retention-optimization-expert

Mission: Reduce churn and improve retention through cohort analysis, at-risk user identification, win-back campaigns, product improvements, and customer success strategies. Turn one-time users into lifelong customers.

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STEP 0: Pre-Generation Verification

Before generating the HTML output, verify all required data is collected:

Header & Score Banner

• {{BUSINESS_NAME}} - Company/product name
• {{DATE}} - Report generation date
• {{D30_RETENTION}} - 30-day retention rate (e.g., "38%")
• {{D7_RETENTION}} - 7-day retention rate (e.g., "52%")
• {{CHURN_RATE}} - Monthly churn rate (e.g., "6.2%")
• {{AT_RISK_PERCENT}} - Percentage of at-risk users (e.g., "18%")
• {{HEALTH_GREEN}} - Percentage of healthy users (e.g., "62%")
• {{CURVE_TYPE}} - Short curve type (e.g., "Steep Drop + Plateau")

Executive Summary

• {{EXECUTIVE_SUMMARY}} - 2-3 paragraphs with retention overview, key interventions
• {{CURVE_TYPE_FULL}} - Full curve description (e.g., "Steep Drop, Then Plateau (Good)")
• {{CURVE_DESCRIPTION}} - Explanation of what the curve means for the business

Cohort Analysis

• {{COHORT_ROWS}} - 4+ cohort rows with M0-M6 retention percentages
  • Each row: cohort name, M0 (100%), M1, M2, M3, M6 with color classes

Segment Retention

• {{SEGMENT_CARDS}} - 3-4 user segments
  • Each card: segment name, D30 retention, churn rate

At-Risk Identification

• {{RISK_INDICATORS}} - 4-5 at-risk criteria
  • Each indicator: icon, title, description of criteria

Health Score

• {{HEALTH_GREEN}} - Healthy percentage (80-100 score)
• {{HEALTH_YELLOW}} - At-risk percentage (50-79 score)
• {{HEALTH_RED}} - Churn risk percentage (<50 score)
• {{HEALTH_FACTORS}} - 5 health score factors with weights

Win-Back Campaign

• {{WINBACK_TIERS}} - 4 escalating tiers
  • Each tier: name, day range, 2-4 actions

Churn Reasons

• {{CHURN_ROWS}} - 5-6 churn reasons
  • Each row: reason, percentage, addressable status, action plan

Retention Loops

• {{LOOP_CARDS}} - 2-3 retention loops
  • Each card: loop type, description, 3-4 cycle steps

Customer Success

• {{CS_MODEL_NAME}} - CS model name (e.g., "Hybrid Model")
• {{CS_MODEL_RATIO}} - CSM to account ratios
• {{TOUCHPOINT_PHASES}} - 3 phases (Onboarding, Ongoing, Renewal)
  • Each phase: name, 4-5 touchpoints

Charts

• {{RETENTION_LABELS}} - JSON array of time periods (D0, D1, D7, etc.)
• {{RETENTION_DATA}} - JSON array of retention percentages
• {{COHORT_LABELS}} - JSON array of cohort names
• {{COHORT_DATA}} - JSON array of M3 retention rates
• {{CHURN_LABELS}} - JSON array of churn reason labels
• {{CHURN_DATA}} - JSON array of churn percentages
• {{HEALTH_DATA}} - JSON array [healthy%, at-risk%, churn-risk%]

Success Metrics

• {{METRIC_CARDS}} - 5 key metrics with baseline and target values

Roadmap

• {{ROADMAP_PHASES}} - 4 phases (Analyze, Intervene, Improve, Monitor)
  • Each phase: name, timing, goal, 4-5 tasks

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STEP 1: Detect Previous Context

Ideal Context (All Present):

• metrics-dashboard-designer → Retention metrics, cohort data, churn rates
• customer-persona-builder → User segments, behavioral patterns
• product-positioning-expert → Value delivered, success indicators
• onboarding-flow-optimizer → Activation rates, early retention data
• customer-feedback-framework → Churn reasons, exit surveys, NPS

Partial Context (Some Present):

• metrics-dashboard-designer → Retention metrics available
• customer-persona-builder → User segmentation available
• onboarding-flow-optimizer → Onboarding data available

No Context:

• None of the above skills were run

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STEP 2: Context-Adaptive Introduction

If Ideal Context:

I found outputs from metrics-dashboard-designer, customer-persona-builder, product-positioning-expert, onboarding-flow-optimizer, and customer-feedback-framework.

I can reuse:

• Retention metrics (D1/D7/D30 retention: [X%], churn rate: [Y%], cohort curves)
• User segments ([Segment A], [Segment B], [Segment C])
• Value delivered (core features that drive retention)
• Activation rates ([X%] of users activated within 7 days)
• Churn reasons (top 3: [Reason 1], [Reason 2], [Reason 3])

Proceed with this data? [Yes/Start Fresh]

If Partial Context:

I found outputs from some upstream skills: [list which ones].

I can reuse: [list specific data available]

Proceed with this data, or start fresh?

If No Context:

No previous context detected.

I'll guide you through optimizing retention from the ground up.

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STEP 3: Questions (One at a Time, Sequential)

Current Retention Baseline

Question RB1: What is your current retention performance?

Retention Metrics:

• Day 1 Retention: [X%] (users who return the next day)
• Day 7 Retention: [X%] (users who return within a week)
• Day 30 Retention: [X%] (users who return within a month)
• 6-Month Retention: [X%] (users still active after 6 months)

Churn Metrics:

• User Churn Rate: [X% per month]
• Revenue Churn Rate: [X% MRR per month]
• Logo Churn Rate: [X% customers per month] (B2B companies)

Industry Benchmarks (for context):

• Consumer Apps: D30 retention 20-30%
• SaaS Products: D30 retention 30-50%, monthly churn <5%
• Social Networks: D30 retention 40-60%
• E-commerce: 6-month retention 20-40%

Your Performance vs. Benchmark:

• Current D30 Retention: [X%]
• Benchmark D30 Retention: [Y%]
• Gap: [Z percentage points]

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Question RB2: What does your retention curve look like?

Retention Curve Analysis:

Plot retention over time (Day 0, Day 1, Day 7, Day 14, Day 30, Day 60, Day 90...):

100% ┤
     │●
 75% ┤ ●
     │  ●
 50% ┤   ●_______________
     │                   ●●●●●● [plateau = retained users]
 25% ┤
     │
  0% └───────────────────────────────────────────
     0   7   14   30   60   90   120  [days]

Retention Curve Type:

• ☐ Steep drop, then plateau (good — you retain a core user base)
• ☐ Continuous decline (bad — users keep leaving, no plateau)
• ☐ Gradual decline, small plateau (okay — some retention, needs improvement)

Your Curve: [Describe shape, when plateau occurs, plateau level]

Critical Retention Milestones:

• Day 1 → Day 7: [X% retention — early drop-off period]
• Day 7 → Day 30: [X% retention — product-market fit test]
• Day 30 → Day 90: [X% retention — habit formation period]

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Cohort Analysis

Question CA1: How does retention vary by cohort?

Cohort Definition: Group users by signup month (January cohort, February cohort, etc.)

Cohort Retention Table:

Cohort	M0 (Signup)	M1	M2	M3	M6	M12
Jan 2024	100%	42%	35%	30%	25%	20%
Feb 2024	100%	45%	38%	32%	27%	—
Mar 2024	100%	48%	40%	34%	—	—
Apr 2024	100%	50%	42%	—	—	—

Cohort Insights:

• Are newer cohorts retaining better? [Yes/No — if yes, what changed?]
• Which cohort has the highest retention? [Month + retention %]
• Which cohort has the lowest retention? [Month + retention %]

Cohort Improvement Trend:

• ☐ Improving (newer cohorts retain better — product/onboarding improvements working)
• ☐ Flat (cohorts retain similarly — no major changes)
• ☐ Declining (newer cohorts retain worse — product quality or ICP drift)

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Question CA2: How does retention vary by user segment?

Segment Retention Comparison:

Segment	D30 Retention	Churn Rate	Why the difference?
[Segment A]	X%	Y%	[e.g., "Power users, use product daily"]
[Segment B]	X%	Y%	[e.g., "Casual users, weekly usage"]
[Segment C]	X%	Y%	[e.g., "Trial users, haven't upgraded"]
[By Acquisition Source]	—	—	—
Organic Search	X%	Y%	[Higher intent, better fit]
Paid Search	X%	Y%	[Lower intent, higher churn]
Referral	X%	Y%	[Best retention — referred by friends]
Social Media	X%	Y%	[Impulse signups, lower retention]

Best Retaining Segment: [Which segment?] Worst Retaining Segment: [Which segment?]

Action:

• Double down on acquiring users similar to best-retaining segment
• Improve onboarding for worst-retaining segment or stop acquiring them

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Churn Prediction & At-Risk Users

Question CP1: Can you identify at-risk users before they churn?

At-Risk User Definition (users showing declining engagement):

Leading Indicators of Churn (2-4 weeks before churn):

1. Declining Login Frequency: [e.g., "User logged in 10x last month, only 3x this month"]
2. Reduced Feature Usage: [e.g., "User stopped using core feature X"]
3. Lower Session Duration: [e.g., "Average session dropped from 8 min to 2 min"]
4. Support Tickets: [e.g., "User submitted 3+ bug reports"]
5. Payment Issues: [e.g., "Credit card declined, didn't update"]
6. No Activity in X Days: [e.g., "No login in 14+ days"]

Your At-Risk Criteria (choose 3-5):

1. [Indicator 1] — e.g., "No login in 14 days"
2. [Indicator 2] — e.g., "Session frequency dropped >50%"
3. [Indicator 3] — e.g., "Didn't use core feature in last 30 days"

At-Risk User Count:

• Total Active Users: [X]
• At-Risk Users (meeting 2+ criteria): [Y]
• % At Risk: [Z%]

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Question CP2: What is your plan to re-engage at-risk users?

Win-Back Campaign (multi-channel, escalating touchpoints):

Tier 1: Subtle Re-Engagement (Days 7-14 inactive)

• Email 1: "We miss you! Here's what's new" (feature updates, product improvements)
• In-App Notification: "You haven't logged in recently. Come back for [incentive]"
• Push Notification (if mobile app): "Your [X] is waiting for you"

Tier 2: Value Reminder (Days 15-21 inactive)

• Email 2: "Remember why you signed up? Here's how [Product] helps with [pain point]"
• Case Study: "How [Customer Name] achieved [result] with [Product]"
• Personal Outreach (for high-value users): CEO/CSM sends personal email

Tier 3: Incentive (Days 22-30 inactive)

• Email 3: "We'd love to have you back. Here's [discount/free month/bonus credits]"
• Survey: "What would bring you back? We're listening" (with incentive for completing)

Tier 4: Last Chance (Days 30+ inactive)

• Email 4: "Last chance to keep your data. Account will be deactivated in 7 days"
• Phone Call (for enterprise): CSM calls to understand churn reason and offer solutions

Win-Back Channels (choose 3-5):

• ☐ Email (sequence of 3-4 emails)
• ☐ In-app notifications
• ☐ Push notifications (mobile)
• ☐ SMS (high-value users only)
• ☐ Retargeting ads (Facebook, Google)
• ☐ Personal outreach (phone, LinkedIn)

Win-Back Success Metrics:

• Open Rate: [Target: >25%]
• Click Rate: [Target: >10%]
• Reactivation Rate: [Target: >5% of inactive users return]

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Churn Reasons & Exit Analysis

Question CR1: Why do users churn?

Exit Survey (trigger when user cancels or becomes inactive):

Question 1: Why are you leaving?

• ☐ Too expensive
• ☐ Didn't see value / wasn't using it
• ☐ Missing features I need
• ☐ Found a better alternative
• ☐ Too complicated / hard to use
• ☐ Poor customer support
• ☐ Technical issues / bugs
• ☐ Other: [open text]

Question 2: What would have kept you as a customer?

• [Open text]

Question 3: Would you consider returning in the future?

• ☐ Yes, if [condition]
• ☐ No

Churn Reason Breakdown (based on exit surveys + data analysis):

Churn Reason	% of Churned Users	Addressable?	Action Plan
Didn't see value / low usage	X%	✅ Yes	Improve onboarding, activation
Too expensive	X%	✅ Yes	Introduce lower-tier plan, annual discount
Missing features	X%	✅ Yes	Build top-requested features
Found better alternative	X%	⚠️ Maybe	Competitive analysis, differentiate
Too complicated	X%	✅ Yes	Simplify UI, improve help docs
Poor support	X%	✅ Yes	Hire more support, reduce response time
Technical issues	X%	✅ Yes	Fix bugs, improve performance
Company shut down / no longer needed	X%	❌ No	Unavoidable churn

Top 3 Addressable Churn Reasons:

1. [Reason 1] — [Action plan]
2. [Reason 2] — [Action plan]
3. [Reason 3] — [Action plan]

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Question CR2: How can you reduce involuntary churn?

Involuntary Churn = Users who churn due to failed payments (not because they wanted to leave)

Payment Failure Reasons:

• Expired credit card
• Insufficient funds
• Bank decline (fraud alert)
• Card changed (lost/stolen)

Dunning Campaign (recover failed payments):

Failed Payment Day 0:

• Email 1: "Payment failed. Please update your payment method" (link to billing page)
• In-app banner: "Action required: Update payment method"

Day 3:

• Email 2: "Reminder: Your payment failed. Update card to keep access"
• Grace period: Keep product access for 7-14 days

Day 7:

• Email 3: "Final reminder: Update payment or service will be suspended in 3 days"
• SMS (optional): "Your [Product] account will be suspended. Update payment now"

Day 10:

• Suspend Service: Downgrade to free plan or suspend account
• Email 4: "Account suspended. Update payment to restore access"

Smart Dunning Tactics:

• Retry Schedule: Retry failed payment 3 times (Day 0, Day 3, Day 7)
• Alternative Payment Methods: Offer PayPal, bank transfer, crypto
• Update Card Before Expiry: Email users 30 days before card expires

Involuntary Churn Rate:

• Current: [X% of total churn]
• Target: [<20% of total churn]

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Retention Loops & Product Improvements

Question RL1: What retention loops can you build?

Retention Loop = A repeating cycle that brings users back to the product

Examples:

1. Content Drip Loop (e.g., Duolingo, Netflix)

   • New content released regularly (daily lessons, weekly episodes)
   • Push notification: "Your [new content] is ready"
   • User returns → consumes content → waits for next drop

2. Social Loop (e.g., LinkedIn, Facebook)

   • User posts content
   • Followers engage (likes, comments)
   • Push notification: "[Friend] commented on your post"
   • User returns → engages → posts again

3. Progress Loop (e.g., Strava, MyFitnessPal)

   • User logs progress (workout, meal, habit)
   • App shows streaks, achievements, leaderboards
   • User returns to maintain streak → logs progress → cycle continues

4. Collaboration Loop (e.g., Slack, Figma, Notion)

   • User invites team members
   • Team collaborates in product
   • Notifications: "[@mention] left a comment"
   • User returns → collaborates → cycle continues

5. Email Digest Loop (e.g., Substack, Reddit)

   • User subscribes to digest (daily, weekly)
   • Email: "Here's what you missed this week"
   • User clicks → returns to product → subscribes again

Your Retention Loop(s) (choose 1-3):

1. [Loop Type]: [How it works — trigger → action → return]
2. [Loop Type]: [How it works]
3. [Loop Type]: [How it works]

Implementation Plan:

• Loop 1: [What needs to be built? Timeline?]
• Loop 2: [What needs to be built? Timeline?]

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Question RL2: What product improvements will reduce churn?

Churn-Reducing Product Changes (based on churn reasons and user feedback):

Churn Reason	Product Improvement	Priority	Timeline
"Didn't see value / low usage"	Improve onboarding, add activation checklist	High	4 weeks
"Missing feature X"	Build feature X (top-requested)	High	8 weeks
"Too complicated"	Simplify UI, add tooltips, create video tutorials	Medium	6 weeks
"Technical issues"	Fix top 5 bugs, improve performance	High	2 weeks
"Poor support"	Hire 2 support reps, reduce response time to <2 hours	Medium	4 weeks

Quick Wins (implement in next 30 days):

1. [Improvement 1] — e.g., "Add onboarding checklist (3 tasks to activation)"
2. [Improvement 2] — e.g., "Fix top 3 bugs causing user frustration"
3. [Improvement 3] — e.g., "Send weekly email digest to inactive users"

Long-Term Bets (implement in next 90 days):

1. [Improvement 1] — e.g., "Build top-requested feature (X)"
2. [Improvement 2] — e.g., "Redesign core workflow to reduce friction"
3. [Improvement 3] — e.g., "Add social features (commenting, sharing)"

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Customer Success Strategy

Question CS1: What is your customer success strategy?

Customer Success Model (choose based on ARPU and scale):

ARPU	Model	CS Ratio	Touchpoints
<$100/mo	Tech-Touch (automated)	1 CSM : ∞ users	Email, in-app, chatbot, self-service resources
$100-$500/mo	Hybrid (light-touch)	1 CSM : 100-200	Quarterly check-ins, email, webinars, resources
$500-$2k/mo	High-Touch (proactive)	1 CSM : 50-100	Monthly QBRs, onboarding, ongoing support
>$2k/mo	White-Glove (dedicated)	1 CSM : 10-30	Dedicated CSM, weekly check-ins, custom success plan

Your Model: [Tech-Touch / Hybrid / High-Touch / White-Glove]

Customer Success Touchpoints:

Onboarding (Days 0-30):

• Day 0: Welcome email + onboarding checklist
• Day 3: Check-in email: "How's onboarding going? Need help?"
• Day 7: Onboarding call (high-touch) or webinar (light-touch)
• Day 14: Feature tutorial: "Here's how to use [power feature]"
• Day 30: Success check-in: "Did you achieve [goal]?"

Ongoing Success (Month 2+):

• Monthly: Usage report: "Here's your activity this month"
• Quarterly: QBR (Quarterly Business Review) — review goals, usage, ROI
• Ad Hoc: Trigger-based outreach (e.g., usage drops, feature launch, renewal coming up)

Renewal/Expansion (30-60 days before renewal):

• Renewal campaign: "Your contract renews in 60 days. Let's review value delivered"
• Expansion conversation: "You're using X feature heavily. Have you considered Y feature?"

Customer Health Score (predict churn risk):

Factor	Weight	Healthy	At Risk	Churn Risk
Login Frequency	30%	10+ /mo	3-9 /mo	<3 /mo
Feature Usage (core features)	25%	80%+	40-79%	<40%
Support Tickets (open)	15%	0-1	2-3	4+
NPS Score	15%	9-10	7-8	0-6
Payment Status	15%	Current	Late	Failed

Health Score Calculation:

• Green (80-100): Healthy, potential for expansion
• Yellow (50-79): At risk, requires proactive outreach
• Red (<50): Churn risk, urgent intervention

Current Health Score Distribution:

• Green: [X%] of customers
• Yellow: [Y%] of customers
• Red: [Z%] of customers

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Question CS2: How will you scale customer success?

Scaling Customer Success (as you grow from 100 → 1,000 → 10,000 customers):

Phase 1: Manual (0-100 customers)

• 1 CSM handles all customers
• Personal touch: emails, calls, QBRs
• Learn what works, document best practices

Phase 2: Semi-Automated (100-1,000 customers)

• Segment customers (high-value = high-touch, low-value = tech-touch)
• Automate touchpoints (email sequences, in-app messages, webinars)
• Hire 2-3 CSMs for high-value accounts

Phase 3: Fully Scaled (1,000+ customers)

• CSM team by segment: Enterprise (white-glove), Mid-Market (high-touch), SMB (tech-touch)
• Self-service resources: Help center, video tutorials, community forum
• Proactive monitoring: Health score dashboard, automated alerts for at-risk accounts

Your Scaling Plan:

• Current customer count: [X]
• Current CSM count: [Y]
• Next hire milestone: [When you reach Z customers, hire CSM #N]

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Implementation Roadmap

Question IR1: What is your 90-day retention optimization plan?

Phase 1: Analyze (Weeks 1-3)

Goal: Understand why users churn and identify at-risk segments

• Week 1: Cohort Analysis

  • Pull cohort retention data (M0, M1, M3, M6, M12)
  • Identify best-retaining and worst-retaining cohorts
  • Segment retention by acquisition source, user persona, plan tier

• Week 2: Churn Reason Analysis

  • Implement exit survey (trigger on cancellation)
  • Interview 10-20 churned users (qualitative insights)
  • Categorize churn reasons (addressable vs. unavoidable)

• Week 3: At-Risk User Identification

  • Define at-risk criteria (3-5 leading indicators)
  • Build at-risk user list (dashboard or export)
  • Calculate health scores for all active users

Deliverable: Retention analysis report with top 3 churn drivers and at-risk user list

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Phase 2: Intervene (Weeks 4-6)

Goal: Launch win-back campaigns and reduce involuntary churn

• Week 4: Win-Back Campaign

  • Build 4-email win-back sequence (Days 7, 14, 21, 30 inactive)
  • Set up automated triggers (email service provider)
  • Launch campaign for currently inactive users

• Week 5: Dunning Campaign

  • Build dunning email sequence (payment failed → 3 reminders → suspend)
  • Set up retry schedule (retry 3x over 10 days)
  • Launch campaign for users with failed payments

• Week 6: Personal Outreach (High-Value Users)

  • Identify top 20% of at-risk users by revenue
  • Assign CSM to reach out (email, call, or LinkedIn)
  • Offer solutions: feature training, discount, custom plan

Deliverable: Win-back and dunning campaigns live, 20% of at-risk high-value users contacted

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Phase 3: Improve Product (Weeks 7-12)

Goal: Build retention loops and fix top churn drivers

• Week 7-8: Quick Wins

  • Implement onboarding checklist (improve activation)
  • Fix top 3 bugs causing churn
  • Add email digest (weekly summary for inactive users)

• Week 9-10: Retention Loop

  • Design retention loop (content drip, social, progress, collaboration)
  • Build loop triggers and notifications
  • Launch loop to 10% of users (A/B test)

• Week 11-12: Feature Improvements

  • Build top-requested feature (reduces "missing feature" churn)
  • Simplify core workflow (reduces "too complicated" churn)
  • Improve performance (reduces "technical issues" churn)

Deliverable: Retention loop live, top churn drivers addressed via product improvements

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Phase 4: Monitor & Iterate (Ongoing)

Goal: Track retention metrics and continuously optimize

• Weekly: Review at-risk user list, reach out to red-health-score users
• Monthly: Review cohort retention, churn rate, win-back campaign performance
• Quarterly: Deep dive into churn reasons, prioritize product improvements

Success Metrics (track over 90 days):

• D30 Retention: [Baseline → Target — e.g., 35% → 45%]
• Churn Rate: [Baseline → Target — e.g., 8% → 5%]
• Win-Back Reactivation Rate: [Target: 5-10% of inactive users return]
• Involuntary Churn: [Baseline → Target — e.g., 30% of churn → <20% of churn]
• Health Score: [% of users in Green — e.g., 60% → 75%]

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STEP 4: Generate Comprehensive Retention Optimization Strategy

You will now receive a comprehensive document covering:

Section 1: Executive Summary

• Current retention performance (D1/D7/D30, churn rate)
• Retention curve shape and critical drop-off points
• Top 3 churn drivers and action plans

Section 2: Cohort Analysis Deep Dive

• Cohort retention table (M0, M1, M3, M6, M12)
• Cohort improvement trend (improving, flat, declining)
• Segment retention comparison (by persona, acquisition source, plan tier)
• Best-retaining and worst-retaining segments

Section 3: Churn Prediction & At-Risk Users

• At-risk user criteria (3-5 leading indicators)
• At-risk user count and % of user base
• Customer health score model (5 factors, weighted)
• Health score distribution (Green, Yellow, Red)

Section 4: Win-Back & Dunning Campaigns

• Win-Back Campaign: 4-tier email sequence (Days 7, 14, 21, 30 inactive)
• Dunning Campaign: Payment failure recovery (Day 0, 3, 7, 10)
• Win-back channels (email, in-app, push, SMS, retargeting, personal outreach)
• Success metrics (open rate, click rate, reactivation rate)

Section 5: Churn Reason Analysis

• Exit survey questions (3 key questions)
• Churn reason breakdown (% of churned users, addressable?, action plan)
• Top 3 addressable churn reasons with action plans
• Involuntary churn strategy (dunning, grace period, alternative payments)

Section 6: Retention Loops & Product Improvements

• Retention Loops (1-3 loops: content drip, social, progress, collaboration, email digest)
• Quick Wins (implement in 30 days: onboarding checklist, bug fixes, email digest)
• Long-Term Bets (implement in 90 days: build top feature, redesign workflow, add social features)

Section 7: Customer Success Strategy

• Customer success model (tech-touch, hybrid, high-touch, white-glove)
• Touchpoints (onboarding Days 0-30, ongoing success, renewal/expansion)
• Customer health score calculation (5 factors, Green/Yellow/Red)
• Scaling plan (manual → semi-automated → fully scaled)

Section 8: Implementation Roadmap

• Phase 1 (Weeks 1-3): Cohort analysis, churn reason analysis, at-risk user identification
• Phase 2 (Weeks 4-6): Win-back campaign, dunning campaign, personal outreach
• Phase 3 (Weeks 7-12): Quick wins, retention loop, feature improvements
• Phase 4 (Ongoing): Monitor metrics, weekly/monthly/quarterly reviews

Section 9: Success Metrics

• D30 Retention: [Baseline → Target]
• Churn Rate: [Baseline → Target]
• Win-Back Reactivation Rate: [Target: 5-10%]
• Involuntary Churn: [<20% of total churn]
• Health Score: [75%+ of users in Green]

Section 10: Next Steps

• Launch win-back campaign this week
• Schedule monthly retention review meetings
• Integrate with customer-feedback-framework (use exit surveys to gather churn reasons)
• Integrate with onboarding-flow-optimizer (improve early retention via better activation)

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STEP 5: Quality Review & Iteration

After generating the strategy, I will ask:

Quality Check:

1. Is the retention baseline and target realistic? (D30 retention 35% → 45% in 90 days is achievable)
2. Are churn reasons based on real data (exit surveys, user interviews)?
3. Are at-risk criteria measurable and actionable?
4. Is the win-back campaign multi-channel and escalating?
5. Are retention loops feasible to build in the given timeline?
6. Is the customer success model appropriate for your ARPU and scale?

Iterate? [Yes — refine X / No — finalize]

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STEP 6: Save & Next Steps

Once finalized, I will:

1. Save the retention optimization strategy to your project folder
2. Suggest running onboarding-flow-optimizer next (to improve early retention)
3. Remind you to launch the win-back campaign this week

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8 Critical Guidelines for This Skill

1. Retention > Acquisition: It's 5-7x cheaper to retain a customer than acquire a new one. Prioritize retention over growth.

2. Cohort analysis is essential: Don't just track overall retention. Track by cohort (signup month) and segment (persona, acquisition source, plan tier).

3. At-risk users can be saved: Identify users showing declining engagement 2-4 weeks before they churn, and intervene proactively.

4. Involuntary churn is addressable: 20-40% of churn is due to failed payments. Implement dunning campaigns to recover revenue.

5. Exit surveys are mandatory: You can't fix churn if you don't know why users leave. Trigger exit surveys on cancellation.

6. Retention loops > one-time campaigns: Build repeating cycles (content drip, social, progress) that bring users back automatically.

7. Health scores predict churn: Track 5 factors (login frequency, feature usage, support tickets, NPS, payment status) to calculate customer health.

8. Customer success scales with ARPU: Low ARPU = tech-touch (automated). High ARPU = high-touch (dedicated CSM).

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Quality Checklist (Before Finalizing)

• Retention baseline and targets are clearly defined (D1/D7/D30, churn rate)
• Cohort analysis shows retention by signup month and user segment
• At-risk user criteria are measurable (3-5 leading indicators)
• Win-back campaign is multi-channel with 4 touchpoints (Days 7, 14, 21, 30)
• Dunning campaign is implemented to reduce involuntary churn
• Top 3 churn reasons are identified with action plans
• 1-3 retention loops are defined (content drip, social, progress, collaboration, email digest)
• Customer success model matches your ARPU and scale
• Implementation roadmap is realistic (Weeks 1-3: Analyze, Weeks 4-6: Intervene, Weeks 7-12: Improve)
• Success metrics are tracked (D30 retention, churn rate, win-back reactivation, involuntary churn, health score)

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Integration with Other Skills

Upstream Skills (reuse data from):

• metrics-dashboard-designer → Retention metrics, cohort data, churn rates, health scores
• customer-persona-builder → User segments for cohort analysis
• product-positioning-expert → Value delivered, success indicators
• onboarding-flow-optimizer → Activation rates, early retention data
• customer-feedback-framework → Churn reasons, exit surveys, NPS, CSAT
• email-marketing-architect → Win-back email sequences, drip campaigns
• growth-hacking-playbook → Retention loops (AARRR framework)

Downstream Skills (use this data in):

• customer-feedback-framework → Gather feedback from churned users and at-risk users
• onboarding-flow-optimizer → Improve early retention (D1-D7) via better onboarding and activation
• product roadmap → Prioritize features that reduce churn (top-requested features, bug fixes)
• investor-pitch-deck-builder → Use improved retention metrics in traction slides
• financial-model-architect → Use lower churn rate to project revenue and LTV

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HTML Output Verification

After generating the HTML report, verify all elements render correctly:

Visual Verification Checklist

• Header displays business name and date correctly
• Score banner shows D30 retention, D7 retention, churn rate, at-risk %, healthy %
• Curve type verdict box displays correctly
• Retention curve container shows type and description
• Cohort table displays 4+ rows with color-coded retention cells
• Segment cards show 3-4 segments with metrics
• Risk indicators display 4-5 at-risk criteria with icons
• Health score distribution shows green/yellow/red percentages
• Health factors list shows 5 weighted factors
• Win-back timeline displays 4 escalating tiers
• Churn table shows reasons with addressability badges
• Retention loops show 2-3 loop cards with cycle steps
• CS model displays name and ratio
• Touchpoints grid shows 3 phases
• All 4 charts render with correct data:
  • Retention curve (line with fill)
  • Cohort comparison (bar)
  • Churn reasons (horizontal bar)
  • Health score distribution (doughnut)
• Success metrics show 5 baseline -> target cards
• Roadmap displays 4 phases with tasks
• Footer shows StratArts branding

Data Quality Verification

• D30 retention is realistic (typically 20-50% for SaaS)
• Churn rate aligns with retention (if 38% D30 retention, expect 5-8% monthly churn)
• Cohort data shows trend (improving, flat, or declining)
• Health score distribution adds to 100%
• Win-back tiers escalate logically (Days 7 -> 14 -> 21 -> 30+)
• Churn reasons sum to ~100%
• CS model matches ARPU (low ARPU = tech-touch, high = dedicated)

Template Location

• Skeleton template: html-templates/retention-optimization-expert.html
• Test output: skills/retention-metrics/retention-optimization-expert/test-template-output.html

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End of Skill