Shapes as Product Differentiation ︎︎︎

with Eric Schulman, Kristen Grauman, Santhosh Ramakrishnan

arxiv ︎︎︎ slides ︎︎︎

Nov 2022

Many differentiated products have key attributes that are high-dimensional (e.g., design, text). We consider one of the simplest design products, fonts, and quantify their shapes by constructing neural network embeddings. Using the embeddings and data from the world's largest online market place for fonts, we study the causal effect of a merger on the merging firm's creative decisions of product differentiation.

*This project is featured in a typography magazine ︎︎︎ and included in the MIT graduate machine learning course ︎︎︎.

© Sukjin Han