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mariokarras

Churn Prevention

by Mario Karras · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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/install abm-churn-prevention
Description
When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when...
README (SKILL.md)

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-context.md exists (or .claude/product-marketing-context.md 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:

  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

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 — 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

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 \x3C40% 50-60% 70%+
Hard decline recovery \x3C10% 20-30% 40%+
Overall payment recovery \x3C30% 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 \x3C5% B2C, \x3C2% 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-test-setup 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

  • 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
Usage Guidance
This skill appears coherent and low-risk: it is a text-only playbook for churn prevention and does not request credentials or install anything. The one practical caution is the optional local file it instructs the agent to read (.agents/product-marketing-context.md or .claude/product-marketing-context.md). Before installing or enabling the skill, inspect that file (if present) and remove any sensitive data (customer PII, payment info, API keys, or secrets). If you want stricter safety, run the skill in a workspace/environment that limits agent file access or provide the needed product-marketing context only via a sanitized input. Finally, note the skill references other skills (email-sequence, paywall-upgrade-cro); if you enable integrations with those, review them separately.
Capability Analysis
Type: OpenClaw Skill Name: abm-churn-prevention Version: 1.0.0 The churn-prevention skill bundle is a comprehensive set of strategic instructions and documentation designed to help an AI agent assist users with SaaS retention strategies. The content in SKILL.md, cancel-flow-patterns.md, and dunning-playbook.md focuses entirely on business logic, such as designing cancellation flows, mapping save offers to customer feedback, and managing failed payment recovery (dunning). There is no evidence of malicious code, data exfiltration, or harmful prompt injection; the instructions are well-aligned with the stated purpose of reducing subscription churn.
Capability Assessment
Purpose & Capability
The name/description match the SKILL.md content: guidance for cancel flows, save offers, dunning and health-score recommendations. No unrelated dependencies, binaries, or credentials are requested.
Instruction Scope
Instructions are prescriptive and focused on churn prevention. They explicitly ask the agent to read .agents/product-marketing-context.md (or .claude/product-marketing-context.md) if present — this is relevant context for the task but means the agent will access a local file. The rest of the guidance stays within churn-related scope and defers cross-functional tasks (email sequences, paywalls) to other named skills.
Install Mechanism
No install spec or code files to execute; this is instruction-only, so nothing will be downloaded or written by an installer.
Credentials
The skill declares no environment variables, no primary credential, and no config paths beyond the optional product-marketing-context file. Requested access is proportional to the described task.
Persistence & Privilege
always is false, autonomous invocation is allowed (the platform default), and the skill does not request permanent presence or modify other skills. There are no indications it would escalate privileges or persist beyond normal agent behavior.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install abm-churn-prevention
  3. After installation, invoke the skill by name or use /abm-churn-prevention
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial public release of the churn-prevention skill. - Provides structured guidance for reducing churn in SaaS, covering both voluntary and involuntary churn prevention. - Includes detailed cancel flow design, dynamic save offer recommendations, and best practices for exit surveys. - Outlines key user/business context questions to gather before starting churn reduction work. - Supports workflows for building new cancel experiences, optimizing existing flows, and setting up payment recovery (dunning). - Incorporates practical UI/UX patterns, offer-to-reason mapping, and actionable strategy explanations.
Metadata
Slug abm-churn-prevention
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Churn Prevention?

When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when... It is an AI Agent Skill for Claude Code / OpenClaw, with 143 downloads so far.

How do I install Churn Prevention?

Run "/install abm-churn-prevention" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Churn Prevention free?

Yes, Churn Prevention is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Churn Prevention support?

Churn Prevention is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Churn Prevention?

It is built and maintained by Mario Karras (@mariokarras); the current version is v1.0.0.

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