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Frontiers in Business Innovations and Management

Open Access Peer Review International
Open Access

Augmented Business Intelligence for Predictive Customer Segmentation

DOI https://doi.org/10.64917/fbim/Volume03Issue01-01
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Kuljeet Kaur 1
4 Independent Researcher, United States.

Abstract

This paper delves into the impactful influence that Augmented Business Intelligence (BI) has over predictive customer segmentation. It mainly focuses on artificial intelligence (AI) and machine learning (ML) integration into today’s analytics platforms. The paper also shows how BI has transitioned from being a reporting system that produced stagnant reports to a lively, AI-powered environment that not only makes suggestions automatically but also uses natural language processing (NLP) and permits prescriptive analytics. Furthermore, the research points out critical methods such as K-Means, Hierarchical Clustering, and the most sophisticated neural network models (CNN, LSTM) that leads to a remarkable increase in both the accuracy of segmentation and business value. The empirical studies that were conducted in e-commerce, telecommunications, and financial sectors show that customer lifetime value (CLV), retention, and ROI have all experienced positive changes that are directly measurable. To conclude, the paper speculates on the directions of future research, which would include generative AI, federated learning, and the integration of real-time analytics, thus providing insights that would be greatly beneficial to both the academic environment and the practitioners who are keen on optimizing the BI-driven marketing intelligence.

How to Cite

Kaur, K. (2026). Augmented Business Intelligence for Predictive Customer Segmentation. Frontiers in Business Innovations and Management, 3(01), 01–14. https://doi.org/10.64917/fbim/Volume03Issue01-01
⬇ Endnote/Zotero/Mendeley (RIS) ⬇ BibTeX

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