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Machine Learning for Enhanced Advertisement Effectiveness: A Computational Model to Analyze Persuasion in Visual Advertisements

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Název: Machine Learning for Enhanced Advertisement Effectiveness: A Computational Model to Analyze Persuasion in Visual Advertisements
Autoři: Vemulapalli, Vijay
Informace o vydavateli: Zenodo
Rok vydání: 2024
Sbírka: Zenodo
Témata: Deep learning, Persuasion strategies, Visual advertisements, U-Net model, ConvNextXLarge, Computational advertising, Advertisement effectiveness, Return on Advertising Spend, Convolutional Neural Network
Popis: The competitive landscape of advertising necessitates a nuanced understanding of persuasion strategies to craft impactful advertisements. In this pursuit, we present a novel framework employing deep learning models to analyze picture advertisements and both predict and locate the embedded persuasion strategies. Our approach leverages the U-Net model and the ConvNextXLarge Convolutional Neural Network (CNN) model to extract and analyze visual features indicative of various persuasion strategies. Through rigorous evaluation, we demonstrate that our models exhibit a promising capability to identify and categorize persuasion strategies effectively. The output of this research not only provides a computational method to gauge the persuasion tactics deployed but also paves the way for an assistive tool for companies aiming to enhance their advertisement effectiveness. By integrating our persuasion prediction model, companies can now analyze the potential effectiveness of their advertisements prior to campaign launches. This, in turn, facilitates data-driven decision-making in advertisement design, potentially leading to improved engagement rates and better Return on Advertising Spend (ROAS). Our work presents a significant stride towards marrying machine learning with advertising domain knowledge, opening avenues for further research and real-world applications in the rapidly expanding field of computational advertising.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://zenodo.org/records/10792336; oai:zenodo.org:10792336; https://doi.org/10.5281/zenodo.10792336
DOI: 10.5281/zenodo.10792336
Dostupnost: https://doi.org/10.5281/zenodo.10792336
https://zenodo.org/records/10792336
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Přístupové číslo: edsbas.2FC85DB9
Databáze: BASE
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  – Url: https://doi.org/10.5281/zenodo.10792336#
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  Data: Machine Learning for Enhanced Advertisement Effectiveness: A Computational Model to Analyze Persuasion in Visual Advertisements
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Vemulapalli%2C+Vijay%22">Vemulapalli, Vijay</searchLink>
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  Data: 2024
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Persuasion+strategies%22">Persuasion strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+advertisements%22">Visual advertisements</searchLink><br /><searchLink fieldCode="DE" term="%22U-Net+model%22">U-Net model</searchLink><br /><searchLink fieldCode="DE" term="%22ConvNextXLarge%22">ConvNextXLarge</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+advertising%22">Computational advertising</searchLink><br /><searchLink fieldCode="DE" term="%22Advertisement+effectiveness%22">Advertisement effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Return+on+Advertising+Spend%22">Return on Advertising Spend</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+Neural+Network%22">Convolutional Neural Network</searchLink>
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  Label: Description
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  Data: The competitive landscape of advertising necessitates a nuanced understanding of persuasion strategies to craft impactful advertisements. In this pursuit, we present a novel framework employing deep learning models to analyze picture advertisements and both predict and locate the embedded persuasion strategies. Our approach leverages the U-Net model and the ConvNextXLarge Convolutional Neural Network (CNN) model to extract and analyze visual features indicative of various persuasion strategies. Through rigorous evaluation, we demonstrate that our models exhibit a promising capability to identify and categorize persuasion strategies effectively. The output of this research not only provides a computational method to gauge the persuasion tactics deployed but also paves the way for an assistive tool for companies aiming to enhance their advertisement effectiveness. By integrating our persuasion prediction model, companies can now analyze the potential effectiveness of their advertisements prior to campaign launches. This, in turn, facilitates data-driven decision-making in advertisement design, potentially leading to improved engagement rates and better Return on Advertising Spend (ROAS). Our work presents a significant stride towards marrying machine learning with advertising domain knowledge, opening avenues for further research and real-world applications in the rapidly expanding field of computational advertising.
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  Data: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
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        Value: 10.5281/zenodo.10792336
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Persuasion strategies
        Type: general
      – SubjectFull: Visual advertisements
        Type: general
      – SubjectFull: U-Net model
        Type: general
      – SubjectFull: ConvNextXLarge
        Type: general
      – SubjectFull: Computational advertising
        Type: general
      – SubjectFull: Advertisement effectiveness
        Type: general
      – SubjectFull: Return on Advertising Spend
        Type: general
      – SubjectFull: Convolutional Neural Network
        Type: general
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      – TitleFull: Machine Learning for Enhanced Advertisement Effectiveness: A Computational Model to Analyze Persuasion in Visual Advertisements
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              M: 01
              Type: published
              Y: 2024
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