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As the rental market continues to grow, it is increasingly important to subdivide rental customers and formulate targeted marketing management strategies. Aiming at the characteristics of large number of renting customers, long period, and large potential value, a length, recency, frequency, averagemonetary and satisfaction value (LRFAS) model of customer segmentation based on recency, frequency and monetary value improvement is proposed. On this basis, the K-means clustering algorithm is used to subdivide customers, and the entropy weight method is used to determine the weight of each indicator, and the obtained indicator weight is used to calculate the value of the customer. An example of an intermediary company was used to verify the feasibility and effectiveness of the improved model in the field of housing leasing. The results show that the improved length, recency, frequency, averagemonetary and satisfaction value model can more effectively and accurately segment rental customers, and at the same time formulate corresponding marketing strategies for different types of customer needs, helping intermediary companies to gain great core competitiveness in the market.

Rental Customer Segmentation Based on Length, Recency, Frequency, Average-Monetary and Satisfaction Value Model and Cluster Analysis