Segmentation of Beer Consumers in Europe using. K-means Clustering

This study explores how beer consumers are classified in Europe. By using the K-means clustering method, the aim is to uncover patterns in consumption habits and preferences based on economic factors. The beer industry is seeing growth in its product range to match changing consumer preferences. Understanding these consumer groups is important for targeted marketing strategies and refining product offerings. To accomplish this, the research uses a dataset that includes data on beer consumption rates, indicators like GDP per capita and purchasing power parity pricing information, as well as demographic profiles from different European countries. Before analysing the clusters, extensive data preprocessing was carried out, including standardization procedures and handling missing data points. By employing the K-means algorithm, segmentation was identified by evaluating the within-cluster sum of squares (WCSS) using the Elbow Method to determine cluster numbers. Validation, through silhouette scores, ensured that the identified segments were meaningful and distinct, aiding in visualizing and interpreting clustering results.