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International Journal of Science, Strategic Management and Technology

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AI-DRIVEN BUSINESS ANALYTICS SYSTEM FOR SALES, CUSTOMER INSIGHTS, AND FORECASTING

AUTHORS:
Barath L
Suganthavanathan M
Pooja D
Mentor
Dr. Perumal S
Affiliation
Department of Computer Science and Information Technology, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai, India
DOI: 10.55041/ijsmt.v2i4.528
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

In the current digital era, organizations continuously produce large amounts of data through transactions, customer engagements, and online platforms. Transforming this raw data into meaningful information is essential for gaining business value. Business analytics enables this transformation by identifying underlying patterns, trends, and relationships within the data. Traditional reporting methods often struggle with data inconsistency, increasing volumes, and limited analytical capabilities

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 27 2026
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L, B., M, S. & D, P. (2026). AI-Driven Business Analytics System for Sales, Customer Insights, and Forecasting. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.528

L, Barath, et al.. "AI-Driven Business Analytics System for Sales, Customer Insights, and Forecasting." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.528.

L, Barath,Suganthavanathan M, and Pooja D. "AI-Driven Business Analytics System for Sales, Customer Insights, and Forecasting." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.528.

References
[1] Radhakrishnan. AI-driven business intelligence framework for market trend forecasting.
[2] Goswami. AI-driven business intelligence systems for customer data insights.
[3] Zamil. AI-driven business analytics models for financial forecasting.
[4] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2011.
[5] P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2nd ed. Pearson Education, 2019.
[6] A. Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. O'Reilly Media, 2022.
[7] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
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✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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