Volume 21, Number 4 (IJIEPR 2010)                   IJIEPR 2010, 21(4): 239-245 | Back to browse issues page


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Farajian M A, Mohammadi S. Mining the Banking Customer Behavior Using Clustering and Association Rules Methods. IJIEPR. 2010; 21 (4) :239-245
URL: http://ijiepr.iust.ac.ir/article-1-241-en.html

K.N.Toosi University of Technology, Tehran, Iran , mohammadi@kntu.ac.ir
Abstract:   (8068 Views)

  The unprecedented growth of competition in the banking technology has raised the importance of retaining current customers and acquires new customers so that is important analyzing Customer behavior, which is base on bank databases. Analyzing bank databases for analyzing customer behavior is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. Few works have focused on analyzing of bank databases from the viewpoint of customer behavioral analyze. This study presents a new two-stage frame-work of customer behavior analysis that integrated a K-means algorithm and Apriori association rule inducer. The K-means algorithm was used to identify groups of customers based on recency, frequency, monetary behavioral scoring predicators it also divides customers into three major profitable groups of customers. Apriori association rule inducer was used to characterize the groups of customers by creating customer profiles. Identifying customers by a customer behavior analysis model is helpful characteristics of customer and facilitates marketing strategy development .

Full-Text [PDF 529 kb]   (7839 Downloads)    
Type of Study: Research | Subject: Other Related Subject
Received: 2011/05/3

Cited by [3] [PDF 101 KB]  (152 Download)
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