Test retention changes against evidence that can survive a release
Measure one retention hypothesis at a time instead of turning incomplete usage data into an automatic churn prediction.
Skene maps tracking calls and database-backed steps, names gaps, and reviews tracking changes. Your team decides what to build or send from that evidence.
A churn signal is not a churn explanation
Lower usage may indicate risk, a changed workflow, seasonality or broken collection. The evidence needs inspection before intervention.
A habit event stops firing and makes healthy accounts look inactive.
A cohort window changes between checks and invalidates the comparison.
A team treats correlation as a guaranteed retention outcome.
Teams act on incomplete history
When evidence is missing, a change in the report can look like a change in customer behavior.
- •Teams cannot tell whether a metric moved or its collection changed.
- •The people reviewing retention and churn data spend time reconstructing what happened.
- •A later fix restores collection, not the missing historical period.
A dashboard cannot review the pull request that changed its input
Analytics and customer systems remain useful destinations for reports and workflows.
They do not replace a review of the tracking code before merge.
A schema shows what can be observed in the database; it does not prove that a code event fired.
Each evidence source must keep its own meaning.
Grade a retention change without inventing a prediction
Define the behavior, cohort, window and expected direction before launch.
Confirm the required event or database evidence is available.
Review tracking changes to the behavior signal before merge.
Run the check after launch and keep human judgment on cause and response.
Skene protects measurement; your team owns the response
Skene provides reviewable evidence and verdicts. It does not replace your analytics, messaging or customer-success systems.
Skene keeps it trustworthy
- •A lifecycle map grounded in repository and optional schema evidence.
- •An Events inventory that names detected evidence and missing tracking.
- •Pull-request review plus a separate deterministic plan-versus-scan check.
- •Measurement plans that compare a stated target with connected evidence.
You and your agent own
- •Defining the business outcome and the metric that represents it.
- •Choosing what product, messaging or customer action follows a finding.
- •Maintaining the analytics and delivery systems that consume the evidence.
Signals in
- →A repeat behavior or retained-state metric chosen by the team.
- →The code event or database state used to observe it.
- →A fixed comparison window and cohort definition.
- →Connected counts for the current and previous periods.
Outputs
- ←A readiness result for the chosen retention measure.
- ←A specific finding when the behavior signal changes in code.
- ←Current, previous, delta and target after the check.
- ←A verdict, not a churn probability or automated save play.
A good fit for
- ✓Teams shipping product changes faster than they can audit tracking by hand.
- ✓Product, growth and customer teams that need to defend retention decisions.
- ✓Developers who want tracking findings in pull requests or coding-agent workflows.
Not a good fit for
- ×Teams looking for a replacement for their analytics dashboard, CRM or campaign tool.
- ×Teams expecting Skene to invent missing history after an event failed to fire.
- ×Teams that want automated customer interventions without human ownership.
Product
Current use cases
Related guide
Review the evidence behind retention & churn
Run the free audit on a repository. Add read-only schema evidence when it helps, then enable pull-request reviews for ongoing tracking changes.