IDEAS home Printed from https://ideas.repec.org/p/abo/neswpt/w0292.html

Color Analytics for Data-Driven Brand Communications

Author

Listed:
  • 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.

Suggested Citation

  • Daria Dzyabura & Renana Peres & Irina Linevich, 2025. "Color Analytics for Data-Driven Brand Communications," Working Papers w0292, New Economic School (NES).
    • Handle: RePEc:abo:neswpt:w0292

    Download full text from publisher

    References listed on IDEAS

    1. Kunter Gunasti & Berna Devezer, 2016. "How competitor brand names affect within-brand choices," Marketing Letters, Springer, vol. 27(4), pages 715-727, December.
    2. Uttam Chakraborty & Savita Bhat, 2018. "Credibility of online reviews and its impact on brand image," Management Research Review, Emerald Group Publishing Limited, vol. 41(1), pages 148-164, January.
    3. Lee, Jonq-Ying & Gao, Zhifeng & Brown, Mark G., 2010. "A study of the impact of package changes on orange juice demand," Journal of Retailing and Consumer Services, Elsevier, vol. 17(6), pages 487-491.
    4. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
    5. Renaud Lunardo & Camille Saintives & Damien Chaney, 2021. "Food packaging and the color red: How negative cognitive associations influence feelings of guilt," Post-Print hal-04455590, HAL.
    6. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    7. J.J. Brakus & B.H. Schmitt & L. Zarantonello, 2009. "Brand Experience: What Is It? How Do We Measure It? And Does It Affect Loyalty?," Post-Print hal-00799102, HAL.
    8. Michel Wedel & Rik Pieters, 2000. "Eye Fixations on Advertisements and Memory for Brands: A Model and Findings," Marketing Science, INFORMS, vol. 19(4), pages 297-312, October.
    9. Nima Y. Jalali & Purushottam Papatla, 2016. "The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 353-384, December.
    10. Hartmann, Jochen & Exner, Yannick & Domdey, Samuel, 2025. "The power of generative marketing: Can generative AI create superhuman visual marketing content?," International Journal of Research in Marketing, Elsevier, vol. 42(1), pages 13-31.
    11. Uttam Chakraborty & Savita Bhat, 2018. "Credibility of online reviews and its impact on brand image," Management Research Review, Emerald Group Publishing Limited, vol. 41(1), pages 148-164, January.
    12. Ryan Dew & Asim Ansari & Olivier Toubia, 2022. "Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Branding and Logo Design," Marketing Science, INFORMS, vol. 41(2), pages 401-425, March.
    13. Park, C Whan & Milberg, Sandra & Lawson, Robert, 1991. "Evaluation of Brand Extensions: The Role of Product Feature Similarity and Brand Concept Consistency," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(2), pages 185-193, September.
    14. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
    15. Gerald J. Gorn & Amitava Chattopadhyay & Tracey Yi & Darren W. Dahl, 1997. "Effects of Color as an Executional Cue in Advertising: They're in the Shade," Management Science, INFORMS, vol. 43(10), pages 1387-1400, October.
    16. Carlos J. Torelli & Alokparna Basu Monga & Andrew M. Kaikati, 2012. "Doing Poorly by Doing Good: Corporate Social Responsibility and Brand Concepts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 38(5), pages 948-963.
    17. Lunardo, Renaud & Saintives, Camille & Chaney, Damien, 2021. "Food packaging and the color red: How negative cognitive associations influence feelings of guilt," Journal of Business Research, Elsevier, vol. 134(C), pages 589-600.
    18. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    19. Liu Liu & Daria Dzyabura & Natalie Mizik, 2020. "Visual Listening In: Extracting Brand Image Portrayed on Social Media," Marketing Science, INFORMS, vol. 39(4), pages 669-686, July.
    20. Michel Wedel & Rik Pieters, 2015. "The Buffer Effect: The Role of Color When Advertising Exposures Are Brief and Blurred," Marketing Science, INFORMS, vol. 34(1), pages 134-143, January.
    21. Timothy B. Heath & Devon Delvecchio & Michael S. Mccarthy, 2011. "The Asymmetric Effects of Extending Brands to Lower and Higher Quality," Post-Print hal-00668763, HAL.
    22. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    23. Heitmann Mark, 2024. "Generative AI for Marketing Content Creation: New Rules for an Old Game," NIM Marketing Intelligence Review, Sciendo, vol. 16(1), pages 10-17, May.
    24. Henrik Hagtvedt & S. Adam Brasel, 2017. "Color Saturation Increases Perceived Product Size," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(2), pages 396-413.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yaqiu & Meg Lee, Hsin Hsuan & Blasco-Arcas, Lorena, 2025. "Computer vision in branding: A conceptual framework and future research agenda," Journal of Business Research, Elsevier, vol. 193(C).
    2. Kanuri, Vamsi K. & Hughes, Christian & Hodges, Brady T., 2024. "Standing out from the crowd: When and why color complexity in social media images increases user engagement," International Journal of Research in Marketing, Elsevier, vol. 41(2), pages 174-193.
    3. Ryan Dew, 2025. "Adaptive Preference Measurement with Unstructured Data," Management Science, INFORMS, vol. 71(5), pages 3996-4012, May.
    4. Mohammad Mosaffa & Omid Rafieian & Hema Yoganarasimhan, 2025. "Visual Polarization Measurement Using Counterfactual Image Generation," Papers 2503.10738, arXiv.org.
    5. Jeremy Yang & Juanjuan Zhang & Yuhan Zhang, 2025. "Engagement That Sells: Influencer Video Advertising on TikTok," Marketing Science, INFORMS, vol. 44(2), pages 247-267, March.
    6. Bahar Türk, 2021. "Brand's Image, Love, and Loyalty: Is it Enough for Word of Mouth Marketing?," European Journal of Studies in Management and Business, EUROKD, vol. 18, pages 16-27.
    7. Koschmann, Anthony & Bowman, Douglas, 2018. "Evaluating marketplace synergies of ingredient brand alliances," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 575-590.
    8. Cao, Jingcun & Li, Xiaolin & Zhang, Lingling, 2025. "Is relevancy everything? A deep-learning approach to understand the effect of image-text congruence," LSE Research Online Documents on Economics 128215, London School of Economics and Political Science, LSE Library.
    9. Andranik Tumasjan, 2024. "The many faces of social media in business and economics research: Taking stock of the literature and looking into the future," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 389-426, April.
    10. Sandra Rousseau & Machteld Joly & Eline Poelmans, 2024. "Search characteristics, online consumer ratings, and beer prices," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 804-824, October.
    11. Betty Purwandari & Latifatul Khairiyah & Mardiana Purwaningsih & Achmad Nizar Hidayanto & Nur Fitriah Ayuning Budi & Kongkiti Phusavat, 2023. "Why do people donate online? A perspective from dual credibility transfer," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 20(2), pages 393-425, June.
    12. Wang, Yajin, 2022. "A conceptual framework of contemporary luxury consumption," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 788-803.
    13. Hema Yoganarasimhan & Irina Iakovetskaia, 2024. "From Feeds to Inboxes: A Comparative Study of Polarization in Facebook and Email News Sharing," Management Science, INFORMS, vol. 70(9), pages 6461-6472, September.
    14. Catherine Janssen & Joëlle Vanhamme & Adam Lindgreen & Cécile Lefebvre, 2014. "The Catch-22 of Responsible Luxury: Effects of Luxury Product Characteristics on Consumers’ Perception of Fit with Corporate Social Responsibility," Journal of Business Ethics, Springer, vol. 119(1), pages 45-57, January.
    15. Pabla, Harleen & Soch, Harmeen, 2023. "Up in the air! Airline passenger's brand experience and its impact on brand satisfaction mediated by brand love," Journal of Air Transport Management, Elsevier, vol. 107(C).
    16. Xiaodong Zhang & Chunrong Guo, 2024. "Influencing Factors and Formation Mechanism of Brand Preference in Community E-Commerce," Sustainability, MDPI, vol. 16(23), pages 1-26, December.
    17. Herhausen, Dennis & Ludwig, Stephan & Abedin, Ehsan & Haque, Nasim Ul & de Jong, David, 2025. "From words to insights: Text analysis in business research," Journal of Business Research, Elsevier, vol. 198(C).
    18. Riganelli, Chiara & Marchini, Andrea & Polenzani, Bianca & Martino, Gaetano, . "Strategies of Diversification and Brand Extension in SME Food Companies: Which Factors Might Affect the Impact of Consumers’ Preferences," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 9(2).
    19. Huang, Zhiyuan & Dai, Xiaohe & Wang, Li, 2026. "More saturated, better Performance: How color saturation affects product performance perception," Journal of Retailing and Consumer Services, Elsevier, vol. 88(C).
    20. Marlene Vock, 2022. "Luxurious and responsible? Consumer perceptions of corporate social responsibility efforts by luxury versus mass-market brands," Journal of Brand Management, Palgrave Macmillan, vol. 29(6), pages 569-583, November.

    More about this item

    Keywords

    ; ; ; ; ; ; ; ; ; ; ; ;

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abo:neswpt:w0292. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vladimir Ivanyukhin The email address of this maintainer does not seem to be valid anymore. Please ask Vladimir Ivanyukhin to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/nerasru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.