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Marketers are often confronted with datasets that contain many variables but are limited in the number of observations, leading to a“large P, small N” problem. With unstructured data, such as product pictures, commonly used deep learning models require the estimation of a large number of parameters, also resulting in a “large K” problem. In this research, we propose a pipeline to process and exploit such unstructured data. We apply it to a novel dataset aggregating all retail sales of distilled spirits in Pennsylvania. We first reduce the high pixel-based dimensionality of the product pictures using a Conditional Generative Adversarial Variational Auto-Encoder (CGAVAE). We then use the result in a deep learning model to predict sales volumes, using Bayesian estimation to mitigate overfitting issues. We show that using the product pictures’ information, in addition to traditional variables such as price and product characteristics, increases the out-of-sample prediction performance for sales volumes by nearly half its base value (R2 increasing from 0.24 to 0.35). We also propose a method to interpret the results and identify relevant product features, potentially allowing for the creation of new theories. Lastly, we use our model in a design optimization exercise, where we identify classes of bottle designs that are predicted to maximize expected revenue.

Keywords: sustainability, green products, public policy, government incentives, climate change, technology adoption, policy evaluation, quasi-experiments, difference-in-differences, coarsened exact matching

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