A Deep Dive Into Customer Segmentation Through Advanced Data Mining Techniques

This study examines how data mining techniques are used to segment customers to reveal complex customer profiles in a grocery store's database. Customer segmentation is crucial to effectively tailor marketing strategies. This procedure makes it easier to create customized customer profiles, making it possible to create more targeted and effective marketing campaigns. The dataset used in the study was obtained from the database of a well-known grocery company and contains 2.240 data points with 29 different features. These features are grouped into four categories: customer demographics, product information, purchase channels and promotional response data. The study attempts to identify meaningful patterns and groupings among customers using advanced clustering techniques such as K-Means Clustering and Agglomerative Clustering. Another goal of the research is to demonstrate how data mining and machine learning techniques can be effectively applied to customer segmentation, a critical component of adapting to the ever- changing complexity of the market and changes in customer behavior. Within the scope of the research, four customer clusters emerged. Clusters represent meaningful subsets and trends among customers, encompassing a range of features such as demographics, purchasing patterns, and responses to marketing campaigns. The findings provide a useful framework for understanding the complexity of customer profiles and adapting marketing strategies accordingly.