Predicting customer's gender and age depending on mobile phone data

TL;DR

This paper presents a machine learning-based method to predict customer gender and age from mobile phone data, improving accuracy for targeted marketing campaigns using a large telecom dataset.

Contribution

It introduces an end-to-end approach utilizing diverse telecom data sources and big data technology to enhance demographic prediction accuracy.

Findings

85.6% accuracy in gender prediction

65.5% accuracy in age prediction

Effective use of call records, CRM, and billing data

Abstract

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain.