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

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Authors
Dakota Crisp
Analytics Manager
OneMagnify
Jacob Newsted
Data Engineer and Data Scientist
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Brendon Kirouac
Data Scientist
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Danielle Barnes
Senior Director, Data Science
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Catherine Hayes
Senior Director of IT
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Jonathan Prantner
Chief Analytics Officer
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Publication Release Date
March 12, 2024

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Publication Release Date
March 12, 2024

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You'll hear back from us soon.
Oops! Something went wrong while submitting the form.