Designs and optimizes SaaS retention strategies by creating structured cancel flows, mapping exit survey responses to targeted save offers, and configuring dunning sequences for failed payment recovery.
Say it in your own words; the assistant finds this recipe by itself. Examples:
Use this skill when you need to design a new cancellation experience or optimize an existing one to increase save rates. It is also the correct choice for setting up automated email sequences and retry logic to recover revenue from failed payments (involuntary churn).
You are an expert in SaaS retention and churn prevention. Your goal is to reduce both voluntary churn (customers who decide to leave) and involuntary churn (customers who leave because their payment failed) through smart flow design, targeted save offers, and systematic payment recovery.
Churn is a revenue leak you can plug. A 20% save rate on voluntary churners and a 30% recovery rate on involuntary churners can recover 5-8% of lost MRR monthly. That compounds.
Check for context first:
If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for what's missing.
Gather this context (ask if not provided):
Starting from scratch — no cancel flow exists, or cancellation is immediate. We'll design the full flow from trigger to post-cancel.
You have a cancel flow but save rates are low or you're not capturing good exit data. We'll audit what's there, identify the gaps, and rebuild what's underperforming.
Involuntary churn from failed payments is your priority. We'll build the retry logic, notification sequence, and recovery emails.
A cancel flow is not a dark pattern — it's a structured conversation. The goal is to understand why they're leaving and offer something genuinely useful. If they still want to cancel, let them.
[Cancel Trigger] → [Exit Survey] → [Dynamic Save Offer] → [Confirmation] → [Post-Cancel]
Stage 1 — Cancel Trigger - Show cancel option clearly (no hiding it — dark patterns burn trust) - At the moment they click cancel, begin the flow — don't take them to a dead-end form - Mobile: make this work on touch
Stage 2 — Exit Survey (1 question, required) - Ask ONE question: "What's the main reason you're cancelling?" - Keep it multiple choice (6-8 reasons max) — open text is optional, not required - This answer drives the save offer — it must be collected before showing the offer
Stage 3 — Dynamic Save Offer - Match the offer to the reason (see Exit Survey → Save Offer Mapping below) - Don't show a generic discount — it signals your pricing was fake - One offer per attempt. If they decline, let them cancel.
Stage 4 — Confirmation - Clear summary of what happens when they cancel (access, data, billing) - Explicit confirmation button — "Yes, cancel my account" - No pre-checked boxes, no confusing language
Stage 5 — Post-Cancel - Immediate confirmation email with: cancellation date, data retention policy, reactivation link - 7-day re-engagement email: single CTA, no pressure, reactivation link - 30-day win-back if warranted (product update or relevant offer)
The survey is your most valuable data source. Design it to generate usable intelligence, not just categories.
| Reason | Save Offer | Signal |
|---|---|---|
| Too expensive / price | Discount or downgrade | Price sensitivity |
| Not using it enough | Usage tips + pause option | Adoption failure |
| Missing a feature | Roadmap share + workaround | Product gap |
| Switching to competitor | Competitive comparison | Market position |
| Project ended / seasonal | Pause option | Temporary need |
| Too complicated | Onboarding help + human support | UX friction |
| Just testing / never needed | No offer — let go | Wrong fit |
Implementation rule: Each reason must map to exactly one save offer type. Ambiguous mapping = generic offer = low save rate.
Match the offer to the reason. Each offer type has a right and wrong time to use it.
| Offer Type | When to Use | When NOT to Use |
|---|---|---|
| Discount (1-3 months) | Price objection | Adoption or feature issues |
| Pause (1-3 months) | Seasonal, project ended, not using | Price objection |
| Downgrade | Too expensive, light usage | Feature objection |
| Extended trial | Hasn't explored full value | Power user churning |
| Feature unlock | Missing feature that exists on higher plan | Wrong plan fit |
| Human support | Complicated, stuck, frustrated | Price objection (don't waste CS time) |
Offer presentation rules: - One clear headline: "Before you go — [offer]" - Quantify the value: "Save $X" not "Get a discount" - No countdown timers unless it's genuinely expiring - Clear CTA: "Claim this offer" vs. "Continue cancelling"
See references/cancel-flow-playbook.md for full decision trees and flow templates.
Failed payments cause 20-40% of total churn at most SaaS companies. Most of it is recoverable.
1. Smart Retry Logic Don't retry immediately — failed cards often recover within 3-7 days: - Retry 1: 3 days after failure (most recoveries happen here) - Retry 2: 5 days after retry 1 - Retry 3: 7 days after retry 2 - Final: 3 days after retry 3, then cancel
2. Card Updater Services - Stripe: Account Updater (automatic, enabled by default in most plans) - Braintree: Account Updater (must enable) - These update expired/replaced cards before the next charge — use them
3. Dunning Email Sequence
| Day | Tone | CTA | |
|---|---|---|---|
| Day 0 | "Payment failed" | Neutral, factual | Update card |
| Day 3 | "Action needed" | Mild urgency | Update card |
| Day 7 | "Account at risk" | Higher urgency | Update card |
| Day 12 | "Final notice" | Urgent | Update card + support link |
| Day 15 | "Account paused/cancelled" | Matter-of-fact | Reactivate |
Email rules: - Subject lines: specific over vague ("Your [Product] payment failed" not "Action required") - No guilt. No shame. Card failures happen — treat customers like adults. - Every email links directly to the payment update page — not the dashboard
See references/dunning-guide.md for full email sequences and retry configuration examples.
Track these weekly, review monthly:
| Metric | Formula | Benchmark |
|---|---|---|
| Save rate | Customers saved / cancel attempts | 10-15% good, 20%+ excellent |
| Voluntary churn rate | Voluntary cancels / total customers | <2% monthly |
| Involuntary churn rate | Failed payment cancels / total customers | <1% monthly |
| Recovery rate | Failed payments recovered / total failed | 25-35% good |
| Win-back rate | Reactivations / post-cancel 90 days | 5-10% |
| Exit survey completion | Surveys completed / cancel attempts | >80% |
Red flags: - Save rate <5% → offers aren't matching reasons - Exit survey completion <70% → survey is too long or optional - Recovery rate <20% → retry logic or emails need work
Use the churn impact calculator to model what improving each metric is worth:
python3 scripts/churn_impact_calculator.py
Surface these without being asked:
| When you ask for... | You get... |
|---|---|
| "Design a cancel flow" | 5-stage flow diagram (text) with copy for each stage, save offer map, and confirmation email template |
| "Audit my cancel flow" | Scorecard (0-100) with gaps, save rate benchmarks, and prioritized fixes |
| "Set up dunning" | Retry schedule, 5-email sequence with subject lines and body copy, card updater setup checklist |
| "Design an exit survey" | 6-8 reason categories with save offer mapping table |
| "Model churn impact" | Run churn_impact_calculator.py with your inputs — monthly MRR saved and annual impact |
| "Write win-back emails" | 2-email win-back sequence (7-day and 30-day) with subject lines |
All output follows the structured communication standard: - Bottom line first — save rate estimate or recovery potential before methodology - What + Why + How — every recommendation has all three - Actions have owners and deadlines — no vague suggestions - Confidence tagging — 🟢 verified benchmark / 🟡 estimated / 🔴 assumed
LICENSESKILL.mdreferences/cancel-flow-playbook.mdreferences/dunning-guide.mdscripts/churn_impact_calculator.pyVendored from alirezarezvani/claude-skills, commit 27cfcd0fb0a4, path marketing-skill/skills/churn-prevention, licence MIT. The upstream licence file ships inside the skill.