An end-to-end data analytics case study designed to move past surface-level aggregate financial success to isolate, quantify, and mitigate hidden corporate profit leakage.
💡 Click the dashboard image above to view and interact with the live application on Tableau Public, where you can cross-filter metrics in real-time.
- Interactive Dashboard: Live Tableau Public Dashboard
- Formal Business Report: Available in Markdown format here or via PDF.
- The Aggregate Trap: The enterprise maintains an overall healthy margin of 12.5% ($286K profit on $2.30M sales), hiding massive value destruction in specific segments.
- The 20% Discount Cliff: Promotional discounts exceeding 20% completely invert the P&L, cascading to a catastrophic -119.2% margin at the 50%+ discount band, resulting in $135,376 of destroyed profit.
- The Tactical Target: 7.6% of historical order volume (Standard Class shipments intersecting with Furniture/Office Supplies carrying over 20% discounts) accounts for nearly half ($65,387) of all promotional value destruction.
/charts: Exported high-resolution data visualizations mapping out margin behavior./data: Contains the historical transaction database./deliverables: Holds the final executive briefing documentation, compiled PDFs, and interactive system screenshots.
- Data Engineering: Python (Pandas) for multi-dimensional transaction auditing and custom financial band calculations.
- Business Intelligence: Tableau Public for dual-axis volume modeling, highlight matrix generation, and interactive dashboard deployment.
The dataset used in this analysis is a historical transaction log representing retail enterprise operations.
- Primary Source: Publicly hosted via the Salesforce Trailblazer Community.
- Curation Archive: Sourced from the Superstore Dataset Archive on Kaggle.
- Acknowledgements: All credit belongs to the original creators and authors at Tableau/Salesforce. This project is structured strictly for educational and portfolio demonstration purposes under fair use.
If you have any questions about the methodology used in this analysis, want to discuss the findings, or are looking to collaborate, feel free to reach out!
- Name: Pranav M S Krishnan
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