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


  • Xiaofeng Wang Zhengzhou Shengda University of Economics, Business & Management



deep learning, enterprise marketing, data management, data integration


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.


Journal article: Abdulaal, R. M. S., Makki, A. A., Alfilali, I. Y., & Li, Ms. E. (2023). A Novel Hybrid Approach for Prioritizing Investment Initiatives to Achieve Financial Sustainability in Higher Education Institutions Using MEREC-G and RATMI. 65(65), 131–179.

Journal article: Caldevilla-Domínguez, D., Martínez-Sala, A.-M., & Báez, A. B. (2021). Tourism and ICT. Bibliometric Study on Digital Literacy in Higher Education. Education Sciences, 11(4), 172.

Journal article: Claudet, J., Loiseau, C., & Pebayle, A. (2021). Critical gaps in the protection of the second-largest exclusive economic zone in the world. Marine Policy, 124, 104379.

Journal article: Cui, Y., Jiao, H., & Zhao, G. (2021). A Heuristic for All? A Multiple Needs Approach to Fairness Heuristic Formation in Digital Transformation in Chinese Work Organizations. IEEE Transactions on Engineering Management, PP(99), 1–13.

Journal article: Gazzola, P., Grechi, D., Papagiannis, F., & Marrapodi, C. (2021). The sharing economy in a digital society: Youth consumer behavior in Italy. Kybernetes, 50(1), 147–164.

Journal article: Gopal, V. (2021). Conference Proceedings National Level Virtual Conference on E-Education, E-Learning, E-Management and E-Business (NC4E) Organizing Committee of NC4E. 45(45), 67–89.

Journal article: Karpovich, I. (2022). Adjustment of ESP curriculum to the needs of the digital university environment. Uchenye Zapiski St. Petersburg University of Management Technologies and Economics, 44(44), 34–73.

Journal article: Khan, A., Shah, A., Arafa, A., Soomro, N. E. H., & Advocate, M. (2021). Plurilateral Negotiation of WTO E-commerce in the Context of Digital Economy: Recent Issues and Developments. 34(34), 213–277.

Journal article: Klymchuk, M., Achkasov, I., Klymchuk, S., & Poliak, O. (2021). Influence of Risk Management on the Formation of the Enterprise’s Business Process Management Strategy in the Digital Economy: The International Experience. Business Inform, 1, 272–278.

Journal article: Kumbhojkar, N. R., & Menon, A. B. (2022). Integrated Predictive Experience Management Framework (IPEMF) for Improving Customer Experience: In the Era of Digital Transformation. International Journal of Cloud Applications and Computing, 163(163), 1–56.

Journal article: Luhtala, H., Erkkil-Vlimki, A., Eliasen, S. Q., & Tolvanen, H. (2021). Business sector involvement in maritime spatial planning – Experiences from the Baltic Sea region. Marine Policy, 123, 104301.

Journal article: Makrydakis, N. (2021). Perceptions of Marketing & Digital Transformation in Greek Public Higher Education Organizations in the Context of Digital Darwinism. 1, 142–188.

Journal article: Mukherjee, S. (2023). The business model canvas of women-owned micro-enterprises in the urban informal sector. Journal of Enterprising Communities: People and Places in the Global Economy, 17(2), 398–418.

Journal article: Prokhorova, V. V., & Chobitok, V. I. (2021). The Organizational and Managerial Provision of Business Processes Reengineering at Enterprise in the Conditions of Digitalization. Business Inform, 1(516), 279–285.

Journal article: Rumyantseva, A., Sintsova, E., Sukhacheva, V., & Tarutko, O. (2021). The Impact of Digital Transformation on the Process of Russian Enterprises Entering International Financial Markets. IV International Scientific and Practical Conference, 122(122), 34–71.

Journal article: Schwarzbach, C., Eden, T., Werth, O., Lohse, U., Breitner, M. H., & Schulenburg, J. M. G. V. D. (2023). Digital Transformation in Back-Offices of German Insurance Companies. International Journal of Innovation and Technology Management, 20(08).

Journal article: Sukhareva, E., Kakhalnikov, M., Korolkova, A., & Sobolev, A. (2021). The organizational and economic mechanism for creating a digital environment in the energy sector. 147(112), 55–78.

Journal article: Sun, H. (2021). Research on the Development of National Education Modernization under the Guidance of Concept Modernization. 46(46), 133–178.

Journal article: Tuan, N. M., Hung, N. Q., & Hang, N. T. (2021). Digital transformation in the business: A solution for developing cash accounting information systems and digitizing documents. T?P Chí Phát Tri?N Khoa h?C và Cng Ngh?, 3(2), 45–77.

Journal article: Tyshchenko, S. (2022). Univariate Analysis of Variance as a Method of Solving Professional Pedagogical Tasks in Higher Education. Modern Economic, 67(67), 78–101.

Journal article: Xia Peng. (2022). Analysis and Suggestions on the Trend of Promoting Digital Transformation in Petroleum and Petrochemical Enterprises. Economic management, 3, 44–47.




How to Cite

Wang X. Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning. EAI Endorsed Scal Inf Syst [Internet]. 2024 Mar. 22 [cited 2024 Apr. 20];. Available from:



Research articles