AI Dynamic Pricing Engine

Overview

An e-commerce company selling 5,000+ SKUs was using cost-plus pricing set quarterly. Meanwhile, competitors adjusted prices daily based on demand. The company was losing sales on price-sensitive items and leaving money on the table on items where customers would pay more. We built an AI dynamic pricing engine that optimizes every product's price in real-time based on demand elasticity, competitive positioning, inventory levels, and margin targets.

The Challenge

Pricing 5,000 SKUs dynamically requires understanding demand elasticity for each product (how sensitive is demand to price changes?), monitoring competitor prices continuously, respecting MAP (minimum advertised price) agreements, maintaining margin floors, and avoiding price changes so frequent they confuse customers. The system also needed guardrails to prevent pricing errors — a wrong decimal point could be catastrophic.

Our Approach

We built an elasticity model for each product category using historical sales data at different price points. A competitive intelligence module monitors competitor prices across major marketplaces. The optimization engine runs every hour, calculating the optimal price for each SKU that maximizes revenue within defined constraints (margin floors, MAP compliance, maximum daily change limits). A/B testing validates the model continuously, and human-reviewable approval workflows handle edge cases.

Key Features

• Hourly price optimization across all SKUs
• Demand elasticity modeling per product
• Real-time competitor price monitoring
• Margin floor and MAP compliance enforcement
• Price change guardrails and approval workflows
• A/B testing framework for pricing strategies
• Revenue and margin impact dashboard

Results

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