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Predicting future successful designs and corresponding market opportunity isa fundamental goal of product design firms. There is accordingly a long historyof quantitative approaches that aim to capture diverse consumer preferences,and then translate those preferences to corresponding "design gaps" in themarket. We extend this work by developing a deep learning approach to predictdesign gaps in the market. These design gaps represent clusters of designs thatdo not yet exist, but are predicted to be both (1) highly preferred byconsumers, and (2) feasible to build under engineering and manufacturingconstraints. This approach is tested on the entire U.S. automotive market usingof millions of real purchase data. We retroactively predict design gaps in themarket, and compare predicted design gaps with actual known successful designs.Our preliminary results give evidence it may be possible to predict designgaps, suggesting this approach has promise for early identification of marketopportunity.

Predicting "Design Gaps" in the Market: Deep Consumer Choice Models under Probabilistic Design Constraints