Download pdf
Research on Predicting Customers' Next Purchase Based on Shopping Basket Data
- 1 University of Toronto
* Author to whom correspondence should be addressed.
Abstract
Thanks to the rapid development of e-commerce and online shopping, a large amount of shopping basket data has been accumulated. How to mine the useful information in shopping cart data to predict customers' next purchase is an important problem in commercial data analysis, which is widely used in the fields of online advertising and product recommendation. In this paper, three prediction methods are proposed, including frequency-based prediction, rule-based prediction and similarity-based prediction. Moreover, evaluations and analysis of these three methods are conducted on the public dataset. It is found that the items that were frequently purchased by consumers in the past are more likely to continue to be purchased due of the higher prediction accuracy of the frequency-based methods. On the other hand, similarity-based item prediction methods also yielded good results because there is also a significant overlap in the items that similar users want to purchase. Therefore, it is concluded that in practical applications, frequency and similarity-based prediction methods can be applied to predict consumers' next purchases.
Keywords
next purchase prediction, shopping basket, associate rules, similarity-based prediction, frequency-based prediction
[1]. Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized markov chains for next-basket recommendation[C]//WWW. 2010: 811-820.
[2]. Mostafa M M. Knowledge discovery of hidden consumer purchase behaviour: a market basket analysis[J]. International Journal of Data Analysis Techniques and Strategies, 2015, 7(4): 384-405.
[3]. Wedel M, Kannan P K. Marketing analytics for data-rich environments[J]. Journal of Marketing, 2016, 80(6): 97-121.
[4]. Arthur L. Big data marketing: engage your customers more effectively and drive value[M]. John Wiley & Sons, 2013.
[5]. Guidotti R, Rossetti G, Pappalardo L, et al. Personalized market basket prediction with temporal annotated recurring sequences[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(11): 2151-2163.
[6]. Kraus M, Feuerriegel S. Personalized purchase prediction of market baskets with Wasserstein-based sequence matching[C]// SIGKDD. 2019: 2643-2652.
[7]. Guo G, Wang H, Bell D, et al. KNN model-based approach in classification[C]//OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". Springer, Berlin, Heidelberg, 2003: 986-996.
[8]. Agrawal R, Srikant R. Fast algorithms for mining association rules[C]//Proc. 20th int. conf. very large data bases, VLDB. 1994, 1215: 487-499.
[9]. LE D T, LAUW H W, FANG Y. Correlation-sensitive next-basket recommendation.(2019)[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macau, China, 2019 August 10. 16: 2808-2814.
[10]. Smith, Karl J. Precalculus: A functional approach to graphing and problem solving. Jones & Bartlett Publishers, 2011.
[11]. Itakura F. Minimum prediction residual principle applied to speech recognition[J]. IEEE Transactions on acoustics, speech, and signal processing, 1975, 23(1): 67-72.
[12]. Guo G, Wang H, Bell D, et al. KNN model-based approach in classification[C]//OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". Springer, Berlin, Heidelberg, 2003: 986-996.
Cite this article
Chen,Y. (2023). Research on Predicting Customers' Next Purchase Based on Shopping Basket Data. Advances in Economics, Management and Political Sciences,11,353-361.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Business and Policy Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).