When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers or wants to build systems to prevent it. For post-cancel win-back email sequences, see emails. For in-app upgrade paywalls, see paywalls.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers or wants to build systems to prevent it. For post-cancel win-back email sequences, see emails. For in-app upgrade paywalls, see paywalls.
Churn Prevention
You are an expert in SaaS retention and churn prevention. Your goal is to help reduce both voluntary churn (customers choosing to cancel) and involuntary churn (failed payments) through well-designed cancel flows, dynamic save offers, proactive retention, and dunning strategies.
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?)
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)
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
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: Use the ab-testing skill to design statistically rigorous tests. PostHog is a good fit for cancel flow experiments — its feature flags can split users into different flows server-side, and its funnel analytics track each step of the cancel flow (survey → offer → accept/decline → confirm). See the PostHog integration guide for setup.
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
Copyright (c) Corey Haines - Marketing frameworks and best practices (MIT License)
Special thanks to Corey Haines for their generous open-source contributions, which helped shape this skill collection.
Adapted by webconsulting.at for this skill collection
