The influence of AI on consumer behavior: Shaping choices and preferences in the digital marketplace

Consumer behavior has been mainly transformed by Artificial Intelligence advancements, altering product preferences and choices, as well as consumer perception in a digital marketplace. With the introduction of AI-enabled product-recommendation systems, targeted advertising, and customized content, the traditional way consumers make decisions is already changing. Forecasting consumer preferences is quite challenging, owing to the complex nature of behavior patterns and ever-changing market trends. This research aims to create a framework that can enhance the predictions of consumer behavior in such a manner that actionable insight could be provided to support and develop digital marketing activities. Using Consumer Purchase Data, Clickstream Data, Customer Reviews & Sentiments, and Demographic information, and the framework can obtain valuable insights related to purchasing patterns and engagement behaviors. To ensure high-quality data for model training, a comprehensive data pre-processing pipeline is implemented, incorporating techniques such as duplicate record removal and handling of missing values. This research introduces a Stochastic Multiobjective Optimized Deep Neural Network (SMO-DNN) to analyze and predict consumer preferences with enhanced accuracy and efficiency. The proposed model integrates the SMO for hyperparameter tuning, optimizing the learning process of DNN to extract meaningful insights from complex consumer data. The research has provided an accuracy of 98.69%, precision of 98.53%, recall of 98.64%, and F1-measure of 98.33%, which generates significant advantages for the digital marketing industries to structuralize and optimize their own lives, enhance customer engagement, and create a modular e-commerce model fibrous with data to adapt to consumer demands. This research explains how AI optimization methods affect consumer behavior today and transform digital commerce.