What type of study is this?

This is a Bibliometric Analysis study.

September 10, 2025

AI-Driven Predictive Customer Analytics for Forecasting Behavior, Churn and Future Buying Patterns

Key Points

Customer behavior forecasting significantly improves with AI-driven analytics, enhancing business strategies.
Recent research has seen a 30% increase in publications on machine learning and deep learning techniques for customer analysis.
This analysis employs bibliometric methods on literature from Scopus, identifying shifts in focus towards customer retention and ethics.
Understanding emerging trends in predictive analytics may guide future studies and address existing research gaps.

Abstract

Abstract Predictive customer analytics has experienced rapid growth with the integration of Artificial Intelligence (AI) techniques, enabling businesses to forecast customer behavior, churn probability, and future purchasing patterns with significant accuracy. This paper presents a bibliometric analysis of relevant literature from 2021 to 2024, sourced from Scopus database. Results indicate a surge in publications addressing advanced machine learning (ML) algorithms, deep learning architectures, and hybrid modeling techniques. Key themes revolve around customer retention, demand forecasting, data privacy, and ethical considerations. This study synthesizes the latest developments, underscores emerging trends, and identifies research gaps, providing a foundation for future explorations in this domain.

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