Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194953,
        author = {Ms.K. Kalyani and G. Santhi and K. Lavanya Rekha and A. Siddu Sairam and D. Baba Nagendra Varma},
        title = {RETAIL SALES PERFORMANCE ANALYSIS AND REVENUE INSIGHTS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8141-8146},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194953},
        abstract = {The retail sector generates large volumes of transactional data that must be systematically analyzed to extract actionable business insights. This paper presents a Retail Sales Performance Analysis and Revenue Insights system developed using Python, SQL, and Stream lit to evaluate business performance through structured data analytics and visualization. The study begins with Exploratory Data Analysis to understand data distribution, detect missing values, identify sales patterns, and examine relationships between revenue, profit, discount, product categories, and regional performance. Cleaned data is stored in a relational database and analyzed with SQL aggregation functions to calculate key performance indicators including total revenue, profit margin, growth rate, category contribution, and regional distribution. The analytical outputs are presented through an interactive dashboard that enables dynamic filtering and performance monitoring. The system focuses on descriptive and diagnostic analytics rather than predictive modeling, showing how structured query analysis combined with visualization tools can support data-driven decision-making in retail management.},
        keywords = {Business Intelligence, Exploratory Data Analysis, Retail Analytics, Revenue Insights, SQL Aggregation, Sales Performance, stream lit Dashboard},
        month = {March},
        }