CUSTOMER SEGMENTATION WITH K-MEANS ALGORITHM AND BUSINESS STRATEGY BUSINESS INTELLIGENCE IN VEGETABLE ONLINE RETAILING - Fitriana, Rina; Sugiarto, Dedy; Nurachman, Nurochman (Jurnal Teknologi Industri Pertanian, 2025)
Most MSMEs still have obstacles to growing and developing at the business level. Applying a business intelligence system is expected to assist in making appropriate and quick business decisions so that MSMEs can grow and develop. This research aimed to determine customer segmentation based on product clustering that consumers demand. In addition, this study aims to determine the benefits of business intelligence in providing business performance information to make decisions. This research uses the k-means algorithm for clustering. Business intelligence uses Power BI software for visualisation. Based on the results of analysing product clustering with the k-means algorithm, the optimal number of clusters is 2 (k = 2). Determination of the value of k = 2 uses an average centroid distance of 121,624,275,127, and validation of the minimum DBI value = 0.052. Based on the clustering results, cluster 0 (28%) and cluster 1 (72%) are two consumer segments. Insights on the sales dashboard are daily sales fluctuations, the dominance of certain products in demand, and products with low sales. Strategy initiatives for the long term are customer segmentation for more personalized promos, focus on subscriptions and repeat orders, optimising digital marketing, and the use of predictive analytics to forecast sales trends. On the dashboard of production, order, and stock, information, such as daily production tends to exceed orders, leading to overstock, while orders fluctuate inconsistently. The key challenges are unbalanced production and demand, overstock on certain products, unstable orders, and underproduced products. Keywords: business intelligence, data analytics, k-means algorithm, Micro Small Medium Enterprise