CUSTOMER SEGMENTATION WITH K-MEANS ALGORITHM AND BUSINESS STRATEGY BUSINESS INTELLIGENCE IN VEGETABLE ONLINE RETAILING
DOI:
https://doi.org/10.24961/j.tek.ind.pert.2025.35.2.118Abstract
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
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