The Design and Application of Precision Marketing Strategies Driven by RFM Data in Enterprises

Starting with the company's marketing data and creating an RFM model, we integrate multiple dimensions such as time of last purchase, frequency of purchase, etc. to study customer consumption behavior, identify the value characteristics of customer groups, and formulate differentiated and accurate marketing strategies. During the process of data cleansing and modeling, quantitative scoring and cluster analysis are used to classify high-value groups and customize their services. The empirical results show that for high-value customers, the response rate to a reactivation strategy is 85% with a 30% increase in sales and a 35% increase in loyalty; a reactivation strategy targeting low-value groups has a response rate of only 15%, while sales increase by 5%. Data analysis shows that the RF management model can effectively optimize resource allocation and improve loyalty and sales for high-value customers, but there is still much to discover.