---
title: "Predict customer churn risk for energy suppliers"
method: POST
path: "/api/business-intelligence/churn-prediction"
tags: ["Business Intelligence"]
---

# Predict customer churn risk for energy suppliers

`POST /api/business-intelligence/churn-prediction`

## Request

- Base URL: `https://api.cernion.de`
- URL: `https://api.cernion.de/api/business-intelligence/churn-prediction`
- Auth: none declared

Predict and prevent customer churn with ML-powered risk scoring. **Churn scoring system (0-100 points)**:

**Scoring Components**:
- **Contract status (30 points)**: <3 months to contract end = **CRITICAL** (30 points)
- **Price sensitivity (25 points)**: Price complaints, >10% above market, recent price increase, competitor comparison
- **Service quality (20 points)**: >5 support contacts, unresolved tickets, NPS Detractor (<6), negative feedback
- **Payment behavior (15 points)**: >2 late payments, payment method changes, manual vs. automatic payment
- **Usage pattern (10 points)**: Consumption declining >20%, no app usage (3+ months), no login activity

**Customer Segments & Business Value**:
- **Residential**: ARPU 600€, LTV 3k€, churn 10-12%, retention budget max 600€
- **Prosumer**: ARPU 1,450€, LTV 10k€, churn 5-8%, budget 2,000€ (**HIGH PRIORITY! Must protect**)
- **Commercial**: ARPU 10k€, LTV 50k€, churn 3-5%, budget unlimited (**MUST WIN! Strategic accounts**)
- **Premium**: ARPU >5k€, LTV >25k€, churn <3%, no budget limit (**VIP treatment required**)

**Retention Strategies by Risk Level**:
- **HIGH RISK (80-100)**: Immediate personal call + retention offer (max 200€ for residential, 2k€ for prosumer)
- **MEDIUM RISK (60-80)**: Automated email campaign + targeted incentive (max 100€ for residential)
- **LOW RISK (40-60)**: Proactive engagement (loyalty program, app activation, community features)

**Use Cases**:
- **Retention campaigns**: Target high-risk customers before contract ends
- **Customer lifetime value protection**: Focus on prosumer and commercial segments
- **Segmented marketing**: Different strategies for different risk/value segments
- **Service quality improvement**: Identify systemic issues causing churn
- **Budget optimization**: Allocate retention budget to highest ROI segments

**ROI Metrics**:
- Typical retention cost: 150-2,000€ depending on segment
- Expected retention success rate: 40-60% with proactive intervention
- Prosumer segment ROI: 19.3x (1,500€ cost saves 29k€ revenue)
- Commercial segment ROI: 33x+ (unlimited ROI for strategic accounts)

**Critical Success Factors**:
- **Act early**: 3-6 months before contract end (not 1 month!)
- **Segment ruthlessly**: Prosumer/Commercial = high touch, Residential = automated
- **Personalize approach**: Different reason = different retention strategy
- **Measure religiously**: Track retention rate by segment, reason, intervention type

## Query parameters

- `token` string

## Request body

- object
  - `customerSegment` 'residential' | 'prosumer' | 'commercial' | 'premium' | 'all', required: Customer segment to analyze
  - `region` string, required: Region/postal code
  - `riskThreshold` 'high' | 'medium' | 'low': Minimum risk level to include
  - `limit` number
  - `predictionWindowMonths` number: Prediction timeframe in months
  - `includeRetentionStrategy` boolean
  - `includeValueSegmentation` boolean
  - `includeChurnReasons` boolean
  - `includeCompetitiveAnalysis` boolean
  - `token` string: Optional Cernion MCP token for this request. Overrides CERNION_TOKEN when provided.

## Response `200`

Successful response

- object

---

[API](https://skmtc.dev/cernion/apis/cernion-energy-tools-api.md) · [All operations](https://skmtc.dev/cernion/apis/cernion-energy-tools-api/llms.txt) · [OpenAPI document](https://skmtc.dev/cernion/apis/cernion-energy-tools-api/revisions/cdb94473b538?raw)