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Page No: 360-368
Abstract : Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. We investigate the integration of optimization methods like gradient descent, Bayesian optimization, and genetic algorithms with commonly used ML models such as decision trees, support vector machines, and neural networks. The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.
Keyword Consumer Behaviour Analytics, Machine Learning Algorithms, Optimization Techniques, Data Preprocessing, Predictive Modeling
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