Circulos AI

Support AI Skill

Churn Risk Intervention

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

Integration Points

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