PRODIGY: Product Design Guidance at Scale

Sambeet Tiady, Anirban Majumder, Deepak Gupta

Abstract: Growth of e-commerce has enabled the creation of thousands of small-scale brands. However, these brands lack information on a) what new products to develop and b) how to refine existing products to improve on business metrics. We present a comprehensive Product Design Insights and Guidance service (named PRODIGY) that mines product attributes data available on e-commerce platforms and surface insights on a) new product development and b) product refinement. Our core contribution is a novel demand forecasting model for product designs based on a notable extension of the recently proposed FTTransformer architecture combined with a self-supervised pre-training task, akin to Masked Language Modeling (MLM) objective. For the product refinement use-case, we present a novel algorithm by embedding the design search in a data-density approximator, namely Conditional Variational Autoencoder. We run a thorough and comprehensive set of experiments and establish that PRODIGY achieves significant improvement in demand prediction as compared to state-of-the-art alternatives. Finally, we present our findings from an online experiment where PRODIGY helps to launch new products with +20% lift in sales and +1.3% lift in product ratings.