Dynamic strategy adjustment and efficiency enhancement of e-commerce marketing management based on big data analysis

In the current wave of the rapid development of big data technology, the e-commerce industry is undergoing an unprecedented and profound transformation. This study focuses on the optimization and upgrading of e-commerce marketing management strategies in the context of big data. By deeply analyzing the internal influence mechanism of big data on e-commerce marketing and applying empirical research methods, it accurately reveals the inherent connections between key influencing factors and marketing performance, and accordingly proposes highly targeted strategic optimization plans. The research findings indicate that big data plays an irreplaceable and crucial role in core aspects such as precise customer positioning, personalized marketing customization, and scientific evaluation of marketing effectiveness. It can significantly enhance the overall performance of e-commerce marketing. To better adapt to the development trends of the big data era, enterprises should focus on strengthening their data integration and in-depth analysis capabilities, constructing an accurate and efficient marketing system, and further optimizing their customer relationship management strategies, so as to effectively improve their market competitiveness [1]. This study not only provides a solid theoretical foundation for e-commerce enterprises to formulate scientific, reasonable, and practical marketing management strategies but also offers valuable practical guidance for their actual operations, enabling these enterprises to stand out in the fierce market competition.

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Publication Details

Journal
Edelweiss Applied Science and Technology
Published
2025-03-06
DOI
https://doi.org/10.55214/25768484.v9i3.5221
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Dynamic strategy adjustment and efficiency enhancement of e-commerce marketing management based on big data analysis

Yongjun Tan, Bin Zhu
Edelweiss Applied Science and Technology
Big Data and Business Intelligence
article

Dynamic strategy adjustment and efficiency enhancement of e-commerce marketing management based on big data analysis

Yongjun Tan, Bin Zhu
article en

Abstract

In the current wave of the rapid development of big data technology, the e-commerce industry is undergoing an unprecedented and profound transformation. This study focuses on the optimization and upgrading of e-commerce marketing management strategies in the context of big data. By deeply analyzing the internal influence mechanism of big data on e-commerce marketing and applying empirical research methods, it accurately reveals the inherent connections between key influencing factors and marketing performance, and accordingly proposes highly targeted strategic optimization plans. The research findings indicate that big data plays an irreplaceable and crucial role in core aspects such as precise customer positioning, personalized marketing customization, and scientific evaluation of marketing effectiveness. It can significantly enhance the overall performance of e-commerce marketing. To better adapt to the development trends of the big data era, enterprises should focus on strengthening their data integration and in-depth analysis capabilities, constructing an accurate and efficient marketing system, and further optimizing their customer relationship management strategies, so as to effectively improve their market competitiveness [1]. This study not only provides a solid theoretical foundation for e-commerce enterprises to formulate scientific, reasonable, and practical marketing management strategies but also offers valuable practical guidance for their actual operations, enabling these enterprises to stand out in the fierce market competition.

Edelweiss Applied Science and TechnologyVol. 9(3)
Binary University of Management & Entrepreneurship (MY)
Industry, innovation and infrastructure
Openalex Percentile: Top 94%
Big Data and Business Intelligence
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