Consumer Segmentation for Vitamin Purchases: A Machine Learning Approach

This study applies machine learning methods to Nielsen Homescan Panel data to understand the segmentation of U.S. vitamin consumers. Consumer segmentation is crucial for understanding purchasing behavior and optimizing strategies to maximize profits. We employ the RFM (Recency–Frequency–Monetary) framework with demographic attributes, and apply the K-means algorithm to classify households into distinct segments. Considering COVID-19 as an exogenous economic shock, we then compare segment composition and purchasing behavior before and during the pandemic and estimate segment-specific price elasticities. We provide targeted marketing recommendations tied to each segment, offering guidance for businesses to enhance consumer engagement in the post-pandemic dietary supplement market.