Discount bundling via dense product embeddings
Name
1191221749-MIT.pdf
Size
3.03 MB
Format
Adobe PDF
Checksum (MD5)
2040af1780021e25f3798d01d976ba23
Author(s)
Kumar, Madhav(Scientist in business management)Massachusetts Institute of Technology.
Advisor(s)
Sinan Aral.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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. 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 click-stream 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.
Description
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 51-52).
Subjects
Sloan School of Management.
MIT Department
Sloan School of Management
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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