customer & apos; s purchasing interest may fluctuate for different reasons and it is important to find the
declining or increasing trends whenever they happen. It is important to study these fluctuations
to improve customer relationships. There are different methods to increase the customer & apos; s
willingness such as planning good promotions, an increase on advertisement, etc. This paper
proposes a new methodology to measure customer & apos; s behavioral trends called customer
electrocardiogram. The proposed model of this paper uses K-means clustering method with
RFM analysis to study customer & apos; s fluctuations over different time frames. We also apply the
proposed electrocardiogram methodology for a real-world case study of food industry and the
results are discussed in details
How to cite this paper
Hamzehei, A., Fathian, M., Gholamian, M & Farvaresh, H. (2011). A new methodology to study customer electrocardiogram using RFM analysis and clustering.Management Science Letters , 1(2), 139-148.
Refrences
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Möller-Levet, C.S., Klawonn, F., Cho, K.-H., & Wolkenhauer, O. (2003). Fuzzy clustering of short time series and unevenly distributed sampling points. Proceedings of the 5th International Symposium on Intelligent Data Analysis, Berlin, Germany, August 28–30.
Ngai, E. W. T., Xiu L., & Chau, D. C. K. (2009). Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification. Expert Systems with Applications, 36, 2592–2602.
Tsai, C. & Chiu, C. (2004). A purchase-based market segmentation methodology. Expert Systems with Applications, 27, 265–276.
Weiwen, X., Liang, C., Zhiyong, Z. & Zhuqiang, Q. (2008). RFM Value and Grey Relation Based Customer Segmentation Model in the Logistics Market Segmentation. International Conference on Computer Science and Software Engineering, 5, 1298 -1301.
Xiang-Bin, Y. & Yi-Jun, L. (2006). Customer Segmentation based on Neural Network with Clustering Technique, Proceedings of the 5th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Madrid, Spain, February 15-17, 265-268.
Yeh, C., Yang, K. & Ting, T. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36, 5866–5871.
Ying, L. & Feng, L. (2008). Customer segmentation analysis based on SOM clustering. IEEE International Conference on Service Operations and Logistics, and Informatics, 1, 15-19.
Yuantao, J. & Siqin, Y. (2008). Mining E-Commerce Data to Analyze the Target Customer Behavior. First International Workshop on Knowledge Discovery and Data Mining, 406-409.
Cheng, C., & Chen, Y. (2008). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 4176–4184.
Farvaresh, H., & Sepehri, M. M. (2010). A data mining framework for detecting subscription fraud in telecommunication. Engineering Applications of Artificial Intelligence, 24(1), 182-194.
Han, J., & Kambert, M. (2001). Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco.
Huang, S., Chang, E. & Wu, H. (2009). A case study of applying data mining techniques in an outfitter's customer value analysis. Expert Systems with Applications, 3957-4174.
Kaefer, F., Heilman C.M., & Ramenofsky, S. D. (2005). A neural network application to consumer classification to improve the timing of direct marketing activities. Computer & Operations Research, 32(10), 2595–2615.
Kim, Y. & Nick Street, W. (2004). An intelligent system for customer targeting: A data mining approach. Decision Support Systems, 37, 215–228.
Lingras, P., Hogo, M., Snorek M., & West, C. (2005). Temporal analysis of clusters of supermarket customers: conventional versus interval set approach. Information Sciences, 172 (1–2), 215–240.
Minghua H., (2008). Customer Segmentation Model Based on Retail Consumer Behavior Analysis.. IITAW '08. International Symposium on Intelligent Information Technology Application Workshops, 914-917.
Möller-Levet, C.S., Klawonn, F., Cho, K.-H., & Wolkenhauer, O. (2003). Fuzzy clustering of short time series and unevenly distributed sampling points. Proceedings of the 5th International Symposium on Intelligent Data Analysis, Berlin, Germany, August 28–30.
Ngai, E. W. T., Xiu L., & Chau, D. C. K. (2009). Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification. Expert Systems with Applications, 36, 2592–2602.
Tsai, C. & Chiu, C. (2004). A purchase-based market segmentation methodology. Expert Systems with Applications, 27, 265–276.
Weiwen, X., Liang, C., Zhiyong, Z. & Zhuqiang, Q. (2008). RFM Value and Grey Relation Based Customer Segmentation Model in the Logistics Market Segmentation. International Conference on Computer Science and Software Engineering, 5, 1298 -1301.
Xiang-Bin, Y. & Yi-Jun, L. (2006). Customer Segmentation based on Neural Network with Clustering Technique, Proceedings of the 5th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Madrid, Spain, February 15-17, 265-268.
Yeh, C., Yang, K. & Ting, T. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36, 5866–5871.
Ying, L. & Feng, L. (2008). Customer segmentation analysis based on SOM clustering. IEEE International Conference on Service Operations and Logistics, and Informatics, 1, 15-19.
Yuantao, J. & Siqin, Y. (2008). Mining E-Commerce Data to Analyze the Target Customer Behavior. First International Workshop on Knowledge Discovery and Data Mining, 406-409.