  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.
