ASA Marketing Section Webinars
Introduction
American Statistical Association, Marketing Section events, Sep-Nov 2024
INTEREST CATEGORY: MARKETING RESEARCH
POSTING TYPE: Events
Posted by: Shibo Li
American Statistical Association (ASA) Marketing Section Webinars in Fall 2024
The is pleased to announce its webinar series in Fall 2024. It aims to facilitate more research, interests, and discussions on the interactions between marketing and statistics given the exciting and rapid developments in both fields. The webinars will be 9pm-10pm Eastern time Tuesday evenings (45-min presentation and 15-min Q&A). The webinar link is . The list of speakers and dates for the fall is given below:
- September 3 (Madhav Kumar, Post Doc at MIT (PhD from MIT): )
- September 24 (Nitin Mehta, Ellison Professor of Marketing, Univ of Toronto: )
- Late October (Bruno Jacobs, Assistant Professor of Marketing, University of Maryland: )
- November 12 (Max Matthe, Assistant Professor of Marketing, Indiana University Bloomington: )
- November 26 (Hortense Fong, Assistant Professor of Business, Columbia Business School: )
Please mark your calendars and you are welcome to distribute it to anyone with interests in the webinars.
The first speaker is who is a postdoc at MIT. He is interested in solving core marketing problems in recommendations, targeting, and pricing for online platforms. He is also the chief research scientist and part of the founding team of where he builds pricing algorithms and design large-scale experiments. The time and date of the webinar are 9-10pm Eastern time, September 3, 2024, with the webinar link: . His presentation title and abstract are given below:
Title: Scalable Bundling via Dense Product Embeddings
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 approach 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 representations (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 the 4,500 focal products and test their performance using a field experiment with a large retailer. We use the experimental data to optimize the bundle design policy with offline policy learning. Our optimized policy is robust across product categories, generalizes well to the retailer’s entire assortment, and provides an expected improvement of 35% (~$5 per 100 visits) in revenue from bundles over a baseline policy using product co-purchase rates.
We look forward to seeing you in the webinars.