Identifying granular patterns of differentiation and learning predictors of product performance are key drivers to capitalize on competitive market segments. In this paper, we propose an approach to identify granular product patterns by using Hierarchical Clustering, and to learn predictors of product performance from historical data by using Genetic Programming. Computational experiments using more than twenty thousand vehicle models collected over the last thirty years shows (1) the feasibility to identify vehicle differentiation at different levels of granularity by hierarchical clustering, and (2) the good predictive ability of learned fuel consumption predictors in vehicle cluster. We believe our approach introduces the building blocks to further advance on studies regarding product differentiation and market segmentation by using data-intensive approaches.