Research on customer segmentation in retail market based on RFM clustering algorithm
Customer relationship management as an important part of enterprise management,its customer segmentation function directly affects the enterprise marketing strategy.In order to better segment the retail market customers,the RFM model and four clustering algorithms including K-means,DBSCAN,AGNES and GMM are verified in UCI Online Retail by applying a British retailer data set.The results of customer classification on retail retailer data set are compared with those of the above four clustering algorithms by using profile coefficient,Kalinsky Harabas Index(CHI)and Davidson Burger Index(DBI).The empirical results show that K-means and AGNES algorithm have better clustering effect on the selected retailer data set,while DBSCAN and GMM algorithm have less clustering effect,aiming to provide reference and reference for Machine Learning Clustering Algorithm in customer classification based on RFM model.It is recommended that enterprises attach importance to the output data,improving the system related to enterprise data,and combining the characteristics of customer data with the sales characteristics of the enterprise itself to use clustering algorithms for targeted customer segmentation,assisting in summarizing customer profiles,and thus developing targeted marketing strategies.
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