• Fundamentals
• 34 min
  1. Bayesian essentials and bayesian regression

  • Prof. Peter Rossi
• 28 min
  2. Introduction to MCMC methods and the Gibbs sampler

  • Prof. Peter Rossi
• 53 min
  3. Hierarchical models, conditional independence and data augmentation

  • Prof. Greg M. Allenby
• 61 min
  4. Metropolis algorithms, logit and quantile regression estimation

  • Prof. Greg M. Allenby
• 52 min
  5. Unit-level models and discrete demand

  • Prof. Greg M. Allenby
• 65 min
  6. Heterogeneity

  • Prof. Greg M. Allenby
• 27 min
  7. Model choice and decision theory

  • Prof. Peter Rossi
• 28 min
  8. Bayesian instrumental variables and simultaneity

  • Prof. Peter Rossi
• Applications
• 43 min
  9. The value of HB in conjoint/choice analysis

  • Mr. Bryan K. Orme
• 41 min
  10. The SoV Probit

  • Dr. Jeff Brazell
• 56 min
  11. Trade-up and trade-down: models for capturing change in consideration set

  • Mr. Mark Garratt
• 37 min
  12. Bayesian modeling of social network data

  • Prof. Asim Ansari
• 52 min
  13. Joint choice decisions

  • Prof. Neeraj Arora
• 47 min
  14. Estimating an item's category role

  • Dr. Peter Boatwright
• 39 min
  15. Bayesian stochastic dynamic models for internet auctions

  • Prof. Eric T. Bradlow
• 42 min
  16. Hierarchical effects of advertising

  • Prof. Sandeep R. Chandukala
• 41 min
  17. Investigating the strategic influence of customer satisfaction on firm financial performance

  • Dr. Jeffrey Dotson
• 45 min
  18. A Bayesian approach to attribute based consideration sets

  • Prof. Timothy J. Gilbride
• 27 min
  19. Variety: models of multiple-discreteness

  • Prof. Jaehwan Kim
• 33 min
  20. Models for upper levels of a hierarchy

  • Dr. Qing Liu
• 75 min
  21. Making better pricing decisions with informative priors

  • Dr. Alan L. Montgomery
• 36 min
  22. Marketing mix modeling

  • Prof. Thomas Otter
• 45 min
  23. Reporting bias in survey data

  • Dr. Sha Yang

Printable Handouts

PDF

Navigable Slide Index

1. Introduction
2. Introduction to mix modeling
3. The role of managers
4. What about the standard tools?
5. Simple example no. 1
6. Simple example no. 2 (1)
7. Simple example no. 2 (2)
8. More realistic example
9. Analysis for multiple decision units (1)
10. Analysis for multiple decision units (2)
11. Analysis for multiple decision units (3)
12. Simulation study
13. Simulation results
14. Gains from reallocation
15. Summary of introduction
16. Modeling challenges
17. Modeling marketing expenditures and outcomes
18. Modeling strategy - demand equation
19. Modeling strategy - allocative errors
20. Modeling strategy - supply-side likelihood
21. Estimation strategy (1)
22. Estimation strategy (2)
23. Testing for the supply-side model
24. Non-informative vs. flat priors
25. Predictive test
26. Application
27. Inputs
28. New customers per 1000 population
29. Inputs per 1000 population
30. Analysis
31. Model fit - multiplicative model
32. Estimates
33. Hierarchy of effects
34. Conditional allocation: promotion given branches
35. Summary
36. Acknowledgements and references
37. Full list of references used in the paper
38. Thank you

Topics Covered

• The role of managers
• What about standard tools?
• Simple example
• More realistic example
• Simulation study
• gains from reallocation
• Modeling challenges
• Modeling strategy
• Estimation strategy
• Testing for the supply-side model
• Non-informative versus flat priors
• Predictive test
• Application
• Analysis

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010