RE-RFME: Real-Estate RFME Model for customer segmentation

TL;DR

This paper introduces RE-RFME, a comprehensive pipeline that utilizes a novel RFME model and K-means clustering to effectively segment online real estate customers based on their behavioral features, aiding targeted marketing.

Contribution

The paper presents a new RFME model for capturing customer behaviors and an end-to-end segmentation pipeline tailored for real estate platforms, improving marketing strategies.

Findings

Effective segmentation into four customer groups

Validated on real-world Housing.com data

Improves targeted marketing efforts

Abstract

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.