Abstract visualization of fonts in a network.

Fontastic Fortunes: How Design Shapes Markets and Mergers

Soraya Malik in Business & Economy December 2025 • 4 min read.

"Unlocking the Secrets of Product Differentiation Through Font Design and Economic Analysis"

In today's diverse marketplace, products are distinguished by many factors, including design and text, which are often complex and high-dimensional. Ignoring these aspects in economic models means missing important information. Quantifying these seemingly unmeasurable attributes can unlock meaningful insights into economic behavior. Consider fonts, one of the most fundamental design elements. By examining fonts, we can better understand product differentiation and the consequences of mergers, offering lessons applicable far beyond the realm of typography.

A recent study analyzes font shapes using an original dataset from the world's largest online font marketplace. By applying deep convolutional neural networks, researchers have successfully quantified font shapes, mapping them into low-dimensional vectors. This innovative approach assumes designers engage in spatial competition, much like businesses in a Hotelling-type model. Image embeddings then provide quantifiable measures of design differentiation, which can be used to study how mergers influence a firm's creative choices.

This new methodology marks a significant step forward. By transforming unstructured design elements into measurable data, we can answer complex questions about market dynamics. How do mergers affect creative decisions? Can increased product differentiation offset the risk of cannibalization? These questions are now within reach, offering valuable insights for businesses and policymakers alike.

Decoding Font Shapes: How Neural Networks Quantify Design

Abstract visualization of fonts in a network.

The core of this research lies in its ability to quantify font shapes using neural networks. High-dimensional design attributes, such as the minute curves and lines of a typeface, pose a significant challenge to economic analysis. Researchers overcame this by constructing embeddings from a deep convolutional neural network. Each font's shape is mapped into a low-dimensional vector, capturing essential visual characteristics.

This embedding process allows designers to compete in a product space, similar to the Hotelling model of spatial competition. In this model, businesses strategically position themselves to capture market share. By using image embeddings, the study constructs measures that capture the degree of design differentiation. Two primary measures are used:

Distance to Averia: Measures the Euclidean distance between a font and the average font in the marketplace, indicating how much a font deviates from the norm.
Gravity Measure: Assesses how a font is located relative to other fonts, reflecting the competitive landscape and a designer’s strategic positioning.

The use of convolutional neural networks is critical for this analysis. Font shapes involve complex interactions between neighboring pixels. Traditional models often miss these nuances, whereas deep neural networks excel at capturing spatial correlations, leading to a more accurate representation of font aesthetics. The study also uses pangrams—sentences containing all letters of the alphabet—as inputs, enabling the network to recognize design elements like spacing and ligature, which are crucial for font design but can be lost when analyzing individual letters.

The Big Picture: Applications Beyond Fonts

The analytical framework presented in this study extends far beyond font design. By demonstrating how unstructured attributes can be quantified and integrated into economic models, this research paves the way for understanding market dynamics in various creative industries. From fashion to furniture design, the ability to measure and analyze visual characteristics unlocks new insights into consumer behavior, market competition, and the strategic decisions of firms.

About this Article -

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Everything You Need To Know

How does the study quantify font shapes, and why is this approach significant?

The study quantifies font shapes using deep convolutional neural networks. These networks transform high-dimensional design attributes, like the curves and lines of a typeface, into low-dimensional vectors. This is significant because it allows researchers to convert unstructured design elements into measurable data, a crucial step for economic analysis. By creating image embeddings, the study can assess design differentiation and analyze market dynamics, offering insights into how mergers affect creative choices and how products compete in the marketplace.

What are the 'Distance to Averia' and 'Gravity Measure,' and how do they contribute to the understanding of font design and market competition?

The 'Distance to Averia' measures the Euclidean distance between a font and the average font in the marketplace, indicating how much a font deviates from the norm. The 'Gravity Measure' assesses how a font is located relative to other fonts, reflecting the competitive landscape. Both measures, derived from image embeddings, capture the degree of design differentiation. They allow researchers to understand how designers strategically position their fonts within the market and how this positioning influences competition.

How does the study's methodology relate to the Hotelling model of spatial competition?

The study applies the concept of spatial competition, similar to the Hotelling model, by assuming that font designers strategically position their fonts within a product space to capture market share. The use of image embeddings enables the researchers to create quantifiable measures of design differentiation, which mirrors the spatial relationships in the Hotelling model. In this context, fonts that are more differentiated from each other are akin to businesses that are located further apart in the Hotelling model.

What role do pangrams play in the neural network analysis of font design, and why are they essential?

Pangrams, sentences containing all letters of the alphabet, serve as inputs for the deep convolutional neural networks. They enable the network to recognize design elements like spacing and ligatures, which are critical aspects of font design but can be overlooked when analyzing individual letters in isolation. By using pangrams, the network captures a more holistic understanding of font aesthetics, contributing to a more accurate representation of font design's visual characteristics.

Beyond font design, what broader implications does this research have for other creative industries?

The analytical framework developed in this study extends beyond font design, offering insights applicable to various creative industries. By demonstrating how unstructured attributes can be quantified and integrated into economic models, this research helps understand market dynamics in sectors like fashion and furniture design. The ability to measure and analyze visual characteristics unlocks new insights into consumer behavior, market competition, and the strategic decisions of firms in these industries. This methodology facilitates a deeper understanding of product differentiation and how firms can differentiate themselves in the marketplace.