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What "stickiness" actually measures

· 4 min read
Arisht Jain
Founder, BreakGround

"Stickiness" is the most-used word in product retention talks and the least-defined. Pinned to a specific number, it's DAU/MAU — daily active users divided by monthly active users. As a piece of language, it's stretched to cover everything from "users come back" to "users like the product" to "the product has a moat."

The metric and the word have come apart. The metric is fine; the way it's interpreted has gotten loose.

The DAU/MAU number​

DAU/MAU expresses, on a 0–1 scale, what fraction of a product's monthly active users were also active today. A score of 1.0 means every monthly user was active today (impossible at scale). A score of 0.05 means roughly one in twenty monthly users showed up today.

Useful benchmarks for the metric, roughly:

  • 0.5+ — daily-use products (consumer messaging, social, daily-use B2B tools).
  • 0.2–0.4 — weekly-use products (project management, design tools, B2B apps).
  • 0.05–0.15 — occasional-use products (tax software, travel booking, expense reports).
  • Below 0.05 — products where the metric is structurally low and not the right thing to measure.

The most common mistake is benchmarking your DAU/MAU against a product in a different usage cadence, then concluding your product is "less sticky" — when really it's just used differently.

What it doesn't measure​

DAU/MAU does not measure:

  • Whether your product is good. A product can have a high DAU/MAU because it has push notifications hounding users back, a low one because users open it once a week and accomplish a lot. The first is not better.
  • Whether users are getting value. A user logging in once a week to do their actual job is more valuable than a user logging in seven times a week to dismiss a streak counter.
  • Cohort behavior. DAU/MAU is a single number rolled across all users; the actual interesting structure is per-cohort retention curves, which tell you whether your retention is improving over time.
  • Quality of session. A 30-second session and a 30-minute session both count as one DAU. The first might be a user dropping in to do one thing efficiently; the second might be a user lost in your interface.

If you're optimizing for DAU/MAU directly, you're going to make a worse product. The metric is a thermometer, not a steering wheel.

What it does measure, when read carefully​

Read directionally, DAU/MAU is informative about:

  • Cadence shift. If your DAU/MAU is moving up or down without any obvious change in user count, the cadence of use is shifting — users are coming back more or less often. That's worth investigating.
  • New-feature stickiness. A new feature that increases DAU/MAU after launch is one that users return for; one that doesn't is a feature users used once and never again. The number can tell you which kind of feature you shipped, after the fact.
  • Cohort-level differences. The most useful version of the metric is "DAU/MAU for users on plan X, in their second month, in segment Y." That's a number you can act on.

The retention metric I'd rather you used​

If you're going to track one number for retention, use Wn retention — the percentage of users active in week 1 who are also active in week N. Plot it as a curve, by cohort, with each cohort getting its own line.

This number tells you what DAU/MAU can't:

  • Whether retention is improving over time (cohorts above earlier cohorts on the curve).
  • Whether the retention shape is the right one (flattening curves are healthy; falling curves are sick).
  • Where in the lifecycle users are quitting (the elbows in the curve).

DAU/MAU is the executive-summary metric. Wn retention is the operational one. Most teams report the executive-summary metric internally, which is why most teams have no idea where their actual retention work should go.