Prepaid Customer Churn Reduction
Telecommunications Sector
From >32% Churn Rate to <15% Stabilization with Predictive ML Model
Churn Rate Reduced from >32% to <15%
XGBoost-Based Churn Probability Prediction
Cross-Functional Anti-Churn Strategies Implemented
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case demonstrates a machine learning-powered anti-churn solution that dramatically stabilized a high-churn prepaid customer base for a leading telecommunications company—reducing churn from over 32% to under 15% through predictive modeling, data enrichment, and coordinated business interventions.
Business Challenge
A major telecommunications provider faced a prepaid customer churn rate exceeding 32%, causing significant revenue leakage and complicating long-term planning, customer retention efforts, handset investments, product development, and value communication. Data quality inconsistencies further hindered accurate churn analysis and predictive modeling, limiting the ability to proactively identify and engage at-risk customers.
Implemented Solution
- 1Conducted deep Exploratory Data Analysis (EDA) and hypothesis testing to identify data quality issues, validate assumptions, and enrich datasets by integrating additional sources into the company's Data Warehouse.
- 2Developed an embedded anti-churn model using XGBoost algorithms for churn probability prediction, based on customers' transaction histories, combined with anomaly detection techniques.
- 3Collaborated cross-functionally: Commercial Team created tailored call center scripts for high-risk customers; Marketing Team defined targeted actions by probability ranges; Analytics Team integrated the model into the broader data pipeline for consistent, real-time predictions.
- 4Ensured the model was actionable and embedded into daily operations for proactive retention.
Obtained Results
Reduced churn rate dramatically from over 32% to stabilized under 15% through targeted communication strategies and organization-wide education.
Enabled proactive engagement with at-risk customers, significantly improving retention and revenue stability.
Fostered cross-departmental alignment: Commercial, Marketing, Analytics, and BI teams collaborated as a unified front to combat churn.
Transformed the organization toward a more data-driven culture with increased awareness of churn metrics at all levels.
Laid the foundation for future enhancements, including expansion into a Customer Lifetime Value (CLTV)-based retention framework.
Evolution in Simov Labs
This anti-churn prediction project is now fully integrated into our portfolio at Simov Labs. We evolve these XGBoost-based models into modern, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML for real-time churn scoring, automated intervention triggers, multi-channel retention orchestration, continuous model retraining, anomaly detection enhancements, and explicit ROI measurement.
Aligned with DAMA and TDWI governance frameworks, our current solutions emphasize business-first analysis, radical honesty in opportunity assessment, and long-term partnership—ensuring churn reduction delivers sustainable revenue protection, customer loyalty, and competitive advantage in telecommunications. No promises. Just results.
Ready to predict and prevent customer churn with accurate, AI-driven models that stabilize revenue and boost retention?
Book a free 30-minute consultation. We'll discuss your churn challenges and where predictive modeling can deliver the most impact.