Modelling End of Online Session from Streaming Data

ACM iKDD Conference on Data Science

Publication date: March 9, 2017

Moumita Sinha, Harsh Jhamtani, Sanket Mehta, Balaji Vasan Srinivasan

Engagement of consumers has become increasingly important for online marketers. When a potential consumer arrives on its online platform and interacts with it, two important and interrelated questions arise. One whether the consumer is engaged in the session or has completed the session. Two, upon completion of a session whether the consumer will return to the site. Real time answers to both these questions benefit the marketer directly by facilitating more effective retargeting, determination of which is a significant problem in online commerce. We address this problem of retargeting by using automated predictive models. Our model allows a marketer to decide in a real time manner whether a click is the last click of the session. Then the model identifies real time the consumer's propensity to return when the session actually ends. This propensity is used to decide whether and whom to retarget with a message. Tests of our model on real data from internet e-commerce sites perform well. The proposed approach is a considerable improvement over the current approach of having to wait for a pre-specified amount of time after a click, in order to identify the end of the session.

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