activation

Also called: activation rate, activation criterion, activation metric, aha moment, user activation

The moment a new user first gets the product's core value, defined by a specific action within a specific time, such as 'added an expense with at least one other member within 3 days'.

Activation is the point where a new user has actually experienced what the product is for, not just signed up. It's written as a precise criterion: an action (or a few), how many times, and within what time after sign-up. For a bill-splitting app that could be "added an expense shared with at least one other member within 3 days"; for a note-taking app, "created three notes on two different days in the first week".

The activation rate is the share of a sign-up cohort that meets the criterion:

$\text{activation rate} = \frac{\text{new users who met the criterion within the window}}{\text{all new users in the cohort}}$

A good criterion has three properties. It predicts retention: users who meet it come back far more often than users who don't. It is early, so the team can influence it during onboarding. And it is reachable: a meaningful share of new users can hit it, otherwise it describes power users, not a first success.

To find one, list candidate actions, and for each compare later retention (for example day-30) between users who did and didn't do it in the window. Prefer the candidate with the biggest gap that still covers a decent share of users, then check the time window by plotting how the gap changes with 1, 3 and 7 days. Remember this is a correlation: the criterion marks users who were already likely to stay. Only an experiment that pushes more people to the action shows whether it causes retention. Once chosen, the activation rate becomes the target for onboarding work and a leading indicator that moves weeks before retention does.

Example

Halves has 1,200 users who signed up in one week; 296 of them are active on day 30 (296 / 1,200 = 24.7%). Two candidate criteria:

Candidate	Did it	Day-30 retained	Didn't	Day-30 retained
A. Created a group on day 0	780	250 / 780 = 32.1%	420	46 / 420 = 11.0%
B. Shared expense with another member within 3 days	420	218 / 420 = 51.9%	780	78 / 780 = 10.0%

Both rows add up: 250 + 46 = 218 + 78 = 296 retained users. Candidate A separates users by 32.1 − 11.0 = 21.1 pp; candidate B by 51.9 − 10.0 = 41.9 pp, and still covers 35% of new users. Halves picks B, so the activation rate for this cohort is 420 / 1,200 = 35.0%.

Theo's reading, "force everyone to add an expense and retention doubles", doesn't follow. The criterion describes who stays; whether nudging people toward it lifts retention needs an A/B test.

Common mistakes

Calling sign-up "activation". Creating an account delivers no value; the criterion must describe the first real success.
No time window. "Added an expense ever" can't be measured for recent users and can't guide onboarding.
Picking the action with the highest retention alone. An action only 3% of users reach separates well but describes enthusiasts; check coverage too.
Reading the gap as cause. Users who share an expense early were already more motivated (selection bias). Test the nudge before promising a retention lift.
Letting automated actions count. Server-created events, such as recurring bills, can meet the criterion without the user doing anything.

Learn it in the course

Activation: finding the aha moment · Find your product's aha moment: the week-one action that separates users who stay from users who leave, and know what that comparison can't prove.
Case: fix the onboarding funnel · Find where new Halves users drop off and what to fix first.