A Framework for Predicting Home Network Problems Using Diverse Data Sources

Providing uninterrupted high quality service is very important for Telecommunications Service Providers to avoid customer churn and to minimize the cost of customer care. Predicting service disruption and degradation, followed by proactive corrective action, helps Service Providers mitigate issues before they are noticed by subscribers. In this paper, we present a framework and a set of algorithms for the prediction of home network problems. More specifically, we discuss data collection, pre-processing and model building steps as applied to diverse data sets arriving from home network devices such as network interface devices, home routers, and customer care systems. We also present the results of a performance evaluation study where we applied the presented framework to a dataset from a Tier 1 Service Provider. Our results show that, as an example use case, our techniques were able to predict 76% of the "Cannot Connect to Internet" problem, which was the top call driver to the customer care organization.