№2, 2023
This paper delves into the utilization of big data analytics and Machine Learning (ML) in the realm of customer behavior analysis for digital marketing. It explores the practical application of ML algorithms and the ML pipeline in the development of predictive models. The primary objectives revolve around forecasting customer churn, identifying prospects with a high propensity to convert, determining optimal communication channels, and leveraging sentiment analysis to enhance the overall customer experience. Concrete real-world examples and compelling case studies are employed to illustrate the efficacy of ML in analyzing customer behavior. Moreover, the paper acknowledges the existing limitations and challenges in this domain, while also outlining potential directions for future research. By offering a comprehensive guide, the aim is to empower businesses with the knowledge and tools needed to effectively leverage big data analytics and ML for customer behavior analysis in the digital marketing landscape. The paper concludes by addressing limitations, challenges, and future research directions in this field, aiming to provide a comprehensive guide to leveraging big data analytics and ML for customer behavior analysis (pp.61-67).
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