A global fashion company faced challenges in optimizing its pricing strategy due to strong seasonality, concentrated demand in discount periods, and an over-reliance on long-term promotional discounts. The demand fluctuations impacted inventory levels and growth, requiring a sophisticated solution to balance demand forecasting with real-time dynamic pricing.

We implemented ML-driven OptimizationAi to enable dynamic pricing, allowing real-time updates based on sales data, customer behavior, and external factors like weather and competitor promotions. This approach maximized revenue opportunities by adapting prices to market conditions instantly. Additionally, we developed a robust demand forecasting model that used time series data, macroeconomic trends, and sales patterns to predict demand surges, optimizing inventory levels and minimizing stockouts or overstock issues. We further enhanced our strategy by conducting A/B testing across product categories, comparing dynamic pricing with traditional models to assess their impact on profitability.