Intent Based Association Modeling for E-commerce

Abstract

Online e-commerce sites track user behavior through use of in-house analytics or by integrating with third party platforms such as Google Analytics. Understanding user behavioral data assists with strategies for user retention, buy-in loyalty and optimizing objective completions. One of the more difficult problems though is understanding user intent that can be dynamic or built over time. Knowing user intent is key to enabling user conversions - the term used to denote completion of a particular goal. Current industry approaches for intent inference have an inherent disadvantage of having the need for embedded tracking code per site-sections as well as the inability to track user’s intent over longer periods. In this paper, we present our work on mining dynamic as well as evolving user’s intents, using a latent multi-topic estimation approach over user’s web browsing activity. Further, based on the intent patterns, we look at generating association rules that model purchasing behavior. Our studies show that users typically go through multiple states of intent behavior, dependent on key features of products under consideration. We test the behavioral model by coupling it with Google Analytics platform to augment a re-marketing campaign, analyzing purchasing behavior changes. We prove statistically that user conversions are possible, provided purchase category dependent associations are effectively used.