Are there alternatives to target stores

Retail Store Segmentation for Target Marketing

tip

Access other chapters in this book by swiping

2015 | OriginalPaper | Book chapter

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.

Would you like to get access to this content? Then find out more about our products now:

Springer Professional "Business + Technology"

With Springer Professional "Business + Technology" you get access to:

  • above 69,000 books
  • above 500 magazines

from the following fields:

  • Automobile + engines
  • Construction + real estate
  • Business IT + informatics
  • Electrical engineering + electronics
  • Energy + environment
  • Finance + Banking
  • Management + leadership
  • Marketing + sales
  • Mechanical engineering + materials
  • Insurance + risk

Try now for 30 days free of charge.

Springer Professional "Technology"

With Springer Professional "Technology" you get access to:

  • above 50,000 books
  • above 380 magazines

from the following fields:

  • Automobile + engines
  • Construction + real estate
  • Business IT + informatics
  • Electrical engineering + electronics
  • Energy + environment
  • Mechanical engineering + materials



Try now for 30 days free of charge.

Springer Professional "Economy"

With Springer Professional "Economy" you get access to:

  • above 58,000 books
  • above 300 magazines

from the following fields:

  • Construction + real estate
  • Business IT + informatics
  • Finance + Banking
  • Management + leadership
  • Marketing + sales
  • Insurance + risk



Try now for 30 days free of charge.

appendix
Table 5.
Frequent item - 2 itemsets - 3itemsets for the entire data and each cluster
Table 6.
Association rules with support and confidence measures, for the entire transaction data and for each cluster
literature
Go back to reference Chen, I.J., Popovich, K .: Understanding CRM, people, process and technology. J. Bus. Process Manage. 9 (5), 672-688 (2003) CrossRef Chen, I.J., Popovich, K .: Understanding CRM, people, process and technology. J. Bus. Process Manage. 9 (5): 672-688 (2003) CrossRef
Go back to reference 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) CrossRef 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) CrossRef
Go back to reference Berson, A., Smith, S., Thearling, K .: Building Data Mining Applications for CRM. McGraw-Hill, New York (1999) MATH Berson, A., Smith, S., Thearling, K .: Building Data Mining Applications for CRM. McGraw-Hill, New York (1999) MATH
Go back to reference Kolyshkina, I., Nankani, E .: Retail analytics in the context of segmentation, targeting, optimization of the operations of convenience store franchises. In: Anzmac 2010 Kolyshkina, I., Nankani, E .: Retail analytics in the context of segmentation, targeting, optimization of the operations of convenience store franchises. In: Anzmac 2010
Go back to reference Jain, A.K., Murthy, M.N., Flynn, P.J .: Data clustering: a review. ACM Comput. Surv. 31 (3), 264-323 (1999) CrossRef Jain, A.K., Murthy, M.N., Flynn, P.J .: Data clustering: a review. ACM Comput. Surv. 31 (3): 264-323 (1999) Cross Ref
Go back to reference Hastie, T., Tibshirani, R., Friedman, J .: The Elements of Statistical Learning. Springer, New York (2009) MATHCrossRef Hastie, T., Tibshirani, R., Friedman, J .: The Elements of Statistical Learning. Springer, New York (2009) MATHCrossRef
Go back to reference 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) 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)
About this chapter
title
Retail Store Segmentation for Target Marketing
DOI
https://doi.org/10.1007/978-3-319-20910-4_3
Authors:
Emrah Bilgic
Mehmed Kantardzic
Ozgur Cakir

premium partner