AI-Driven Consumer Behavior Analysis for Personalized Marketing Strategies

Abstract

This paper outlines a Hybrid Deep Learning Model (CNN + LSTM) to help analyze the behavior of consumers to maximize personalized marketing strategies by AI. The model suggested would combine both Convolutional Neural Networks (CNN) to extract meaningful features in both structured and unstructured consumer data and the Long Short-Term Memory (LSTM) networks to identify sequential patterns in consumer behavior. When these methods are combined, the model can be used to give much more precise forecasts regarding consumer behavior and allow marketing plans to be highly personalized. Supervised model development was done using PyTorch and PyTorch Lightning, which proved useful in training the models, scaling, and was simple to experiment with. The findings indicate that the hybrid model is much more effective than the traditional ones, including decision trees and support vehicle machines, since it has a better accuracy and conversion rate in personalized marketing campaigns. This effort demonstrates the possibility of AI-based consumer behavior analysis to understand the customer better and create the most effective marketing strategy in a dynamic, data-intensive environment.