Applying RFM model and K-means method in customer value analysis of an outfitter

Hsin Hung Wu; En Chi Chang; Chiao Fang Lo

Applying RFM model and K-means method in customer value analysis of an outfitter

Hsin Hung Wu, En Chi Chang, Chiao Fang Lo

MSM Marketing

Research output: Chapter in Book/Conference proceeding › Conference contribution

Abstract

This case study applies RFM model and K-means method in the value analysis of the customer database of an outfitter in Taipei, Taiwan. By considering gender, birth date, shopping frequency, and the total spending, six clusters have been found among 675 member customers from the company's database. In addition to the clustering analysis, different promotion strategies for the members of different clusters are provided. The analyses show that Clusters 5 and 6 are the two most important groups that the company has to devote resources into. Moreover, the company might ration resources for the customers in Clusters 1 and 2 because they do not contribute enough values to the outfitter. © Springer-Verlag London Limited 2009.

Original language	English
Title of host publication	Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering\|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng.
Pages	665-672
Number of pages	7
Publication status	Published - 2009
Event	16th ISPE International Conference on Concurrent Engineering, CE 2009 - Taipei Duration: 1 Jul 2009 → …

Conference

Conference	16th ISPE International Conference on Concurrent Engineering, CE 2009
City	Taipei
Period	1/07/09 → …

Keywords

Customer value analysis
Data mining
K-means method
Rfm model

Cite this

Wu, H. H., Chang, E. C., & Lo, C. F. (2009). Applying RFM model and K-means method in customer value analysis of an outfitter. In Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng. (pp. 665-672)

Wu, Hsin Hung ; Chang, En Chi ; Lo, Chiao Fang. / Applying RFM model and K-means method in customer value analysis of an outfitter. Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng.. 2009. pp. 665-672

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title = "Applying RFM model and K-means method in customer value analysis of an outfitter",

abstract = "This case study applies RFM model and K-means method in the value analysis of the customer database of an outfitter in Taipei, Taiwan. By considering gender, birth date, shopping frequency, and the total spending, six clusters have been found among 675 member customers from the company's database. In addition to the clustering analysis, different promotion strategies for the members of different clusters are provided. The analyses show that Clusters 5 and 6 are the two most important groups that the company has to devote resources into. Moreover, the company might ration resources for the customers in Clusters 1 and 2 because they do not contribute enough values to the outfitter. {\textcopyright} Springer-Verlag London Limited 2009.",

keywords = "Customer value analysis, Data mining, K-means method, Rfm model",

author = "Wu, {Hsin Hung} and Chang, {En Chi} and Lo, {Chiao Fang}",

year = "2009",

language = "English",

isbn = "9781848827615",

pages = "665--672",

booktitle = "Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng.",

note = "16th ISPE International Conference on Concurrent Engineering, CE 2009 ; Conference date: 01-07-2009",

}

Wu, HH, Chang, EC & Lo, CF 2009, Applying RFM model and K-means method in customer value analysis of an outfitter. in Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng.. pp. 665-672, 16th ISPE International Conference on Concurrent Engineering, CE 2009, Taipei, 1/07/09.

Applying RFM model and K-means method in customer value analysis of an outfitter. / Wu, Hsin Hung; Chang, En Chi; Lo, Chiao Fang.
Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng.. 2009. p. 665-672.

Research output: Chapter in Book/Conference proceeding › Conference contribution

TY - GEN

T1 - Applying RFM model and K-means method in customer value analysis of an outfitter

AU - Wu, Hsin Hung

AU - Chang, En Chi

AU - Lo, Chiao Fang

PY - 2009

Y1 - 2009

N2 - This case study applies RFM model and K-means method in the value analysis of the customer database of an outfitter in Taipei, Taiwan. By considering gender, birth date, shopping frequency, and the total spending, six clusters have been found among 675 member customers from the company's database. In addition to the clustering analysis, different promotion strategies for the members of different clusters are provided. The analyses show that Clusters 5 and 6 are the two most important groups that the company has to devote resources into. Moreover, the company might ration resources for the customers in Clusters 1 and 2 because they do not contribute enough values to the outfitter. © Springer-Verlag London Limited 2009.

AB - This case study applies RFM model and K-means method in the value analysis of the customer database of an outfitter in Taipei, Taiwan. By considering gender, birth date, shopping frequency, and the total spending, six clusters have been found among 675 member customers from the company's database. In addition to the clustering analysis, different promotion strategies for the members of different clusters are provided. The analyses show that Clusters 5 and 6 are the two most important groups that the company has to devote resources into. Moreover, the company might ration resources for the customers in Clusters 1 and 2 because they do not contribute enough values to the outfitter. © Springer-Verlag London Limited 2009.

KW - Customer value analysis

KW - Data mining

KW - K-means method

KW - Rfm model

M3 - Conference contribution

SN - 9781848827615

SP - 665

EP - 672

BT - Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng.

T2 - 16th ISPE International Conference on Concurrent Engineering, CE 2009

Y2 - 1 July 2009

ER -

Applying RFM model and K-means method in customer value analysis of an outfitter

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Keywords

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