Abstract
In this paper, we use Data Mining techniques such as clustering and association rules, for the purpose of target marketing strategy. Our goal is to develop a methodology for retailers on how to segment their stores based on multiple data sources and how to create marketing strategies for each segment rather than mass marketing. We have analyzed a supermarket chain company, which has 73 stores located in the Istanbul area in Turkey. First, stores are segmented in 5 clusters using a hierarchical clustering method and then association rules are applied for each cluster.
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References
Chen, I.J., Popovich, K.: Understanding CRM, people, process and technology. J. Bus. Process Manage. 9(5), 672–688 (2003)
Ling, R., Yen, D.C.: Customer relationship management: an analysis framework and implementation strategies. J. Comput. Inf. Syst. 41, 82–97 (2001)
Bermingham, P., Hernandez, T., Clarke, I.: Network planning and retail store segmentation, a spatial clustering approach. Int. J. Appl. Geospatial Res. 4(1), 67–79 (2013)
Smith, W.: Product differentiation and market segmentation as alternative marketing strategies. J. Mark. 21, 3–8 (1956)
Segal, M.N., Giacobbe, R.W.: Market segmentation and competitive analysis for supermarket retailing. Int. J. Retail Distrib. Manage. 22, 38–48 (1994)
Berson, A., Smith, S., Thearling, K.: Building Data Mining Applications for CRM. McGraw-Hill, New York (1999)
Kolyshkina, I., Nankani, E.: Retail analytics in the context of segmentation, targeting, optimisation of the operations of convenience store franchises. In: Anzmac 2010
Mendes, A., Cardoso, M.: Clustering supermarkets: the role of experts. J. Retail. Consum. Serv. 13, 231–247 (2006)
Kumar, V., Karande, K.: The effect of retail store environment on retailer performance. J. Bus. Res. 49(2), 167–181 (2000)
Jain, A.K., Murthy, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Gower, J.C.: A general coefficient of similarity and some of its properties. J. Biometrics 27(4), 857–871 (1971)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New Jersey (1990)
Hennig, C., Liao, T.F.: How to find an appropriate clustering for mixed type variables with application to socio-economic stratification. J. Roy. Stat. Soc. Appl. Stat. Part 3 62, 309–369 (2013)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)
Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31, 274–295 (2014)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993 Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Turkish Statistical Institute. www.turkstat.gov.tr
Real Estate Index. www.hurriyetemlak.com
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Bilgic, E., Kantardzic, M., Cakir, O. (2015). Retail Store Segmentation for Target Marketing. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_3
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DOI: https://doi.org/10.1007/978-3-319-20910-4_3
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