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.

翻译：市场营销是所有在线平台的高成本活动之一。随着客户数量的增加，基于动态行为理解客户以制定有效的营销策略至关重要。客户细分是一种广泛使用的方法，将客户划分为不同类别，并针对每个群体单独设计营销策略。因此，本文提出了一个端到端流水线RE-RFME，将客户分为四类：高价值客户、潜力客户、需关注客户和需激活客户。具体而言，我们提出了一种新颖的RFME（最近消费时间、频率、金额与参与度）模型来追踪客户的行为特征，并将其划分为不同类别。最后，我们训练K-means聚类算法将用户归入上述四类之一。我们在真实世界的Housing.com数据集上展示了该方法对网站和移动应用用户的有效性。