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Enterprise marketing strategy using big data mining technology combined with XGBoost model in the new economic era

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  • Huijun Chen

Abstract

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

Suggested Citation

  • Huijun Chen, 2023. "Enterprise marketing strategy using big data mining technology combined with XGBoost model in the new economic era," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-22, June.
    • Handle: RePEc:plo:pone00:0285506
    DOI: 10.1371/journal.pone.0285506
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    References listed on IDEAS

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    1. Azeroual, Otmane & Saake, Gunter & Schallehn, Eike, 2018. "Analyzing data quality issues in research information systems via data profiling," International Journal of Information Management, Elsevier, vol. 41(C), pages 50-56.
    2. Dmitry Ivanov & Christopher S. Tang & Alexandre Dolgui & Daria Battini & Ajay Das, 2021. "Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2055-2078, April.
    3. Kar, Arpan Kumar & Dwivedi, Yogesh K., 2020. "Theory building with big data-driven research – Moving away from the “What” towards the “Why”," International Journal of Information Management, Elsevier, vol. 54(C).
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    Cited by:

    1. Matthew Smith & Francisco Alvarez, 2025. "Machine Learning for Applied Economic Analysis: Gaining Practical Insights," Working Papers 2025-03, FEDEA.

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