A Price Driven Hazard Approach to User Retention

Customer loyalty is crucial for internet services since retaining users of a service to ensure the staying time of the service is of significance for increasing revenue. It demands the retention of customers to be high enough to meet the needs for yielding profit for the internet servers. Besides, the growing of rich purchasing interaction feedback helps in uncovering the inner mechanism of purchasing intent of the customers. In this work, we exploit the rich interaction data of user to build a customers retention evaluation model focusing on the return time of a user to a product. Three aspects, namely the consilience between user and product, the sensitivity of the user to price and the external influence the user might receive, are promoted to effect the purchase intents, which are jointly modeled by a probability model based on Cox's proportional hazard approach. The hazard based model provides benefits in the dynamics in user retention and it can conveniently incorporate covariates in the model. Extensive experiments on real world purchasing data have demonstrated the superiority of the proposed model over state-of-the-art algorithms.

[1]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[2]  Le Song,et al.  Isotonic Hawkes Processes , 2016, ICML.

[3]  David M. Blei,et al.  Dynamic Poisson Factorization , 2015, RecSys.

[4]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.

[5]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[6]  D. Stewart,et al.  Customer Experience Management in Retailing: Understanding the Buying Process , 2009 .

[7]  Dhruv Grewal,et al.  The Effect of Store Name, Brand Name and Price Discounts on Consumers' Evaluations and Purchase Intentions , 1998 .

[8]  Mingxuan Sun,et al.  A hazard based approach to user return time prediction , 2014, KDD.

[9]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[10]  Alan L. Montgomery,et al.  Prospects for Personalization on the Internet , 2008 .

[11]  Kevin C. Almeroth,et al.  The Dynamics of Price, Revenue, and System Utilization , 2001, MMNS.

[12]  Le Song,et al.  Time-Sensitive Recommendation From Recurrent User Activities , 2015, NIPS.

[13]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

[14]  Hiroshi Sawada,et al.  Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-Rescaling Model , 2014, CIKM.

[15]  Jie Liu,et al.  Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs , 2017, WWW.

[16]  A. Hawkes Spectra of some self-exciting and mutually exciting point processes , 1971 .

[17]  Young-Gul Kim,et al.  Identifying key factors affecting consumer purchase behavior in an online shopping context , 2003 .

[18]  Hong Cheng,et al.  Why It Happened: Identifying and Modeling the Reasons of the Happening of Social Events , 2015, KDD.

[19]  Richard G. Netemeyer,et al.  Price Perceptions and Consumer Shopping Behavior: A Field Study , 1993 .

[20]  Na Liu,et al.  Recommend products with consideration of multi-category inter-purchase time and price , 2018, Future Gener. Comput. Syst..

[21]  Jure Leskovec,et al.  The role of social networks in online shopping: information passing, price of trust, and consumer choice , 2011, EC '11.

[22]  Lars Schmidt-Thieme,et al.  Pairwise interaction tensor factorization for personalized tag recommendation , 2010, WSDM '10.

[23]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[24]  David M. Blei,et al.  Bayesian Nonparametric Poisson Factorization for Recommendation Systems , 2014, AISTATS.

[25]  Takashi Sonoda,et al.  Model of Personal Discount Sensitivity in Recommender Systems , 2016, IxD&A.

[26]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[27]  W. Reinartz,et al.  On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical Investigation and Implications for Marketing , 2000 .

[28]  Kun Guo,et al.  Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining , 2012 .

[29]  Sunil Gupta,et al.  Stochastic Models of Interpurchase Time with Time-Dependent Covariates , 1991 .

[30]  David M. Blei,et al.  Scalable Recommendation with Hierarchical Poisson Factorization , 2015, UAI.

[31]  Kristina Lerman,et al.  Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior , 2015, WSDM.

[32]  Utkarsh Upadhyay,et al.  Recurrent Marked Temporal Point Processes: Embedding Event History to Vector , 2016, KDD.

[33]  Markus Zanker,et al.  Case-studies on exploiting explicit customer requirements in recommender systems , 2009, User Modeling and User-Adapted Interaction.

[34]  Isabel Valera,et al.  Modeling Adoption and Usage of Competing Products , 2014, 2015 IEEE International Conference on Data Mining.