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Predictive Churn Analysis

Telecom Provider · Telecommunications

Created predictive ML models identifying at-risk customers before churn occurs for proactive retention.

40%
Churn Prevention
฿15M
Annual Savings
87%
Model Accuracy
850%
ROI

! 挑战

The telecom provider was losing 8–12% of customers per quarter with no early-warning system, making timely retention interventions impossible.

我们的解决方案

Built a churn prediction model analyzing 200+ features from usage, billing, and support interactions, paired with automated retention workflows.

成果

  • 40% of at-risk customers successfully retained
  • ฿15M annual cost savings
  • 87% model accuracy (AUC-ROC)
  • 850% ROI within 12 months

技术栈

Python XGBoost SHAP MLflow Airflow PostgreSQL Tableau

项目周期

4 months

关键成果

40% churn prevention, saved ฿15M/year

Tags

Predictive AnalyticsChurnMLTelecom
工作流程

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3

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