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Customer Classification and Market Basket Analysis Using K-Means Clustering and Association Rules: Evidence from Distribution Big Data of Korean Retailing Company

군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로

  • Received : 2018.08.29
  • Accepted : 2018.11.25
  • Published : 2018.12.31

Abstract

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

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