A Scalable Digital Marketing Framework Based on Distributed Computing and Automated Machine Learning

The advancement of computational technologies, particularly in artificial intelligence and machine learning, has fundamentally transformed digital marketing capabilities. However, technical implementation barriers and resource constraints prevent many Small and Medium-sized Enterprises (SMEs) from leveraging these sophisticated marketing technologies effectively. This study develops an innovative technical framework incorporating distributed computing architectures and automated machine learning approaches to address this critical gap. Through the implementation of deep learning algorithms and real-time data processing pipelines, we established a scalable solution optimized for resource-constrained environments. Our research employs quantitative methodology across multiple SME case studies, utilizing containerized microservices and privacy-preserving computation techniques. The developed hierarchical customer profiling architecture achieves an 89% feature preservation rate, while our targeting algorithm demonstrates a 37% improvement in purchase propensity prediction accuracy compared to conventional methods. The hybrid clustering approach for market segmentation yields a 42% enhancement in segment stability with homogeneity scores above $\mathbf{0. 8 5}$. Implementation results validate the framework’s effectiveness, showing a 28% increase in campaign response rates and 31% reduction in customer acquisition costs. This study contributes to both theoretical understanding of scalable marketing systems and provides practical implementation guidelines for SMEs seeking to enhance their digital marketing capabilities through advanced technological solutions.