Customer Behavior Analysis Using Big Data Analytics and Machine Learning

Abstract

This thesis explores the use of big data analytics and Machine Learning (ML) for customer behavior analysis in the context of digital marketing. The primary objective is to uncover patterns and trends in customer behavior and leverage this information to drive data-driven decisions related to marketing strategy, product development, and customer service. The thesis commences with an in-depth overview of the fundamental concepts of big data and ML, elucidating their applicability within the realm of digital marketing. This includes a comprehensive discussion of various types of ML algorithms and the ML pipeline employed to construct and deploy predictive models for customer behavior analysis. Subsequently, the thesis delves into specific applications of ML in customer behavior analysis. It investigates the utilization of ML techniques to predict customer churn, identify high-potential prospects, and determine optimal communication channels for distinct customer segments. Furthermore, the thesis explores the integration of sentiment analysis in marketing, showcasing how ML can effectively assess customer sentiment and enhance the overall customer experience. Throughout the thesis, real-world examples and compelling case studies are presented to exemplify the efficacy of ML in customer behavior analysis. These instances provide tangible evidence of how ML techniques have yielded actionable insights and facilitated decision-making processes within marketing contexts. Concluding the thesis, an examination of the limitations and challenges associated with utilizing ML for customer behavior analysis is presented. Additionally, the thesis outlines prospective avenues for future research in this domain. By encompassing the various facets of ML in customer behavior analysis, this thesis aspires to serve as a comprehensive guide for professionals seeking to harness the potential of big data analytics and ML in their organizations. By the conclusion of this thesis, readers will have acquired a profound understanding of the advantages offered by these technologies. Furthermore, they will be equipped with practical insights necessary for implementing and integrating big data analytics and ML methodologies effectively within their business frameworks.