Abstract
In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.
Highlights
In the e-business world, online shopping has become the most popular trading pattern in China
Statistics show that the national online retail sales reached RMB 10,632.4 billion in 2019
Interested readers may refer to references [1,2,3,4]. ey provide comprehensive reviews of data mining techniques and their industrial applications
Summary
In the e-business world, online shopping has become the most popular trading pattern in China. Statistics show that the national online retail sales reached RMB 10,632.4 billion in 2019 In such an online environment, customer purchase behaviors change dynamically. An excellent customer-oriented marketing strategy for predicting customer online behaviors based on data mining is much needed by selling enterprises. Data mining, which can discover hidden knowledge of great pertinence from enormous amounts of online transaction data, is the most suitable method for customer purchase behavior analysis. Mathematical Problems in Engineering a standardized dataset for further analysis On this basis, we utilize a RFM model and K-means algorithm to conduct customer segmentation and value analysis. Customers are classified into four groups based on their purchase behaviors On this basis, different CRM strategies are brought forward to gain a high level of customer satisfaction.
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