Consumer Purchase Behavior Prediction and Marketing Decision Optimization Using Hybrid Deep Learning Models

The rapid growth of e-commerce platforms has led to massive consumer behavior data, offering opportunities for accurate purchase prediction and personalized marketing. This paper presents a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with advanced attention mechanisms to capture complex spatio-temporal patterns in consumer actions. The model employs sophisticated feature fusion and multi-scale temporal modeling techniques to handle heterogeneous data effectively, while ensuring interpretability through tailored attention modules. We validate our approach using the UCI Online Retail dataset, containing over 540,000 transactions. The proposed architecture achieves a prediction accuracy of 78.3% and an AUC of 0.834, outperforming traditional machine learning models by 12.7% and single deep learning models by 4.0 percentage points. The experimental results demonstrate the model’s superior capability in capturing complex consumer behavior patterns and its potential for various business applications in consumer analytics and marketing optimization.