In this course, students will learn that managing price difference between transactions and customers requires both management insights and quantitative metrics (The Problem). This course will demonstrate how that with machine learning and quantitative methods, relevant data-based facts can be deployed to drive intelligence augmentation pricing decisions (The Solution).

Learning Objectives

1. Define trade-offs between price variances and volume growths through the volume hurdle developed from various scenario analysis
2. Optimize prices with elasticity while acknowledging the pitfalls of a machine learning only approach which is best overcome through the symbiotic use of human insight in an intelligence augmented decision approach
3. At a qualitative level, define the pros and cons of price variances to enable managerial trade-offs
4. At a quantitative modeling level, demonstrate the economic pros and cons of price variances and discuss the expected shape of the product specific demand curves
5. Visualize price variances in the four most-useful different plotting techniques
6. Conduct machine learning using Excel for intelligence augmentation on a sample data set
7. Identify the three key management tools to constrain price variances towards profitable customer relationships
8.