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?)

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
Individual recommendation rating is 0–6 (detractor)	Medium	1-3 months before cancel

Health Score Model

Build a simple health score (0-100) from weighted signals. Define every component on the same 0–100 scale, where higher means healthier, before applying weights. Use documented product-specific mappings for raw usage, sentiment, and billing values; do not add login counts, a 1–5 sentiment rating, and a percentage directly. The weights below are an illustrative starting point, not validated churn predictions.

Treat missing or stale signals as unavailable, rather than automatically healthy or zero. Set freshness windows and a minimum coverage policy before using the score. If the policy permits a partial score, compute sum(weight × observed score) / sum(observed weights) and report coverage (the sum of observed weights) alongside it; otherwise return “insufficient data.” All unavailable means no score. For example, only login (80) and feature usage (60) produce (0.30×80 + 0.25×60)/0.55 = 70.91, with 55% coverage. Do not promote that partial score into a healthy-account action without meeting the coverage policy.

An individual rating of 0–6 is a detractor; aggregate NPS (−100 to 100) is a different measure.

Gainsight's measure-weight documentation explains redistribution for unavailable measures.

Illustrative complete-data model:

Health Score = (
  Login frequency score × 0.30 +
  Feature usage score   × 0.25 +
  Support sentiment     × 0.15 +
  Billing health        × 0.15 +
  Engagement score      × 0.15
)

Validate score mappings and intervention thresholds against later observed renewal/churn outcomes before operational use; the following tiers are illustrative.

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: 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

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

Related Skills

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