캠핑 예약 플랫폼의 고객 행동 데이터 기반 고객 세그멘테이션 전략: RFM 분석 모델과 K-Means 클러스터링 기법 비교를 기반으로

초록

This study aims to analyze the practical applicability of customer segmentation by comparing traditional RFM analysis and machine learning-based K-means clustering, using actual customer data from Korea's leading camping reservation platform, 'ThankYouCamping'. Customers were divided into five distinct groups based on RFM analysis model and divided into eight distinct groups based on K-means clustering, showing differences in booking patterns, review behavior, and channel usage. While RFM enabled simple segmentation based on customer value, K-means offered more detailed clusters based on behavioral data. Based on these results, customized marketing strategies were proposed for each customer segment. This research offers a foundational segmentation strategy for effectively utilizing customer data in digital platform environments.

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