Optimizing Pricing Strategies Through Learning the Market Structure

Ezgi Karabulut Türkseven

This talk presents recent work on integrating market structure learning into pricing strategies to
maximize revenue in e-commerce and retail environments. The focus is on the challenge of
determining the optimal price for a single product in a market of heterogeneous consumers
segmented by their product valuations. Pricing strategies are analyzed under varying levels of
prior knowledge about the market structure. The proposed approach leverages customer
preference data through a two-stage process: an offline learning stage dedicated to uncovering
key aspects of the market structure—such as the number of consumer segments, their sizes, and
valuations—and an online learning stage that emphasizes revenue maximization while
iteratively refining these estimates. Experimental results illustrate how the methods adapt to
different levels of market knowledge and highlight the economic value of learning market
structure. Notably, the findings show that even with limited prior information, firms can employ
incremental learning strategies to achieve revenues close to those attainable under full
information.

Short Bio

Ezgi Karabulut Türkseven is an assistant professor in the Industrial Engineering program at
Sabancı University. She holds BSc and MSc degrees from the Industrial Engineering department
at Boğaziçi University and received her PhD in Operations Research from the Georgia Institute
of Technology. Her research lies at the intersection of operations research, machine learning,
and optimization, with a particular focus on integrating preference learning into decision-making
models. Her recent work explores the use of data-driven approaches in multi-period optimization
problems with multiple agents.

Venue

Friday, March 28th, 2025, 4:00 pm