Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Add-on selling strategies in an online open market

  • Shim, Beomsoo (Korea Hydro & Nuclear Power Co., Ltd., Central research institute) ;
  • Lee, Hanjun (Korea Institute for Defense Analyses)
  • Received : 2015.03.31
  • Accepted : 2015.06.05
  • Published : 2015.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Add-on selling can provide new chances to increase sellers' profits and meet customers' needs. Although prior studies have advocated add-on selling for its business value, there is an argument that add-on selling can cause customer repulsion. Therefore, we need to understand customer purchasing pattern related to add-on selling in order to promote it and to mitigate the customer repulsion. To that end, we applied data mining techniques to the 24,925 transactions of data from an online open market in Korea. We then conducted feature selection to investigate the most influential factors that can explain the characteristics of add-on selling transactions using a classification model. We also identified association rules among add-on selling and promotions. Finally, based on the findings in our experiments, we proposed add-on selling strategies for the target online market.

Keywords

References

  1. Adomavicius, G. and Tuzhilin, A. (2001). Expert-driven validation of rule-based user models in personal-ization applications. Data Mining and Knowledge Discovery, 5, 33-58. https://doi.org/10.1023/A:1009839827683
  2. Aggarval, C. C. and Yu, P. S. (2002). Finding localized associations in market basket data. IEEE Transac-tions on Knowledge and Data Engineering, 14, 51-62. https://doi.org/10.1109/69.979972
  3. Agresti, A. (2012). Categorical data analysis, 3rd ed., Wiley, New York.
  4. Blattberg, R., Getz, G. and Thomas, J. (2001). Customer Equity . Harvard Business School Press, Boston.
  5. Bortiz, J. E. and Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9, 503-512. https://doi.org/10.1016/0957-4174(95)00020-8
  6. Bunkley, Nick. (2008). Joseph Juran, 103, pioneer in quality control, dies, New York Times, http://www.nytimes.com/2008/03/03/business/03juran.html.
  7. Changchien, S. W., Lee, C. F. and Hsu, Y. J. (2004). On-line personalized sales promotion in electronic commerce. Expert Systems with Applications, 27, 35-52. https://doi.org/10.1016/j.eswa.2003.12.017
  8. Chen, M. C., Chiu, A. L. and Chang, H. H. (2005). Mining changes in customer behaviour in retail CRM. Expert Systems with Applications, 28, 773-781. https://doi.org/10.1016/j.eswa.2004.12.033
  9. Cheng, C. H. and Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36, 4176-4184. https://doi.org/10.1016/j.eswa.2008.04.003
  10. Clement, T. M., Cloodt, M. M. A. H. and Verhoef, L. H. J. (2010). Differences in the Business model design;CRM & Add-on selling in Central Asia, Master Theses, Technology University Eindhoven, Eindhoven.
  11. Dennis, C., Marsland, D. and Cockett, T. (2001). Data mining for shopping centres customer knowledge management framework. Journal of Knowledge Management, 5, 368-374. https://doi.org/10.1108/13673270110411797
  12. Ebster, C.,Wagner, U. and Valis, S. (2006). The effectiveness of verbal prompts on sales. Journal of Retailing and Consumer Services, 13, 169-76. https://doi.org/10.1016/j.jretconser.2005.08.003
  13. Fletcher, D. and Goss, E. (1993). Forecasting with neural networks: An application using bankruptcy data. Information and Management, 24, 159-167. https://doi.org/10.1016/0378-7206(93)90064-Z
  14. Lee, H., Shim, B. and Suh, Y. (2015). VIP-targeted CRM strategies in an open market. Journal of the Korean Data & Information Science Society, 26, 1-13. https://doi.org/10.7465/jkdi.2015.26.1.1
  15. Hosseini, M., Anahita, M. and Mohammad, R. G. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37, 5259-5264. https://doi.org/10.1016/j.eswa.2009.12.070
  16. Hughes, A. M. (1994). Strategic data base CRM, University of Chicago Press, Chicago.
  17. Hwang, H., Jung, T. and Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications, 26, 181-188. https://doi.org/10.1016/S0957-4174(03)00133-7
  18. Thomas, J. S. (2001). A methodology for linking customer acquisition to customer retention. Journal of Marketing Research, 38, 262-268. https://doi.org/10.1509/jmkr.38.2.262.18848
  19. Khajvanda, M. and Tarokhb, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332. https://doi.org/10.1016/j.procs.2011.01.011
  20. Kim, S. Y., Jung, T. S., Suh, E. H. and Hwang, H. S. (2006). Customer segmentation and strategy develop-ment based on customer lifetime value: A case study. Expert Systems with Applications, 31, 101-107. https://doi.org/10.1016/j.eswa.2005.09.004
  21. Kim, Y. S. (2006). Toward a successful CRM: Variable selection, sampling and ensemble. Decision Support Systems, 41, 542-553. https://doi.org/10.1016/j.dss.2004.09.008
  22. Kivetz, R. and Simonson (2003). The idiosyncratic fit Heuristic: Effort advantage as a determinant of consumer response to loyalty programs. Journal of Marketing Research, 40, 454-467. https://doi.org/10.1509/jmkr.40.4.454.19383
  23. Kracklauer, A. H., Mills, D. Q. and Seifert, D. (2004). Customer management as the origin of collaborative customer relationship management. Collaborative Customer Relationship Management - taking CRM to the next level, 3-6.
  24. Kubat, M., Hafez, A., Raghavan, V. V., Lekkala, J. R. and Chen, W. K. (2003). Item set trees for targeted association querying. IEEE Transactionon Knowledge and Data Engineering, 15, 1522-1534. https://doi.org/10.1109/TKDE.2003.1245290
  25. Langley, P. and Simon, H. A. (1995). Applications of machine learning and rule induction. Communication of the ACM, 38, 55-64.
  26. Lau, H. C. W., Wong, C. W. Y., Hui, I. K. and Pun, K. F. (2003). Design and implementation of an integrated knowledge system. Knowledge-Based Systems, 16, 69-76. https://doi.org/10.1016/S0950-7051(02)00052-7
  27. Ling, R. and Yen, D. C. (2001). Customer relationship management: An analysis framework and implemen-tation strategies. Journal of Computer Information Systems, 41, 82-97.
  28. McCarthy, T. (1998). Secrets to suggestive selling. Lodging Hospitality, 54, 18-21.
  29. Mirman, R. (1982). Performance management in sales organizations. In Handbook of organizational behavior management, 427-475, edited by L. W. Frederiksen, John Wiley, New York.
  30. Newell, F. (1997). The new rules of CRM: How to use one-to-one relationship CRM to be the leader in your industry, McGraw-Hills, NewYork.
  31. Parvatiyar, A. and Sheth, J. N. (2001). Customer relationship management: Emerging practice, process and discipline. Journal of Economic & Social Research, 3, 1-34.
  32. Polonsky, M. J., Cameron, H., Halstead, S., Ratcliffe, A., Stilo, P. and Watt, G. (2000). Exploring companion selling: Does the situation affect customers' perceptions? International Journal of Retail and Distribution Management, 28, 37-45. https://doi.org/10.1108/09590550010306764
  33. Salchenberger, L. M., Cinar, E. M. and Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23, 899-916. https://doi.org/10.1111/j.1540-5915.1992.tb00425.x
  34. Su, C. T., Hsu, H. H. and Tsai, C. H. (2002). Knowledge mining from trained neural networks. Journal of Computer Information Systems, 42, 61-70.
  35. Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies, Prentice-Hall, Upper Saddle River, New Jersey.
  36. Tam, K. Y. and Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, 926-947. https://doi.org/10.1287/mnsc.38.7.926
  37. Wu, C. H., Kao, S. C., Su, Y. Y. and Wu, C. C. (2005). Targeting customers via discovery knowledge for the insurance industry. Expert Systems with Applications, 29, 291-299. https://doi.org/10.1016/j.eswa.2005.04.002
  38. Wu, J. and Lin, Z. (2005). Research on customer segmentation model by clustering. ACM International Conference Proceeding Series, 113.
  39. Yu, J. X., Ou, Y., Zhang, C. and Zhang, S. (2005). Identifying interesting visitors through web log classifi-cation. IEEE Intelligent Systems, 20, 55-59. https://doi.org/10.1109/MIS.2005.47
  40. Zhang, G., Hu, M. Y., Patuwo, B. E. and Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross validation analysis. European Journal of Operational Research, 116, 16-32. https://doi.org/10.1016/S0377-2217(98)00051-4