Churn Prevention

Reduce voluntary churn (customers choosing to cancel) and involuntary churn (failed payments) via cancel flows, dynamic save offers, proactive retention, and dunning.

Before Starting

Product context: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it first and tailor output to it; only ask for what it doesn't cover.

Gather this context (ask if not provided):

1. Current churn situation — Monthly churn rate (voluntary vs. involuntary if known)? Active subscribers? Average MRR per customer? Existing cancel flow, or instant cancel?
2. Billing & platform — Provider (Stripe, Chargebee, Paddle, Recurly, Braintree)? Monthly, annual, or both intervals? Pausing or downgrades supported? Retention tooling (Churnkey, ProsperStack, Raaft)?
3. Product & usage data — Feature usage tracked per user? Engagement drop-offs identifiable? Cancellation reason data from past churns? 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)? Offboarding brand tone (empathetic, direct, playful)?

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How This Skill Works

Two churn types, two 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 is typically 50-70% of total churn; involuntary is 30-50% but often easier to fix.

Three modes:

1. Build a cancel flow — Design from scratch with survey, save offers, and confirmation
2. Optimize an existing flow — Analyze cancel data and improve save rates
3. Set up dunning — Failed payment recovery with retries and email sequences

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Cancel Flow Design

The Cancel Flow Structure

Trigger → Survey → Dynamic Offer → Confirmation → Post-Cancel

1. Trigger — Customer clicks "Cancel subscription" in account settings.
2. Exit Survey — Ask why; the reason determines which save offer to show.
3. Dynamic Save Offer — Targeted offer based on reason (discount, pause, downgrade, etc.)
4. Confirmation — If they still cancel, confirm clearly with end-of-billing-period messaging.
5. Post-Cancel — Set expectations, offer easy reactivation, trigger win-back sequence.

Exit Survey Design

The 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)
• Most common reasons first (review data quarterly)
• No guilt trips — "Help us improve" framing beats "Why are you leaving?"

Dynamic Save Offers

Match the offer to the reason. A discount won't save someone who isn't using the product; a roadmap won't save someone who can't afford it.

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%+ (trains customers to cancel for deals)
• Time-limit it ("This offer expires when you leave this page")
• Show dollar amount saved, not just the percentage

Pause subscription

• 1-3 month maximum (longer pauses rarely reactivate)
• 60-80% of pausers eventually return to active
• Auto-reactivation with advance notice email; keep data and settings intact

Plan downgrade

• Lower tier instead of full cancellation; show what they keep vs. lose
• Position as "right-size your plan," with an easy path back up

Feature unlock / extension

• Unlock an untried premium feature or extend a higher-tier trial; 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, or a personal founder email at 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 "continue cancelling" 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.

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Churn Prediction & Proactive Retention

The best save happens before the customer ever clicks "Cancel."

Risk Signals

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

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

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 — paywall-upgrade-cro 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

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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

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:

• 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
• Plain text outperforms 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.

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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

Running experiments: Use the ab-test-setup skill for statistically rigorous tests. PostHog fits cancel flow experiments — feature flags split users into flows server-side, and funnel analytics track each step (survey → offer → accept/decline → confirm). See the PostHog integration guide for setup.

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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; some churned users will want to come back

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Tool Integrations

For implementation, see the tools registry.

Retention Platforms

Tool	Best For	Key Feature
Churnkey	Full cancel flow + dunning	AI-powered adaptive offers, 34% avg save rate
ProsperStack	Cancel flows with analytics	Advanced rules engine, Stripe/Chargebee integration
Raaft	Simple cancel flow builder	Easy setup, good for early-stage
Chargebee Retention	Chargebee customers	Native integration, was Brightback

Billing Providers (Dunning)

Provider	Smart Retries	Dunning Emails	Card Updater
Stripe	Built-in (Smart Retries)	Built-in	Automatic
Chargebee	Built-in	Built-in	Via gateway
Paddle	Built-in	Built-in	Managed
Recurly	Built-in	Built-in	Built-in
Braintree	Manual config	Manual	Via gateway

Related CLI Tools

Tool	Use For
stripe	Subscription management, dunning config, payment retries
customer-io	Dunning email sequences, retention campaigns
posthog	Cancel flow A/B tests via feature flags, funnel analytics
mixpanel / ga4	Usage tracking, churn signal analysis
segment	Event routing for health scoring

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Related Skills

• email-sequence: For win-back email sequences after cancellation
• paywall-upgrade-cro: For in-app upgrade moments and trial expiration
• pricing-strategy: For plan structure and annual discount strategy
• onboarding-cro: For activation to prevent early churn
• analytics-tracking: For setting up churn signal events
• ab-test-setup: For testing cancel flow variations with statistical rigor