Products & Services

Seatmap Testing and Research

When reserved-seating venues reopen, they will need to enforce seatmaps to follow social-distance guidelines. Locational choice experiments conducted on nationally representative panels can allow you to understand how consumers will pick seats, and which room designs will lead to the highest occupancy.

Machine Learning for Seat Recommendation

Consumer preference for seats differ widely. We have developped data science models for recommending specific seats that lower non-purchase rates and maximize occupancy.

Funding & Partners

Marketing Science Institute

Our work on predicting locational choice benefited from a grant of the Marketing Science Institute. [Access Report]

Penn State Center for Sports Business Research

Our research has also received a grant from Penn State.


We have also received the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

Knowledge Base

What are locational choice experiments?

Ever wondered how consumers would react to fewer rows, blocked out seats or VIP pricing? Using our software backend, create and test ways to present seat maps with large representative panels. Configurations are customizable.

Hybrid Convolutional Neural Networks Choice Model for Seat Preference Predictions

Consumer preference for seats differ widely. We have developped and compared models for recommending specific seats to consumers based on experimental and real ticket purchase data.

Get in touch

We have made available locational choice experiment datasets, R and Python codes for analysis (1) (2), and provided a sample interface for locational choice experiments. If you have questions or would like to know more about how we can help, send us an e-mail via the form below.