Today, more and more companies are providing web-based product configuration systems in order to better meet individual customer preferences. In many cases, pre-defined product specifications are additionally offered to facilitate the corresponding choice decisions. Against this background, we present a Poisson regression approach for analyzing customer preferences and a genetic algorithm for determining preference-based default products. The basis for this is transaction data, as they are automatically generated when configuring a product online. The potentials of the suggested methodology are demonstrated by means of two case studies referring to different product categories.