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A Comparative Methodology of Supervised Machine Learning Algorithms for Predicting Customer Churn Using Neuromarketing Techniques

A Comparative Methodology of Supervised Machine Learning Algorithms for Predicting Customer Churn Using Neuromarketing Techniques

Martin Muduva, Thanks Hondoma, Ronald Chiwariro, Fungai Jacqueline Kiwa
Copyright: © 2024 |Pages: 29
ISBN13: 9798369321652|ISBN13 Softcover: 9798369344866|EISBN13: 9798369321669
DOI: 10.4018/979-8-3693-2165-2.ch001
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MLA

Muduva, Martin, et al. "A Comparative Methodology of Supervised Machine Learning Algorithms for Predicting Customer Churn Using Neuromarketing Techniques." AI-Driven Marketing Research and Data Analytics, edited by Reason Masengu, et al., IGI Global, 2024, pp. 1-29. https://doi.org/10.4018/979-8-3693-2165-2.ch001

APA

Muduva, M., Hondoma, T., Chiwariro, R., & Kiwa, F. J. (2024). A Comparative Methodology of Supervised Machine Learning Algorithms for Predicting Customer Churn Using Neuromarketing Techniques. In R. Masengu, O. Chiwaridzo, M. Dube, & B. Ruzive (Eds.), AI-Driven Marketing Research and Data Analytics (pp. 1-29). IGI Global. https://doi.org/10.4018/979-8-3693-2165-2.ch001

Chicago

Muduva, Martin, et al. "A Comparative Methodology of Supervised Machine Learning Algorithms for Predicting Customer Churn Using Neuromarketing Techniques." In AI-Driven Marketing Research and Data Analytics, edited by Reason Masengu, et al., 1-29. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-2165-2.ch001

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Abstract

This chapter presents an approach to using supervised machine learning and neuromarketing techniques to predict customer churn. It explores how combining neuromarketing strategies with machine learning algorithms improves churn forecast accuracy. The chapter highlights the significance of choosing the most appropriate techniques for churn prediction by contrasting several algorithms combined with various neuromarketing methodologies, such as biometric analysis and neuroimaging. It discusses the connection between customer attrition and neuromarketing, highlighting studies on customer relationship characteristics, neuroscience methods, and the role of emotions in churn prediction. Marketers can leverage machine-learning algorithms and evaluation metrics while adhering to privacy regulations, conducting algorithm testing, ensuring interpretability and practicing responsible use to create predictive models, minimize biases, and maintain trust in customer relationships.

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