Summary

This study delves into the application of predictive analytics within the telco sector, with a particular focus on prepaid mobile services. It investigates the prediction of customer behaviors, such as the top-up propensity within 2 to 4 days and account balance before top-ups. The goal is to empower telco operators with data-driven insights to tailor their marketing strategies more precisely.

This research evaluates the effectiveness of Neural Network (NN) models, underscored by rigorous hyperparameter tuning and cross-validation processes, against the traditional Machine Learning (ML) models, namely Random Forest (RF) and Gradient Boosting Trees (GBT), which are currently in production at Altice Labs. Innovatively, it incorporates pre-processing and feature selection techniques not previously used in traditional ML model development. The results demonstrate a significant performance leap of NN models over existing ML counterparts in accurately predicting customer actions. By providing telco operators with a more nuanced understanding of customer behavior patterns, this study offers insights into enhancing predictive models in the telco sector