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Communication Service Providers (CSPs) are in a unique position to utilizetheir vast transactional data assets generated from interactions of subscriberswith network elements as well as with other subscribers. CSPs could leverageits data assets for a gamut of applications such as service personalization,predictive offer management, loyalty management, revenue forecasting, networkcapacity planning, product bundle optimization and churn management to gainsignificant competitive advantage. However, due to the sheer data volume,variety, velocity and veracity of mobile subscriber datasets, sophisticateddata analytics techniques and frameworks are necessary to derive actionableinsights in a useable timeframe. In this paper, we describe our journey from arelational database management system (RDBMS) based campaign managementsolution which allowed data scientists and marketers to use hand-written rulesfor service personalization and targeted promotions to a distributed Big DataAnalytics platform, capable of performing large scale machine learning and datamining to deliver real time service personalization, predictive modelling andproduct optimization. Our work involves a careful blend of technology,processes and best practices, which facilitate man-machine collaboration andcontinuous experimentation to derive measurable economic value from data. Ourplatform has a reach of more than 500 million mobile subscribers worldwide,delivering over 1 billion personalized recommendations annually, processing atotal data volume of 64 Petabytes, corresponding to 8.5 trillion events.

Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets