The skill
Install once in Claude. It plots your retention curve, finds your activation metric, scores early-churn risk, and drafts onboarding nudges from your cohort data.
What's inside
Three files, one job: stop the early leak
Built for growth and product teams, not data scientists.
Install guide
A short setup walkthrough, plus a no-install option if you cannot add skills.
Plan B prompt
A single prompt you paste into any Claude chat to run the same analysis without installing anything.
How it works
Three steps, from cohort data to a fix.
Step 1Bring your cohort data
Signup dates plus per-user event flags or timestamps, with enough history for the curve to settle, and your activation event or persona segments if you want the metric and nudge modes.
Step 2Claude reads the shape
It plots N-day retention, classifies the curve as flatten or decay, benchmarks activation against the SaaS reference of about 37 percent, then finds your activation metric by correlation or scores early-churn risk.
Step 3Get a decision and the fix
A flatten-or-decay verdict, your candidate activation metric with the causal test to confirm it, risk segments, and persona-branched onboarding nudges with a holdout plan.
The skill works from the data you bring, it does not connect to your product analytics. It also treats any activation metric as correlational until you run the experiment it prescribes.
How to reduce early churn
Early churn is the highest-leverage lever most growth teams ignore, because the bucket leaks fastest in the first days, before users ever reach value. Reducing it starts with reading your cohort retention curve correctly, then finding the activation metric that predicts whether a user stays. Here is how to diagnose it and what to fix first.
Why early churn is the number that caps growth
Early churn is enormous and front-loaded. The average app loses about 77 percent of its daily users within three days, 90 percent within 30 days, and over 95 percent within 90 days, and in self-serve SaaS 40 to 60 percent of all churn lands in the first 30 days. Most of it is users leaving before they ever reach value, which means the leak sits at the top of the funnel, not across the customer lifetime. Fixing that early drop-off raises the LTV ceiling that makes paid acquisition viable, so it is higher leverage than tuning bids.
How to read your cohort retention curve
Plot retention by cohort over time and read the shape. A healthy curve drops steeply early then flattens to a plateau, which is the signature of product-market fit and a retained core you can grow from. A curve that decays toward zero means there is no retained core, and no onboarding flow, nudge or churn model will fix it, because that is a value-proposition problem. The skill makes this call for you and overlays the Sean Ellis test, where 40 percent or more of active users saying they would be very disappointed without the product signals fit.
How to find your activation metric
The activation metric is the early action that predicts retention, and the honest way to find it is from your own data, not by copying a famous number. The skill ranks candidate early actions by correlation with downstream retention and names the strongest as your candidate. Then it prescribes the part most teams skip, a causal test that pushes more users to take the action and checks whether retention actually rises against a control. Until that experiment confirms it, the metric stays correlational.
How to score early-churn risk and act on it
Once you know what activation looks like, you can catch users who are about to miss it. The skill fits an early-churn-risk model from first-session or first-week behavior, segments users by risk, and surfaces the features that drive it. The point is to intervene while users are still active, with onboarding nudges branched by persona toward the activation event, and to measure every intervention as incremental lift on retained usage against a holdout, never raw clicks.
What the skill cannot do, and how to stay honest
It is an analysis tool, not a fix for a broken product. Correlation is not causation, so it states every activation metric as correlational until your experiment proves it. It cannot manufacture product-market fit, and if your retention curve never flattens no nudge will save it. It also flags the most expensive misdiagnosis, churn that is really wrong-customer acquisition wearing an onboarding mask, which better onboarding cannot fix. For the full argument and the evidence behind early churn as the growth ceiling, read Your Growth Ceiling Is Early Churn, Not CAC.