Predicting customer behavior via calling links

Machine learning techniques have been used to predict customer behavior in telecommunications industry. Typically, several data sources, including historical usage, billing, payment, network, customer service, and demographic data, can be used in a predictive model. However, in some cases, e.g., in the prepaid customer segment, there is often little data available except for the call detail record (CDR) data. In this paper, we tackle this challenging problem, using significantly delayed CDR data as the primary data source to predict customer behavior. We extract calling links, i.e., who called whom, from the CDR data, and propose several distance measures based on calling links. We demonstrate that, by using information derived from the calling links alone as inputs to a neural network model, an acceptable accuracy for predicting churn (customer switching from one service provider to another) can be achieved. Calling links can also be used to identify calling communities, which may be used for targeted marketing campaigns and help predict acceptance of marketing offers.