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Printable Handouts
Navigable Slide Index
- Introduction
- Talk outline
- Bayesian theory and informative priors
- Characterizing the Bayesian approach
- Visualizing the prior
- Subjectivity and prior beliefs
- Where does the prior come from?
- Statistical models for making pricing decisions
- Goal
- Weekly movement and price of TropPrem64
- Movement vs. price of TropPrem64
- Movement vs. price of TropPrem64 - log plot
- Sales response models
- Applications of this model
- Hierarchical Bayesian models
- The problem with the usual regression approach
- Our solution: shrinkage
- Rewriting our model in vector notation
- A Bayesian version of our model
- Hierarchical Bayesian setup
- Our Bayesian setup
- Implication: shrinkage estimators
- What are we shrinking towards?
- Illustrating how shrinkage works
- Out-of-sample predictions
- Store-level strategies
- Movement vs. price in two Chicago stores
- Profitability of TropPrem64 in two stores
- Store-level national brand / store brand price gaps
- Findings
- Theory based priors
- Informative priors
- A typical sales response model
- Additive utility structure
- A prior based upon a simple additive utility model
- Our approach to specifying the prior
- A simulated example
- Implementation
- Adaptive shrinkage
- Demonstrating adaptivity with a simulation
- Application to retailer scanner data
- Comparison of goodness-of-fit
- Goodness-of-fit for various estimators
- Marginal prior distributions
- Elasticity estimates - unrestricted least squares
- Elasticity estimates - Bayes
- Conclusions (1)
- Eliciting priors using experts
- Constraints as business rules
- Constraints represent prior information
- Example
- Practical problem
- Log-linear demand example
- Computing the implied prior (1)
- Computing the implied prior (2)
- Visualizing the implied prior
- Solution
- Another example
- Implied prior
- Distribution of optimal prices for various priors
- Empirical application
- Rejection sampling
- Implied prior of own-price on TropPrem64
- Impact on the posterior of own-price coefficients
- Posterior distribution of optimal price
- Conclusions (2)
- Final recommendations
- Suggestions
Topics Covered
- Bayesian theory and informative priors
- Using statistical models for making pricing decisions
- Sales response models
- Exchangeable priors: hierarchical Bayesian models
- Implication: shrinkage estimators
- Store-level strategies
- Theory based priors
- Informative priors
- Additive utility structure
- A simulated example
- Elasticity estimates
- Eliciting priors using experts
- Constraints as business rules
- Empirical application
- Rejecting sampling
- Final recommendations
Talk Citation
Montgomery, A.L. (2010, January 28). Making better pricing decisions with informative priors [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved December 12, 2024, from https://doi.org/10.69645/XWRD1076.Export Citation (RIS)
Publication History
Making better pricing decisions with informative priors
Published on January 28, 2010
75 min