Churn Prevention
Design cancel flows, save offers, dunning, and customer health scoring to cut voluntary and involuntary SaaS churn.
Overview
Churn Prevention is an agent skill for the Grow phase that designs voluntary and involuntary churn reduction through cancel flows, save offers, dunning, and health scoring for SaaS products.
Install
npx skills add https://github.com/coreyhaines31/marketingskills --skill churn-preventionWhat is this skill?
- Frames strategy for both voluntary churn (cancel intent) and involuntary churn (failed payments)
- Cancel flow: trigger → exit survey → dynamic save offer → confirmation → post-cancel nurture
- Exit survey organized across seven cancellation-reason categories with mapped save offers
- Dunning stack for payment recovery: pre-dunning, smart retry, email sequence, grace period
- Customer health score model recommendations for proactive outreach
- Cancel flow framework with five stages: trigger → exit survey → dynamic save offer → confirmation → post-cancel nurture
- Exit survey uses seven cancellation-reason categories with mapped save offers
Adoption & trust: 59.2k installs on skills.sh; 32.4k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your SaaS loses paying customers every month and you lack a cancel flow, payment recovery playbook, or health signals to intervene before they leave.
Who is it for?
Subscription SaaS founders with recurring revenue, measurable churn, and capacity to implement cancel flows and billing retries.
Skip if: Pre-revenue products with no billing, one-time purchase shops without subscriptions, or teams unwilling to change cancel and payment UX.
When should I use this skill?
User asks to reduce churn, design cancel flows, recover failed payments, or build retention and health scoring for a SaaS product.
What do I get? / Deliverables
You leave with a prioritized churn prevention plan covering cancel UX, mapped save offers, dunning for failed payments, and a health score model to operationalize retention.
- Voluntary and involuntary churn strategy
- Cancel flow and dunning implementation plan with priorities
Recommended Skills
Journey fit
Churn prevention is a Grow-phase retention job: it targets paying customers leaving or failing to pay, not pre-launch validation. Lifecycle fits because the skill centers on cancellation moments, payment recovery, and ongoing account health—not top-of-funnel SEO or raw analytics instrumentation.
How it compares
Use instead of generic “send a discount email” advice—this skill specifies staged cancel flows, reason-mapped saves, and a dunning stack for involuntary churn.
Common Questions / FAQ
Who is churn-prevention for?
Solo and indie SaaS builders who own pricing, billing, and lifecycle and need a concrete retention strategy when monthly churn hurts growth.
When should I use churn-prevention?
In Grow when monthly churn is high, cancel reasons are unknown, or a large share of churn is failed payments—after you have paying customers and billing in place.
Is churn-prevention safe to install?
It is advisory content for marketing and product decisions; review the Security Audits panel on this page and treat billing or cancel-flow changes as production-impacting when you implement recommendations.
SKILL.md
READMESKILL.md - Churn Prevention
{ "skill_name": "churn-prevention", "evals": [ { "id": 1, "prompt": "Our SaaS product has a 7% monthly churn rate and we need to bring it down. We're a $49/month project management tool with about 2,000 paying customers. Can you help us design a churn prevention strategy?", "expected_output": "Should check for product-marketing.md first. Should address both voluntary and involuntary churn. Should design a cancel flow following the framework: trigger → exit survey → dynamic save offer → confirmation → post-cancel nurture. Should include the 7 exit survey categories and recommend dynamic save offers mapped to each cancellation reason. Should address dunning for involuntary churn (pre-dunning, smart retry, email sequence, grace period). Should recommend a health score model. Should provide prioritized implementation plan.", "assertions": [ "Checks for product-marketing.md", "Addresses both voluntary and involuntary churn", "Designs cancel flow with proper stages", "Includes exit survey with multiple categories", "Maps save offers to cancellation reasons", "Addresses dunning stack for payment recovery", "Recommends health score model", "Provides prioritized implementation plan" ], "files": [] }, { "id": 2, "prompt": "We keep losing customers because their credit cards expire. About 15% of our churn is from failed payments. How do we fix this?", "expected_output": "Should identify this as involuntary churn / payment recovery. Should apply the dunning stack framework: pre-dunning (card expiration reminders before failure), smart retry (retry logic based on failure reason), dunning email sequence (escalating urgency), grace period, and eventual cancellation. Should provide specific timing for each stage. Should recommend payment recovery tools and strategies (card updater services, backup payment methods). Should include recovery rate benchmarks.", "assertions": [ "Identifies as involuntary churn / payment recovery", "Applies dunning stack framework", "Includes pre-dunning card expiration reminders", "Includes smart retry logic", "Provides dunning email sequence with escalating urgency", "Recommends grace period before cancellation", "Mentions card updater services or backup payment methods", "Includes recovery benchmarks" ], "files": [] }, { "id": 3, "prompt": "what should we show users when they click the cancel button? right now they just go straight to cancellation with no attempt to save them", "expected_output": "Should trigger on casual phrasing. Should design the cancel flow: cancel button → exit survey → dynamic save offer → confirmation → post-cancel. Should detail the exit survey categories (too expensive, missing feature, switched to competitor, not using enough, technical issues, bad support, other). Should provide dynamic save offers matched to each reason (e.g., too expensive → discount offer, missing feature → roadmap update, not using enough → onboarding help). Should include copy recommendations for each screen. Should warn against dark patterns (making it impossible to cancel).", "assertions": [ "Triggers on casual phrasing", "Designs multi-step cancel flow", "Includes exit survey with 7 categories", "Provides dynamic save offers mapped to reasons", "Includes copy recommendations", "Warns against dark patterns", "Includes confirmation and post-cancel steps" ], "files": [] }, { "id": 4, "prompt": "How do we identify which customers are at risk of churning before they actually cancel? We want to be proactive.", "expected_output": "Should apply the health score model framework. Should define health score components: product usage signals (login frequency, feature adoption, key actio