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Customising generative AI: Harnessing document retrieval and fine-tuning alternatives for dynamic marketing insights

Author

Listed:
  • Crisp, Dakota

    (Senior Manager of Data Science, OneMagnify, USA)

  • Newsted, Jacob

    (Data Engineer and Data Scientist, OneMagnify, USA)

  • Kirouac, Brendon

    (Data Scientist, OneMagnify, USA)

  • Barnes, Danielle

    (Senior Director of Data Science, OneMagnify, USA)

  • Hayes, Catherine

    (Senior Director of IT, OneMagnify, USA)

  • Prantner, Jonathan

    (Chief Analytics Officer, OneMagnify, USA)

Abstract

This study delves into the transformative impact of leveraging large language models (LLMs) in marketing analytics, particularly emphasising a paradigm shift from fine-tuning models to the strategic application of document retrieval techniques and more. Focusing on innovative methods, such as retrieval augmented generation and low-rank adaptation, the paper explores how marketers can now activate against vast and unstructured datasets, such as call centre transcripts, unlocking valuable insights that were previously overlooked. By harnessing the power of document retrieval and adaptation, marketers can bring their data to life, enabling a more nuanced and adaptive approach to understanding consumer behaviour and preferences. This research contributes to the evolving landscape of applied marketing analytics by demonstrating the efficacy of document retrieval in enhancing the utilisation of LLMs for dynamic and data-driven marketing strategies.

Suggested Citation

  • Crisp, Dakota & Newsted, Jacob & Kirouac, Brendon & Barnes, Danielle & Hayes, Catherine & Prantner, Jonathan, 2024. "Customising generative AI: Harnessing document retrieval and fine-tuning alternatives for dynamic marketing insights," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 10(1), pages 18-31, June.
    • Handle: RePEc:aza:ama000:y:2024:v:10:i:1:p:18-31
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    More about this item

    Keywords

    generative AI; marketing analytics; call centre; natural language processing; document retrieval techniques; retrieval augmented generation; low-rank adaptation;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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