2014 AHazardbasedApproachtoUserRetur

• (Kapoor et al., 2014) ⇒ Komal Kapoor, Mingxuan Sun, Jaideep Srivastava, and Tao Ye. (2014). “A Hazard based Approach to User Return Time Prediction.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623348

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+A+Hazard+based+Approach+to+User+Return+Time+Prediction
• http://dl.acm.org/citation.cfm?id=2623330.2623348&preflayout=flat#citedby

Quotes

Author Keywords

• Customer relationship management; growth and retention; hazard based methods; online user behavior; web-based services

Abstract

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 which makes it crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we address this problem by proposing a new retention metric for web services by concentrating on the rate of user return. We further apply predictive analysis to the proposed retention metric on a service, as a means for characterizing lost customers. 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 be superior.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 AHazardbasedApproachtoUserRetur	Jaideep Srivastava Komal Kapoor Mingxuan Sun Tao Ye			A Hazard based Approach to User Return Time Prediction				10.1145/2623330.2623348		2014