Predicting online purchase conversion for retargeting

Jinyoung Yeo, Sungchul Kim, Eunyee Koh, Seung Won Hwang, Nedim Lipka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Generally 2% of shoppers make a purchase on the first visit to an online store while the other 98% enjoys only window-shopping. To bring people back to the store and close the deal, "retargeting" has been a vital online advertising strategy that leads to "conversion" of window-shoppers into buyers. As such retargeting is more effective as a focused tool, in this paper, we study the problem of identifying a conversion rate for a given product and its current customers, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customeror product-level conversion pattern, we propose a joint modeling of both level patterns based on the well-studied buying decision process. To evaluate the effectiveness of our method, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs. The evaluation results show that conversion predictions by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment.

Original languageEnglish
Title of host publicationWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages591-600
Number of pages10
ISBN (Electronic)9781450346757
DOIs
Publication statusPublished - 2017 Feb 2
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: 2017 Feb 62017 Feb 10

Publication series

NameWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

Other

Other10th ACM International Conference on Web Search and Data Mining, WSDM 2017
CountryUnited Kingdom
CityCambridge
Period17/2/617/2/10

Bibliographical note

Funding Information:
This work was partially supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2720-16- 0007) supervised by the IITP (Institute for Information & communications Technology Promotion).

Publisher Copyright:
© 2017 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications
  • Software

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