Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Branding and Logo Design

74 Pages Posted: 28 Jun 2019 Last revised: 8 Apr 2021

See all articles by Ryan Dew

Ryan Dew

University of Pennsylvania - Marketing Department

Asim Ansari

Columbia University - Columbia Business School, Marketing

Olivier Toubia

Columbia University - Columbia Business School, Marketing

Date Written: November 25, 2019

Abstract

Logos serve a fundamental role as the visual figureheads of brands. Yet, due to the difficulty of using unstructured image data, prior research on logo design has largely been limited to non-quantitative studies. In this work, we explore the interplay between logo design and brand identity creation from a data-driven perspective. We develop both a novel logo feature extraction algorithm that uses modern image processing tools to decompose pixel-level image data into meaningful features, and a multiview representation learning framework that links these visual features to textual descriptions, consumer ratings of brand personality, and other high-level tags describing firms. We apply this framework to a unique dataset of brands, to understand which brands use which logo features, and how consumers evaluate these brands’ personalities. Moreover, we show that manipulating the model’s learned representations through what we term “brand arithmetic” yields new brand identities, and can help with ideation. Finally, through an application to fast food branding, we show how our model can be used as a decision support tool for suggesting typical logo features for a brand, and for predicting consumers’ reactions to new brands or rebranding efforts.

Keywords: logos, branding, machine learning, multiview learning, deep generative modeling, image processing

Suggested Citation

Dew, Ryan and Ansari, Asim and Toubia, Olivier, Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Branding and Logo Design (November 25, 2019). Columbia Business School Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=3406857 or http://dx.doi.org/10.2139/ssrn.3406857

Ryan Dew (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

Asim Ansari

Columbia University - Columbia Business School, Marketing ( email )

New York, NY 10027
United States

Olivier Toubia

Columbia University - Columbia Business School, Marketing ( email )

New York, NY 10027
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
1,208
Abstract Views
3,761
Rank
31,921
PlumX Metrics