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alirezarezvani

churn-prevention

by Alireza Rezvani · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install cs-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 cance...
README (SKILL.md)

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 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 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 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 \x3C2% monthly
Involuntary churn rate Failed payment cancels / total customers \x3C1% 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 \x3C5% → offers aren't matching reasons
  • Exit survey completion \x3C70% → survey is too long or optional
  • Recovery rate \x3C20% → 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

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 → \x3C70% 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.
Usage Guidance
This skill appears to be a straightforward playbook and a harmless local calculator script. Before enabling or invoking it: 1) Check any local file named marketing-context.md (the skill will read it if present) and remove or redact secrets or payment credentials. 2) Don’t paste live API keys or full account credentials into chat if the agent asks for connector details—provide high-level info (e.g., 'Stripe' vs. an API key). 3) Review the churn_impact_calculator.py if you plan to run it locally — it takes JSON input or falls back to sample data, and it makes no network calls. 4) If you plan to implement playbook steps that touch billing systems, ensure those integrations are done in your secure backend (not by pasting credentials into an agent). Overall: coherent and reasonable, but always avoid exposing secrets in workspace files the skill might read.
Capability Analysis
Type: OpenClaw Skill Name: cs-churn-prevention Version: 1.0.0 The skill bundle is a legitimate tool for SaaS churn prevention and dunning management. It contains well-structured instructions in SKILL.md, comprehensive reference guides in the references/ directory, and a safe Python script (scripts/churn_impact_calculator.py) for financial modeling. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found.
Capability Assessment
Purpose & Capability
Name/description (churn prevention) align with included artifacts: a detailed cancel-flow playbook, a dunning guide, and a local churn impact calculator script. The skill does not request unrelated binaries, environment variables, or config paths.
Instruction Scope
SKILL.md stays within the expected scope (designing cancel flows, building dunning sequences). It asks the agent to read marketing-context.md if present — reasonable for gathering context but means the agent will access files in the workspace. The playbooks instruct implementers to store cancel_reason and offer outcomes in customer records (implementation guidance), but the skill does not instruct network exfiltration or use of secrets. Review any workspace file the agent may read for sensitive data before invoking.
Install Mechanism
No install spec (instruction-only plus one small local Python script). Nothing is downloaded from external URLs and no archives are extracted. Low install risk.
Credentials
The skill declares no required environment variables, primary credential, or config paths. References to payment processors (Stripe, Chargebee, etc.) are contextual and do not force credentials to be provided by the platform.
Persistence & Privilege
always:false (not forced into every agent run) and default autonomous invocation allowed. The skill does not request persistent modifications to other skills or system-wide settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install cs-churn-prevention
  3. After installation, invoke the skill by name or use /cs-churn-prevention
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial publish (prefixed slug)
v2.1.1
v2.1.1: optimization, reference splits
Metadata
Slug cs-churn-prevention
Version 1.0.0
License MIT-0
All-time Installs 2
Active Installs 2
Total Versions 2
Frequently Asked Questions

What is churn-prevention?

Reduce voluntary and involuntary churn through cancel flow design, save offers, exit surveys, and dunning sequences. Use when designing or optimizing a cance... It is an AI Agent Skill for Claude Code / OpenClaw, with 325 downloads so far.

How do I install churn-prevention?

Run "/install cs-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 Alireza Rezvani (@alirezarezvani); the current version is v1.0.0.

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