K-means를 적용한 고객 가치 사례 분석
Case Study of Customer Value Analysis using K-means
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Customer identification for company is very valuable for direct marketing and increase of profit to target the population who are to become most profitable customer to the company based on target customer analysis and customer segmentation. Customer value analysis involves seeking the profitable groups of customers through analysis of customer’s attributes. Data mining techniques can help to accomplish to extract or detect hidden customer values and behaviors from big data. The objective of this paper is to propose customer value analysis based on RFM (R: Recency, F: Frequency, M: Monetary) model to identify the profitable segments (top target customer) of customer based on customer’ underlying characteristics. We use the case study of S-company (122 customers with 6639 transactions from 2017/09/01 to 2018/08/31) to show the procedure of customer value analysis based on RFM model. We show how we can make the scores of RFM attributes and segment customers. K-means is one of the most important technique in data mining. K-means is used for five group market segmentations based on valid index intra-cluster distance which is a popular and efficient data clustering method. Our experiments and simulation results show the 26 top target customers out of 122 customers. We also propose the product recommend system based on RFM model for efficient marketing strategy with high priority.
Abstract
Customer identification for company is very valuable for direct marketing and increase of profit to target the population who are to become most profitable customer to the company based on target customer analysis and customer segmentation. Customer value analysis involves seeking the profitable groups of customers through analysis of customer’s attributes. Data mining techniques can help to accomplish to extract or detect hidden customer values and behaviors from big data. The objective of this paper is to propose customer value analysis based on RFM (R: Recency, F: Frequency, M: Monetary) model to identify the profitable segments (top target customer) of customer based on customer’ underlying characteristics. We use the case study of S-company (122 customers with 6639 transactions from 2017/09/01 to 2018/08/31) to show the procedure of customer value analysis based on RFM model. We show how we can make the scores of RFM attributes and segment customers. K-means is one of the most important technique in data mining. K-means is used for five group market segmentations based on valid index intra-cluster distance which is a popular and efficient data clustering method. Our experiments and simulation results show the 26 top target customers out of 122 customers. We also propose the product recommend system based on RFM model for efficient marketing strategy with high priority.
참고문헌 (14)
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