| By: |
Daria Dzyabura (New Economic School);
Renana Peres (Hebrew University of Jerusalem);
Irina Linevich (MIT Sloan School of Management) |
| Abstract: |
Color is an important component in brand visual communication. Firms select
brand colors to align with the brand's strategic positioning goals. Despite
their importance, brand color decisions are often driven by intuition and
trial and error. We introduce BRACE (BRand Attribute and Color Engine), a
predictive model and genetic-algorithm based optimization framework, that
generates color palettes that reflect combinations of brand characteristics.
Using theory on color combinations and color harmonies, the model avoids
contradictions across characteristics while maintaining visual harmony. For
example, if a brand seeks to be perceived as Friendly and Glamorous, or highly
Outdoorsy but not Young, we recommend aesthetically appealing color palettes
that best capture these attribute combinations. We validate the algorithm
through a series of experiments. We also find that real ads recolored with
recommended palettes are rated significantly higher on the intended brand
characteristics. We further use topic modeling to provide interpretable
insights into the relationships between characteristics and colors, and how
these relationships vary across product categories. This paper is a major step
towards data-driven brand visual communication that can better align creative
choices with communication goals. |
| Keywords: |
Image analytics, branding, color, machine learning, genetic algorithm, topic modeling, brand personality, BRACE. JEL Classifications: M31, M37, C45, C55, D12 |
| Date: |
2025–12 |
| URL: |
https://d.repec.org/n?u=RePEc:abo:neswpt:w0292 |