Abstract
The rapid growth in customer data has driven companies to develop smarter and more effective marketing strategies. One efficient approach is customer segmentation, which involves dividing a market or group of customers into smaller segments based on similar characteristics or behaviors. Customer segmentation improves understanding of customer needs, preferences, and behavior. This study uses customer segmentation based on purchase potential at Fast Moving Consumer Goods (FMCG). Analyzing potential purchases can help identify market opportunities, implement more effective pricing, target promotions, manage stock and distribution, and develop new products to enhance customer satisfaction. The most commonly used segmentation method is the K-Means Clustering algorithm, which groups data into homogeneous clusters. This study aims to segment customers based on potential purchases using the K-Means Clustering method. The customer dataset in FMCG stores was divided into three clusters using seven attributes: Sex, Marital Status, Age, Education, Income, Occupation, and Settlement Size. The results, calculated in Microsoft Excel, concluded after four iterations with three clusters: k1 (Cluster 1) with 535 customers having low purchase potential, k2 (Cluster 2) with 685 customers having high purchase potential, and k3 (Cluster 3) with 7810 customers having medium purchase potential.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
