort you're measuring - how many users started on Day 0.
  2. Enter your Day 1 retention percentage - the share still active a day later.
  3. Enter your Day 7 and Day 30 retention percentages.
  4. Read the projected curve out to Day 90, 180 and 365, and the fit check against your real Day 7 number.

Questions

How does this project retention past Day 30?
It fits a power-law decay curve - retention(t) = Day1 x t^-b - through your Day 1 and Day 30 numbers, then reads Day 90, 180 and 365 off that same curve. Power-law decay is the standard shape for app retention: steep losses in the first days, a long flattening tail, which is close to what nearly every published retention curve looks like.
What is the 'fit check' number?
It plugs Day 7 into the curve fitted from your Day 1 and Day 30 numbers and compares that prediction to the Day 7 you actually entered. A close match means your retention is decaying smoothly. A big gap usually means something happened around Day 7 specifically - a push campaign, an update, a shift in who you were acquiring - that a two-point curve cannot see.
Why does the model use Day 1 and Day 30, not Day 7 too?
Two points are enough to solve a power curve exactly, and leaving Day 7 out of the fit is what lets it act as an independent check instead of being baked in. If Day 7 agrees with the curve, trust the Day 90-plus projections more. If it does not, treat them as rough.
Will my retention really keep decaying forever?
No - almost no real app does. Power-law decay pushes toward zero, but most apps flatten at a small loyal-user floor above zero once the casual users who were never going to stay have already left. Treat every projection past Day 30 as a lower bound, not a prediction, and Day 180 and 365 as rougher than Day 90.
What counts as good Day 1, Day 7 and Day 30 retention?
As a rough rule of thumb for consumer apps: Day 1 above 25 to 30 percent, Day 7 above 10 to 12 percent, and Day 30 above 4 to 6 percent are generally considered solid. Games and social apps typically run higher, utility apps often run lower - retention benchmarks vary a lot by category, so treat these as a general compass, not a grade.
Is anything sent to a server?
No. It is JavaScript running in your browser. Nothing is uploaded, nothing is stored, and closing the tab erases it.

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