New Data for Consumer Insights Conference 鈥� Kilts Center

天美传媒

NECESSARY COOKIES These cookies are essential to enable the services to provide the requested feature, such as remembering you have logged in.	ALWAYS ACTIVE
	Reject \| Accept
PERFORMANCE AND ANALYTIC COOKIES These cookies are used to collect information on how users interact with Chicago Booth websites allowing us to improve the user experience and optimize our site where needed based on these interactions. All information these cookies collect is aggregated and therefore anonymous.
FUNCTIONAL COOKIES These cookies enable the website to provide enhanced functionality and personalization. They may be set by third-party providers whose services we have added to our pages or by us.
TARGETING OR ADVERTISING COOKIES These cookies collect information about your browsing habits to make advertising relevant to you and your interests. The cookies will remember the website you have visited, and this information is shared with other parties such as advertising technology service providers and advertisers.
SOCIAL MEDIA COOKIES These cookies are used when you share information using a social media sharing button or 鈥渓ike鈥� button on our websites, or you link your account or engage with our content on or through a social media site. The social network will record that you have done this. This information may be linked to targeting/advertising activities.

New Data for Consumer Insights Conference

May 30-31, 2025

Paper Submissions: Closed

Conference Co-chairs:

Giovanni Compiani, Assistant Professor of Marketing, Chicago Booth

Malika Korganbekova, Assistant Professor of Marketing, Chicago Booth

Estimating consumer preferences is a key building block of many empirical analyses in marketing and economics, including pricing, merger policy, product design and branding.
Standard approaches focus on one product category at a time and model demand as a function of a few marketing mix variables and product attributes. Recently, new sources of data have become available, including:

• Unstructured data (e.g., texts, images and videos)
• Big data (e.g., data from a large number of possibly interrelated product categories)
• Clickstream data (e.g., search sessions on e-commerce platforms)
• Data produced by generative AI (e.g., ChatGPT)
• Novel survey data

The conference aims to bring together scholars in marketing, economics and statistics/AI/ML who use Machine Learning, NLP, and other tools to extract valuable insights from new types of data. We hope the conference can serve as a bridge between different fields. Extended abstracts and full papers are welcome for both methodological and empirical submissions.

In addition to contributed sessions, the conference will feature keynote lectures by and .

Programming:
Friday, May 30, 2025 12:00 - 9:00 p.m. CT | Chicago Booth, Gleacher Center
Saturday, May 31, 2025 9:00 a.m. - 5:00 p.m. CT | Chicago Booth, Gleacher Center

Agenda:
View programming agenda here.

Registration is closed.

Pricing:
Faculty: $150 USD
University of Chicago Faculty: No cost (The conference is free to attend for University of Chicago faculty, but due to venue space limitations, we expect all registered participants to make every effort to attend).
Speaker/Discussant: No cost
PhD Students: No cost

For questions, please contact Flora Alvarado.

Keynote Speakers

Susan Athey is the Economics of Technology Professor at Stanford. Her current research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning.

Greg Lewis works in economics, causal machine learning and marketplace design. He has previously been a senior principal economist at Amazon, senior principal researcher at Microsoft Research and associate professor of economics at Harvard.