Profiling Subscribers According to Their Internet Usage Characteristics and Behaviors

Abstract

Providers (SP) are wishing to increase their Return of Investment (ROI) by utilizing the data assets generated by tracking subscriber behaviors. This results in the ability of applying personalized policies, monitoring and controlling the service traffic to subscribers and gaining more revenues through the usage of subscriber data with ad networks. In this paper, a framework is developed to monitor and analyze the Internet access of the subscribers of a regional SP in order to categorize the subscribers into an interest category from The Interactive Advertising Bureau (IAB) categories. The study employs the categorization engine to build category vectors for all subscribers. The simulation results show that once a subscriber has been classified into a category the click rate for the same subscriber group can be improved by correlating the interests of the subscribers with the advertisements.