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Churn Propensity Model

Model Details · Telecom AI Model Registry

Model Details

Churn Propensity Model

Algorithm: XGBoostAPPROVED DESIGN
Code
MDL-CHURN-PROP
Version
v3.2
Owner
Head of Customer Analytics
Governance owner
Model Risk Committee

Governance configuration shown for review and validation. Read-only.

Business purpose

Identify high-risk churn segments for targeted retention

Production-recommended design specification. This record does not mean the model has been trained, fitted, deployed or validated.

Technical specification

Model family
Gradient Boosted Decision Trees
Algorithm
XGBoost
Implementation library
XGBClassifier
Model type
Binary Classification
Explainability method
SHAP TreeExplainer

Implementation governance

Governance status
APPROVED DESIGN
Implementation status
MODEL DEVELOPMENT REQUIRED
Validation status
VALIDATION REQUIRED
Lifecycle stage
Design
Current performance
Not yet measured
Refresh cadence
Weekly
Last reviewed
14 Sep 2026
Next review due
14 Dec 2026
Historical sample reference
AUC 0.84 — earlier sample figure, kept for provenance only; not a validated model result.

Business

Mission area
Churn & Retention
Decision journey
Churn & Retention
Target variable
Churn probability (next 30 days)
Output
Segment-level churn risk scores and revenue at risk

Input features

Governed variables, in feature-rank order

  1. 1.30-day usage decline %Change in usage vs prior 30 days
  2. 2.Recharge frequencyRecharges per 30 days
  3. 3.Tenure monthsMonths since activation
  4. 4.Complaint count 30dComplaints in last 30 days
  5. 5.network_quality_scoreExperienced network quality for the subscriber's serving sites (0–100).
  6. 6.Plan typePrepaid, hybrid or postpaid
  7. 7.Digital activity scoreApp and wallet engagement score

Related Intelligence

Linked governed records

Decision journey