Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning

INTRODUCTION: To explore the integration and innovation path of enterprise marketing data management based on deep learning to adapt to today's competitive business environment. With the continuous development of information technology, enterprises are faced with a large amount of marketing data, and how to efficiently manage and integrate these data has become an essential issue for enterprises to improve their market competitiveness. Deep learning, as a necessary technical means of artificial intelligence, provides enterprises with more intelligent and precise data processing tools. OBJECTIVES: The primary purpose of the study is to solve the problems of marketing data management in traditional enterprises and to achieve better integration and management of data through deep learning technology. Specifically, the goal is to explore the potential of deep learning in improving data processing efficiency and accurately analyzing user behavior and trends. By achieving these goals, organizations can better understand market needs, develop more effective marketing strategies, and stand out in a competitive marketplace. METHODS: This study adopts a comprehensive approach, including a literature review, case study, and empirical analysis of deep learning algorithms. First, the main issues of current enterprise marketing data management and the latest progress in deep learning were understood through an in-depth study of the literature in related fields. Second, several enterprise cases were selected to gain a deeper understanding of the challenges and needs of enterprises in marketing data management through field research and data collection. Finally, a series of deep learning algorithms were designed and implemented to validate their effectiveness in real-world applications and analyze their impact on data integration and innovation paths. RESULTS: The results of the study show that deep learning has significant advantages in enterprise marketing data management. By using deep learning algorithms, enterprises are able to handle large-scale marketing data more efficiently and achieve intelligent data integration and accurate analysis. This not only improves the efficiency of data processing but also provides enterprises with deeper market insights that help develop more targeted marketing strategies. CONCLUSION: The results of the study are of guiding significance for enterprises to realize data-driven marketing decision-making, which provides strong support for enterprises to maintain their competitive advantages in the highly competitive market. Future research can further explore the application of deep learning in different industries and scenarios, as well as how to optimize deep learning algorithms further to meet the changing needs of enterprises.