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Consumer segmentation with large language models
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DOI：10.1016/j.jretconser.2024.104078.png)
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摘要
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Consumer segmentation is vital for companies to customize their offerings effectively. Our study explores the application of Large Language Models (LLMs) in marketing research for consumer segmentation. We developed a workflow leveraging LLMs to perform clustering analysis based on consumer survey data, with a focus on textbased multiple-choice and open-ended questions. Firstly, we employed a LLMs model to embed text for clustering, demonstrating that LLMs enhance clustering accuracy over traditional models. Secondly, we created persona chatbots using LLMs, which achieved over 89% accuracy in simulating consumer preferences. Our findings underscore the potential of our LLMs framework in marketing research.
Keyword:
Consumer segmentation
Large language model
Text analysis
Marketing research
期刊
IF:
13.1
论文数:
3.8K
被引数:
3.1W
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