№2, 2023

Leyla G. Muradkhanli, Zaman M. Karimov

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).

Keywords:Big data, Customer behavior analysis, Machine learning, Artificial intelligence, Customer lifetime value, Big data analytics

Bhardwaj, S. et al. (2023). Proposing an integrative data-analytics framework for micro, small and medium enterprises: a systematic review substantiated by evidence from two case studies. Annals of Operations Research.

Chien-Chang Hsu et al. (2004). An Intelligent Interface for Customer Behaviour Analysis from Interaction Activities in Electronic Commerce. Innovations in Applied Artificial Intelligence, 315-324.

da Silva Wegner et al. (2023). Performance analysis of social media platforms: evidence of digital marketing. Journal of Marketing Analytics.

Ducange, P. et al. (2018). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing 22(1), 325–342.


Kailash Hambarde et al. (2020). Augmentation of Behavioral Analysis Framework for E-Commerce Customers Using MLP-Based ANN. Advances in Data Science and Management, 45-50.

Kühl, N. et al. (2020).  Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media. Electron Markets 30, 351–367.

Li, H. (2022). Intelligent business framework for interactive data visualization of small and medium-sized enterprises in developing countries. Annals of Operations Research, 1-17.

Meshal Alduraywish et al. (2022). Application of Artificial Intelligence in Recommendation Systems and Chatbots for Online Stores in Fast Fashion Industry. Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021), 558–567.

O’Leary, P.N., et al. (2017). Blurred Lines: Ethical Implications of Social Media for Behavior Analysts. Behavior Analysis in Practice 10, 45–51.

Oh, S., Ji, H., Kim, J. et al. (2022). Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism 24, 109–126.

Pin-Liang Chen et al. (2017). Social Network and Consumer Behavior Analysis: A Case Study in the Shopping District. Frontier Computing, 879–890.

Singh, N. et al. (2020). An inclusive survey on machine learning for CRM: a paradigm shift. Decision 47, 447–457.https://doi.org/10.1007/s40622-020-00261-7 

Stavros Anastasios Iakovou et al. (2016). Customer Behavior Analysis for Recommendation of Supermarket Ware. Artificial Intelligence Applications and Innovations, 471–480.

Walk, N. et al. (2001). Real-time database analysis: Customer knowledge as a value-determining factor in e-commerce. Journal of Database Marketing & Customer Strategy Management 8, 143–149.