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

Bujor Dragoș () and Ene Constantin Andreea Bianca ()
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Bujor Dragoș: Bucharest University of Economic Studies, Bucharest, Romania
Ene Constantin Andreea Bianca: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 981-994

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.

Keywords: artificial intelligence; customer; future buying patterns; consumption behavior; sustainable development (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:981-994:n:1010

DOI: 10.2478/picbe-2025-0077

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