While we have all heard about biases in AI LLMs regarding gender and race, we wondered what other biases might be lurking beneath the surface that we can’t readily see. On April 9, we dove into a study from Galileo Research & Strategy Consultancy about Americans’ Health & Wellness behaviors and attitudes. Attendees discovered what was learned and its implications for using AI in research studies.
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This study explores the potential of large language models (LLMs) to revolutionize marketing research. By partnering with a Fortune 500 food company, the authors replicated qualitative and quantitative studies using GPT-4. The findings indicate that LLMs can effectively generate synthetic respondents, moderate in-depth interviews and perform data analysis tasks, matching or even surpassing human performance in certain aspects. The study highlights the benefits of a Human-LLM hybrid approach, where LLMs assist in various stages of the research process, from study design to data analysis. This approach not only enhances efficiency but also uncovers new insights that might be overlooked by human researchers alone.
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Using data to microtarget messages to Chobani customers produced a positive sales impact, according to this case study. The authors show how loyalty card data, linked to household address and then to a digital address was used to identify key audience segments to whom messages were specifically directed – with implications for future efforts.
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The authors describe a very broad approach to creating omnichannel marketing effectiveness. It calls for real time indicators of campaign success. They create a personal level database through integration of online and offline data and then aggregate persons into clusters with a homogeneity of location and behaviors. That allows them to buy more traditional media but to highly targeted clusters of people.
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