Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact

Abstract

Abstract Online companies face large user populations, making segmentation a daunting exercise. Demonstrating an approach that facilitates user segmentation, this research leverages product dissemination and product impact metrics with normalized Shannon entropy. Using 4,653 products from an international news and media organization with 134,364,449 user-product engagements, we isolate the key products with the widest product dissemination and the least product impact using entropy-based measures, effectively capturing the engagement levels. We demonstrate that a small percentage (0.33% in our dataset) of products are so widely disseminated that they are non-discriminatory, and a large percentage of products (17.02%) are discriminatory but have so little dissemination that their impact is negligible. Our approach reduces the product dataset by 17.35% and the number of user segments by 8.18%. Implications are that organizations can isolate impactful products useful for user segmentation to enhance the user focus.