White paper “NN-based predictive
analytics for prepaid mobile
services in telco sector”

Discover how Neural Network models outperform traditional Machine Learning methods, offering a deeper understanding of customer pattern

White paper “NN-based predictive analytics for prepaid mobile services in telco sector”

Discover how Neural Network models outperform traditional Machine Learning methods, offering a deeper understanding of customer pattern

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

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

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