Machine Learning and Quantitative Methods for Intelligence Augmented Pricing - Tim Smith

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Problem: Managing price differences between transactions and customers requires both management insights and quantitative metrics. 

Solution: With machine learning and quantitative methods, this course demonstrates how relevant data-based facts can be developed to drive intelligence augmentation pricing decisions.

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.  

Course Details

Welcome
PPT Presentation
Transcription
Session
Module 1
Module 1 - Review Quiz
Session
Module 2
Module 2 - Review Quiz
Session
Module 3
Module 3 - Review Quiz
Session
Module 4
Module 4 - Review Quiz
Session
Module 5
Module 5 - Review Quiz
Session
Module 6
Module 6 - Review Questions
Session
Module 7 - Part 1
Module 7 - Part 2
Module 7 - Part 3
Module 7 - Part 4
Module 7 - Part 5
Module 7 - Part 6
Module 7 - Part 7
Module 7 - Review Quiz
Session
Module 8
Module 8 - Review Quiz
Course Assessment
Final Quiz
Course Survey
Machine Learning Survey
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