인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This study proposed a method using the Analytic Hierarchy Process (AHP) to derive RFM weights suitable for the characteristics of each industry, and compared and analyzed this with the existing traditional RFM weighting method using customer data from CompWany C, an actual coffee franchise company. AHP has the advantage of being able to calculate weights based on the subjective judgment of evaluators, so it can quantitatively express quatlitative factors. Since evaluators belong to the industry, it can effectively reflect the characteristics of the industry. In this study, customers were subdivided through the K-Means Clustering algorithm using the RFM score, and the quality of the cluster according to the two weighting methods was compared. As a result of the study, it was confirmed that the proposed weight derivation method showed higher cluster quality than the existing traditional method, and that the segmentation suitable for the coffee industry was made. Future research requires empirical research through quantitative expansion of customer data, advancement of the preprocessing process for RFM variables, improvement of the AHP stage, and application of various machine learning techniques
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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