IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-032-23493-3_21.html

Optimising Digital Advertising Using Generative AI: A Design Science Approach to Keyword Generation and Decision Support

Author

Listed:
  • Syed Farhan

    (University of Surrey)

  • Colin Fu

    (University College London)

Abstract

This study presents a design science artefact that operationalises the Cognitive AI Framework (Fu and Ioannou, 2025) to enhance digital advertising decision-making. By integrating generative models (BART, GPT-3.5) with predictive analytics (Random Forest), the system moves beyond content creation to prescribe optimal keyword allocation based on predicted click-through rates (CTR). Using the Cognitive AI stages of Engage (generation), Examine (prediction), and Formulate (decision), the system demonstrates how AI tools can function as prescriptive decision support systems rather than mere creative assistants. Empirical findings show transformer-based models significantly outperform traditional LSTM in accuracy, while the predictive layer acts as a governance gate for campaign planning. A locally deployable prototype with voice input via Whispr illustrates how such a framework functions in applied contexts. Conceptually, we identify five affordances, i.e. creative scalability, performance-aware prescription, multimodal interaction, domain portability, and transparency that enable marketers to transition from descriptive analytics to prescriptive action. These insights extend recent work on AI-enabled advertising (Singh et al., 2024; Korst et al., 2025) and provide a blueprint for experimental adoption.

Suggested Citation

  • Syed Farhan & Colin Fu, 2026. "Optimising Digital Advertising Using Generative AI: A Design Science Approach to Keyword Generation and Decision Support," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-23493-3_21
    DOI: 10.1007/978-3-032-23493-3_21
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:lnopch:978-3-032-23493-3_21. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.