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

Abstract

Managing and utilizing marketing data presents numerous challenges for businesses; this analysis sheds light on a few of these limitations. These obstacles, which range from data fragmentation to the complexities of real-time analytics, must be overcome to allow for marketing insights to reach their full potential. Issues that arise in the management of an organization's marketing records include concerns about scalability, the necessity of real-time processing, the need for trustworthy predictive modeling, and the integration of data from numerous sources. These problems prevent companies from making the most of their advertising data while making important strategic decisions. The solution proposed in this research is Deep Learning Empowered Enterprise Marketing Data Management (DL-EEMDM). It incorporates deep learning techniques with enterprise marketing statistics management. DL-EEMDM enhances predictive analytics, enables scalable processing, and provides seamless statistics integration. Using this method, insights from complex and massive advertising datasets can be effectively extracted. A number of examples of DL-EEMDM's applications include character-based marketing campaigns, buyer sentiment research, recommendation systems, defection prediction, and customer segmentation. Organizations can tap into new opportunities for personalized marketing and consumer engagement with the help of deep learning algorithms. A simulated experiment demonstrates the efficacy of DL-EEMDM in real-world scenarios. Through comparative investigations and overall performance evaluations, the recommended approach outperforms conventional procedures. Results from the simulation demonstrate the accuracy, scalability, and performance gains that DL-EEMDM brings to the table when it comes to controlling marketing data for corporations.