A Hazard Based Approach to User Return Time Prediction

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A Hazard Based Approach to User Return Time Prediction

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In the competitive environment of the internet, retaining and growing one's user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good. Hence, it is crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we first propose a new retention metric for web services concentrating on the rate of user return. Secondly, we apply predictive analysis to the proposed retention metric on a service. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox's proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to far surpass our baselines.



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Kapoor, Komal; Sun, Mingxuan; Srivastava, Jaideep; Ye, Tao. (2013). A Hazard Based Approach to User Return Time Prediction. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215936.

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