
Optimizing marketing strategies and personalized recommendation systems through precision advertising and customer segmentation with artificial intelligence and business intelligence
- 1 City University of Hong Kong (Dongguan), Guangdong, China
* Author to whom correspondence should be addressed.
Abstract
Modern marketing strategies have transformed through the combined power of Artificial Intelligence (AI) and Business Intelligence (BI) which improve customer segmentation and personalize marketing activities. This research examines how AI recommendation systems alongside BI tools influence marketing performance through customer interaction and conversion metrics. The research shows how AI and BI technologies produce effective marketing initiatives by analyzing consumer behavior data from transaction histories, browsing patterns, and social media activities. The study shows major enhancements in essential performance metrics including click-through rates and conversion rates with increased customer satisfaction when businesses implement AI-based systems over traditional marketing techniques. The research indicates that businesses using BI tools to implement AI-based customer segmentation achieve better conversion rates across different consumer demographics. Organizations that utilize both AI and BI systems can develop market advantages by improving customer targeting methods and enhancing their advertising approaches. The study offers important information that helps businesses boost their marketing performance while keeping pace with changing consumer behaviors in a competitive environment.
Keywords
artificial intelligence, business intelligence, marketing strategies, personalized recommendation systems, customer segmentation
[1]. Wu, C.-W., & Monfort, A. (2023). Role of artificial intelligence in marketing strategies and performance. Psychology & Marketing, 40(3), 484-496.
[2]. Umamaheswari, D. (2024). Role of Artificial Intelligence in Marketing Strategies and Performance. Migration Letters, 21(S4), 1589-1599.
[3]. Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50.
[4]. Yau, K.-L. A., Mat Saad, N., & Chong, Y.-W. (2021). Artificial intelligence marketing (AIM) for enhancing customer relationships. Applied Sciences, 11(18), 8562.
[5]. Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38-68.
[6]. Verma, S., et al. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002.
[7]. Haleem, A., et al. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119-132.
[8]. Noranee, S., & Othman, A. K. B. (2023). Understanding consumer sentiments: Exploring the role of artificial intelligence in marketing. JMM17: Jurnal Ilmu Ekonomi dan Manajemen, 10(1), 15-23.
[9]. Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663-680.
[10]. Shaily, S. A., & Emma, N. N. (2021). Integration of artificial intelligence marketing to get brand recognition for social business. International Review of Management and Marketing, 11(4), 29.
[11]. Hicham, N., Nassera, H., & Karim, S. (2023). Strategic framework for leveraging artificial intelligence in future marketing decision-making. Journal of Intelligent Management Decision, 2(3), 139-150.
[12]. Krishna, S. R., et al. (2023). Artificial Intelligence Integrated with Big Data Analytics for Enhanced Marketing. 2023 International Confe.rence on Inventive Computation Technologies (ICICT). IEEE, 2023.
Cite this article
Wang,Z. (2025). Optimizing marketing strategies and personalized recommendation systems through precision advertising and customer segmentation with artificial intelligence and business intelligence. Advances in Operation Research and Production Management,4(1),18-22.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Journal:Advances in Operation Research and Production Management
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