Predicting Transactional Profitability to Protect Business Margins
The average retailer loses 15-20% of their potential margin to "Silent Profit Erosion"—unprofitable transactions that are hidden within high-volume sales data. I developed an AI-Driven Decision Intelligence Engine that moves beyond static reporting to predict loss-making transactions before they impact the bottom line.
Key Result: Automated 100% of the profitability audit process, saving 15+ manual hours/week with 85.2% prediction accuracy.
Most retail businesses focus heavily on Top-line Revenue while neglecting Bottom-line Profit.
High discount rates (>20%) and complex logistics costs often create "silent losses" that standard accounting reports fail to flag in real-time.
Traditional Excel-based analysis is "post-mortem"—it tells you that you lost money after the quarter is over.
Scaling sales without a profitability filter leads to "scaling losses," where increased volume actually decreases net margin.
This system serves as a "Profit Gatekeeper." It doesn't just show "what happened"; it provides actionable intelligence on "what to do next."
A high-precision CatBoost classifier that predicts transaction profitability in <0.5 seconds.
An interactive sandbox for managers to test pricing and discount strategies before they are implemented.
A robust, modular ML pipeline covering data ingestion, feature engineering, and real-time inference.
| Metric | Manual Process (Before) | AI-Driven System (After) |
|---|---|---|
| Audit Time | 15-20 Hours/Week (Manual Excel) | Instant / Automated |
| Decision Logic | Intuition & Reactive Reporting | 85.2% Accurate ML Insights |
| Response Speed | 2-3 Days for deep-dive analysis | Real-time (<1 second) |
| Margin Risk | High (Invisible margin leaks) | Proactive Margin Protection |
Python, Pandas, NumPy (Modular, Object-Oriented Architecture).
CatBoost Classifier, Scikit-Learn, MLflow for experiment tracking.
Streamlit Cloud for production hosting, Plotly for interactive visualization.
Comprehensive logging, custom exception handling, and version-controlled codebase.
Standard Business Intelligence (BI) tools answer the question: "How much did we sell?"
This system answers the question: "Which sales actually generated profit—and how can we repeat them?"
This is the shift from Reporting to Intelligence.
Are you looking to protect your business margins with custom AI solutions?