AI-Powered Turnaround
in Retail
About the
project
UrbanCart, a fast-growing retail chain, faced a paradox—rising revenue but shrinking profits. Despite strong sales, their financials suffered due to:
- Overstocking low-demand items, leading to dead inventory.
- Stockouts of bestsellers, causing lost sales opportunities.
- Inefficient discounting strategies, eroding profit margins.
Their data told a story, but they weren’t reading it right. Instead of chasing short-term sales boosts, I built an AI-driven inventory intelligence system that optimized stock levels, predicted trends, and maximized revenue per shelf space.
Key Solutions Implemented
- AI Demand Forecasting – Trained an LLM-based model to predict buying trends, reducing stockouts by 65%.
- Dynamic Pricing Engine – Applied real-time demand-based discounting, increasing revenue per product by 27%.
- Dead Stock Liquidation Strategy – Used an AI-powered bundle pricing approach, cutting dead inventory by 50% in 3 months.
Skillset Applied
AI Demand Forecasting & Predictive Modeling
Inventory & Supply Chain Strategy
Revenue Optimization & Profitability Engineering
Data-Driven Pricing & Discounting Algorithms
UrbanCart transformed into a data-first retailer, improving profit margins by 35% in just six months—all without expanding operations.
Insights from the project
- Revenue is vanity—profit is reality. Selling more isn’t success unless your margins scale with it.
- Stocking without forecasting is gambling. Retailers often lose money before they realize the mistake.
- Pricing should adapt, not react. Discounts should maximize profit, not just clear stock.
