skip to main content
10.1145/2983323.2983665acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Discovering Temporal Purchase Patterns with Different Responses to Promotions

Published:24 October 2016Publication History

ABSTRACT

The supermarkets often use sales promotions to attract customers and create brand loyalty. They would often like to know if their promotions are effective for various customers, so that better timing and more suitable rate can be planned in the future. Given a transaction data set collected by an Australian national supermarket chain, in this paper we conduct a case study aimed at discovering customers' long-term purchase patterns, which may be induced by preference changes, as well as short-term purchase patterns, which may be induced by promotions. Since purchase events of individual customers may be too sparse to model, we propose to discover a number of latent purchase patterns from the data. The latent purchase patterns are modeled via a mixture of non-homogeneous Poisson processes where each Poisson intensity function is composed by long-term and short-term components. Through the case study, 1) we validate that our model can accurately estimate the occurrences of purchase events; 2) we discover easy-to-interpret long-term gradual changes and short-term periodic changes in different customer groups; 3) we identify the customers who are receptive to promotions through the correlation between behavior patterns and the promotions, which is particularly worthwhile for target marketing.

References

  1. P. Adamopoulos and V. Todri. The effectiveness of marketing strategies in social media: Evidence from promotional events. In Proceedings of the 21th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pages 1641--1650. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. G. M. Allenby, R. P. Leone, and L. Jen. A dynamic model of purchase timing with application to direct marketing. Journal of the American Statistical Association, 94(446):365--374, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  3. R. E. Bucklin, S. Gupta, and S. Siddarth. Determining segmentation in sales response across consumer purchase behaviors. Journal of Marketing Research, pages 189--197, 1998.Google ScholarGoogle Scholar
  4. C. Chatfield and G. J. Goodhardt. A consumer purchasing model with erlang inter-purchase times. Journal of the American Statistical Association, 68(344):828--835, 1973.Google ScholarGoogle Scholar
  5. D. Dong and H. M. Kaiser. Studying household purchasing and nonpurchasing behaviour for a frequently consumed commodity: two models. Applied Economics, 40(15):1941--1951, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. S. Ehrenberg. The pattern of consumer purchases. Applied Statistics, pages 26--41, 1959.Google ScholarGoogle Scholar
  7. T. Iwata, S. Watanabe, T. Yamada, and N. Ueda. Topic tracking model for analyzing consumer purchase behavior. In Proc. of the 22nd Int'l Joint Conf. on Artificial Intelligence, pages 1427--143. AAAI Press, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. H. Kim, N. Takaya, and H. Sawada. Tracking temporal dynamics of purchase decisions via hierarchical time-rescaling model. In Proc. of the 23rd ACM Int'l Conf. on Information and Knowledge Management, pages 1389--1398. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. K. Kopperschmidt and W. Stute. Purchase timing models in marketing: a review. AStA Advances in Statistical Analysis, 93(2):123--149, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  10. P. Kotler and G. Armstrong. Principles of marketing. pearson education, 2010.Google ScholarGoogle Scholar
  11. B. Li, X. Zhu, R. Li, C. Zhang, X. Xue, and X. Wu. Cross-domain collaborative filtering over time. In Proc. of the 22nd Int'l Joint Conf. on Artificial Intelligence, pages 2293--2298. AAAI Press, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. L. Luo, B. Li, I. Koprinska, S. Berkovsky, and F. Chen. Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. In Pacific Asia Conference on Knowledge Discovery and Data Mining, volume 9651, pages 527--539. Springer, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  13. D. G. Morrison and D. C. Schmittlein. Generalizing the NBD model for customer purchases: What are the implications and is it worth the effort? Journal of Business & Economic Statistics, 6(2):145--159, 1988.Google ScholarGoogle Scholar
  14. P. E. Rossi, R. E. McCulloch, and G. M. Allenby. The value of purchase history data in target marketing. Marketing Science, 15(4):321--340, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. N. Sheth, B. Mittal, and B. I. Newman. Customer behavior: consumer behavior and beyond. Dryden Press Fort Worth, TX., 1999.Google ScholarGoogle Scholar

Index Terms

  1. Discovering Temporal Purchase Patterns with Different Responses to Promotions

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
            October 2016
            2566 pages
            ISBN:9781450340731
            DOI:10.1145/2983323

            Copyright © 2016 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]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 24 October 2016

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            CIKM '16 Paper Acceptance Rate160of701submissions,23%Overall Acceptance Rate1,861of8,427submissions,22%

            Upcoming Conference

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader