Color Analytics for Data-Driven Brand Communications

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.