Optimalisasi Strategi Pemasaran Melalui Analisis RFM pada Dataset Transaksi Ritel Menggunakan Python

Andy Hermawan; Nila Rusiardi Jayanti; Aji Saputra; Cahaya Tambunan; Dzaky Muhammad Baihaqi; Muhammad Alif Syahreza; Zacharia Bachtiar

doi:10.55606/mri.v2i4.3246

Authors

Andy Hermawan Universitas Indraprasta PGRI
Nila Rusiardi Jayanti Universitas Indraprasta PGRI
Aji Saputra Universitas Khairun
Cahaya Tambunan Universitas Negeri Medan
Dzaky Muhammad Baihaqi Universitas Pasundan
Muhammad Alif Syahreza Asia Pacific University of Innovation and Technology
Zacharia Bachtiar Telkom University

DOI:

https://doi.org/10.55606/mri.v2i4.3246

Keywords:

Customer Segmentation, Kaggle, Marketing Strategy, RFM Analysis, Retail Data

Abstract

This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on a retail transaction dataset obtained from Kaggle. The dataset contains 64,682 transactions from 5,242 SKUs involving 22,625 customers over one year. Data cleaning and RFM analysis were conducted to segment customers based on recency, frequency, and monetary values. The findings reveal that customers were segmented into groups such as Champions, Loyal Customers, and At Risk. These segments provide valuable insights for developing targeted marketing strategies, such as loyalty programs for high-value customers and retention campaigns for at-risk customers. The study demonstrates that RFM analysis is effective in identifying valuable customer segments and optimizing marketing efforts based on customer behavior. This approach can increase customer retention and improve the return on investment (ROI) in marketing campaigns.

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