Customer Segmentation Analysis in E-Commerce Platforms Using the RFM Model: A Case Study of E-Commerce Transaction Data (January 2009 – December 2011) from Kaggle.

olivia, olivia (2025) Customer Segmentation Analysis in E-Commerce Platforms Using the RFM Model: A Case Study of E-Commerce Transaction Data (January 2009 – December 2011) from Kaggle. Tesis (S2) - thesis, Universitas Bakrie.

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Abstract

This study examines customer segmentation in e-commerce platforms using the Recency, Frequency, and Monetary (RFM) model to optimize marketing strategies and customer retention. A quantitative descriptive approach is applied, analyzing e-commerce transaction data from Kaggle (January 2009 – December 2011). The research involves data preprocessing, RFM scoring, and clustering techniques to classify customers into eight distinct segments, including Brand Royalty, Rising Stars, and Vanishing Buyers. Findings reveal that high-frequency, high-spending customers contribute most to revenue, whereas fading and vanishing buyers require re-engagement efforts. The study recommends tailored marketing campaigns, predictive analytics, and loyalty programs to enhance customer retention. Future research should integrate machine learning and psychographic data to refine segmentation accuracy.

Item Type:	Thesis (Tesis (S2) - )
Uncontrolled Keywords:	E-commerce, RFM Model, Customer Segmentation, Data-Driven Marketing
Subjects:	Business > Entrepreneur Management > Business Plan > Business > Entrepreneur Thesis > Thesis (S2) Knowledge Management > knowledge management frameworks Knowledge Management > Knowledge Management Models
Divisions:	Fakultas Ekonomi dan Ilmu Sosial > Program Studi Magister Management
Depositing User:	Olivia Olivia
Date Deposited:	11 Mar 2025 03:15
Last Modified:	11 Mar 2025 03:15
URI:	https://repository.bakrie.ac.id/id/eprint/11520

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