← All tools
Analytics utility · Audience forecasting

DAU forecaster.

Project how daily active users build from your install plan and retention curve — and how much a retention lift compounds over a year.

Enter average daily installs, how installs grow month to month, and your D1 and D30 retention. The second D30 value lets you compare an improvement scenario.

Your numbers

Use a negative number for a declining plan.
DAU at horizon——
DAU at day 90—Base curve
Active days / user—Two-year cap
Scenario uplift——

—

Projected DAU
Base retentionImproved D30
How it's calculated

The method, in plain terms.

  1. Retention curve R(t) = D1 × t^(−b), with b = ln(D1 ÷ D30) ÷ ln 30
  2. DAU(day) = Σ over past cohorts (installs × R(age)), install day counted as active
  3. Expected active days per user = 1 + Σ R(t) (capped at two years)
Worked example

At 5,000 installs a day with D1 40% and D30 8%, DAU keeps climbing for months as each cohort’s long tail stacks. The steady-state level is roughly daily installs × expected active days per user.

Defaults on this page are illustrative starting points, not benchmarks for your game. Replace them with your own numbers.

The thinking behind it
Terms on this page

DAU · Retention rate · Cohort · DAU/MAU (stickiness)

Embed this calculator

Free to embed on your blog, wiki or course page with the credit link intact.

FAQ

Common questions.

How do you forecast DAU for a mobile game?

DAU on any day is the sum of every past install cohort multiplied by the share of that cohort still active at its age. Fit a retention curve to your D1 and D30, apply it to your daily install plan, and add the cohorts up day by day.

Why does DAU keep growing even with flat installs?

Each day adds a new cohort while older cohorts decay slowly along the long tail of the retention curve. DAU rises until the users lost from old cohorts balance the users added by new ones — which can take many months for games with flat long-term retention.

What retention curve does this use?

A power law, R(t) = D1 × t^(−b), fitted exactly through your D1 and D30 values. Power laws describe mobile retention decay well in the first months; beyond your observed data they are an assumption, so treat long-horizon numbers as scenarios.

How much does a retention improvement change DAU?

More than the headline suggests, because every future cohort benefits and the gains stack across cohorts. Use the “improved D30” input to compare two curves directly.

Can I use this for non-game apps?

Yes. The mechanics are the same for any product measured by daily actives; just use your own D1 and D30 definitions consistently.

Calculator by The Game Scientist ↗