Clustering Online-Shopping Customers in Order to Analyze Customers Satisfaction

Ali Rezaeian¹ Sajjad Shokouhyar² Fariba Dehghan³

1) Shahid Beheshti University, faculty of management and accounting, Email:
2) Shahid Beheshti University, faculty of management and accounting, Email:
3) Shahid Beheshti University, faculty of management and accounting, Email:

IOI Code : XYCF-ADKAD
Publication : 2nd.International Congress on Management, Economy, Humanities and Business Development(icmba.ir)
Abstract :
    Today the use of modern technologies in the daily life for satisfying the needs is unavoidable. Follow the news and searching through the internet has affected on organizations to provide platform on the internet for availability of information for the customers. With the development of e-commerce, online shopping plays an increasingly important role in people’s life. With the use of data technique prospect, managers of this site can analyze preferences and purchasing patterns of online customers in order to custom product recommendations. Data mining helps to provide services in accordance with customers’ requirements. The aim of this research is to identify the customers’ requirements in online shopping and cluster these customers based on gender, product classification, recency, frequency and monetary. For this purpose, the data related to nyazco website that is an e-commerce website with a variety of products, were examined as a case study in the period of 7 months. IBM SPSS Modeler 14.2 was applied for data mining analysis and clustering customers. Initially, the RFM algorithm was used in order to behavior analysis of customers that its indicators are recency, frequency and monetary. Then, RFM algorithm output was used as an input for K-Means algorithm in order to extract clusters. Four clusters based on silhouette index were identified. Finally, suitable strategies were presented by ranking identified clusters.

Keywords : "e commerce" "online shopping" "data mining" "clustering"