Objective
A retention system the business can run: a churn diagnosis that names which churn type is actually costing money, an engagement health score with defined signals and thresholds, intervention playbooks per risk tier, a win-back sequence, involuntary-churn prevention, and a 90-day implementation roadmap with the metrics to watch.
Diagnose before prescribing. A loyalty program aimed at voluntary churn does nothing when the real leak is failed payments, and a dunning sequence does nothing when customers leave in week three because onboarding never delivered value.
Done when
Work type strategy-plan — floor E3/C3/O1 (harness/eco-floors.yaml). The system is a plan; nothing leaves the workspace until a human sends it.
- E3 — a named human approved the retention playbook, the engagement scoring spec, and the email sequences.
- C3 —
banned_word_checkclean on all customer-facing copy, every email sequence at 10+/16 on Four U's, and a non-author read the system end to end. Max 2 auto-retry cycles on gate failures for email content. - O1 — the plan names its metric with baseline, threshold, window, and owner: monthly churn rate, cohort retention curve, health score distribution, NPS trend, and expansion versus contraction revenue.
Sending any sequence is separate work under email-lifecycle (E5/C3/O3), where unsubscribe and sender-identity compliance is a C4 field-standard item, not a lint.
Constraints
- Read
MARKETING.mdfrom the project root first. If it does not exist, build it from the codebase — CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, email/ad/analytics config — using the template from/kai-email-system, and confirm the draft. Do not ask the user what the product is. - Seven things must be known before diagnosis: business model (SaaS, ecommerce, services, marketplace); current churn rate; customer count and average revenue per customer; retention efforts already running; known churn reasons from exit surveys, support tickets, or the cancellation flow; whether product usage and feature adoption are tracked; and customer segments (free vs. paid, plan tiers, cohorts).
- No banned Tier 1 words in any customer-facing copy.
- Win-back emails comply with CAN-SPAM — see
harness/references/cold-email-rules.md. - Loyalty rewards must not erode margins below profitability. Redemption options drive retention, not margin destruction.
- Discounts in rescue plays cap at 20% unless the user approves higher, and all email sequences target 10+/16 on Four U's before they count as deliverable.
- Match the prescription to the maturity level. Predictive churn scoring proposed to a Level 0 business is a plan that never gets built.
| Level | Current state |
|---|---|
| 0 | No retention effort beyond the product itself |
| 1 | Basic cancellation flow plus occasional check-in emails |
| 2 | Lifecycle emails, usage tracking, support triggers |
| 3 | Predictive churn scoring, proactive intervention, loyalty program |
Context
| Need | Load |
|---|---|
| Retention mechanics, churn tactics, loyalty design | knowledge/playbooks/customer-retention.md |
| Retention as a growth loop | knowledge/playbooks/growth-loops-applied.md |
| Lifecycle email structure and triggers | knowledge/channels/email-lifecycle.md |
| Which persona is churning | knowledge/personas/_persona-index.md |
| CAN-SPAM compliance for win-back sends | harness/references/cold-email-rules.md |
| Lifecycle email format contract and gate minimums | harness/skill-contracts/email-lifecycle.yaml |
| Product, ICP, voice, current channels | MARKETING.md (project root) |
Churn types — separate them before proposing anything. Voluntary: the customer actively cancels (dissatisfaction, budget, switched). Involuntary: payment failure, expired card, billing issue. Passive: stops using without cancelling (ghost users).
Churn timeline — most exits cluster: first 30 days (onboarding failure), 60–90 days (value not realized), at renewal (annual decision point), or after a price increase or feature change. Leading indicators: login frequency decline, feature usage drop, support ticket spike, NPS/CSAT decline, billing page visits.
Engagement score (0–100) — the rubric:
| Signal | Weight | Scoring |
|---|---|---|
| Login frequency (last 14 days) | 25% | Daily=100, Weekly=60, Monthly=20, None=0 |
| Core feature usage | 25% | Used all=100, Used some=50, Used none=0 |
| Support interactions | 15% | Positive=80, Neutral=50, Negative=20 |
| Account expansion signals | 15% | Upgraded=100, Stable=50, Downgraded=10 |
| Referral/advocacy | 10% | Referred=100, NPS promoter=60, Passive=30 |
| Billing health | 10% | Current=100, Late=30, Failed=0 |
Risk tiers and the play for each:
| Tier | Score | Play |
|---|---|---|
| Red | 0–39 | Immediate rescue: personal outreach within 24 hours, a concession (discount, extended trial, premium support), escalation to customer success, win-back sequence |
| Yellow | 40–69 | Proactive nurture: usage tips for unused features, office hours or webinar invite, relevant case study, a short feedback survey (not NPS) |
| Green | 70–100 | Expansion and advocacy: referral or testimonial request, early access, advisory board or beta invite, relevant cross-sell or upsell |
Win-back for already-churned customers: a 3-email sequence at Day 1, Day 7, Day 30, each addressing a different churn reason, each carrying a specific offer or product update.
Involuntary churn prevention: a dunning sequence of 3–5 emails over 14 days, smart retry logic for failed payments, and a card-update reminder before expiration.
Loyalty program, where the model supports one: reward mechanics (points, tiers, milestones, or referral credits), earning actions mapped to business goals, redemption options that drive retention, and a launch communication plan.
Gates, on every email file:
bashpython scripts/quality_gates/banned_word_check.py <file> python scripts/quality_gates/four_us_score.py <file>
Escalate when
- Churn rate, cohort data, or usage data is unavailable and the diagnosis would be guesswork.
- The stated churn reason from the user conflicts with what exit surveys or support tickets show.
- A rescue play needs a discount above 20%.
- The proposed loyalty mechanics would push unit economics negative.
- Churn is driven by the product rather than by marketing — say so; a retention campaign cannot fix broken onboarding.
- Win-back targets contacts whose consent basis or suppression status is unclear.
