What counts as a good day-1 retention figure?

There is no figure that holds independently of context. What determines yours is the product's natural usage rhythm, the event chosen as active, the mix and quality of acquisition channels, the platform and the market. Rather than searching for an external target, keep the definition fixed and compare this month's cohort with earlier ones.

Why is our retention so far from a similar product's published number?

Check the other side's definition before drawing conclusions: the curve type, the event treated as active, and whether the denominator was all signups or only users past activation. In most cases much of the gap comes from those three choices rather than from the products themselves.

Retention Benchmark

Intermediate

Updated Jul 11, 2026 Shahin editorial 3 min read

PronunciationEN/rɪˈtɛnʃən ˈbɛntʃmɑːrk/

A retention benchmark is a reference figure that teams look at to judge their own numbers, usually taken from an industry report or an analytical article.

Every published retention figure carries its own definition with it: whether the measurement was N-day or rolling, which event counted as active, which channels the users arrived from, and which market and platform were involved. Changing any of these moves the number more than a genuine product difference would, which is why an imported figure rarely transfers. The most useful reference is a team's own earlier cohorts measured with an unchanged definition. The dangerous consequence of adopting an external target is that the definition gets optimised instead of the product: loosening active from a completed purchase to an app open lifts the curve without any change in behaviour.

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