Predicting Customer Behavior Using Machine Learning and Deep Learning: A Comprehensive Review
Keywords:
Consumer analytics, Customer behavior prediction, Customer churn, Data mining, Deep learning, LSTM, Machine learning, Predictive modeling, Recommendation systems, Sentiment analysis, Social media analytics, TransformerAbstract
To make customers happy, predict who will leave, and give them unique experiences, businesses need to know how their customers act. Recent improvements in Machine Learning (ML) and Deep Learning (DL) have changed the way businesses look at and predict what customers will do in areas like e-commerce, banking, and social media. This study gives a full overview of the modern methods used to predict and analyze customer behavior using a variety of ML algorithms, such as Decision Trees, Random Forest, Logistic Regression, Support Vector Machines, Gradient Boosting, Naïve Bayes, and advanced DL models like Long Short-Term Memory (LSTM) and Transformer-based networks. The works that were looked at show how to use big structured and unstructured datasets and models for things like churn prediction, sentiment analysis, product recommendation, and trend forecasting. The review also identifies the growing significance of social media analytics, ethical concerns related to data use, and the superior performance of ensemble and deep learning models in capturing customer intent. By synthesizing these findings, this paper highlights the state-of-the-art in predictive customer behavior modeling and suggests future directions for more interpretable, privacy-conscious, and context-aware intelligent systems.
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