Lilly Tech Systems
AI Dynamic Pricing
Master machine learning-driven pricing strategies that maximize revenue in real time. Learn elasticity modeling, demand forecasting, competitive pricing intelligence, and systematic A/B testing approaches to optimize every price point.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
The landscape of AI-driven pricing: how machine learning transforms static price lists into dynamic, revenue-maximizing strategies.
2. Pricing Models
ML algorithms for pricing: regression models, reinforcement learning, and neural networks that learn optimal price points from data.
3. Demand Forecasting
Predict demand curves with time-series models, elasticity estimation, and seasonal pattern recognition for smarter pricing.
4. Competitive Pricing
AI-powered competitive intelligence: scraping, monitoring, and responding to competitor prices in real time.
5. Implementation
Deploy dynamic pricing systems: A/B testing frameworks, guardrails, real-time optimization engines, and integration patterns.
6. Best Practices
Ethical pricing, fairness constraints, regulatory compliance, and building customer trust in algorithmic pricing.
What You'll Learn
By the end of this course, you'll be able to:
Build Pricing Models
Implement ML algorithms that automatically determine optimal price points based on demand, competition, and customer segmentation.
Forecast Demand
Use time-series analysis and elasticity modeling to predict how price changes will affect sales volume and revenue.
Monitor Competition
Deploy AI systems that track competitor pricing in real time and automatically adjust your prices to maintain market position.
Test & Optimize
Run rigorous A/B tests on pricing strategies and use reinforcement learning to continuously improve pricing decisions.