Rental Customer Segmentation Based on Length, Recency, Frequency, Average-Monetary and Satisfaction Value Model and Cluster Analysis
- https://doi.org/10.2991/aebmr.k.200908.057How to use a DOI?
- renting customers, customer segmentation, LRFAS model, K-means clustering
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, average-monetary 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, average-monetary 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.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Fu Tao AU - Xindi Wang PY - 2020 DA - 2020/09/08 TI - Rental Customer Segmentation Based on Length, Recency, Frequency, Average-Monetary and Satisfaction Value Model and Cluster Analysis BT - Proceedings of the 3rd International Conference on Economy, Management and Entrepreneurship (ICOEME 2020) PB - Atlantis Press SP - 346 EP - 350 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.200908.057 DO - https://doi.org/10.2991/aebmr.k.200908.057 ID - Tao2020 ER -