research-article

Predicting Online Purchase Conversion for Retargeting

Authors:

Seung-won Hwang,

Nedim LipkaAuthors Info & Claims

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Pages 591 - 600

https://doi.org/10.1145/3018661.3018715

Published: 02 February 2017 Publication History

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 customer- or 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.

References

[1]

B. Akbaş, D. Karahoca, A. Karahoca, and A. Güngör. Predicting newspaper sales by using data mining techniques. In CompSysTech, 2014.

Digital Library

[2]

J. Bakker, M. Pechenizkiy, et al. Towards context aware food sales prediction. In ICDM Workshops, 2009.

[3]

S. Bellman, G. L. Lohse, and E. J. Johnson. Predictors of online buying behavior. Communications of the ACM, 1999.

Digital Library

[4]

D. Ben-Shimon, A. Tsikinovsky, M. Friedmann, B. Shapira, L. Rokach, and J. Hoerle. Recsys challenge 2015 and the yoochoose dataset. In RecSys, 2015.

[5]

R. Bennett. Ticket sales forecasting methods and performance of uk theatre companies. International Journal of Arts Management, pages 36--49, 2002.

[6]

D. Bertsimas, A. J. Mersereau, and N. R. Patel. Dynamic classification of online customers. In SDM, 2003.

[7]

S. Bhagat, A. Goyal, and L. V. Lakshmanan. Maximizing product adoption in social networks. In WSDM, 2012.

Digital Library

[8]

P. Butler and J. Peppard. Consumer purchasing on the internet:: Processes and prospects. European management journal, 16(5):600--610, 1998.

[9]

C. Comegys, M. Hannula, and J. Väisänen. Longitudinal comparison of finnish and us online shopping behaviour among university students: The five-stage buying decision process. Journal of Targeting, Measurement and Analysis for Marketing, 14(4):336--356, 2006.

[10]

G. Cui, H.-K. Lui, and X. Guo. The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 2012.

Digital Library

[11]

eMarketer. Us retail commerce sales highest for computers. In consumer electronics, April, 2014.

[12]

R. J. Hyndman and G. Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.

[13]

S. Kim, J. Yeo, E. Koh, and N. Lipka. Purchase influence mining: Identifying top-k items attracting purchase of target item. In WWW, 2016.

[14]

P. Kotler, K. L. Keller, D. Manceau, and A. Hémonnet-Goujot. Marketing management, volume 14. Prentice Hall Englewood Cliffs, NJ, 2015.

[15]

N. B. Lassen, R. Madsen, and R. Vatrapu. Predicting iphone sales from iphone tweets. In EDOC, 2014.

Digital Library

[16]

J. Lee, M. Podlaseck, E. Schonberg, R. Hoch, and S. Gomory. Analysis and visualization of metrics for online merchandising. In WEBKDD'99 Workshop.

[17]

P. Meulstee and M. Pechenizkiy. Food sales prediction: "if only it knew what we know". In ICDM Workshops, 2008.

Digital Library

[18]

W. W. Moe. Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of consumer psychology, 2003.

[19]

W. W. Moe and P. S. Fader. Dynamic conversion behavior at e-commerce sites. Management Science, 2004.

Digital Library

[20]

A. L. Montgomery, S. Li, K. Srinivasan, and J. C. Liechty. Modeling online browsing and path analysis using clickstream data. Marketing Science, 23(4):579--595, 2004.

Digital Library

[21]

C. Park, D. Kim, J. Oh, and H. Yu. Predicting user purchase in e-commerce by comprehensive feature engineering and decision boundary focused under-sampling. In RecSys, 2015.

Digital Library

[22]

W. P. Putsis Jr and N. Srinivasan. Buying or just browsing? the duration of purchase deliberation. Journal of marketing research, 1994.

[23]

S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW, 2010.

Digital Library

[24]

C. Sismeiro and R. E. Bucklin. Modeling purchase behavior at an e-commerce web site: A task-completion approach. Journal of marketing research, 41(3):306--323, 2004.

[25]

D. Van den Poel and W. Buckinx. Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2):557--575, 2005.

[26]

C. Wang and Y. Wang. Discovering consumer's purchasing behavior based on efficient association rules. In FSKD, 2011.

[27]

J. Wang and Y. Zhang. Opportunity model for e-commerce recommendation: right product; right time. In SIGIR, 2013.

Digital Library

[28]

X. Wu, Q. Liu, E. Chen, L. He, J. Lv, C. Cao, and G. Hu. Personalized next-song recommendation in online karaokes. In RecSys, 2013.

Digital Library

[29]

L. Xu, J. A. Duan, and A. Whinston. Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Science, 2014.

Digital Library

[30]

R. Yao and J. Chen. Predicting movie sales revenue using online reviews. In GrC, 2013.

[31]

H. Yuan, W. Xu, and M. Wang. Can online user behavior improve the performance of sales prediction in e-commerce? In SMC, 2014.

Cited By

Chen SXu ZXu DGou X(2024)Customer purchase prediction in B2C e-business: A systematic review and future research agendaExpert Systems with Applications10.1016/j.eswa.2024.124261252(124261)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124261
Nishimura NSukegawa NTakano YIwanaga J(2023)Predicting Online Item-Choice Behavior: A Shape-Restricted Regression ApproachAlgorithms10.3390/a1609041516:9(415)Online publication date: 29-Aug-2023
https://doi.org/10.3390/a16090415
Altameem AHafez A(2022)Behavior Prediction Scheme Using Hierarchical Clustering and Deep Neural NetworksJournal of Nanoelectronics and Optoelectronics10.1166/jno.2022.326117:5(861-872)Online publication date: 1-May-2022
https://doi.org/10.1166/jno.2022.3261
Show More Cited By

Index Terms

Predicting Online Purchase Conversion for Retargeting
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
  2. World Wide Web
    1. Online advertising
      1. Display advertising
    2. Web applications
      1. Electronic commerce
        Online shopping

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cover image ACM Conferences

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

February 2017

868 pages

ISBN:9781450346757

DOI:10.1145/3018661

General Chairs:
Maarten de Rijke
University of Amsterdam
,
Milad Shokouhi
Microsoft
,
Program Chairs:
Andrew Tomkins
Google
,
Min Zhang
Tsinghua University

Copyright © 2017 ACM.

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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Publication History

Published: 02 February 2017

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IITP (Institute for Information & communications Technology Promotion)
Korea government (MSIP)

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WSDM 2017

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WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining

February 6 - 10, 2017

Cambridge, United Kingdom

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WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Chen SXu ZXu DGou X(2024)Customer purchase prediction in B2C e-business: A systematic review and future research agendaExpert Systems with Applications10.1016/j.eswa.2024.124261252(124261)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124261
Nishimura NSukegawa NTakano YIwanaga J(2023)Predicting Online Item-Choice Behavior: A Shape-Restricted Regression ApproachAlgorithms10.3390/a1609041516:9(415)Online publication date: 29-Aug-2023
https://doi.org/10.3390/a16090415
Altameem AHafez A(2022)Behavior Prediction Scheme Using Hierarchical Clustering and Deep Neural NetworksJournal of Nanoelectronics and Optoelectronics10.1166/jno.2022.326117:5(861-872)Online publication date: 1-May-2022
https://doi.org/10.1166/jno.2022.3261
Alves Gomes MMeyes RMeisen PMeisen TAl Hasan MXiong L(2022)Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using EmbeddingsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557127(2873-2882)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557127
Wen YYang HPeng W(2022)Mining Willing-to-Pay Behavior Patterns from Payment DatasetsACM Transactions on Intelligent Systems and Technology10.1145/348584813:1(1-19)Online publication date: 6-Feb-2022
https://dl.acm.org/doi/10.1145/3485848
Xiao YHe WZhu YZhu J(2022)A click-through rate model of e-commerce based on user interest and temporal behaviorExpert Systems with Applications10.1016/j.eswa.2022.117896207(117896)Online publication date: Nov-2022
https://doi.org/10.1016/j.eswa.2022.117896
Moriwaki DHayakawa YMatsui ASaito YMunemasa IShibata M(2021)A Real-World Implementation of Unbiased Lift-based Bidding System2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671800(1877-1888)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671800
Tariq MBabar MPoulin MKhattak A(2021)Distributed model for customer churn prediction using convolutional neural networkJournal of Modelling in Management10.1108/JM2-01-2021-003217:3(853-863)Online publication date: 20-May-2021
https://doi.org/10.1108/JM2-01-2021-0032
Micol Policarpo Lda Silveira Dda Rosa Righi RAntunes Stoffel Rda Costa CVictória Barbosa JScorsatto RArcot T(2021)Machine learning through the lens of e-commerce initiativesComputer Science Review10.1016/j.cosrev.2021.10041441:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100414
Yeo JHwang SKim SKoh ELipka N(2020)Conversion Prediction from Clickstream: Modeling Market Prediction and Customer PredictabilityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.288446732:2(246-259)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TKDE.2018.2884467
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