Today we will explore the integration of AI with business intelligence (BI) tools in retail, highlighting how AI enhances decision-making, operational efficiency, and customer satisfaction.
Authors
- Junaid Baig Mirza
- MD Anwarul Matin Jony
- Ali Hassan
- Rajesh Paul
- Mohammad Rakibul Hasan
- MD Nadil Khan
- Ayesha Islam Asha
Short DOI: 10.62127/aijmr.2025.v03i01.1123
Link: https://www.aijmr.com/research-paper.php?id=1126
Cite This: AI-Driven Business Intelligence in Retail: Transforming Customer Data into Strategic Decision-Making Tools – MD Nadil khan, Junaid Baig Mirza, MD Mohaiminul Hasan, Rajesh Paul, Mohammad Rakibul Hasan, Ayesha Islam Asha – AIJMR Volume 3, Issue 1, January-February 2025.
AI-driven BI tools improve inventory accuracy by 30–50%, while predictive analytics enable dynamic pricing strategies that boost profit margins by up to 10%
Data Sources
This research draws upon secondary data from peer-reviewed articles, industry reports, and publicly available retail datasets over the past 5–10 years. Datasets related to sales, inventory, and customer behavior were sourced from platforms such as Kaggle and UCI Machine Learning Repository. Case studies from retail sectors were analyzed to provide context, while real-world data enabled the development and testing of AI-driven business intelligence models.
Analysis Techniques
This study applied quantitative analysis using real-world datasets (sales, inventory, customer history) sourced from Kaggle and UCI repositories.
Machine learning models—Linear Regression, Recurrent Neural Networks (RNNs), and Decision Trees—were employed to assess performance trends and behavioral patterns.
Predictive analytics and visualization tools like Power BI and Tableau were used for data interpretation, while maintaining ethical standards through the use of anonymized public data.
Customer segmentation and personalized marketing powered by AI led to a 20% increase in loyalty and up to 25% rise in customer engagement.
Key Findings & Insights
Inventory Accuracy
30-50%, AI improves inventory accuracy.
Profit Margins
5-10%, Dynamic pricing powered by AI increases profit margins.
Customer Engagement
15-25%, Customer segmentation with AI boosts engagement.
Customer Loyalty
20%, Personalized marketing enhances customer loyalty.
Challenges
High implementation costs, lack of skilled workforce, and integration complexities with legacy systems pose significant barriers to AI adoption in retail. Data quality issues and resistance to organizational change further hinder effective deployment. Ensuring compliance with data privacy regulations like GDPR adds another critical layer of complexity.
Recommendations & Future Directions
Adopt AI Tools
Adopt AI-powered tools for dynamic pricing, segmentation, and predictive analytics.
Invest in Training
Invest in workforce training for AI integration and operation.
Implement Data Privacy
Implement strong data privacy frameworks (e.g., GDPR compliance).
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