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.
