The Impact of Artificial Intelligence on Commercial Big Data

Jiacheng Ji

doi:10.54254/2754-1169/2025.GL27475

Advances in Economics, Management and Political SciencesOpen access

The Impact of Artificial Intelligence on Commercial Big Data

Research Article

Open Access

The Impact of Artificial Intelligence on Commercial Big Data

Jiacheng Ji ^1*

¹ Suzhou Science & Technology Town Foreign Language School

^*Corresponding author: jijiacheng114514@gmail.com

Published on 2 October 2025

AEMPS Vol.222

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-403-8

ISBN (Online): 978-1-80590-404-5

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Abstract

This article focuses on the impact of artificial intelligence, or AI, on the application of commercial big data and its functional dimensions, and explores all its specific values in the retail industry. First of all, this article elaborates in detail on the connotations of artificial intelligence and commercial big data, their respective characteristics, and development situations. It also analyzed a transformation model such as "data - algorithm - business value" formed after the integration of the two, which has brought about significant changes to the traditional analysis methods. This article then specifically elaborates on the role that artificial intelligence can play in customer analysis, how it functions in marketing optimization, what assistance it offers to operational efficiency improvement, as well as its roles in risk control and dynamic cycle mechanisms. Subsequently, it discusses the application strategies in different scenarios, demonstrating the feasibility of these strategies from the perspectives of technology, cost, and benefit. This article summarizes the research results, points out the limitations of the research and the future development direction. These contents can provide some references for enterprises to formulate intelligent data strategies.

Keywords:

Artificial intelligence, AI, commercial big data, retail industry, application strategies

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References

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References

[4]. Unknown. (2024) Artificial intelligence-driven big data analytics for business intelligence in SaaS products. Information Systems Frontiers , 26 , 890-905

[7]. Unknown. (2023) Artificial intelligence integrated with big data analytics for enhanced marketing. Journal of Interactive Marketing , 62 , 1-18

[8]. Ding, C.J., Li, K., Chen, L.S. and Shang, S. (2025) Parallel online similarity join over trajectory streams. Data & Knowledge Engineering , 148 , 102100-102118

[9]. Li, X., Wang, H., Zhao, J. and Chen, Y. (2021) Deep learning for customer lifetime value prediction: A comparative study. Computational Economics , 58 , 1200-1225

[10]. Wang, Q., Li, J., Zhang, S. and Liu, M. (2022) Reinforcement learning for dynamic marketing budget allocation. Marketing Science , 41 , 850-875

[11]. Zhao, Y., Chen, W., Huang, Z. and Yang, L. (2021) Predictive maintenance in manufacturing: A deep learning approach. Journal of Manufacturing Systems , 59 , 300-320

[12]. Zhang, H. and Wang, L. (2022) Federated learning in financial fraud detection: A practical framework. Computers & Security , 112 , 102500-102518

Cite this article

Ji,J. (2025). The Impact of Artificial Intelligence on Commercial Big Data. Advances in Economics, Management and Political Sciences,222,1-9.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Financial Framework's Role in Economics and Management of Human-Centered Development

ISBN: 978-1-80590-403-8(Print) / 978-1-80590-404-5(Online)

Editor: Lukáš Vartiak, Habil. Florian Marcel Nuţă

Conference website: https://2025.icftba.org/Galati.html

Conference date: 17 October 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.222

ISSN: 2754-1169(Print) / 2754-1177(Online)

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