--- name: churn-risk-intervention description: Predict customer churn risk and execute intervention playbooks to retain at-risk accounts. Use when building churn prediction models, designing retention playbooks, identifying at-risk accounts, executing save campaigns, or measuring retention program effectiveness. Triggers on phrases like "churn risk", "customer retention", "at-risk accounts", "save campaign", "churn prediction", "retention playbook", "customer win-back", "deflection retention", "renewal risk", "account health churn". --- # Churn Risk Prediction & Intervention Identify customers at risk of churning and execute targeted retention interventions. ## Workflow ### Churn Prediction Model Setup Trigger: Quarterly model refresh; new data source availability; model accuracy decline: 1. **Data preparation**: Aggregate customer data — usage metrics (login frequency, feature adoption, session duration), support signals (ticket volume, CSAT, escalation rate), billing signals (payment failures, downgrade requests), engagement signals (NPS, survey responses, community activity). 2. **Feature engineering**: Calculate derived metrics — usage trend (30-day change %), support interaction frequency, days since last login, feature adoption gap, contract renewal timeline; normalize features for model input. 3. **Model training**: Train classification model (logistic regression or gradient boosting) on historical data (labeled: churned vs. retained in last 12 months); achieve target AUC > 0.80; validate on holdout set. 4. **Risk scoring**: Output churn probability (0–100%) for each active customer; classify risk tiers — High (>70%), Medium (40–70%), Low (<40%); review calibration monthly. 5. **Intervention assignment**: Based on risk tier and account value — assign appropriate playbook (CSE outreach, discount offer, success plan, executive check-in); prioritize by revenue at risk. 6. **Intervention execution**: CSE/support team executes playbook within 48 hours of flag; track intervention type, timing, customer response, outcome; log all actions in CRM. 7. **Outcome tracking**: Measure retention rate by risk tier and intervention type; calculate save rate (at-risk customers retained vs. lost); compare to baseline (no intervention). 8. **Model improvement**: Retrain quarterly with new outcome data; add new features from domain experts; A/B test intervention strategies; iterate on risk thresholds. ### Churn Risk Scoring Framework ``` CHURN RISK SCORING — FEATURE WEIGHTS ======================================== Signal Category Weight Example Features ---------------------- ------- ------------------------------------------ Usage decline 25% - Login frequency down > 50% (30-day trend) - Active features down > 2 - Session duration down > 30% - Key workflow abandonment Support signals 20% - CSAT score < 3.0 (last 3 tickets) - Ticket volume spike (2× normal) - Escalation in last 30 days - Negative sentiment in tickets Billing signals 15% - Payment failure in last 30 days - Downgrade request - Discount request - Billing dispute Engagement signals 15% - NPS score < 6 (promoter → passive/detractor) - Survey non-response (disengagement) - Community participation stopped - Training/webinar attendance declined Contract signals 10% - Renewal within 90 days - No expansion in 12+ months - Competitor mentioned in support ticket - Decision maker changed (new contact) External signals 10% - Industry downturn (sector risk) - Company funding news (down round, layoffs) - Competitive threat detected - Market intelligence flag - News monitoring alert - Financial health decline - M&A activity detected - Regulatory changes affecting industry - Economic indicator shift - Customer sentiment on social media - Review site rating change - Employee review score drop Risk Tier Thresholds: High risk: Score > 70% → Immediate intervention (48 hours) Medium risk: Score 40–70% → Proactive outreach (7 days) Low risk: Score < 40% → Standard monitoring Monthly Review: - Validate predictions: % of High risk that actually churned (target > 60%) - False positive rate: % of High risk that didn't churn (target < 40%) - Model drift detection: Compare recent predictions to baseline ``` ### Intervention Playbooks ``` CHURN INTERVENTION PLAYBOOKS ============================== Playbook A: High-Value, High-Risk (Enterprise, > $10K ARR) Trigger: Churn risk > 70% AND ARR > $10,000 Owner: VP Customer Success + Account Executive Timeline: 48 hours to first contact Step 1: Data review (hour 0–24) - Pull full account history: usage, support, billing, key contacts - Identify churn driver: usage decline? support dissatisfaction? competitor? pricing? - Prepare personalized intervention: specific actions based on driver Step 2: Executive outreach (hour 24–48) - VP CS sends personalized email: "I noticed X, I want to understand" - Schedule 30-minute call within 3 business days - Offer: Dedicated success plan, priority support, custom training Step 3: Resolution plan (week 1–2) - Address root cause: fix support issues, enable unused features, provide competitive differentiation - Offer incentives if appropriate: extended term, pricing adjustment (requires CFO approval) - Document action items with timelines Step 4: Follow-up (week 3–4) - Weekly check-in call for 4 weeks - Track usage recovery: login frequency, feature adoption - Re-assess churn risk score monthly Playbook B: Mid-Value, Medium-Risk ($1K–$10K ARR) Trigger: Churn risk 40–70% AND ARR $1,000–$10,000 Owner: Customer Success Manager Timeline: 7 days to first contact Step 1: Automated outreach - Email: "We want to make sure you're getting the most out of [Product]" - Include: Personalized usage tips, relevant training resources, offer to schedule call Step 2: CSE follow-up (if no response in 3 days) - Phone call: "I'm reaching out because I noticed you haven't been using [feature]" - Offer: 15-minute onboarding call, troubleshooting session Step 3: Resolution - If engagement resumes: Monitor for 30 days; reduce risk score - If still at-risk: Escalate to Playbook A if revenue justifies Playbook C: Low-Value, Self-Service (< $1K ARR) Trigger: Churn risk > 60% AND ARR < $1,000 Owner: Automated Timeline: Immediate Step 1: In-app message + email - "We miss you! Here are 3 things you might find helpful:" - Links to relevant tutorials, FAQs, community forum Step 2: Second touch (7 days later, if no engagement) - Email: "Need help? Our support team is here for you" - Include: Direct link to create support ticket Step 3: Win-back (30 days later) - Email: Special offer (discount, free month) to reactivate - Track: Reactivation rate, lifetime value recovery ``` ## Edge Cases - **Churn due to external factors** (customer company going out of business, budget cuts, M&A): - Detection: News monitoring alerts (layoffs, funding loss, acquisition); sudden usage drop across entire organization - Response: Immediate outreach from executive ("We heard about [event], we want to understand how we can help") - Outcome: May not be saveable; focus on knowledge transfer, graceful offboarding, and maintaining relationship for future - Prevention: Diversify customer base across industries; avoid concentration risk - **Silent churn** (customer stops using but doesn't cancel — "quiet quit"): - Detection: Usage drops to near-zero but subscription active; no support interaction; no communication - Impact: Still counts as revenue but at high risk of actual churn at renewal - Intervention: Aggressive re-engagement campaign (email sequence, in-app messages, phone call) - Metric: "Ghost account" rate — % of active subscriptions with < 1 login in 30 days (target < 5%) - **Competitive displacement** (customer evaluating or switching to competitor): - Detection: Competitor mentioned in support ticket; G2/Capterra review comparison; sales intel - Response: Competitive battle card prepared by AE; highlight differentiation; offer migration assistance if switching back - Pricing: Competitive pricing review; match-or-beat policy (requires approval) - Differentiation: Emphasize unique features, integration depth, customer success value - **Post-purchase churn** (customer churns within 30 days of signup): - Root cause: Failed onboarding; product doesn't meet expectations; wrong buyer fit - Prevention: Better qualification during sales; proactive onboarding (dedicated CSM for first 30 days) - Detection: Usage < 20% of expected within first week; onboarding tasks incomplete after 14 days - Intervention: Immediate CSM outreach; free extended trial; personalized success plan - Metric: 30-day churn rate (target < 5% for enterprise, < 15% for SMB) - **Churn model false positives** (model flags customer as high-risk but customer is happy): - Cause: Seasonal usage patterns; planned maintenance periods; temporary staff changes - Detection: CSE feedback loop ("This customer is fine, model is wrong") - Mitigation: Allow manual risk score override; retrain model with corrected labels - Balance: Prefer false positives (unnecessary outreach) over false negatives (missed churn) - Metric: Precision (true positives / all flagged) target > 60% ## Integration Points - **CRM**: Salesforce, HubSpot — customer data, account value, renewal dates, key contacts - **Product analytics**: Mixpanel, Amplitude, Segment — usage data, feature adoption, login frequency - **Help desk**: Zendesk, Freshdesk — support history, CSAT, sentiment analysis - **Billing**: Stripe, Chargebee, Recurly — payment history, subscription status, revenue data - **Customer success**: Gainsight, Totango, Planhat — health scores, engagement tracking, QBR notes - **ML platform**: DataBricks, Databricks, SageMaker — model training, prediction scoring - **Email marketing**: Customer.io, Iterable, SendGrid — intervention outreach, win-back campaigns - **News monitoring**: Google Alerts, BuzzSumo, custom — external factor detection - **Data warehouse**: Snowflake, BigQuery — unified data aggregation, historical analysis - **Workflow automation**: Zapier, Make, custom — automated intervention triggers