Consumer behavior analysis and precision marketing strategy optimization driven by deep learning

Traditional consumer behavior analysis methods have many limitations, and deep learning has brought new breakthroughs in this field with its powerful learning and feature extraction capabilities. The method framework including data preprocessing, deep learning model construction and precision marketing strategy optimization is studied and constructed. The data preprocessing module performs operations such as normalization and sequence processing on consumer behavior data, in the deep learning model, multi-layer perceptron (MLP) combined with Transformer architecture of self-attention mechanism is adopted to effectively extract consumer behavior characteristics. The optimization of precision marketing strategies is based on extracted features for consumer segmentation, personalized recommendations, and dynamic pricing strategy formulation. The experiment used a dataset of three months of behavioral logs from 300,000 users on an e-commerce platform. The results showed that this approach outperformed traditional methods in clustering effectiveness, recommendation performance, and marketing outcomes. For instance, the clustering silhouette score reached 0.63, the click-through rate (CTR) was 4.6%, the conversion rate was 1.9%, and the marketing ROI (marketing revenue/cost) improved by an average of 2.3 times. These results validate the significant advantages of deep learning technology in enhancing the accuracy of consumer behavior analysis and the effectiveness of precision marketing, providing strong support for enterprises to optimize their marketing strategies.