Product comparison matrices (PCMs) provide a
convenient way to document the discriminant features of a family of related products and now abound on the internet. Despite their apparent simplicity, the information present in existing PCMs can be very heterogeneous, partial, ambiguous, hard to exploit by users who desire to choose an appropriate product. Variability Models (VMs) can be employed to formulate in a more precise way the semantics of PCMs and enable automated reasoning such as assisted configuration. Yet, the gap between PCMs and VMs should be precisely understood and automated techniques should support the transition between the two. In this paper, we propose
variability patterns that describe PCMs content and conduct an empirical analysis of 300+ PCMs mined from Wikipedia. Our findings are a first step toward better engineering techniques for maintaining and configuring PCMs.