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

TL;DR

This paper introduces a deep learning model to predict market 'design gaps'—potentially successful, feasible new product designs—by analyzing consumer preferences and constraints, tested on the U.S. automotive market with promising preliminary results.

Contribution

The paper presents a novel deep learning framework for predicting market design gaps considering consumer preferences and engineering constraints, extending prior quantitative methods.

Findings

Model successfully retroactively predicts known successful designs.

Predicted design gaps align with actual market opportunities.

Approach shows promise for early market opportunity detection.

Abstract

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