Predict and prevent customer churn early

Identify at-risk customer segments and generate intervention strategies

SaaS companies with recurring revenue and predictable churn patterns, where early intervention on at-risk segments preserves more revenue than win-back campaigns after churn.

• · Customer segments to score (predefined CRM segments or behavioral cohorts with engagement signals)
• · Behavioral signals: email engagement trends, purchase frequency/recency, login/usage patterns, support sentiment
• · CRM data source (Salesforce, HubSpot, or other)
• · Intervention budget (optional; if provided, prioritizes high-LTV-at-risk segments)
• · Lookback period (optional; defaults to 90 days)
• · Custom churn signals (optional; brand-specific indicators that precede churn)

• · Churn risk score per segment (0-100, composite of weighted behavioral signals)
• · Risk tier classification: low risk, medium risk, high risk, urgent (churn imminent)
• · Intervention playbook per tier: specific actions, timing windows, channel recommendations, messaging approach
• · LTV-at-risk quantification per segment (revenue exposure if segment churns)
• · Intervention budget allocation across segments (by LTV-at-risk and intervention cost)
• · Monitoring cadence recommendation (monthly review for low-risk, weekly for urgent)

• · CRM MCP: Salesforce or HubSpot (pull behavioral data)
• · churn-predictor.py (scoring algorithm)
• · intervention-recommender.py (playbook generation)

• · CRM is connected and has 90+ days of behavioral data
• · Customer segments are pre-defined in CRM or user specifies cohort criteria
• · At least 100 customers per segment for reliable scoring
• · Email engagement, purchase, and usage data are tracked
• · LTV is calculated (if available, for optimization)

• · Scoring done on too-recent data (30-day window misses slow-burn churn)
• · All signals weighted equally (purchase recency should outweigh historical behavior)
• · Intervention playbook same for all tiers (urgent vs medium risk need different urgency/cadence)
• · Interventions executed without segmentation (mass re-engagement email to everyone performs worse)
• · No measurement of intervention success (can't tell if playbook works)

• · Composite risk score using weighted signals: purchase recency (highest weight), engagement trend direction, support sentiment, usage pattern breaks
• · Risk tier framework with named tiers: low, medium, high, urgent
• · Intervention playbook per tier with specific actions (retention offer vs win-back differs)
• · LTV-at-risk quantification (which segments matter most by revenue exposure)