User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on pre- dicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.
There are sporadic works that have been proposed to exploit historical statuses for attrition prediction @cite_36 @cite_28 . Although these works have tried to incorporate historical statuses of users, they have two issues. First, the whole historical observation periods are divided into multiple sub-periods for model training with handcrafted efforts. In this case, the correlation across different sub-periods cannot be fully explored. Second, the decaying impact of statuses within different sub-periods on attrition prediction within the target time period is out of consideration. The survival analysis framework has been proposed to capture the time-to-event of attrition @cite_33 . It utilizes the initial information at the start of the user enrollment to perform model learning for predicting survival time of subscriptions. Here the inherent problem is that the evolving user activities are not incorporated into the attrition prediction, which are crucial to the attrition modeling according to our experiments. Aside from the above works purely based on attrition, profit-driven discussion and simulation studies were also performed based on a potential intervention assumption (e.g., bonus, discount) @cite_7 .
@cite_36 @cite_28 @cite_33 @cite_7
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Abstract As deregulation, new technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers. A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory. Churn prediction is an increasingly pressing issue in today?s ever-competitive commercial environments, especially in mobile communication arena. In this paper, a Mixed Process Neural Network (MPNN) based on fourier orthogonal base function has been proposed to support churn management, which can deal with both static value and time-varied continuous value simultaneously. To further improve its performance, an optimized network, c- MPNN, has been presented, which adopts fourier expansion based preprocessing and hidden layer combination techniques to optimize MPNN?s structure. Most important of all, our method has been used in real applications in China Mobile. Experiments based on the real datasets also show that our proposed churn prediction method has good maneuverability and performance. We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include legit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47000 USA domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.