IntermediateAgent Skill

Churn Prevention

Design subscription cancel flows, save offers, exit surveys, and failed-payment recovery sequences.

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Claude Code · Codex
01

What this Skill does

Design subscription cancel flows, save offers, exit surveys, and failed-payment recovery sequences. The source separates voluntary cancellation from involuntary churn and includes a local impact calculator.

02

What makes it different

01

Approach and advantage

The source separates voluntary cancellation from involuntary churn and includes a local impact calculator.

02

What you can use it for

Design a clear cancellation and exit-survey flow; Match approved save offers to cancellation reasons; Draft failed-payment reminders and scenario estimates.

03

Before you install

Inputs to prepare

Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.

Agent and runtime

Use a Skill-capable agent with file access. Python 3 is required for the bundled Python helpers; inspect their sample schemas before supplying data.

Scope to confirm

This is subscription-retention design, not physical-product returns processing. It does not charge cards, retry payments, or deploy cancel flows.

04

Install and get started

Codex

The repository installer copies this complete Skill into your Codex skills directory. Git and the installer’s shell/Python requirements must be available.

  1. Choose the method for your agent and install the complete package, not just the displayed SKILL.md.
  2. Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.
  3. Start a new agent session and explicitly ask to use churn-prevention. Try one use case below with a small sample and review the output.
git clone https://github.com/alirezarezvani/claude-skills.git cd claude-skills git checkout 6d2f02ea077eead1bee5411a31bcc6db5a11a70c ./scripts/codex-install.sh --skill churn-prevention
05

Original Skill.md

This is the unchanged source SKILL.md for the reviewed version. Install the complete package to retain referenced scripts, assets, and instructions. Starter prompts below are EcomAgentTools examples.

Source and package files reviewed on October 25, 2026: GitHub 6d2f02ea077e. This is a static review, not an installation or business-outcome test.

View the reviewed source · MIT

License notice
MIT License

Copyright (c) 2025 Alireza Rezvani

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
---
name: "churn-prevention"
description: "Reduce voluntary and involuntary churn through cancel flow design, save offers, exit surveys, and dunning sequences. Use when designing or optimizing a cancel flow, building save offers, setting up dunning emails, or reducing failed-payment churn. Trigger keywords: cancel flow, churn reduction, save offers, dunning, exit survey, payment recovery, win-back, involuntary churn, failed payments, cancel page. NOT for customer health scoring or expansion revenue — use customer-success-manager for that."
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: marketing
  updated: 2026-03-06
---

# Churn Prevention

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.

## Before Starting

**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):

### 1. Current State
- Do you have a cancel flow today, or is cancellation instant/via support?
- What's your current monthly churn rate? (voluntary vs. involuntary split if known)
- What payment processor are you on? (Stripe, Braintree, Paddle, etc.)
- Do you collect exit reasons today?

### 2. Business Context
- SaaS model: self-serve or sales-assisted?
- Price points and plan structure
- Average contract length and billing cycle (monthly/annual)
- Current MRR

### 3. Goals
- Which problem is primary: too many cancellations, or failed payment churn?
- Do you have a save offer budget (discounts, extensions)?
- Any constraints on cancel flow friction? (some platforms penalize dark patterns)

## How This Skill Works

### Mode 1: Build Cancel Flow
Starting from scratch — no cancel flow exists, or cancellation is immediate. We'll design the full flow from trigger to post-cancel.

### Mode 2: Optimize Existing Flow
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.

### Mode 3: Set Up Dunning
Involuntary churn from failed payments is your priority. We'll build the retry logic, notification sequence, and recovery emails.

---

## Cancel Flow Design

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.

### The 5-Stage Flow

```
[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)

---

## Exit Survey Design

The survey is your most valuable data source. Design it to generate usable intelligence, not just categories.

### Recommended Reason 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.

---

## Save Offer Playbook

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](references/cancel-flow-playbook.md) for full decision trees and flow templates.

---

## Involuntary Churn: Dunning Setup

Failed payments cause 20-40% of total churn at most SaaS companies. Most of it is recoverable.

### Recovery Stack

**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 | Email | 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](references/dunning-guide.md) for full email sequences and retry configuration examples.

---

## Metrics & Benchmarks

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:

```bash
python3 scripts/churn_impact_calculator.py
```

---

## Proactive Triggers

Surface these without being asked:

- **Instant cancellation flow** → Revenue is leaking immediately. Any friction saves money — flag for priority fix.
- **Single generic save offer** → A discount shown to everyone depresses average revenue and trains customers to wait for deals. Map offers to exit reasons.
- **No dunning sequence** → If payment fails and nothing happens, that's 20-40% of churn going unaddressed. Flag immediately.
- **Exit survey is optional** → <70% completion = bad data. Make it required (one question, fast).
- **No post-cancel reactivation email** → The 7-day window is the highest win-back moment. Missing it leaves money on the table.
- **Churn rate >5% monthly** → At this rate, the company is likely contracting. Churn prevention alone won't fix it — flag for product/ICP review alongside retention work.

---

## Output Artifacts

| 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 |

---

## Communication

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

---

## Related Skills

- **customer-success-manager**: Use for health scoring, QBRs, and expansion revenue. NOT for cancel flow or dunning.
- **email-sequence**: Use for lifecycle nurture and onboarding emails. NOT for dunning (use this skill for dunning).
- **pricing-strategy**: Use when churn root cause is pricing or packaging mismatch. NOT for save offer design (use this skill).
- **campaign-analytics**: Use for analyzing which acquisition channels produce high-churn customers. NOT for setting up retention tracking.
- **signup-flow-cro**: Use for reducing drop-off at signup. NOT for post-signup retention.
06

Get started

Starter prompts for the main use cases—copy and use them directly.

01

Design a clear cancellation and exit-survey flow

Design a clear cancellation and exit-survey flow Use for: Design a clear cancellation and exit-survey flow. Prepare: Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.

Show prompt and variablesHide prompt and variables
Use the installed churn-prevention Skill. Task: Design a clear cancellation and exit-survey flow. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. This is subscription-retention design, not physical-product returns processing. It does not charge cards, retry payments, or deploy cancel flows.

Replace these variables

[INPUT]
Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.
[CONSTRAINTS]
Scope, allowed actions, output format, and review requirements.
02

Match approved save offers to cancellation reasons

Match approved save offers to cancellation reasons Use for: Match approved save offers to cancellation reasons. Prepare: Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.

Show prompt and variablesHide prompt and variables
Use the installed churn-prevention Skill. Task: Match approved save offers to cancellation reasons. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. This is subscription-retention design, not physical-product returns processing. It does not charge cards, retry payments, or deploy cancel flows.

Replace these variables

[INPUT]
Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.
[CONSTRAINTS]
Scope, allowed actions, output format, and review requirements.
03

Draft failed-payment reminders and scenario estimates

Draft failed-payment reminders and scenario estimates Use for: Draft failed-payment reminders and scenario estimates. Prepare: Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.

Show prompt and variablesHide prompt and variables
Use the installed churn-prevention Skill. Task: Draft failed-payment reminders and scenario estimates. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. This is subscription-retention design, not physical-product returns processing. It does not charge cards, retry payments, or deploy cancel flows.

Replace these variables

[INPUT]
Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics.
[CONSTRAINTS]
Scope, allowed actions, output format, and review requirements.
07

Risks and operating notes

Known limits

This is subscription-retention design, not physical-product returns processing. It does not charge cards, retry payments, or deploy cancel flows.

Evidence and review

Source and package files reviewed on October 25, 2026: GitHub 6d2f02ea077e. This is a static review, not an installation or business-outcome test.

Before acting on the result

Use authorized inputs and keep secrets out of prompts. Review customer-facing claims, calculations, code changes, and any proposed external action before applying them.

Frequently asked questions

What does it do, and what is the advantage?

Design subscription cancel flows, save offers, exit surveys, and failed-payment recovery sequences. The source separates voluntary cancellation from involuntary churn and includes a local impact calculator.

How do I install it and start?

Choose the method for your agent and install the complete package, not just the displayed SKILL.md. Subscription terms, churn split, cancellation reasons, approved offers, payment process, and baseline metrics. Start a new agent session and explicitly ask to use churn-prevention. Try one use case below with a small sample and review the output.

Which situations is it useful for?

Design a clear cancellation and exit-survey flow; Match approved save offers to cancellation reasons; Draft failed-payment reminders and scenario estimates

What limitations should I know?

This is subscription-retention design, not physical-product returns processing. It does not charge cards, retry payments, or deploy cancel flows.

What was checked for this page?

Source and package files reviewed on October 25, 2026: GitHub 6d2f02ea077e. This is a static review, not an installation or business-outcome test.

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Author / maintainer

Alireza Rezvani

Source and package files reviewed on October 25, 2026: GitHub 6d2f02ea077e. This is a static review, not an installation or business-outcome test.

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