IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v41y2022i2p401-425.html
   My bibliography  Save this article

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

Author

Listed:
  • Ryan Dew

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Asim Ansari

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Olivier Toubia

    (Columbia Business School, Columbia University, New York, New York 10027)

Abstract

Logos serve a fundamental role as the visual figureheads of brands. Yet, because of the difficulty of using unstructured image data, prior research on logo design has largely been limited to nonquantitative 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 data set 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.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:41:y:2022:i:2:p:401-425
    DOI: 10.1287/mksc.2021.1326
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2021.1326
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2021.1326?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Endrissat, Nada & Islam, Gazi & Noppeney, Claus, 2016. "Visual organizing: Balancing coordination and creative freedom via mood boards," Journal of Business Research, Elsevier, vol. 69(7), pages 2353-2362.
    3. Boyoun (Grace) Chae & JoAndrea Hoegg, 2013. "The Future Looks "Right": Effects of the Horizontal Location of Advertising Images on Product Attitude," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(2), pages 223-238.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hui Li & Jian Ni & Fangzhu Yang, 2024. "Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data," Papers 2405.15929, arXiv.org, revised Jun 2024.
    2. Alex Burnap & John R. Hauser & Artem Timoshenko, 2023. "Product Aesthetic Design: A Machine Learning Augmentation," Marketing Science, INFORMS, vol. 42(6), pages 1029-1056, November.
    3. Alireza Aghasi & Arun Rai & Yusen Xia, 2024. "A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 616-634, March.

    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. Alex Burnap & John R. Hauser & Artem Timoshenko, 2023. "Product Aesthetic Design: A Machine Learning Augmentation," Marketing Science, INFORMS, vol. 42(6), pages 1029-1056, November.
    2. Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.
    3. Aparna Sundar & James J. Kellaris, 2017. "How Logo Colors Influence Shoppers’ Judgments of Retailer Ethicality: The Mediating Role of Perceived Eco-Friendliness," Journal of Business Ethics, Springer, vol. 146(3), pages 685-701, December.
    4. Kareklas, Ioannis & Muehling, Darrel D. & King, Skyler, 2019. "The effect of color and self-view priming in persuasive communications," Journal of Business Research, Elsevier, vol. 98(C), pages 33-49.
    5. Shen Liu & Hongyan Liu, 2021. "Tagging Items Automatically Based on Both Content Information and Browsing Behaviors," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 882-897, July.
    6. Alim Al Ayub Ahmed & Sugandha Agarwal & IMade Gede Ariestova Kurniawan & Samuel P. D. Anantadjaya & Chitra Krishnan, 2022. "Business boosting through sentiment analysis using Artificial Intelligence approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 699-709, March.
    7. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    8. Xing Qin & Shuangge Ma & Mengyun Wu, 2023. "Two‐level Bayesian interaction analysis for survival data incorporating pathway information," Biometrics, The International Biometric Society, vol. 79(3), pages 1761-1774, September.
    9. Youngseon Lee & Seongil Jo & Jaeyong Lee, 2022. "A variational inference for the Lévy adaptive regression with multiple kernels," Computational Statistics, Springer, vol. 37(5), pages 2493-2515, November.
    10. Liu, Jie & Ye, Zifeng & Chen, Kun & Zhang, Panpan, 2024. "Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    11. Nathaniel Tomasetti & Catherine Forbes & Anastasios Panagiotelis, 2019. "Updating Variational Bayes: Fast Sequential Posterior Inference," Monash Econometrics and Business Statistics Working Papers 13/19, Monash University, Department of Econometrics and Business Statistics.
    12. Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    13. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    14. Wang, Yajin, 2022. "A conceptual framework of contemporary luxury consumption," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 788-803.
    15. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    16. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    17. Li, Yi-Na & Li, Yan & Chen, Haipeng (Allan) & Wei, Jiuchang, 2023. "How verbal and non-verbal cues in a CEO apology for a corporate crisis affect a firm’s social disapproval," Journal of Business Research, Elsevier, vol. 167(C).
    18. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    19. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    20. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.

    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:inm:ormksc:v:41:y:2022:i:2:p:401-425. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.