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Dynamic Price Optimization

Moving beyond "gut feel" pricing. We built a machine learning engine to model elasticity and recommend optimal price points.

Pricing curves visualization

The Challenge

A mid-sized retailer was leaving margin on the table. They priced products based on simple "cost-plus" logic, ignoring competitor signals, seasonality, and customer willingness to pay. They feared changing prices would alienate loyal customers.

Our Solution

We implemented a three-stage optimization engine:

  • Demand Modeling: Regression models to calculate price elasticity for each SKU.
  • Constraint Optimization: Finding the profit-maximizing price within business rules (e.g., "never change price by >5%").
  • A/B Testing Framework: A system to test specific price points in select regions before global rollout.

Impact

"We saw a 12% lift in gross margin in the pilot category without impacting sales volume."