Implementing deep learning models for clustering customers into segments is the most effective technique to utilize NVIDIA GPUs for extracting actionable insights from a large customer behavior dataset. Deep learning models (e.g., autoencoders, neural networks) excel at unsupervised clustering of complex, high- dimensional data, identifying subtle trends and patterns for recommendations. NVIDIA GPUs accelerate these models via libraries like cuDNN and frameworks like PyTorch, as noted in NVIDIA's "Deep Learning Institute (DLI)" and "AI Infrastructure for Enterprise" resources, making them ideal for GPU-powered data mining.
Spreadsheets (A) and SQL queries (B) lack scalability and GPU utilization. Decision trees (D) are simpler but less effective for large-scale pattern discovery. Deep learning on GPUs is NVIDIA's recommended approach.