A new bundling model to promote the worst selling products with deep learning approaches

Abstract

It is essential that managers have information on how to design bundling incentives to increase sales for the worst selling products. Therefore, this study attempts to identify which items are suitable for promoting the worst selling products by different measures and further predict the sales of products by using deep learning approaches, recurrent neural network and long short-term memory (RNN and LSTM), as well as two conventional methods, linear regression and XGBRegressor. Experimental results from a real-life dataset show that the proposed measure, PromoteRate, outperforms the other traditional measures, lift, cosine, and allconf. That is, PromoteRate can discover better bundling promotion items for the worst selling products. LSTM shows superior predictive performance compared to other approaches. Finally, the experiment also includes results for products with lower sales, not solely focused on the worst performers.

Keywords

marketing product bundling neural networks data mining

Get full access to this article

View all access options for this article.

References

Aggarwal

C. C.

P. S.

(1998). A new framework for itemset generation. In In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems (pp. 18–24). ACM.

Agrawal

Imieliński

Swami

(1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216. ACM. https://doi.org/10.1145/170036.170072

Agrawal

Srikant

(1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases. VLDB, 1215(2), 487–499. https://mahoto.wordpress.com/wp-content/uploads/2013/10/dmct-lab7-apriori.pdf.

Ahn

K. I.

(2012). Effective product assignment based on association rule mining in retail. Expert Systems with Applications, 39(16), 12551–12556. https://doi.org/10.1016/j.eswa.2012.04.086

Anderson

(2004). “The long tail”. Wired, Issue 12.10, Oct 2004. https://www.wired.com/2004/10/tail/ (accessed 2/10/2024).

Brin

Motwani

Silverstein

(1997). Beyond market baskets: Generalizing association rules to correlations. ACM SIGMOD Record, 26(2), 265–276. ACM. https://doi.org/10.1145/253262.253327

Chen

Y. L.

Weng

C. H.

(2008). Mining association rules from imprecise ordinal data. Fuzzy Sets and Systems, 159(4), 460–474. https://doi.org/10.1016/j.fss.2007.10.005

Chiang

W. Y.

(2011). To mine association rules of customer values via a data mining procedure with improved model: An empirical case study. Expert Systems with Applications, 38(3), 1716–1722. https://doi.org/10.1016/j.eswa.2010.07.097

Cil

(2012). Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611–8625. https://doi.org/10.1016/j.eswa.2012.01.192

10.

Gautam

(2022). Transfer Learning for COVID-19 cases and deaths forecast using LSTM network. ISA Transactions, 124(1), 41–56. https://doi.org/10.1016/j.isatra.2020.12.057

11.

Gopagoni

P. K.

M. R.

(2020). Distributed elephant herding optimization for grid-based privacy association rule mining. Data Technologies and Applications, 54(3), 365–382. https://doi.org/10.1108/dta-07-2019-0104

12.

Han

Kamber

(2006). Data mining, Southeast Asia Edition: Concepts and techniques. Morgan kaufmann.

13.

G. T.

W. H.

C. H.

Tse

Y. K.

(2012). Using a fuzzy association rule mining approach to identify the financial data association. Expert Systems with Applications, 39(10), 9054–9063. https://doi.org/10.1016/j.eswa.2012.02.047

14.

Hochreiter

Schmidhuber

(1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

15.

Huang

Duan

(2011). Mining association rules to support resource allocation in business process management. Expert Systems with Applications, 38(8), 9483–9490. https://doi.org/10.1016/j.eswa.2011.01.146

16.

Jia

Wang

(2019). A regression-based algorithm for frequent itemsets mining. Data Technologies and Applications, 54(3), 259–273. https://doi.org/10.1108/dta-03-2019-0037

17.

Kamsu-Foguem

Rigal

Mauget

(2013). Mining association rules for the quality improvement of the production process. Expert Systems with Applications, 40(4), 1034–1045. https://doi.org/10.1016/j.eswa.2012.08.039

18.

Kuo

R. J.

Pai

C. M.

Lin

R. H.

Chu

H. C.

(2015). The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation. Applied Mathematics and Computation, 250(10), 958–972. https://doi.org/10.1016/j.amc.2014.11.015

19.

H. Q.

Arch-Int

Nguyen

H. X.

Arch-Int

(2013). Association rule hiding in risk management for retail supply chain collaboration. Computers in Industry, 64(7), 776–784. https://doi.org/10.1016/j.compind.2013.04.011

20.

Lee

Song

Park

(2012). Design of convergent product concepts based on functionality: An association rule mining and decision tree approach. Expert Systems with Applications, 39(10), 9534–9542. https://doi.org/10.1016/j.eswa.2012.02.099

21.

Lee

Park

S. H.

Moon

(2013). Utility-based association rule mining: A marketing solution for cross-selling. Expert Systems with Applications, 40(7), 2715–2725. https://doi.org/10.1016/j.eswa.2012.11.021

22.

Lee

Y. K.

Kim

W. Y.

Cai

Y. D.

Han

(2003). CoMine: Efficient mining of correlated patterns. In In ICDM (pp. 581–584).

23.

Wong

Feng

Tan

Y. P.

(2005). Relative risk and odds ratio: A data mining perspective. In Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (pp. 368–377). ACM.

24.

X. D.

J. K.

Chow

T. W.

(2005). Approximation of dynamical time-variant systems by continuous-time recurrent neural networks. IEEE Transactions on Circuits and Systems II: Express Briefs, 52(10), 656–660. https://doi.org/10.1109/tcsii.2005.852006

25.

Olah

(2015). Understanding LSTM networks. Retrieved December 28, 2024 from https://colah.github.io/posts/2015-08-Understanding-LSTMs/

26.

Omiecinski

E. R.

(2003). Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering, 15(1), 57–69. https://doi.org/10.1109/tkde.2003.1161582

27.

Pascanu

Mikolov

Bengio

(2013). On the difficulty of training recurrent neural networks. In International conference on machine learning (pp. 1310–1318). PMLR.

28.

Salinger

M. A.

(1995). A graphical analysis of bundling. Journal of Business, 68(1), 85–98. https://doi.org/10.1086/296654

29.

Sarkar

De Bruyn

(2021). LSTM response models for direct marketing analytics: Replacing feature engineering with deep learning. Journal of Interactive Marketing, 53(1), 80–95. https://doi.org/10.1016/j.intmar.2020.07.002

30.

Savasere

Omiecinski

Navathe

(1998). Mining for strong negative associations in a large database of customer transactions. In Data Engineering, 1998. Proceedings 14th international conference on (pp. 494–502). IEEE.

31.

Schilke

Wirtz

B. W.

(2012). Consumer acceptance of service bundles: An empirical investigation in the context of broadband triple play. Information & Management, 49(2), 81–88. https://doi.org/10.1016/j.im.2011.12.003

32.

Shim

Choi

Suh

(2012). CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Systems with Applications, 39(9), 7736–7742. https://doi.org/10.1016/j.eswa.2012.01.080

33.

Stremersch

Tellis

G. J.

(2002). Strategic bundling of products and prices: A new synthesis for marketing. Journal of Marketing, 66(1), 55–72. https://doi.org/10.1509/jmkg.66.1.55.18455

34.

Tian

Pan

(2015). Predicting short-term traffic flow by long short-term memory recurrent neural network. In 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity) (pp. 153–158). IEEE.

35.

Venkatesh

Mahajan

(1993). A probabilistic approach to pricing a bundle of products or services. Journal of Marketing Research, 30(4), 494–508. https://doi.org/10.2307/3172693

36.

Weng

C. H.

(2011). Mining fuzzy specific rare itemsets for education data. Knowledge-Based Systems, 24(5), 697–708. https://doi.org/10.1016/j.knosys.2011.02.010

37.

Weng

C. H.

(2016). Identifying association rules of specific later-marketed products. Applied Soft Computing, 38(5), 518–529. https://doi.org/10.1016/j.asoc.2015.09.047

38.

Weng

C. H.

Huang

T. C. K.

(2018). Knowledge acquisition of association rules from the customer-lifetime-value perspective. Kybernetes, 47(3), 441–457. https://doi.org/10.1108/k-03-2016-0042

39.

Zhang

(2004). Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems, 22(3), 381–405. https://doi.org/10.1145/1010614.1010616

40.

Zheng

Yang

Liu

Dai

H. N.

Zhang

(2019). Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3927–3939. https://doi.org/10.1109/tits.2019.2909904