Consumers’ Cart-Building Behavior in Online Grocery: A Structural Approach Using Generative AI
Speakers
Sandeep Chitla
NYU Stern School of Business
Sandeep Chitla is a final-year Ph.D. candidate in the Operations Management department at NYU Stern School of Business, advised by Professor Srikanth Jagabathula. His research uses ML and Generative AI methods to predict customer purchase choices in online marketplaces to help firms enhance their operational strategies. He accurately models customer decision-making processes by integrating often-overlooked factors such as sequential decision-making, consideration sets, and behavioral characteristics into structural choice models. He collaborates with leading companies to acquire large-scale, real-world data encompassing millions of data points from hundreds of thousands of customers. Utilizing state-of-the-art ML techniques, including Bayesian estimation and Generative AI, He effectively estimates these structural models, providing firms with optimal operational strategies for promotions, pricing, and product assortments.
| Date: |
| Thursday, 21 November 2024 |
| Time: |
| 10:00 am - 11:30 am |
| Venue: |
|
NUS Business School Mochtar Riady Building BIZ1-0302 15 Kent Ridge Drive Singapore 119245 (Map) |
Abstract
How do customers build their online grocery shopping carts across multiple product categories, and how do cart-level promotions, such as free delivery thresholds, influence this behavior? We develop a scalable structural framework that models customers as reinforcement learning agents who sequentially select items based on inherent product needs and the expectation of redeeming cart-level promotions. We leverage recent advancements in transformer architectures, commonly applied in large language models, to estimate model parameters efficiently. In collaboration with a leading quick commerce company in India, we trained the model on data from approximately 8.5 million shopping carts representing 350,000 customers. Our counterfactual analyses evaluate the impact of cart-level promotions on order value and the demand for individual products.