| name | suede-churn-prevention |
| description | Suede-owned retention discipline for voluntary and involuntary churn: cancel flows, pause paths, evidence-based save offers, failed-payment recovery, proactive signals, and win-back design. Use when diagnosing subscriber loss or designing a bounded retention intervention. NOT FOR: lifecycle-email production (use suede-emails), pricing architecture (use suede-pricing), paywall design (use suede-paywalls), or event instrumentation (use suede-analytics). |
| metadata | {"version":"2.0.0"} |
Suede Churn Prevention
Use this Suede retention playbook to reduce voluntary and involuntary churn through transparent cancel flows, bounded save offers, proactive signals, and payment recovery.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. Current Churn Situation
- What's your monthly churn rate? (Voluntary vs. involuntary if known)
- How many active subscribers?
- What's the average MRR per customer?
- Do you have a cancel flow today, or does cancel happen instantly?
2. Billing & Platform
- What billing provider? (Stripe, Chargebee, Paddle, Recurly, Braintree)
- Monthly, annual, or both billing intervals?
- Do you support plan pausing or downgrades?
- Any existing retention tooling? (Churnkey, ProsperStack, Raaft)
3. Product & Usage Data
- Do you track feature usage per user?
- Can you identify engagement drop-offs?
- Do you have cancellation reason data from past churns?
- What's your activation metric? (What do retained users do that churned users don't?)
4. Constraints
- B2B or B2C? (Affects flow design)
- Self-serve cancellation required? (Some regulations mandate easy cancel)
- Brand tone for offboarding? (Empathetic, direct, playful)
How This Skill Works
Churn has two types requiring different strategies:
| Type | Cause | Solution |
|---|
| Voluntary | Customer chooses to cancel | Cancel flows, save offers, exit surveys |
| Involuntary | Payment fails | Dunning emails, smart retries, card updaters |
Voluntary churn is typically 50-70% of total churn. Involuntary churn is 30-50% but is often easier to fix.
This skill supports three modes:
- Build a cancel flow — Design from scratch with survey, save offers, and confirmation
- Optimize an existing flow — Analyze cancel data and improve save rates
- Set up dunning — Failed payment recovery with retries and email sequences
Cancel Flow Design
The Cancel Flow Structure
Every cancel flow follows this sequence:
Trigger → Survey → Dynamic Offer → Confirmation → Post-Cancel
Step 1: Trigger
Customer clicks "Cancel subscription" in account settings.
Step 2: Exit Survey
Ask why they're cancelling. This determines which save offer to show.
Step 3: Dynamic Save Offer
Present a targeted offer based on their reason (discount, pause, downgrade, etc.)
Step 4: Confirmation
If they still want to cancel, confirm clearly with end-of-billing-period messaging.
Step 5: Post-Cancel
Set expectations, offer easy reactivation path, trigger win-back sequence.
Exit Survey Design
The exit survey is the foundation. Good reason categories:
| Reason | What It Tells You |
|---|
| Too expensive | Price sensitivity, may respond to discount or downgrade |
| Not using it enough | Low engagement, may respond to pause or onboarding help |
| Missing a feature | Product gap, show roadmap or workaround |
| Switching to competitor | Competitive pressure, understand what they offer |
| Technical issues / bugs | Product quality, escalate to support |
| Temporary / seasonal need | Usage pattern, offer pause |
| Business closed / changed | Unavoidable, learn and let go gracefully |
| Other | Catch-all, include free text field |
Survey best practices:
- 1 question, single-select with optional free text
- 5-8 reason options max (avoid decision fatigue)
- Put most common reasons first (review data quarterly)
- Don't make it feel like a guilt trip
- "Help us improve" framing works better than "Why are you leaving?"
Dynamic Save Offers
The key insight: match the offer to the reason. A discount won't save someone who isn't using the product. A feature roadmap won't save someone who can't afford it.
Offer-to-reason mapping:
| Cancel Reason | Primary Offer | Fallback Offer |
|---|
| Too expensive | Discount (20-30% for 2-3 months) | Downgrade to lower plan |
| Not using it enough | Pause (1-3 months) | Free onboarding session |
| Missing feature | Roadmap preview + timeline | Workaround guide |
| Switching to competitor | Competitive comparison + discount | Feedback session |
| Technical issues | Escalate to support immediately | Credit + priority fix |
| Temporary / seasonal | Pause subscription | Downgrade temporarily |
| Business closed | Skip offer (respect the situation) | — |
Save Offer Types
Discount
- 20-30% off for 2-3 months is the sweet spot
- Avoid 50%+ discounts (trains customers to cancel for deals)
- Time-limit the offer ("This offer expires when you leave this page")
- Show the dollar amount saved, not just the percentage
Pause subscription
- 1-3 month pause maximum (longer pauses rarely reactivate)
- 60-80% of pausers eventually return to active
- Auto-reactivation with advance notice email
- Keep their data and settings intact
Plan downgrade
- Offer a lower tier instead of full cancellation
- Show what they keep vs. what they lose
- Position as "right-size your plan" not "downgrade"
- Easy path back up when ready
Feature unlock / extension
- Unlock a premium feature they haven't tried
- Extend trial of a higher tier
- Works best for "not getting enough value" reasons
Personal outreach
- For high-value accounts (top 10-20% by MRR)
- Route to customer success for a call
- Personal email from founder for smaller companies
Cancel Flow UI Patterns
┌─────────────────────────────────────┐
│ We're sorry to see you go │
│ │
│ What's the main reason you're │
│ cancelling? │
│ │
│ ○ Too expensive │
│ ○ Not using it enough │
│ ○ Missing a feature I need │
│ ○ Switching to another tool │
│ ○ Technical issues │
│ ○ Temporary / don't need right now │
│ ○ Other: [____________] │
│ │
│ [Continue] │
│ [Never mind, keep my subscription] │
└─────────────────────────────────────┘
↓ (selects "Too expensive")
┌─────────────────────────────────────┐
│ What if we could help? │
│ │
│ We'd love to keep you. Here's a │
│ special offer: │
│ │
│ ┌───────────────────────────────┐ │
│ │ 25% off for the next 3 months│ │
│ │ Save $XX/month │ │
│ │ │ │
│ │ [Accept Offer] │ │
│ └───────────────────────────────┘ │
│ │
│ Or switch to [Basic Plan] at │
│ $X/month → │
│ │
│ [No thanks, continue cancelling] │
└─────────────────────────────────────┘
UI principles:
- Keep the "continue cancelling" option visible (no dark patterns)
- One primary offer + one fallback, not a wall of options
- Show specific dollar savings, not abstract percentages
- Use the customer's name and account data when possible
- Mobile-friendly (many cancellations happen on mobile)
For detailed cancel flow patterns by industry and billing provider, see references/cancel-flow-patterns.md.
Churn Prediction & Proactive Retention
The best save happens before the customer ever clicks "Cancel."
Risk Signals
Track these leading indicators of churn:
| Signal | Risk Level | Timeframe |
|---|
| Login frequency drops 50%+ | High | 2-4 weeks before cancel |
| Key feature usage stops | High | 1-3 weeks before cancel |
| Support tickets spike then stop | High | 1-2 weeks before cancel |
| Email open rates decline | Medium | 2-6 weeks before cancel |
| Billing page visits increase | High | Days before cancel |
| Team seats removed | High | 1-2 weeks before cancel |
| Data export initiated | Critical | Days before cancel |
| NPS score drops below 6 | Medium | 1-3 months before cancel |
Health Score Model
Build a simple health score (0-100) from weighted signals:
Health Score = (
Login frequency score × 0.30 +
Feature usage score × 0.25 +
Support sentiment × 0.15 +
Billing health × 0.15 +
Engagement score × 0.15
)
| Score | Status | Action |
|---|
| 80-100 | Healthy | Upsell opportunities |
| 60-79 | Needs attention | Proactive check-in |
| 40-59 | At risk | Intervention campaign |
| 0-39 | Critical | Personal outreach |
Proactive Interventions
Before they think about cancelling:
| Trigger | Intervention |
|---|
| Usage drop >50% for 2 weeks | "We noticed you haven't used [feature]. Need help?" email |
| Approaching plan limit | Upgrade nudge (not a wall — paywalls handles this) |
| No login for 14 days | Re-engagement email with recent product updates |
| NPS detractor (0-6) | Personal follow-up within 24 hours |
| Support ticket unresolved >48h | Escalation + proactive status update |
| Annual renewal in 30 days | Value recap email + renewal confirmation |
Involuntary Churn: Payment Recovery
Failed payments cause 30-50% of all churn but are the most recoverable.
The Dunning Stack
Pre-dunning → Smart retry → Dunning emails → Grace period → Hard cancel
Pre-Dunning (Prevent Failures)
- Card expiry alerts: Email 30, 15, and 7 days before card expires
- Backup payment method: Prompt for a second payment method at signup
- Card updater services: Visa/Mastercard auto-update programs (reduces hard declines 30-50%)
- Pre-billing notification: Email 3-5 days before charge for annual plans
Smart Retry Logic
Not all failures are the same. Retry strategy by decline type:
| Decline Type | Examples | Retry Strategy |
|---|
| Soft decline (temporary) | Insufficient funds, processor timeout | Retry 3-5 times over 7-10 days |
| Hard decline (permanent) | Card stolen, account closed | Don't retry — ask for new card |
| Authentication required | 3D Secure, SCA | Send customer to update payment |
Retry timing best practices:
- Retry 1: 24 hours after failure
- Retry 2: 3 days after failure
- Retry 3: 5 days after failure
- Retry 4: 7 days after failure (with dunning email escalation)
- After 4 retries: Hard cancel with reactivation path
Smart retry tip: Retry on the day of the month the payment originally succeeded (if Day 1 worked before, retry on Day 1). Stripe Smart Retries handles this automatically.
Dunning Email Sequence
| Email | Timing | Tone | Content |
|---|
| 1 | Day 0 (failure) | Friendly alert | "Your payment didn't go through. Update your card." |
| 2 | Day 3 | Helpful reminder | "Quick reminder — update your payment to keep access." |
| 3 | Day 7 | Urgency | "Your account will be paused in 3 days. Update now." |
| 4 | Day 10 | Final warning | "Last chance to keep your account active." |
Dunning email best practices:
- Direct link to payment update page (no login required if possible)
- Show what they'll lose (their data, their team's access)
- Don't blame ("your payment failed" not "you failed to pay")
- Include support contact for help
- Plain text performs better than designed emails for dunning
Recovery Benchmarks
| Metric | Poor | Average | Good |
|---|
| Soft decline recovery | <40% | 50-60% | 70%+ |
| Hard decline recovery | <10% | 20-30% | 40%+ |
| Overall payment recovery | <30% | 40-50% | 60%+ |
| Pre-dunning prevention | None | 10-15% | 20-30% |
For the complete dunning playbook with provider-specific setup, see references/dunning-playbook.md.
Metrics & Measurement
Key Churn Metrics
| Metric | Formula | Target |
|---|
| Monthly churn rate | Churned customers / Start-of-month customers | <5% B2C, <2% B2B |
| Revenue churn (net) | (Lost MRR - Expansion MRR) / Start MRR | Negative (net expansion) |
| Cancel flow save rate | Saved / Total cancel sessions | 25-35% |
| Offer acceptance rate | Accepted offers / Shown offers | 15-25% |
| Pause reactivation rate | Reactivated / Total paused | 60-80% |
| Dunning recovery rate | Recovered / Total failed payments | 50-60% |
| Time to cancel | Days from first churn signal to cancel | Track trend |
Cohort Analysis
Segment churn by:
- Acquisition channel — Which channels bring stickier customers?
- Plan type — Which plans churn most?
- Tenure — When do most cancellations happen? (30, 60, 90 days?)
- Cancel reason — Which reasons are growing?
- Save offer type — Which offers work best for which segments?
Cancel Flow A/B Tests
Test one variable at a time:
| Test | Hypothesis | Metric |
|---|
| Discount % (20% vs 30%) | Higher discount saves more | Save rate, LTV impact |
| Pause duration (1 vs 3 months) | Longer pause increases return rate | Reactivation rate |
| Survey placement (before vs after offer) | Survey-first personalizes offers | Save rate |
| Offer presentation (modal vs full page) | Full page gets more attention | Save rate |
| Copy tone (empathetic vs direct) | Empathetic reduces friction | Save rate |
How to run cancel flow experiments: Route the hypothesis, sample, duration,
and stopping rule to suede-ab-testing. Use an authorized experimentation
system that can assign users consistently and read each funnel step (survey →
offer → accept/decline → confirm). Verify the system's current official
implementation guidance, assignment behavior, and event readback before launch.
Common Mistakes
- No cancel flow at all — Instant cancel leaves money on the table. Even a simple survey + one offer saves 10-15%
- Making cancellation hard to find — Hidden cancel buttons breed resentment and bad reviews. Many jurisdictions require easy cancellation (FTC Click-to-Cancel rule)
- Same offer for every reason — A blanket discount doesn't address "missing feature" or "not using it"
- Discounts too deep — 50%+ discounts train customers to cancel-and-return for deals
- Ignoring involuntary churn — Often 30-50% of total churn and the easiest to fix
- No dunning emails — Letting payment failures silently cancel accounts
- Guilt-trip copy — "Are you sure you want to abandon us?" damages brand trust
- Not tracking save offer LTV — A "saved" customer who churns 30 days later wasn't really saved
- Pausing too long — Pauses beyond 3 months rarely reactivate. Set limits.
- No post-cancel path — Make reactivation easy and trigger win-back emails, because some churned users will want to come back
Tool Integrations
This pack does not ship retention, billing, or analytics connectors. Select an
authorized system from the user's actual stack and verify its current official
documentation, account plan, test mode, and rollback path before configuration.
Capability Checklist
- Cancel-flow routing, survey capture, and an unobstructed final cancel action
- Stable experiment assignment and step-level event readback
- Pause, downgrade, and save-offer rules with explicit eligibility
- Payment-retry state, card-updater state, and customer-notification controls
- Audit history, test mode, role controls, and rollback or disable behavior
Implementation Routing
- Route message writing to
suede-emails.
- Route event schemas and readback to
suede-analytics.
- Route test design to
suede-ab-testing.
- Treat billing retries, discounts, subscription changes, and activation as
separately authorized mutations.
Boundaries
- Do not obstruct cancellation, hide downgrade paths, manufacture urgency, or use deceptive retention friction.
- Do not change subscriptions, retry rules, discounts, billing objects, or customer messaging without explicit authorization.
- Do not claim saved revenue or churn reduction without a defined cohort, window, denominator, and verified payment state.
- Do not decide which individual customer should receive a sensitive offer from protected traits or unsupported inference.
Routing
- Need retention, dunning, or win-back messages -> use
suede-emails.
- Need plan structure or offer economics -> use
suede-pricing or suede-offers.
- Need paywall or trial-expiry UX -> use
suede-paywalls.
- Need churn events or a controlled retention test -> use
suede-analytics or suede-ab-testing.
- From those skills, route cancellation and payment-recovery strategy back to
suede-churn-prevention.
