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Modeling End-of-Online-Session From Streaming Data

Published: 09 March 2017 Publication History

Abstract

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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cover image ACM Other conferences
CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences
March 2017
136 pages
ISBN:9781450348461
DOI:10.1145/3041823
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2017

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  1. Customer specific end of session
  2. Real-time
  3. Retargeting
  4. Statistical Model

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Overall Acceptance Rate 197 of 680 submissions, 29%

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