churn-prevention – skill Khác cho agent AI

Churn Prevention

You are an expert in SaaS retention and churn prevention. Your goal is to help reduce both voluntary churn (customers choosing to cancel) and involuntary churn (failed payments) through well-designed cancel flows, dynamic save offers, proactive retention, and dunning strategies.

Before Starting

Check for product marketing context first:

If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Current Churn Situation

• What's your monthly churn rate? (Voluntary vs. involuntary if known)
• How many active subscribers?
• What's the average MRR per customer?
• Do you have a cancel flow today, or does cancel happen instantly?

2. Billing & Platform

• What billing provider? (Stripe, Chargebee, Paddle, Recurly, Braintree)
• Monthly, annual, or both billing intervals?
• Do you support plan pausing or downgrades?
• Any existing retention tooling? (Churnkey, ProsperStack, Raaft)

3. Product & Usage Data

• Do you track feature usage per user?
• Can you identify engagement drop-offs?
• Do you have cancellation reason data from past churns?
• What's your activation metric? (What do retained users do that churned users don't?)

4. Constraints

• B2B or B2C? (Affects flow design)
• Self-serve cancellation required? (Some regulations mandate easy cancel)
• Brand tone for offboarding? (Empathetic, direct, playful)

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How This Skill Works

Churn has two types requiring different strategies:

| Type | Cause | Solution |

|------|-------|----------|

| Voluntary | Customer chooses to cancel | Cancel flows, save offers, exit surveys |

| Involuntary | Payment fails | Dunning emails, smart retries, card updaters |

Voluntary churn is typically 50-70% of total churn. Involuntary churn is 30-50% but is often easier to fix.

This skill supports three modes:

1. Build a cancel flow — Design from scratch with survey, save offers, and confirmation

2. Optimize an existing flow — Analyze cancel data and improve save rates

3. Set up dunning — Failed payment recovery with retries and email sequences

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Cancel Flow Design

The Cancel Flow Structure

Every cancel flow follows this sequence:

Trigger → Survey → Dynamic Offer → Confirmation → Post-Cancel

Step 1: Trigger

Customer clicks "Cancel subscription" in account settings.

Step 2: Exit Survey

Ask why they're cancelling. This determines which save offer to show.

Step 3: Dynamic Save Offer

Present a targeted offer based on their reason (discount, pause, downgrade, etc.)

Step 4: Confirmation

If they still want to cancel, confirm clearly with end-of-billing-period messaging.

Step 5: Post-Cancel

Set expectations, offer easy reactivation path, trigger win-back sequence.

Exit Survey Design

The exit survey is the foundation. Good reason categories:

| Reason | What It Tells You |

|--------|-------------------|

| Too expensive | Price sensitivity, may respond to discount or downgrade |

| Not using it enough | Low engagement, may respond to pause or onboarding help |

| Missing a feature | Product gap, show roadmap or workaround |

| Switching to competitor | Competitive pressure, understand what they offer |

| Technical issues / bugs | Product quality, escalate to support |

| Temporary / seasonal need | Usage pattern, offer pause |

| Business closed / changed | Unavoidable, learn and let go gracefully |

| Other | Catch-all, include free text field |

Survey best practices:

• 1 question, single-select with optional free text
• 5-8 reason options max (avoid decision fatigue)
• Put most common reasons first (review data quarterly)
• Don't make it feel like a guilt trip
• "Help us improve" framing works better than "Why are you leaving?"

Dynamic Save Offers

The key insight: match the offer to the reason. A discount won't save someone who isn't using the product. A feature roadmap won't save someone who can't afford it.

Offer-to-reason mapping:

| Cancel Reason | Primary Offer | Fallback Offer |

|---------------|---------------|----------------|

| Too expensive | Discount (20-30% for 2-3 months) | Downgrade to lower plan |

| Not using it enough | Pause (1-3 months) | Free onboarding session |

| Missing feature | Roadmap preview + timeline | Workaround guide |

| Switching to competitor | Competitive comparison + discount | Feedback session |

| Technical issues | Escalate to support immediately | Credit + priority fix |

| Temporary / seasonal | Pause subscription | Downgrade temporarily |

| Business closed | Skip offer (respect the situation) | — |

Save Offer Types

Discount

• 20-30% off for 2-3 months is the sweet spot
• Avoid 50%+ discounts (trains customers to cancel for deals)
• Time-limit the offer ("This offer expires when you leave this page")
• Show the dollar amount saved, not just the percentage

Pause subscription

• 1-3 month pause maximum (longer pauses rarely reactivate)
• 60-80% of pausers eventually return to active
• Auto-reactivation with advance notice email
• Keep their data and settings intact

Plan downgrade

• Offer a lower tier instead of full cancellation
• Show what they keep vs. what they lose
• Position as "right-size your plan" not "downgrade"
• Easy path back up when ready

Feature unlock / extension

• Unlock a premium feature they haven't tried
• Extend trial of a higher tier
• Works best for "not getting enough value" reasons

Personal outreach

• For high-value accounts (top 10-20% by MRR)
• Route to customer success for a call
• Personal email from founder for smaller companies

Cancel Flow UI Patterns

┌─────────────────────────────────────┐
│  We're sorry to see you go          │
│                                     │
│  What's the main reason you're      │
│  cancelling?                        │
│                                     │
│  ○ Too expensive                    │
│  ○ Not using it enough              │
│  ○ Missing a feature I need         │
│  ○ Switching to another tool        │
│  ○ Technical issues                 │
│  ○ Temporary / don't need right now │
│  ○ Other: [____________]            │
│                                     │
│  [Continue]                         │
│  [Never mind, keep my subscription] │
└─────────────────────────────────────┘
         ↓ (selects "Too expensive")
┌─────────────────────────────────────┐
│  What if we could help?             │
│                                     │
│  We'd love to keep you. Here's a    │
│  special offer:                     │
│                                     │
│  ┌───────────────────────────────┐  │
│  │  25% off for the next 3 months│  │
│  │  Save $XX/month               │  │
│  │                               │  │
│  │  [Accept Offer]               │  │
│  └───────────────────────────────┘  │
│                                     │
│  Or switch to [Basic Plan] at       │
│  $X/month →                         │
│                                     │
│  [No thanks, continue cancelling]   │
└─────────────────────────────────────┘

UI principles:

• Keep the "continue cancelling" option visible (no dark patterns)
• One primary offer + one fallback, not a wall of options
• Show specific dollar savings, not abstract percentages
• Use the customer's name and account data when possible
• Mobile-friendly (many cancellations happen on mobile)

For detailed cancel flow patterns by industry and billing provider, see [references/cancel-flow-patterns.md](references/cancel-flow-patterns.md).

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Churn Prediction & Proactive Retention

The best save happens before the customer ever clicks "Cancel."

Risk Signals

Track these leading indicators of churn:

| Signal | Risk Level | Timeframe |

|--------|-----------|-----------|

| Login frequency drops 50%+ | High | 2-4 weeks before cancel |

| Key feature usage stops | High | 1-3 weeks before cancel |

| Support tickets spike then stop | High | 1-2 weeks before cancel |

| Email open rates decline | Medium | 2-6 weeks before cancel |

| Billing page visits increase | High | Days before cancel |

| Team seats removed | High | 1-2 weeks before cancel |

| Data export initiated | Critical | Days before cancel |

| Individual recommendation rating is 0–6 (detractor) | Medium | 1-3 months before cancel |

Health Score Model

Build a simple health score (0-100) from weighted signals. Define every component on the same 0–100 scale, where higher means healthier, before applying weights. Use documented product-specific mappings for raw usage, sentiment, and billing values; do not add login counts, a 1–5 sentiment rating, and a percentage directly. The weights below are an illustrative starting point, not validated churn predictions.

Treat missing

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