What It Does
The skill walks you through exit survey design, offer-to-reason mapping, dunning email sequences, health score modeling, and A/B testing frameworks — giving you a complete retention system rather than a single tactic.
It covers both voluntary churn (customers choosing to leave) and involuntary churn (failed payments), which together account for 100% of subscriber loss.
Churn Prevention is an expert-guided skill for SaaS teams looking to reduce subscriber loss through structured cancel flows, dynamic save offers, and failed payment recovery.
Key Features
- Cancel Flow Design with Dynamic Save Offers — Guides you through a five-step cancel flow: trigger → exit survey → dynamic save offer → confirmation → post-cancel path. Crucially, save offers are matched to cancellation reasons — a pause for seasonal users, a discount for price-sensitive ones, a roadmap preview for those missing a feature — rather than applying a blanket discount to everyone.
- Exit Survey Framework — Provides a ready-to-use set of cancellation reason categories with guidance on survey copy, option ordering, and 'help us improve' framing that reduces friction. Includes best practices like limiting to 5-8 options, single-select with optional free text, and quarterly review of reason distribution.
- Involuntary Churn & Dunning Playbook — Covers the full dunning stack: pre-dunning card expiry alerts, smart retry logic by decline type (soft vs. hard vs. authentication-required), a four-email dunning sequence with timing and tone guidance, and recovery benchmarks to measure against. Includes provider-specific notes for Stripe, Chargebee, Paddle, Recurly, and Braintree.
- Churn Prediction & Health Scoring — Defines high-signal leading indicators (login drop, key feature stoppage, billing page visits, data export initiation) and a weighted health score formula (0–100) that maps to action tiers. Includes a proactive intervention table pairing specific triggers to specific outreach actions before customers reach the cancel screen.
- Metrics, Cohort Analysis & A/B Testing — Defines the key churn metrics to track (save rate, offer acceptance rate, pause reactivation rate, dunning recovery rate) with benchmark targets. Provides a structured A/B test menu for cancel flow variables — discount depth, pause duration, survey placement, offer presentation — and recommends PostHog feature flags for implementation.
- Retention Tool & Integration Guidance — Maps leading retention platforms (Churnkey, ProsperStack, Raaft, Chargebee Retention) to use cases, and lists billing provider dunning capabilities side-by-side. Also covers analytics and event-routing tools (Customer.io, PostHog, Mixpanel, Segment) for health scoring and campaign execution.
Use Cases
- Building a first cancel flow from scratch — A self-serve SaaS product with instant cancellation uses this skill to design a survey → save offer → confirmation sequence. The skill provides the UI wireframe, reason category list, offer-to-reason mapping table, and copy guidance, so the team can ship a working flow without starting from a blank page.
- Diagnosing high payment-failure churn — A subscription business notices 40% of cancellations come from failed payments. The skill walks through classifying decline types, configuring smart retry schedules, drafting a four-email dunning sequence with direct payment-update links, and setting up card expiry alerts — turning silent failures into recoverable events.
- Proactive retention for at-risk accounts — A B2B SaaS team wants to intervene before customers cancel. Using the health score model and risk signal table, they instrument usage events, build a scoring pipeline, and set up triggered campaigns (e.g., re-engagement email on 14-day login absence, CS escalation for NPS detractors) that catch churn risk weeks before the cancel button is clicked.
- Optimizing an underperforming cancel flow — A team with an existing cancel flow has a 12% save rate and wants to improve it. The skill's A/B test menu, offer-acceptance benchmarks, and cohort segmentation framework help them identify whether the issue is offer type, discount depth, survey design, or copy tone — and run statistically rigorous tests to find the fix.