APPLICATION OF THE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING CUSTOMERS

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NAJI AZIME Z.; GHORBANPOUR A.

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Journal: Journal of Operational Research in its Applications Year:2015 | Volume:12 | Issue:1 (44) Page(s): 33-47

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Title

APPLICATION OF THE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING CUSTOMERS

Author(s)

NAJI AZIME Z. | GHORBANPOUR A. | Issue Writer Certificate

Keywords

CUSTOMER CLUSTERINGQ2

PARTICLE SWARM OPTIMIZATION ALGORITHMQ2

K-MEAN CLUSTERING ALGORITHMQ2

Abstract

Modern marketing is based on customer segmentation; this is because the product-centric view has been in its place to the customer orientation. So, proficiency in establishing proper communication with the customer is essential to retain the key existing customers. Segmentation is one of the issues in the area of customer relationship management. For this purpose, using a model of customer segmentation is the opportunity for organizations to design and provide their valuable suggestions that fit the needs and wants of targeted sectors and thus improve their performance from a different perspective. The purpose of this study is to use an appropriate model for customers segmenting based on criteria such as the length of the customer relationship, recent exchanges, exchange frequency and monetary value of exchange. For clustering data in this article, combining PARTICLE SWARM OPTIMIZATION ALGORITHM with k-mean to overcome the problems as being sensitive to initial value has been used to trap in local optimum. Research findings show that customers who belong to the first cluster have high mean indicators of “the length customer relationship” and “just buy” and have less than the total customers’ average in indicators of “frequency of purchase” and “sale price”, and also customers who belong to the second cluster have high mean indicators of “just buy” and have less than the total customers’ average indicators of “length customer relationship”, “frequency of purchase” and “sale price”. Therefore, in terms of loyalty matrix, the first cluster customers are loyal customers, and in terms of the values matrix are uncertain customers, and so the second cluster customer in terms of loyalty matrix are new customers and in terms of the values matrix are uncertain customers. In the end it is clear that the algorithm designed to achieve more accurate clustering of customers is more performance than k-mean algorithm.

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APA: Copy

NAJI AZIME, Z., & GHORBANPOUR, A.. (2015). APPLICATION OF THE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING CUSTOMERS. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), 12(1 (44)), 33-47. SID. https://sid.ir/paper/164414/en

Vancouver: Copy

NAJI AZIME Z., GHORBANPOUR A.. APPLICATION OF THE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING CUSTOMERS. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS)[Internet]. 2015;12(1 (44)):33-47. Available from: https://sid.ir/paper/164414/en

IEEE: Copy

Z. NAJI AZIME, and A. GHORBANPOUR, “APPLICATION OF THE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING CUSTOMERS,” JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), vol. 12, no. 1 (44), pp. 33–47, 2015, [Online]. Available: https://sid.ir/paper/164414/en

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