Growth Loops · Session 05 · Dates TBD
Spot churn earlier and earn a useful return
Ship AIPhoenix, ArizonaPress → to begin
A cancellation, a failed payment, and fading use are three different problems.
Treating them as one win-back campaign hides the cause and spends customer attention where the product has nothing useful to say.
Retention showed where repeat value decays. Tonight turns that pattern into a precise risk state and an intervention that helps only when there is unfinished value to resume.
Act one
Churn is a boundary around an entity, an eligible population, an event or absence, and a time interval. The rate is meaningless without all four.
| Loss event | Denominator | |
|---|---|---|
| Logo churn | A customer account ends | Accounts active at period start |
| User churn | An eligible user becomes inactive | Eligible active users |
| Activity churn | The value event stops recurring | Entities expected to repeat it |
| Revenue churn | Recurring revenue contracts | Recurring revenue at period start |
| Involuntary | Payment or billing failure | Renewals attempted |
Expansion and contraction can make revenue move differently from logos. Keep the measures separate before combining them into a net view.
A behavior pattern can support several causes. Pair events with support, billing, interview, and cancellation evidence before choosing an intervention.
Exercise: separate the causes
classify last 20 losses by evidence
8 value-decay candidates5 never activated4 payment failures3 unknown — research, not campaign
Sample recent churned customers and attach the strongest available behavioral and qualitative evidence. Keep unknown as a valid category rather than forcing every loss into a convenient story.
The counts are your own; the terminal output is illustrative. Small samples guide investigation, not population claims.
Act two
A useful signal changes before churn, represents loss of value, and maps to something the product or team can responsibly do.
Worth investigating
Often noisy alone
Compare the same lifecycle stage, segment, and opportunity to act. A signal without eligibility turns normal behavior into false risk.
Without exit rules, the same customer remains at risk forever and receives interventions long after they became irrelevant.
| Useful response | Do not substitute | |
|---|---|---|
| Never activated | Repair the first-value path | A discount |
| Value decay | Resume the unfinished job | A generic win-back blast |
| Poor fit | Qualify, export, or offboard well | More reminders |
| Service failure | Acknowledge, repair, and restore trust | Feature education |
| Payment failure | Dunning and payment recovery | Product-engagement messaging |
Ownership matters. Billing, support, product, and lifecycle teams should not all contact the same person for the same risk moment.
Act three
Re-engagement succeeds when the customer resumes useful behavior and sustains it — not when a message earns a click.
Clicks and opens diagnose delivery. The primary product outcome is a return to value that lasts beyond the message.
The right intervention for some customers can be the wrong intervention for the same metric state in another context.
| Treatment | Holdout | |
|---|---|---|
| Eligibility | Same risk and suppression rules | Same risk and suppression rules |
| Experience | Receives the intervention | Does not receive it |
| Primary | Durable return to value | Natural durable return |
| Incremental lift | Treatment minus holdout | The counterfactual baseline |
| Guardrails | Complaints, opt-outs, errors | Same observation window |
Customers can return without outreach. A holdout estimates how much return the intervention added beyond what would have happened anyway.
The artifact
build risk cohort → trigger → holdout → verify exits
entry + healthy/suppress exits validated10% eligible holdout assigned before sendreturn + repeat-value events firingwrote 05-reengagement/incremental-readout.md
Commit the churn definition, cause evidence, risk cohort, intervention, suppression rules, holdout assignment, and incremental-lift scorecard.
Session six uses the strongest proven behavior from the whole lifecycle to build a loop whose output can feed another measurable turn.
A return is earned when value resumes.
Next
The final session maps one product mechanism from input to output and re-entry, finds its constraint, and ships a bounded experiment with an explicit decision rule.
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