Application Of K-Means Clustering For Customer Segmentation In Grocery Stores In Kenya

Abstract

The retail industry, particularly in the context of grocery stores, plays a vital role in meeting consumers' daily needs. To optimize marketing strategies and enhance customer satisfaction, understanding customer behavior and preferences is crucial. Customer segmentation, a powerful market research technique, enables businesses to group customers with shared characteristics into distinct segments, allowing targeted and personalized approaches. This article explores the application of the K-means clustering algorithm for customer segmentation in grocery stores within the unique context of Kenya. By leveraging transactional and demographic data from diverse grocery stores across Kenya, the study aims to identify homogeneous customer groups with similar purchasing behaviors and preferences. The data collection process involved obtaining consent from store owners and ensuring data privacy and security. Following data preprocessing, K-means clustering was applied, and various validation techniques were utilized to determine the optimal number of clusters. The results yielded valuable insights into customer segments, aiding the identification of key customer groups and their distinct preferences.