Skip to main content
Log in

Direct mail promotion mechanisms and their application in supermarkets

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In this study, we consider the problem of selecting supermarket loyalty program members to receive physical direct mail and promotional electronic direct mail (i.e., direct email). To help marketers choose the target members for physical direct mails, we modify the customer’s preference index of our original model to predict members’ repurchase rates for a physical supermarket’s members. Based on members’ predicted repurchase rates, marketers can design proper marketing strategies for different types of supermarket member to improve marketing effectiveness. In addition, because members can only spend a short amount of time reading direct emails before choosing the products that they like, a recommender system based on a simple combination method is introduced. The system determines the most suitable combination of commodity types under the condition that a customized direct email can include only a small, fixed number of such types. In this study, member transaction records from a well-known Taiwanese supermarket were used as the test data. This supermarket’s marketing department reviewed all the experimental results and confirmed that our approach is not only superior to the current approach employed by the supermarket but also useful in designing appropriate direct-mail marketing strategies for selected supermarket members. Our approach is also suitable for direct email sent by the supermarket.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Chiang R-D, Wang Y-H, Chu H-C (2013) Prediction of members’ return visit rates using a time factor. Electron Commerc Res Appl 12:362–371. https://doi.org/10.1016/j.elerap.2013.06.002

    Article  Google Scholar 

  2. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132. https://doi.org/10.1016/j.knosys.2013.03.012

    Article  Google Scholar 

  3. Luo X, Yunni X, Zhu Q (2012) Incremental collaborative filtering recommender based on regularized Matrix factorization. Knowl Based Syst 27:271–280. https://doi.org/10.1016/j.knosys.2011.09.006

    Article  Google Scholar 

  4. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7:76–80. https://doi.org/10.1109/MIC.2003.1167344

    Article  Google Scholar 

  5. Bell RM, Koren Y (2007) Lessons from the Netflix prize challenge. SIGKDD Explor Newsl 9:75–79. https://doi.org/10.1145/1345448.1345465

    Article  Google Scholar 

  6. Pradel B, Savaneary S, Delporte J, Guerif S, Rouveirol C, Usunier N, Fogelman-Souli F, Dufau-Joel F (2011) A case study in a recommender system based on purchase data. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, pp 377–385

  7. Wang J, Sarwar B, Sundaresan N (2011) Utilizing related products for post-purchase recommendation in e-commerce. In: Proceedings of the Fifth ACM Conference on Recommender Systems. ACM, New York, pp 329–332

  8. Guan H, Huakang Li X, Cheng-Zhong MG (2013) Semi-sparse algorithm based on multi-layer optimization for recommender system. J Supercomput 66(3):1418–1437

    Article  Google Scholar 

  9. Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–101

    Google Scholar 

  10. Bell RM, Koren Y, Volinsky C (2008) The bellkor 2008 solution to the Netflix prize. Statistics Research Department at AT&T Research

  11. Koren Y (2010) Collaborative filtering with temporal dynamics. Commun ACM 53:89–97. https://doi.org/10.1145/1721654.1721677

    Article  Google Scholar 

  12. Xiang L, Yang Q (2009) Time-dependent models in collaborative filtering based recommender system. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT’09). IET, London, pp 450–457

  13. Ding Y, Li X (2005) Time weight collaborative filtering. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. ACM, New York, pp 485–492

  14. Blanco-Fernández Y, Pazos-Arias JJ, López-Nores M, Gil-Solla A, Ramos-Cabrer M, García-Duque J, Fernández-Vilas A, Díaz-Redondo RP (2010) Incentivized provision of metadata, semantic reasoning and time-driven filtering: making a puzzle of personalized e-commerce. Expert Syst Appl 37:61–69. https://doi.org/10.1016/j.eswa.2009.05.022

    Article  MATH  Google Scholar 

  15. Ma S, Li X, Ding Y, Orlowska ME (2007) A recommender system with interest-drifting. In: Web Information Systems Engineering—WISE 2007. Springer, Berlin, pp 633–642

  16. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  17. Luo X, Xia Y, Zhu Q (2012) Incremental collaborative filtering recommender based on regularized matrix factorization. Knowl Based Syst 27:271–280

    Article  Google Scholar 

  18. Wang Z, Liu Y, Chiu S (2016) An efficient parallel collaborative filtering algorithm on multi-GPU platform. J Supercomput 72(6):2080–2094

    Article  Google Scholar 

  19. Wu S-J (2016) Commodities selection of supermarket Email-flyers by recommender systems In: The 5th International Conference on Frontier Computing—Theory, Technologies and Applications (FC 2016), Tokyo

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shih-Jung Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, SJ., Chiang, RD. & Wu, TF. Direct mail promotion mechanisms and their application in supermarkets. J Supercomput 76, 1398–1415 (2020). https://doi.org/10.1007/s11227-018-2259-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2259-z

Keywords

Navigation