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Churn Prevention

  • 567 installs
  • 23.5k repo stars
  • Updated July 17, 2026
  • alirezarezvani/claude-skills

churn-prevention is a Python churn impact calculator skill that models MRR saved from voluntary save-rate and involuntary payment-recovery improvements for developers evaluating subscription retention investments.

About

churn-prevention is a Python 3 churn impact calculator that accepts JSON inputs—MRR, monthly churn rate, voluntary versus involuntary churn split, current and target save rates, and current and target recovery rates—and outputs modeled revenue impact before funding retention programs. The bundled sample uses $50,000 MRR, 4.5% monthly churn, 65% voluntary churn, 8% to 20% save-rate targets, and 15% to 35% recovery targets with $150 average customer MRR. Developers reach for churn-prevention when sizing the dollar value of cancellation saves and failed-payment recovery prior to building churn tooling or lifecycle campaigns.

  • Python churn impact calculator with JSON sample inputs for quick what-if runs
  • Splits voluntary vs involuntary churn and models current vs target save and recovery rates
  • Outputs MRR churned, saves, recoveries, and incremental retained revenue from improvements
  • Uses average customer MRR to translate rate changes into dollar impact
  • Suitable for prioritizing save offers vs billing recovery work

Churn Prevention by the numbers

  • 567 all-time installs (skills.sh)
  • Ranked #140 of 853 Sales & Marketing skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/alirezarezvani/claude-skills --skill churn-prevention

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Installs567
repo stars23.5k
Security audit2 / 3 scanners passed
Last updatedJuly 17, 2026
Repositoryalirezarezvani/claude-skills

How do you model MRR impact of churn reduction?

Model MRR saved by improving voluntary save rates and involuntary payment recovery before you invest in churn programs.

Who is it for?

SaaS developers or product managers quantifying retention-program ROI before investing in save flows or dunning recovery.

Skip if: Implementing actual cancellation UI, payment webhooks, or real-time churn analytics pipelines without scenario modeling.

When should I use this skill?

A user provides MRR, churn percentages, save rates, or recovery rates and asks to estimate revenue impact of churn improvements.

What you get

JSON churn impact projections showing MRR preserved from save-rate and payment-recovery scenario changes.

  • Churn impact JSON output
  • MRR saved projections

By the numbers

  • Sample scenario uses $50,000 MRR and 4.5% monthly churn rate
  • Sample models save-rate lift from 8% to 20% and recovery from 15% to 35%
  • Sample average customer MRR set at $150

Files

SKILL.mdMarkdownGitHub ↗

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

ReasonSave OfferSignal
Too expensive / priceDiscount or downgradePrice sensitivity
Not using it enoughUsage tips + pause optionAdoption failure
Missing a featureRoadmap share + workaroundProduct gap
Switching to competitorCompetitive comparisonMarket position
Project ended / seasonalPause optionTemporary need
Too complicatedOnboarding help + human supportUX friction
Just testing / never neededNo offer — let goWrong 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 TypeWhen to UseWhen NOT to Use
Discount (1-3 months)Price objectionAdoption or feature issues
Pause (1-3 months)Seasonal, project ended, not usingPrice objection
DowngradeToo expensive, light usageFeature objection
Extended trialHasn't explored full valuePower user churning
Feature unlockMissing feature that exists on higher planWrong plan fit
Human supportComplicated, stuck, frustratedPrice 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

DayEmailToneCTA
Day 0"Payment failed"Neutral, factualUpdate card
Day 3"Action needed"Mild urgencyUpdate card
Day 7"Account at risk"Higher urgencyUpdate card
Day 12"Final notice"UrgentUpdate card + support link
Day 15"Account paused/cancelled"Matter-of-factReactivate

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:

MetricFormulaBenchmark
Save rateCustomers saved / cancel attempts10-15% good, 20%+ excellent
Voluntary churn rateVoluntary cancels / total customers<2% monthly
Involuntary churn rateFailed payment cancels / total customers<1% monthly
Recovery rateFailed payments recovered / total failed25-35% good
Win-back rateReactivations / post-cancel 90 days5-10%
Exit survey completionSurveys 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

---

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.

Related skills

FAQ

What inputs does churn-prevention require?

churn-prevention accepts JSON with MRR, monthly_churn_rate_pct, voluntary_churn_pct, current and target save_rate_pct, current and target recovery_rate_pct, and avg_customer_mrr to project retained revenue.

Does churn-prevention separate voluntary and involuntary churn?

Yes. churn-prevention splits churn into voluntary and involuntary portions, modeling save-rate gains on cancellations and recovery-rate gains on failed payments independently.

Is Churn Prevention safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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