Personalised Telecom Plan Recommendation Engine Based on Subscriber Usage Data

Objective: To develop an intelligent engine that recommends tailored telecom plans to subscribers using their historical usage patterns and demographics.

In India, telecom operators face significant challenges in retaining customers and maximizing ARPU due to diverse usage patterns, regional preferences, and intense price competition. Existing generic plans often do not meet individual customer needs, resulting in churn and suboptimal satisfaction.

This project involves analyzing real or simulated subscriber usage datasets—including voice, data, and SMS consumption, recharge frequency, and location—using advanced analytics. The team will design and implement a recommendation engine employing clustering and predictive modeling, integrating demographic and behavioral data.

Deliverables include cleaned datasets, exploratory analysis, model design documentation, a working prototype of the recommendation engine, and a dashboard visualizing personalized plan suggestions. The team will validate the engine using accuracy metrics and simulated business scenarios.

The solution informs product managers and marketing teams about actionable segmentation, enabling targeted plan offerings, improved customer retention, and greater revenue optimization for Indian telecom operators.