| name | churn-risk-detection |
| description | Detects and scores customer churn risk for CPG and retail e-commerce brands using behavioral signals, purchase pattern analysis, and engagement decay metrics. Use when a user needs to identify at-risk customers, build churn prediction models, or design proactive retention interventions. Triggers on requests about churn analysis, customer attrition, retention risk, lapsed customer identification, or win-back targeting.
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| metadata | {"display_name":"Churn Risk Detection","short_description":"Score and predict customer churn risk for retail brands","default_prompt":"Review my churn risk and highlight top risks and next actions","version":"1.0.1","tags":["cpg-retail"],"icon_path":"assets/icon.png"} |
Churn Risk Detection
Overview
This skill identifies customers exhibiting churn signals by analyzing purchase recency decay, frequency decline, engagement drop-off, and behavioral anomalies. It produces a risk-scored customer list with churn probability estimates, time-to-churn predictions, revenue-at-risk quantification, and prescribed intervention strategies. Designed for non-contractual CPG and retail contexts where churn is silent (customers simply stop buying).
When to Use
- Proactively identifying customers likely to churn before they lapse
- Building retention trigger campaigns based on behavioral signals
- Quantifying revenue at risk from customer attrition
- Evaluating the health of a customer base over time
- Designing and prioritizing win-back campaigns
- Setting up automated churn prevention workflows in CRM/ESP
Required Inputs
| Input | Required | Description |
|---|
| Transaction Data | Yes | Customer-level purchase history with dates, amounts, and order counts |
| Customer IDs | Yes | Unique identifiers for each customer |
| Analysis Date | Yes | Current date or reference date for recency calculations |
| Category Purchase Cycle | Recommended | Expected repurchase interval for product category (e.g., 30 days for coffee) |
| Engagement Data | Recommended | Email opens/clicks, site visits, app sessions over time |
| Subscription Status | No | Active, paused, cancelled subscription status (if applicable) |
| Customer Service Data | No | Support tickets, complaints, returns, refund history |
| Acquisition Channel | No | How the customer was acquired (organic, paid, referral, etc.) |
Methodology
Step 1 — Define Churn for the Business
In non-contractual settings, churn must be operationally defined:
Category-Based Churn Definition:
Churn Threshold = Expected Repurchase Cycle × Churn Multiplier
Recommended Multipliers:
Consumables (coffee, supplements, cleaning): 2.0× → e.g., 30-day cycle → churn at 60 days
Semi-durable (skincare, personal care): 2.5× → e.g., 60-day cycle → churn at 150 days
Durable (cookware, appliances): 3.0× → e.g., 180-day cycle → churn at 540 days
Grocery basket: 1.5× → e.g., 14-day cycle → churn at 21 days
If category cycle is unknown, calculate from data:
Median Inter-Purchase Interval (IPI) = Median of (Order Date N+1 - Order Date N) across all customers
Churn Threshold = Median IPI × 2.0 (or use 75th percentile IPI × 1.5)
Step 2 — Behavioral Signal Extraction
Extract churn signals across multiple dimensions:
Purchase Behavior Signals:
| Signal | Calculation | Churn Indicator |
|---|
| Recency Gap | Days since last purchase ÷ Avg IPI | Ratio >1.5 indicates concern; >2.0 critical |
| Frequency Decline | Orders in last 90d vs. prior 90d | >30% decline is a strong churn signal |
| AOV Decline | AOV last 3 orders vs. lifetime AOV | >20% decline indicates reduced commitment |
| Basket Shrinkage | Items per order trending down | Reducing engagement with product range |
| Category Narrowing | # categories last 3 orders vs. historical | Down-trading to fewer categories |
| Promotion Dependency | % of recent orders with discount code | Increasing to >80% signals price-only loyalty |
Engagement Signals:
| Signal | Calculation | Churn Indicator |
|---|
| Email Open Decay | 30-day open rate vs. 90-day average | >40% decline |
| Click-through Decline | 30-day CTR vs. 90-day average | >50% decline |
| Site Visit Frequency | Sessions last 30d vs. prior 30d | >50% decline |
| App Uninstall | App removed or sessions dropped to zero | Strong churn signal |
| Loyalty Inactivity | Points earned last 60d = 0 (for loyalty members) | Disengagement signal |
Service Signals:
| Signal | Churn Indicator |
|---|
| Recent complaint (unresolved) | 2× churn risk elevation |
| Multiple returns (3+ in 90 days) | 3× churn risk elevation |
| Negative review or rating | 1.5× churn risk elevation |
| Subscription downgrade or pause | Immediate intervention needed |
| Delivery failure or late shipment | 1.5× churn risk elevation (compounding) |
Step 3 — Churn Risk Scoring Model
Build a composite churn risk score (0–100):
Weighted Signal Model:
Churn Risk Score =
(Recency Gap Score × 0.30) +
(Frequency Decline Score × 0.25) +
(Engagement Decay Score × 0.20) +
(AOV/Basket Decline Score × 0.10) +
(Service Issue Score × 0.10) +
(Promotion Dependency Score × 0.05)
Each component scored 0–100:
Recency Gap Score = min(100, (Days Since Purchase ÷ Churn Threshold) × 100)
Frequency Decline Score = min(100, max(0, (1 - Recent Freq ÷ Historical Freq) × 100))
...etc.
Risk Tier Classification:
| Tier | Score Range | Estimated Churn Probability | Urgency |
|---|
| Low Risk | 0–25 | <10% | Monitor quarterly |
| Moderate Risk | 26–50 | 10%–30% | Monitor monthly; soft engagement |
| High Risk | 51–75 | 30%–60% | Active intervention within 2 weeks |
| Critical Risk | 76–100 | >60% | Immediate intervention; likely churning now |
Step 4 — Revenue-at-Risk Quantification
Calculate the financial impact of potential churn:
Revenue at Risk (per customer) = Predicted Annual Revenue × Churn Probability
Where Predicted Annual Revenue = Historical AOV × Historical Annual Frequency
Aggregate Revenue at Risk = Σ (Revenue at Risk per customer) for all customers with score >50
Segment-Level Risk Summary:
Segment | Customers at Risk | Avg Churn Score | Revenue at Risk | % of Total Revenue
Champions | XX | XX | $XX,XXX | X%
Loyal | XXX | XX | $XXX,XXX | XX%
At Risk | X,XXX | XX | $XXX,XXX | XX%
Total | X,XXX | -- | $X,XXX,XXX | XX%
Step 5 — Churn Driver Analysis
Identify the primary churn drivers in the customer base:
- Recency-driven churn: Customers simply haven't returned — no engagement decline, just inactivity
- Experience-driven churn: Customers had negative experiences (returns, complaints, delivery issues)
- Value-driven churn: Customers only buy on promotion; churn when discounts stop
- Competition-driven churn: Cross-shopping signals (declining share of wallet)
- Lifecycle-driven churn: Natural category exit (e.g., baby products as children age out)
- Subscription fatigue: Active subscription cancellations or skip frequency increasing
Map each at-risk customer to their primary churn driver for targeted intervention.
Step 6 — Intervention Prescription
Prescribe actions based on risk tier and churn driver:
| Risk Tier | Driver | Intervention | Channel | Timing |
|---|
| High | Recency | "We miss you" + incentive | Email + SMS | Day 1 of high-risk classification |
| High | Experience | Service recovery outreach | Personal email or phone | Within 48 hours |
| High | Value | Exclusive loyalty offer (not discount) | Email | Day 3 |
| Critical | Recency | Escalated offer + free shipping | SMS + push | Immediate |
| Critical | Subscription | Pause option + downsell offer | Email + in-app | Pre-cancellation trigger |
| Moderate | Recency | Content re-engagement (new products, tips) | Email | Weekly cadence |
| Moderate | Value | Bundle offer or subscribe-and-save pitch | Email | Next promotional window |
Incentive Ladder (escalating offers for non-responsive at-risk customers):
Day 0: Personalized product recommendation (no incentive)
Day 7: 10% off next order
Day 14: 15% off + free shipping
Day 21: 20% off + free gift with purchase
Day 30: Final win-back: 25% off "last chance" offer
Day 45: Move to suppression list; reduce marketing spend
Step 7 — Monitoring & Alert System Design
Define ongoing churn monitoring:
- Daily scan: Flag newly critical-risk customers for immediate action
- Weekly digest: Summary of risk tier migration (how many moved from moderate to high?)
- Monthly review: Churn rate trend, intervention effectiveness, revenue-at-risk dashboard
- Quarterly recalibration: Adjust scoring weights based on observed churn vs. predicted churn
Alert Triggers:
- Customer crosses from moderate to high risk → trigger retention workflow
- High-value customer (top 10% LTV) enters high risk → alert customer success team
- Churn rate exceeds baseline by >20% → flag systemic issue for investigation
- Subscription cancellation initiated → trigger save flow
Output Specification
- Churn Risk Scorecard: Every customer scored with risk tier, probability, and primary driver
- Revenue-at-Risk Summary: Aggregate and segment-level revenue exposure
- Top 50 At-Risk Customers: Prioritized list of highest-value customers at greatest risk
- Churn Driver Distribution: Breakdown of primary churn causes across the at-risk population
- Intervention Playbook: Per-tier, per-driver recommended actions with channel and timing
- Monitoring Dashboard Spec: Metrics, thresholds, and alert rules for ongoing churn tracking
Examples
Input: "Identify churn risk for our pet food DTC brand. 25,000 customers, average repurchase cycle is 28 days. We've noticed a spike in subscription cancellations."
Output: 3,200 customers (13%) classified as high or critical risk, representing $420K in annual revenue at risk. Subscription cancellers (800 customers) are the highest-risk cohort; primary driver is subscription fatigue (average tenure 8 months). Recommendation: introduce "pause" option, flexible delivery frequency, and surprise-and-delight program at month 6. Non-subscription at-risk customers show recency-driven patterns; prescribe a 4-step incentive ladder.
Input: "Our beauty brand has 60% first-year churn. Help us understand why and who's most at risk."
Output: Churn analysis reveals 42% of first-year churn happens before the 2nd purchase (within 60 days). Primary driver: post-purchase disengagement (no email engagement after order confirmation). High-risk new customers identified by: no email open within 14 days, no site revisit within 30 days, and single-SKU first order. Intervention: redesigned post-purchase nurture sequence with education content, usage tips, and day-45 reorder incentive.
Guidelines
- Non-contractual churn (CPG/retail) requires probabilistic estimation — there is no single "churn event"
- Always define churn threshold relative to category purchase cycle, not arbitrary time periods
- Combine behavioral signals; no single metric reliably predicts churn alone
- High-value customers warrant more aggressive (and more expensive) retention interventions
- For low-value, high-churn segments, it may be more efficient to let them churn than to invest in retention
- Distinguish between addressable churn (can be prevented) and structural churn (lifecycle exit)
- Track intervention effectiveness: what % of high-risk customers were retained after intervention?
- Avoid "discount addiction" — escalate non-monetary value (exclusive access, content, community) before discounts
- For subscription businesses, monitor skip rate and frequency changes as early warning signals
Validation Checklist
