Presenting a customer classification Pattern with a combined data mining approach (case study :Hygienic and Cosmetic products Industry )

Subject Areas : business management

omid  Bashardoust ¹ (Department of Management; Roudehen branch, Islamic Azad University, Roudehen, Iran)
Ezzatollah  Asgharizadeh ² (Department of Management, Faculty of Management, Tehran University, Tehran, Iran)
moHammadAli  AfsharKazemi ³ (Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)

Received: 2021-03-07 Accepted : 2021-03-30 Published : 2021-06-22

Keywords: Data mining, Clustering, Segmentation, Purchasing Behavior Patterns, WRFM Index,

Abstract :

Due to the accumulated volume of customer purchasing information and the complexity of competition in the present era, the importance of creating a platform for analyzing up-to-date and accurate customer data, with the aim of creating effective relationships with current and loyal customers, more than ever for organizations as It has become a competitive advantage. The purpose of this study was to investigate the behavioral patterns of customers buying Hygienic Products in order to classify them based on the WRFM using data mining methods. 65534 samples were collected from the company databases in the period of 1396-1397 among the customers of Tehran province by the available purposeful sampling method, also with the help of SPSS, the amount of WRFM determined according to the opinion of industry experts and then this field had been to other fields in the research and using Clementine software, customers clustering has been done according to 70% of the data; also, in order to evaluate the quality of clustering, the criteria of Gini Score, error percentage, and normalized mutual information were used. The results indicate the high efficiency of the K-Means clustering method with the number of four clusters with purity percentage (0.761) for customer segmentation.