Dynamic Pricing & Promo Optimization (Auto Parts Retailer)
Industry : E-commerce
Major auto parts and consumables retailer (30,000+ active SKUs)
Task
Transition from manual price management to a smart system that considers inventory levels, competitor prices, and demand for analogues.
AI implementation process
Data collection
Analysis of each SKU to determine optimal price range
Goal setting
Price and promotion recommendations with revenue and margin forecasts
Forecast development
Virtual simulator for testing promotions before launch
Piloting and integration
Implementation: sales data collection, goal setting, demand forecasting, piloting on 50-200 items, full ERP integration via API
Client results during the AI implementation process
Revenue growth with AI
Revenue growth without AI
according to McKinsey & Company
+2.5% gross margin by category
-30% unnecessary discounts
+5% promo uplift
More about the solution
We implemented an intelligent system that helps category managers make pricing decisions based on data, not intuition.
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AI analyzes each SKU (from original parts to cross‑references) in a specific location and time period, determining the price ceiling at which demand remains stable.
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The system provides pricing and promotion recommendations along with a ready forecast of revenue and final margin.
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A manager’s tool that allows testing any promotion (for example, a discount on brake pads of a certain brand) in a virtual environment before launching it in the real network.
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