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

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.

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Publication Details

Journal
Applied marketing analytics
Published
2024-09-01
DOI
https://doi.org/10.69554/wzkb3722
Citations
1
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
0.44
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article

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

David Fogarty, Xinlei Cui
1 citations
Applied marketing analytics
Customer churn and segmentation
0.44
article

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

David Fogarty, Xinlei Cui
article en
1 citations

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.

Applied marketing analyticsVol. 10(2)
National University (US), New York University (US)
Openalex Percentile: Top 28%
Customer churn and segmentation
0.44
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Machine learning and AI in marketing analytics: Leveraging the survey data to find customers — David Fogarty, Xinlei Cui · Applied marketing analytics (2024) | TGRS Research Map | TGRS