  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.
Historically, the focus has been on theoretical studies in the context of
monopolistic firms and assumed product relationships, e.g., complementarity in
usage. We develop a new machine-learning-driven methodology for designing
bundles in a large-scale, cross-category retail setting. We leverage historical
purchases and consideration sets created from clickstream data to generate
dense continuous representations of products called embeddings. We then 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 product and test their
performance using a field experiment with a large retailer. We combine the
results from the experiment with product embeddings using a hierarchical model
that maps bundle features to their purchase likelihood, as measured by the
add-to-cart rate. We find that our embeddings-based heuristics are strong
predictors of bundle success, robust across product categories, and generalize
well to the retailer's entire assortment.
