Telecommunication has a common problem of suffering from high churn rate, e.g. an existing customer may leave the service provider or shift to another. Churn prediction is the task of predicting the likelihood that a certain group of customers churn, and customer segmentation has been widely applied for a target marketing strategy. Previous work has adopted various clustering and classification methods, among which support vector machines and neural networks have achieved better performance, but the possible reason why customers churn is rarely explored in details. This article aims to address the gap of understanding customer churn using customers’ usage of services. To do this, an integrated framework containing two phases was built: we carried out predictive analysis using churn prediction and association analysis (phase one), based on which we provided recommendations in the decision analysis (phase two). The experimental results showed that the proposed Usage-based Decision Tree (UsageDT) achieved better performance in understanding the churners/non-churners in terms of interpretability, higher precision, and much higher true positive rate. The usage-based association analysis was integrated seamlessly with churn prediction, and deepened our understanding of the churn phenomena. Evaluation carried out with two professionals in telecommunication industry also revealed the usefulness of our recommendations.

We show that telco big data can make churn prediction much more easier from the $3$V's perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having $0.96$ precision for the top $50000$ predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value.

With the increasing prevalence of versatile mobile devices and the fast deployment of broadband mobile networks, a huge volume of Mobile Broadband (MBB) data has been generated over time. The MBB data naturally contain rich information of a large number of mobile users, covering a considerable fraction of whole population nowadays, including the mobile applications they are using at different locations and time; the MBB data may present the unprecedentedly large knowledge base of human behavior which has highly recognized commercial and social value. However, the storage, management and analysis of the huge and fast growing volume of MBB data post new and significant challenges to the industrial practitioners and research community. In this demonstration, we present a new, MBB data tailored, distributed analytic system named OceanST which has addressed a series of problems and weaknesses of the existing systems, originally designed for more general purpose and capable to handle MBB data to some extent. OceanST is featured by ( i ) efficiently loading of ever-growing MBB data, ( ii ) a bunch of spatiotemporal aggregate queries and basic analysis APIs frequently found in various MBB data application scenarios, and ( iii ) sampling-based approximate solution with provable accuracy bound to cope with huge volume of MBB data. The demonstration will show the advantage of OceanST in a cluster of 5 machines using 3TB data.