Enhancing Customer Segmentation in Online Transportation Services: A Comprehensive Approach Using K-Means Clustering and RFM Model
Abstract
In the rapidly evolving landscape of online transportation services, companies face complex challenges to maintain and expand their market positions. Understanding customer dynamics has become crucial for success, extending beyond mere acquisition to encompass retention. This study presents a comprehensive approach to customer segmentation in online transportation services using K-Means Clustering and the RFM Model. K-Means Clustering categorizes customers based on behavioral patterns, while the RFM Model provides a detailed insight into customer engagement in acquisition activities. The integration of these methodologies aims to enable companies to tailor services, enhance customer experiences, and formulate targeted marketing strategies. The analysis identifies 5 diverse customer groups: (1) Urban Luxury Commuters, (2) Non-Motorized Urban Users, (3) Tech-Savvy Urban Commuters, (4) Diverse Urban Commuters, and (5) Budget-Conscious Urban Commuters. Among these groups, the (2) Non-Motorized Urban Users group is the focus due to its high monetary value and the second-highest frequency level. Users in this cluster tend to transact frequently, indicating consistent and recent engagement with transportation services. Factors such as high transaction frequency and total transaction value underscore the importance of this cluster in generating overall revenue. Additionally, the research will consider additional factors such as user demographics, travel purposes, and promotional activities to further understand user behavior patterns in this cluster. The goal is to formulate targeted strategies to enhance user satisfaction, engagement, and potential revenue growth for transportation service providers. This study also introduces an RFM-based marketing program targeting different customer segments, such as (1) Platinum Membership, (2) Rush Hour Bonanza, (3) Bundle Extravaganza, (4) Revive and Thrive Offer, and (5) Back in the Saddle Campaign. Furthermore, the Refer-a-Friend Program encompasses all RFM segments, encouraging users to expand the network of online transportation service users. The seamless integration between customer segmentation and RFM-based initiatives has the potential to enhance customer retention, drive revenue growth, and improve operational efficiency, contributing significantly to adaptive business strategies in the dynamic online transportation services sector.
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