MACHINE LEARNING
Definition
Machine learning (ML) is a branch of artificial intelligence in which algorithms learn from data rather than being explicitly programmed. In pricing, ML is used to model complex relationships (elasticity, demand, competition), make predictions, and recommend optimal prices. It transforms a discipline historically based on business expertise into a data-driven and scalable discipline.
Why it's important
- Capturing complexity: ML detects nonlinear relationships between price, demand, and context that traditional models miss.
- Large-scale industrialization: An ML model can process tens of thousands of products simultaneously—something humans cannot do.
- Continuous learning: A machine learning model improves as new data comes in, unlike static rules.
A concrete example
An e-commerce retailer uses a gradient boosting model to optimize prices across 50,000 SKUs. The model incorporates 80 variables: competitor prices, sales history, inventory, seasonality, price elasticity, and calendar events. Every night, it calculates the optimal prices for the following day to maximize margin while adhering to minimum price thresholds and price positioning. A/B testing shows a 6% increase in incremental margin compared to the old rules-based engine.
How to measure/use it
Implementing ML pricing requires: 1) clean, rich historical data (at least 12 to 24 months), 2) a data science team (in-house or via a vendor solution), 3) a production deployment infrastructure (MLOps), and 4) a governance framework to validate recommendations and measure performance. Modern pricing analytics solutions incorporate these components and enable the industrial-scale deployment of ML without the need to build an in-house data team.
Common Mistakes
- Blindly trusting: An ML model can produce absurd recommendations, so a human safety net is essential.
- Underestimating the importance of data quality: 80% of the time spent on an ML project is devoted to data cleaning and preparation.
- Forget about explainability: a "black box" model that isn't justified is poorly received by category managers, which hinders its adoption.
Learn more
- Research & Data: Sales forecasting using AI and predictive modeling.
- Solutions: Pricing Analytics powered by machine learning for the retail industry.
- Tip: Pricing training to help your teams understand machine learning and its limitations.
- Resources: Check out our pricing FAQ to learn how machine learning fits into modern pricing.