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

• {{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

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?

• [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:

• [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

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

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

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

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

• 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

End of Skill