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

Dakota Crisp, Jacob Newsted, Brendon Kirouac, Danielle Barnes, Catherine Hayes and Jonathan Prantner
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Dakota Crisp: Senior Manager of Data Science, OneMagnify, USA
Jacob Newsted: Data Engineer and Data Scientist, OneMagnify, USA
Brendon Kirouac: Data Scientist, OneMagnify, USA
Danielle Barnes: Senior Director of Data Science, OneMagnify, USA
Catherine Hayes: Senior Director of IT, OneMagnify, USA
Jonathan Prantner: Chief Analytics Officer, OneMagnify, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2024, vol. 10, issue 1, 18-31

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.

Keywords: generative AI; marketing analytics; call centre; natural language processing; document retrieval techniques; retrieval augmented generation; low-rank adaptation (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2024
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