Emmanuel Ayodele & Victor O. Sodeinde

Customer segmentation, a crucial tool in contemporary business, enables organizations to enhance their marketing campaigns, optimize resource allocation, and provide tailored experiences to diverse consumer groups. The K-means clustering method has shown to be an effective tool for identifying distinct client segments based on shared characteristics. This study applied K-means clustering algorithm's in customer segmentation, paying particular attention to the advantages, and potential downsides. The K-means approach reduces the sum of squared distances between data points and their associated cluster centroids in order to repeatedly divide a dataset into clusters. This technique helps discover homogeneous groups of customers who share similar behaviors, tastes, and traits in the context of customer segmentation. K-means accommodates multiple data kinds and shapes by using a variety of distance measurements and starting procedures, increasing its applicability to a variety of business settings. K-means reveals hidden patterns in client data through unsupervised learning, empowering businesses to decide on marketing tactics, product recommendations, and tailored communication. However, careful evaluation of potential difficulties is necessary for the effective deployment of the K-means algorithm for consumer segmentation. Due to the algorithm's sensitivity to initial centroid placements, using methods like K-means++ initialization may be necessary to avoid producing inferior results. The accomplishment of meaningful consumer segmentation by K-means clustering is made possible, despite hurdles, by smart parameter tweaking and validation procedures that reduce potential downsides. This research work is to implement K-means clustering algorithm for customer segmentation to predict the future consumption trend of customers. Keywords: Customer segmentation, data analysis, unsupervised learning, marketing strategy, K-means clustering0150