Measuring Online-Shopping Customer Satisfaction by Combining Data Mining Technique and Fuzzy Kano Model (Case Study: Nyazco Website)

Authors

10.35050/JIPM010.2017.010

Abstract

Nowadays, one of the most important things that make companies pay attention to the customer satisfaction is the competition in the world and companies struggle to achieve sustainable competitive advantage and strategic superiority over their competitors. The aims of this research is to identify the customers’ requirement in Nyazco online shopping and cluster these customers based on two methods, k-means algorithm and fuzzy Kano model, in order to offer products according to each customer’s needs. In this study, 1090 records of customers were examined for data mining of these customers in the period of 7 months, and four clusters of customers were defined. To measure the customers’ satisfactions, the fuzzy Kano questionnaire was designed and randomly assigned to 168 customers, who were selected based on Cochran formula, and finally, the requirements of each cluster were investigated and classified by using fuzzy Kano model. The results of this research show that the category of some characteristics is different between some clusters and is the same between other clusters. Also, the satisfaction and dissatisfaction score is calculated for each cluster. The results of this study indicate that the customers of the third cluster are more important for this site because the transaction frequencies of these customers and the total amount of purchasing are high. The result of this study will help other online shopping centers for better presentation of goods to different customers.

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