• 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. Source of volume and cannibalization
3. Product and line optimization questions
4. Quick definitions
5. Red bus / blue bus example
6. What is IID? and IIA?
7. Variables we measure and variables we don't
8. We know IIA/IID gets source-of-volume wrong
9. When should we care about substitution patterns?
10. Is it really a problem?
11. Source of volume calculations
12. Cannibalization = Preference
13. Haven't we solved this one?
14. How do you "handle" IIA in your choice models?
15. Simulating preference shares: state-of-the-art
16. What does proportional substitution look like?
17. We have a problem
18. Industry survey: how do current methods do?
19. What do we need?
20. The SoV Probit (1)
21. A very brief introduction to Probit models
22. Why should I care about correlated errors? (1)
23. Why should I care about correlated errors? (2)
24. Why aren't we all using Probit models?
25. The SoV Probit (2)
26. A structured covariance Probit
27. Distance metric 1
28. Distance metric 2
29. Distance metric 3
30. Distance metric 4
31. Choice experiment
32. Example conjoint task
33. Model fit statistics
34. Parameter estimates
35. WTP comparison
36. Representative correlation matrix
37. Does it work? - For a representative individual
38. Does it work? - For a heterogeneous population
39. Success
40. Summary and implications
41. Thank you!

Topics Covered

• Source of volume and cannibalization
• Product and line optimization questions
• Quick definitions
• Red bus / blue bus example
• What is IID? and IIA?
• We know IIA/IID gets source-of-volume wrong
• When should we care about substitution patterns?
• Source of volume calculations
• Cannibalization = Preference
• How do you "handle" IIA in your choice models?
• Simulating preference shares: state-of-the-art
• What does proportional substitution look like?
• Industry survey: how do current methods do?
• The SoV Probit
• Why should I care about correlated errors?
• Why aren't we all using Probit models?
• A structured covariance Probit
• Distance metrics
• Choice experiment
• Model fit statistics
• Parameter estimates
• WTP comparison
• Representative correlation matrix
• Does it work?

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on March 16, 2010