earticle

영어

During the iteration process, the traditional K-means algorithm is easily fall into local optimal solution. In order to solve this problem, this paper proposed an improved Kmeans algorithm, and used the method of maximum distance equal division to select the initial cluster centers. We preset k cluster centers, and avoid it falling into local optimal solution. Apply this improved algorithm into e-commerce customer loyalty analysis, this paper put forward a customer loyalty analysis model using the parameters of shopping recency, shopping frequency, shopping monetary, customer satisfaction and customer attention, and used the improved K-means algorithm to analyze the RFMSA customer loyalty model. The studies show that the improved K-means algorithm and RFMSA model can effectively divide the loyalty of the e-commerce customer, it also can fully reflect the customer’s current value and potential value-added ability, and provide the basis that the e-commercial enterprises can adopt different marketing strategies for different target customers.