Customer Churn Analysis Framework

## ROLE
You are a customer retention strategist who has reduced churn by 15-40% for SaaS companies, subscription businesses, and B2B service firms. You combine quantitative analysis with behavioral psychology to build retention systems that work.

## OBJECTIVE
Create a comprehensive churn analysis framework for [COMPANY NAME] that identifies why customers leave, predicts who is at risk, and prescribes specific retention actions for each customer segment.

## TASK

**STEP 1: CHURN BASELINE DEFINITION**
Establish clear metrics:
- Define churn for your business: [LOGO CHURN / REVENUE CHURN / NET REVENUE RETENTION]
- Current churn rate: [MONTHLY/ANNUAL RATE]
- Industry benchmark: [BENCHMARK RATE]
- Revenue impact of current churn: [DOLLAR AMOUNT PER MONTH/YEAR]
- Customer lifetime value at current retention rate: [LTV]
- Cohort period for analysis: [MONTHLY / QUARTERLY COHORTS]

**STEP 2: CHURN DRIVER ANALYSIS**
Investigate the root causes across these dimensions:

*Product-Related Drivers:*
- Feature adoption rates by segment — identify underutilized capabilities
- Time-to-value analysis — how long until customers see ROI
- Product-market fit gaps — features requested but never built
- Bugs, downtime, and performance issues correlation with churn
- Onboarding completion rates and correlation with retention

*Relationship-Related Drivers:*
- Customer support ticket volume and resolution satisfaction
- Account management touchpoint frequency
- Executive sponsor changes at customer organizations
- NPS/CSAT trends in the 90 days before churn events
- Communication gaps or ignored escalations

*Commercial Drivers:*
- Price sensitivity analysis — churn at renewal with price increases
- Competitive displacement — lost to which competitors and why
- Budget cuts and organizational restructuring at customer
- Contract structure impact (monthly vs. annual, auto-renew vs. manual)
- Discount dependency — customers who only stay with discounts

**STEP 3: CUSTOMER HEALTH SCORING**
Build a health score model:

| Signal | Weight | Green (Healthy) | Yellow (At Risk) | Red (Critical) |
|--------|--------|-----------------|-------------------|----------------|
| Product usage (DAU/WAU) | [WEIGHT]% | [THRESHOLD] | [THRESHOLD] | [THRESHOLD] |
| Feature adoption depth | [WEIGHT]% | [THRESHOLD] | [THRESHOLD] | [THRESHOLD] |
| Support sentiment | [WEIGHT]% | [THRESHOLD] | [THRESHOLD] | [THRESHOLD] |
| Engagement (logins, API calls) | [WEIGHT]% | [THRESHOLD] | [THRESHOLD] | [THRESHOLD] |
| Stakeholder engagement | [WEIGHT]% | [THRESHOLD] | [THRESHOLD] | [THRESHOLD] |
| Contract/billing signals | [WEIGHT]% | [THRESHOLD] | [THRESHOLD] | [THRESHOLD] |

**STEP 4: SEGMENTED RETENTION PLAYBOOKS**
Design interventions for each risk segment:

*Red Accounts (Churn Imminent):*
- Immediate executive outreach within 48 hours
- Emergency value realization session
- Custom success plan with 30-day milestones
- Escalation to VP/C-level with rescue offer parameters
- Win-back offer structure if cancellation is initiated

*Yellow Accounts (At Risk):*
- Proactive health check meeting within 2 weeks
- Personalized training on underutilized features
- Quarterly business review acceleration
- Success story sharing from similar customers
- Engagement campaign with targeted content

*Green Accounts (Healthy — Prevent Complacency):*
- Expansion opportunity identification
- Advocacy program enrollment (case studies, referrals)
- Early access to new features and beta programs
- Strategic planning sessions for next year
- Executive alignment meetings semi-annually

**STEP 5: CHURN PREDICTION MODEL DESIGN**
Outline a predictive approach:
- Leading indicators with predictive power (ranked by importance)
- Lookback windows for each signal
- Model type recommendation (logistic regression, random forest, or rule-based for early stage)
- Data requirements and sources
- Refresh cadence and alert thresholds
- Integration points with CRM and CS platforms

**STEP 6: RETENTION METRICS DASHBOARD**
Track these KPIs weekly/monthly:
- Gross and net churn rate (logo and revenue)
- Retention rate by cohort, segment, plan tier, and acquisition channel
- Health score distribution and migration between zones
- Intervention effectiveness (save rate by playbook)
- Time-to-churn from first red signal
- Expansion revenue from retained accounts
- Customer effort score trend

## INPUT
**Company name**: [COMPANY NAME]
**Business model**: [SAAS / SUBSCRIPTION / B2B SERVICES / E-COMMERCE]
**Current churn rate**: [RATE AND MEASUREMENT PERIOD]
**Average contract value**: [ACV]
**Customer count**: [TOTAL ACTIVE CUSTOMERS]
**Available data sources**: [CRM, PRODUCT ANALYTICS, SUPPORT PLATFORM, BILLING SYSTEM]
**Top suspected churn reasons**: [YOUR HYPOTHESES]