Retention loops built on behavior data that does not silently break
Design retention as loops built on product behavior so churn stops feeling random, on data you can actually trust.
Skene keeps the behavior events behind your loops complete and unbroken on every PR, then exposes them over MCP. You and your coding agent watch for early risk and run the plays on data you can trust.
You cannot manage churn one ticket or save call at a time
As long as retention lives in ad-hoc reactions, small issues compound silently until another cohort drops. You need repeatable loops instead of last-minute save attempts.
Churn curves look noisy and unpredictable, even when top-of-funnel metrics are healthy.
Teams can point to dashboards but struggle to explain why specific accounts left or stayed.
Retention work often happens as last-minute save attempts rather than as designed loops.
Churn quietly eats growth when retention is not designed
Without explicit loops and signals, it is easy to blame "bad fit" customers while the product and motion stay unchanged.
- •Net revenue retention stalls or declines, making growth expensive to sustain.
- •Roadmaps chase new features while existing customers quietly slip away.
- •Self-serve segments underperform because no one owns their ongoing health.
Dashboards and campaigns alone do not create retention loops
Dashboards summarize churn, but the events behind them quietly break the moment an agent refactors a feature.
Health scores are often coarse blends of logins, NPS, and ticket volume with little connection to real value.
Generic campaign tools cannot see product usage deeply enough to distinguish healthy from fragile accounts.
Hand-built tracking rots as the product changes, so the risk signals your loops depend on go stale without warning.
Retention as loops wired directly into your product
Treat retention as the emergent property of a few core product loops, not as a single metric.
Skene adds the behavior events behind those loops and indexes them against your Supabase schema.
On every PR, Skene catches a write that breaks before the data is gone, so your risk signals stay honest.
Your coding agent queries that trustworthy data over MCP, watches for early risk, and runs the interventions.
Trustworthy data from Skene, the retention plays from you
Skene keeps the data layer honest: complete, schema-validated behavior events behind your loops. You and your agent design the loops, run the plays, and make the calls only humans should make.
Skene keeps it trustworthy
- •The behavior events behind your loops, added and indexed against your Supabase schema.
- •A schema check on every PR that catches a broken or renamed event before the data is gone.
- •A complete, trustworthy record of who is fading and who is sticking, queryable over MCP.
- •Churn-signal and cohort data you can trust, without a bespoke analytics project.
You and your agent own
- •Designing the retention loops and the interventions themselves (your agent builds them on Skene's data).
- •Running the in-product nudges, lifecycle plays, and re-engagement campaigns.
- •The human relationships on complex, high-touch enterprise accounts.
Signals in
- →Loop-specific product usage (for example, workflows run, dashboards reviewed, collaboration events).
- →Account composition and plan details to understand economic impact.
- →Lifecycle and billing milestones such as trial end, contract dates, and upgrade history.
- →Historical retention and expansion patterns across segments and cohorts.
Outputs
- ←Schema-validated behavior events your agent turns into churn-risk signals.
- ←Trustworthy churn-risk signals at user, account, and segment levels, queryable over MCP.
- ←Retention loop participation and cohort data you can trust, showing which behaviors are fading.
- ←A PR comment the moment a write that feeds those signals breaks.
A good fit for
- ✓PLG products with clear recurring workflows and repeatable usage patterns.
- ✓Teams that see decent activation but weak D30, D90, or expansion metrics.
- ✓Leaders who want retention levers they can design and iterate on, not just observe.
Not a good fit for
- ×Products without any repeatable usage motion (pure one-off transactions).
- ×Organizations unwilling to adjust the product surface to support healthy loops.
- ×Teams looking only for a churn report, not trustworthy data their agent runs retention plays on.
