The analysis of marketing performance in E-commerce live broadcast platform based on big data and deep learning.

This study aims to conduct a comprehensive and in-depth analysis of the marketing performance of e-commerce live broadcast platforms based on big data management technology and deep learning. Firstly, by synthesizing large-scale datasets and surveys, the study constructs a series of performance evaluation indicators including user participation, content quality, commodity sales effect, user satisfaction, and platform promotion effect. Secondly, the weight of each indicator is finally determined through the indicator screening of the expert scoring method. Finally, the experimental design and implementation steps such as data collection, experimental environment setting, parameter setting, and performance evaluation are introduced in detail. Through the training and evaluation of the Back Propagation Neural Network (BPNN), each secondary indicator's adjusted weight value and global ranking are obtained, providing a scientific basis for subsequent management opinions. The research results emphasize the importance of comments and ratings, purchase conversion rate, advertising click-through rate, and other indicators in improving user satisfaction, promoting sales, and effective promotion. Overall, this study provides a clear direction for an e-commerce live broadcast platform to optimize user experience, improve sales performance, and strengthen brand promotion.