Abstract
Understanding customer behavior is crucial for businesses aiming to enhance customer satisfaction, predict churn, and deliver personalized experiences. Recent advancements in machine learning (ML) and deep learning (DL) have significantly transformed the way organizations analyze and forecast customer actions across domains such as e-commerce, finance, and social media. This study presents a comprehensive review of contemporary approaches employed to predict and analyze customer behavior using various ML algorithms like Decision Trees, Random Forest, Logistic Regression, Support Vector Machines, Gradient Boosting, Naïve Bayes, and advanced DL models including Long Short-Term Memory (LSTM) and Transformer-based networks. The reviewed works demonstrate the use of large-scale structured and unstructured datasets, applying models for tasks such as churn prediction, sentiment analysis, product recommendation, and trend forecasting.