Deep Learning Approaches to Personalized Marketing and Consumer Response Analysis

Personalized marketing has become a vital strategy in the age of big data and digital transformation because it allows organizations to deliver content, recommendations, and offers tailored to individual consumers. This paper empirically investigates the use of deep learning models, including long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and Transformer-based models, to predict consumer response and optimize personalized marketing. Model performance was assessed on multi-channel customer interaction datasets in predicting click-through rates, purchase likelihood, and engagement measures. In the reported experiments, the deep learning models outperformed traditional machine learning techniques, which the authors attribute to their ability to capture sequential behavior and latent preferences. The results suggest that advanced architectures can improve targeting accuracy and customer experience, although data privacy and model interpretability remain open challenges.