Machine learning and AI in marketing analytics: Leveraging the survey data to find customers

David Fogarty and Xinlei Cui
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David Fogarty: Associate Professor, National University, USA
Xinlei Cui: Graduate Research Assistant, New York University, Stern School of Business, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2024, vol. 10, issue 2, 158-175

Abstract: The field of marketing analysis in the digital era faces numerous challenges. Despite the availability of vast amounts of structured and unstructured data, practitioners have yet to fully harness the potential of machine learning models. This paper addresses this gap by investigating how to find targeted customers and expand the emerging market by implementing machine learning models to process survey text data and provides empirical evidence through model evaluation experiments. The research problem focuses on demonstrating the effectiveness of machine learning and AI models in optimising value creation and enhancing competitive advantages in marketing practices. The paper employs mixed methods and presents experimental results, leading to conclusions highlighting the benefits of improving data quality to strengthen the performance of machine learning models. This research also provides insights into model selection and offers a foundation for future researchers and marketing analysts to interpret and evaluate machine learning models effectively by multiple efficient metrics.

Keywords: machine learning; marketing; artificial intelligence; automated classification; consumer targeting; analytics (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2024
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