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

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.