Scalable bundling via dense product embeddings Madhav Kumar,* Dean Eckles, Sinan Aral Massachusetts Institute of Technology September 26, 2021 Abstract Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well-examined concept in academia. Scholars have largely focused on theoretical studies in the context of monopolistic firms and assumed product relationships (e.g., complementarity in usage). There is, however, little empirical guidance on how to actually create bundles, especially at the scale of thousands of products. We use a machine-learning-driven ap- proach for designing bundles in a large-scale, cross-category retail setting. We leverage historical purchases and consideration sets determined from clickstream data to generate dense represen- tations (embeddings) of products. We put minimal structure on these embeddings and develop heuristics for complementarity and substitutability among products. Subsequently, we use the heuristics to create multiple bundles for each of 4,500 focal products and test their performance using a field experiment with a large retailer. We use the experimental data to optimize the bun- dle design policy with offline policy learning. Our optimized policy is robust across product categories, generalizes well to the retailer’s entire assortment, and provides expected improve- ment of 35% (∼$5 per 100 visits) in revenue from bundles over a baseline policy using product co-purchase rates. *Corresponding author:
[email protected] 1 1 Introduction Bundling is a widespread product and promotion strategy used in a variety of industries such as fast food (meal + drinks), telecommunications (voice + data plan), cable (TV + broadband), and insurance (car + home insurance).