使用生成式人工智能的市场研究和知识:大型语言模型的力量

IF 15.5 1区 管理学 Q1 BUSINESS
Macarena Estevez , María Teresa Ballestar , Jorge Sainz
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引用次数: 0

摘要

生成式人工智能(GAI)凭借其在数据分析、个性化、战略优化和内容生成方面的能力,正在迅速改变营销行业,为旨在建立或保持强大品牌地位的企业提供强大的工具。本研究考察了大型语言模型(llm)在市场研究中的应用,使营销人员能够创造洞察市场行为和客户心理的复杂性,帮助他们更有意义、更有效地与目标受众建立联系。特别是,它旨在评估GAI在多大程度上可以复制传统的市场研究。我们对西班牙的啤酒消费进行了全面的调查,涵盖了从品牌意识到购买的所有转化漏斗阶段。该研究使用了四个著名的llm: ChatGPT (OpenAI)、Gemini(谷歌)、Claude (Anthropic)和LlaMa (Meta),并使用一系列统计方法将这些llm的结果与传统调查的结果进行了比较。我们的研究结果表明,法学硕士对市场研究是有价值的,提供了重要的见解作为可靠的代理。这代表了一种竞争优势,使这类研究更容易获得,成本效益更高,使各种规模的公司都受益。然而,法学硕士不能完全复制传统方法,并且呈现出结果的可变性,这给决策带来了风险,因为数据生成中的潜在错误在没有基准的情况下很难估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Market research and knowledge using Generative AI: the power of Large Language Models
Generative artificial intelligence (GAI) is rapidly transforming the marketing industry with its capabilities in data analysis, personalisation, strategic optimisation, and content generation, providing powerful tools for businesses aiming to establish or maintain a strong brand position. This research examines the application of Large Language Models (LLMs) for market research, enabling marketers to create insights that capture the complexities of market behaviour and customer psychology, helping them to connect with their target audiences more meaningfully and effectively. Particularly, it aims to evaluate the extent to which GAI can replicate traditional market research. We conducted a comprehensive survey on beer consumption in Spain, covering all conversion funnel stages, from Brand Awareness to Purchase. The study was replicated using four prominent LLMs: ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic) and LlaMa (Meta), and the results of these LLMs were compared with those of the traditional survey using a collection of statistical methods. Our results show that LLMs are valuable for market research, offering significant insights as reliable proxies. This represents a competitive advantage by making studies of this kind more accessible and cost-effective, benefitting companies of all sizes. However, LLMs cannot fully replicate traditional methods and present result variability, introducing risks in decision-making because of potential errors in data generation that are complex to estimate without benchmarking.
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来源期刊
CiteScore
16.10
自引率
12.70%
发文量
118
审稿时长
37 days
期刊介绍: The Journal of Innovation and Knowledge (JIK) explores how innovation drives knowledge creation and vice versa, emphasizing that not all innovation leads to knowledge, but enduring innovation across diverse fields fosters theory and knowledge. JIK invites papers on innovations enhancing or generating knowledge, covering innovation processes, structures, outcomes, and behaviors at various levels. Articles in JIK examine knowledge-related changes promoting innovation for societal best practices. JIK serves as a platform for high-quality studies undergoing double-blind peer review, ensuring global dissemination to scholars, practitioners, and policymakers who recognize innovation and knowledge as economic drivers. It publishes theoretical articles, empirical studies, case studies, reviews, and other content, addressing current trends and emerging topics in innovation and knowledge. The journal welcomes suggestions for special issues and encourages articles to showcase contextual differences and lessons for a broad audience. In essence, JIK is an interdisciplinary journal dedicated to advancing theoretical and practical innovations and knowledge across multiple fields, including Economics, Business and Management, Engineering, Science, and Education.
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