- Industry: Telecommunications
- Service Pillar: Data & AI → Data & Analytics
Executive Summary
A leading telecom operator in Southeast Asia was facing high customer churn and declining Net Promoter Scores (NPS) due to service quality issues and fragmented customer insights. Mindmap Technologies delivered a predictive analytics solution using driver tree modeling and advanced visualization to identify root causes of churn and improve customer retention strategies.
Problem Overview
- Rising churn rates and limited visibility into customer sentiment drivers
- Data scattered across CRM, CDRs, ticketing, and QoS systems
- No unified analytics layer to correlate service performance with customer behavior
- Reactive customer engagement rather than predictive retention campaigns
Our Solution
- Unified customer and service data into a centralized analytics model
- Applied SHAP-based driver tree analysis to reveal top churn influencers
- Implemented predictive churn and satisfaction models using historical data
- Integrated insights into CRM for proactive retention offers and service improvements
Technologies Used
Python, Power BI, Azure Synapse, Scikit-learn, SHAP, SQL Server
Key Outcomes
01
14% reduction in churn within two quarters through targeted retention actions
02
9-point increase in NPS across high-value segments
03
Actionable insights into service-level issues driving dissatisfaction
04
Empowered marketing and service teams with self-service analytics dashboards