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In the age of data driven solution, the customer demographic attributes, suchas gender and age, play a core role that may enable companies to enhance theoffers of their services and target the right customer in the right time andplace. In the marketing campaign, the companies want to target the real user ofthe GSM (global system for mobile communications), not the line owner. Wheresometimes they may not be the same. This work proposes a method that predictsusers' gender and age based on their behavior, services and contractinformation. We used call detail records (CDRs), customer relationshipmanagement (CRM) and billing information as a data source to analyze telecomcustomer behavior, and applied different types of machine learning algorithmsto provide marketing campaigns with more accurate information about customerdemographic attributes. This model is built using reliable data set of 18,000users provided by SyriaTel Telecom Company, for training and testing. The modelapplied by using big data technology and achieved 85.6% accuracy in terms ofuser gender prediction and 65.5% of user age prediction. The main contributionof this work is the improvement in the accuracy in terms of user genderprediction and user age prediction based on mobile phone data and end-to-endsolution that approaches customer data from multiple aspects in the telecomdomain.

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