Retention and churn you can design, not just explain after the fact
Model retention as loops built on product behavior so churn stops feeling random and NRR compounds.
Skene turns your code and usage into retention loops, then lets AI agents watch for early risk signals and run targeted plays before customers decide to leave.
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
Vanity dashboards summarize churn but do not propose or run interventions.
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.
One-off "winback" campaigns spike activity briefly but fail to build durable habits or product loops.
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.
Use code and usage to identify the behaviors that correlate most strongly with long-term customers.
Have AI agents watch for early deviations from those healthy patterns and trigger targeted interventions.
Continuously connect loops, behavior, and outcomes so the system learns which plays improve cohorts.
Automated
- •Detection of early churn risk from drops in specific product behaviors, not just reduced logins.
- •Triggering of in-product guidance and lifecycle nudges tailored to the loop that is failing.
- •Protection of high-value accounts with proactive prompts before renewal dates.
- •Cohort analysis that connects interventions with changes in retention and expansion.
Intentionally not automated
- •Guaranteeing retention in the absence of product-market fit.
- •Replacing human relationships on complex, high-touch enterprise accounts.
- •Running broad, brand-level marketing campaigns unrelated to usage patterns.
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
- ←Churn risk flags at user, account, and segment levels.
- ←Retention loop participation metrics that show which behaviors are fading.
- ←Recommendations and triggers for targeted re-engagement and education.
- ←Cohort views that reveal how loops and plays impact NRR over time.
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 a system that proposes and runs plays.