RE-RFME: Real-Estate RFME Model for customer segmentation
This addresses marketing cost reduction for online real estate platforms by enabling targeted strategies, though it is incremental as it builds on existing RFM and clustering methods.
The paper tackles customer segmentation for real estate platforms by proposing RE-RFME, an end-to-end pipeline that uses a novel RFME model to group customers into four categories, demonstrating effectiveness on real-world Housing.com datasets.
Marketing is one of the high-cost activities for any online platform. With the increase in the number of customers, it is crucial to understand customers based on their dynamic behaviors to design effective marketing strategies. Customer segmentation is a widely used approach to group customers into different categories and design the marketing strategy targeting each group individually. Therefore, in this paper, we propose an end-to-end pipeline RE-RFME for segmenting customers into 4 groups: high value, promising, need attention, and need activation. Concretely, we propose a novel RFME (Recency, Frequency, Monetary and Engagement) model to track behavioral features of customers and segment them into different categories. Finally, we train the K-means clustering algorithm to cluster the user into one of the 4 categories. We show the effectiveness of the proposed approach on real-world Housing.com datasets for both website and mobile application users.