Sales Product Clustering Using RFM Calculation Model And K-Means Algorithm on Primskystore

Abstract

The optimization of data processing procedures can yield high-quality information that is then utilized by business owners in the decision-making process. An effective company plan, particularly in the realm of promotion, holds significant importance for store proprietors in order to foster sustained business expansion. Primskystore is an e-commerce platform operating inside the retail industry, where the efficacy of product sales promotion is perceived to be suboptimal. Sales product advertising continues to revolve around a single sort of product. The objective of this work is to develop a data mining model through the implementation of a web-based application that utilizes the K-means clustering approach and the RFM model. The utilization of the K-Means clustering approach and the RFM model can facilitate the clustering of sales products at retailers. The data mining application employed on this website utilizes the Python programming language, MySQL as the database, and the Unified Modeling Language for system model creation. The findings of this research encompass the development of a web-based data mining tool designed to present the outcomes of product sales clustering within retail establishments. The system has the capability to present the outcomes of the RFM calculation model and KMeans clustering. Specifically, it reveals the presence of three distinct clusters, with cluster 0 accounting for 30% of the data, cluster 1 representing 7.5% of the data, and cluster 2 encompassing 62.5% of the data. This web-based data mining program has the potential to assist retailers in determining an optimal promotional business strategy.