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.
STEP 0: Pre-Generation Verification
Before generating the HTML output, verify all required data is collected:
Header & Score Banner
Executive Summary
Cohort Analysis
Segment Retention
At-Risk Identification
Health Score
Win-Back Campaign
Churn Reasons
Retention Loops
Customer Success
Charts
Success Metrics
Roadmap
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
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.
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]
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]
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)
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
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):
Declining Login Frequency : [e.g., "User logged in 10x last month, only 3x this month"]
Reduced Feature Usage : [e.g., "User stopped using core feature X"]
Lower Session Duration : [e.g., "Average session dropped from 8 min to 2 min"]
Support Tickets : [e.g., "User submitted 3+ bug reports"]
Payment Issues : [e.g., "Credit card declined, didn't update"]
No Activity in X Days : [e.g., "No login in 14+ days"]
Your At-Risk Criteria (choose 3-5):
[Indicator 1] — e.g., "No login in 14 days"
[Indicator 2] — e.g., "Session frequency dropped >50%"
[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%]
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]
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?
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 :
[Reason 1] — [Action plan]
[Reason 2] — [Action plan]
[Reason 3] — [Action plan]
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]
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 :
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
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
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
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
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):
[Loop Type] : [How it works — trigger → action → return]
[Loop Type] : [How it works]
[Loop Type] : [How it works]
Implementation Plan :
Loop 1: [What needs to be built? Timeline?]
Loop 2: [What needs to be built? Timeline?]
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):
[Improvement 1] — e.g., "Add onboarding checklist (3 tasks to activation)"
[Improvement 2] — e.g., "Fix top 3 bugs causing user frustration"
[Improvement 3] — e.g., "Send weekly email digest to inactive users"
Long-Term Bets (implement in next 90 days):
[Improvement 1] — e.g., "Build top-requested feature (X)"
[Improvement 2] — e.g., "Redesign core workflow to reduce friction"
[Improvement 3] — e.g., "Add social features (commenting, sharing)"
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
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]
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
Deliverable : Retention analysis report with top 3 churn drivers and at-risk user list
Phase 2: Intervene (Weeks 4-6)
Goal : Launch win-back campaigns and reduce involuntary churn
Deliverable : Win-back and dunning campaigns live, 20% of at-risk high-value users contacted
Phase 3: Improve Product (Weeks 7-12)
Goal : Build retention loops and fix top churn drivers
Deliverable : Retention loop live, top churn drivers addressed via product improvements
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%]
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)
STEP 5: Quality Review & Iteration
After generating the strategy, I will ask:
Quality Check :
Is the retention baseline and target realistic? (D30 retention 35% → 45% in 90 days is achievable)
Are churn reasons based on real data (exit surveys, user interviews)?
Are at-risk criteria measurable and actionable?
Is the win-back campaign multi-channel and escalating?
Are retention loops feasible to build in the given timeline?
Is the customer success model appropriate for your ARPU and scale?
Iterate? [Yes — refine X / No — finalize]
STEP 6: Save & Next Steps
Once finalized, I will:
Save the retention optimization strategy to your project folder
Suggest running onboarding-flow-optimizer next (to improve early retention)
Remind you to launch the win-back campaign this week
8 Critical Guidelines for This Skill
Retention > Acquisition : It's 5-7x cheaper to retain a customer than acquire a new one. Prioritize retention over growth.
Cohort analysis is essential : Don't just track overall retention. Track by cohort (signup month) and segment (persona, acquisition source, plan tier).
At-risk users can be saved : Identify users showing declining engagement 2-4 weeks before they churn, and intervene proactively.
Involuntary churn is addressable : 20-40% of churn is due to failed payments. Implement dunning campaigns to recover revenue.
Exit surveys are mandatory : You can't fix churn if you don't know why users leave. Trigger exit surveys on cancellation.
Retention loops > one-time campaigns : Build repeating cycles (content drip, social, progress) that bring users back automatically.
Health scores predict churn : Track 5 factors (login frequency, feature usage, support tickets, NPS, payment status) to calculate customer health.
Customer success scales with ARPU : Low ARPU = tech-touch (automated). High ARPU = high-touch (dedicated CSM).
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)
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
HTML Output Verification
After generating the HTML report, verify all elements render correctly:
Visual Verification Checklist
Data Quality Verification
Template Location
Skeleton template: html-templates/retention-optimization-expert.html
Test output: skills/retention-metrics/retention-optimization-expert/test-template-output.html
End of Skill