• 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. Agenda
3. Recurring themes in this talk
4. Examples
5. Joint choice models
6. Preference revision and preference concession
7. Data collection challenges
8. Our solution
9. Our modeling framework (1)
10. Initial preference
11. Preference revision (1)
12. Preference revision (2)
13. Preference revision - 4 possible models
14. Concession through utility aggregation
15. Study context
16. Cell phone attributes and levels
17. Data collection flow chart
18. Measuring stated initial preference
19. Conjoint task: initial preference
20. Manipulation of preference
21. Manipulation check
22. Conjoint task: to assess preference revision
23. Joint choice task
24. Sample profile
25. Stated initial preference (1)
26. Stated initial preference (2)
27. Actual vs. manipulated preference congruence
28. Possible models
29. Parameter estimates
30. Preference revision for low price ($79)
31. Our modeling framework (2)
32. Revised preference
33. Predictive tests
34. What if dyadic data are NOT matched?
35. Predictive performance unmatched case
36. Model extensions
37. Summary
38. Conclusions
39. Acknowledgements
40. References

Topics Covered

• A novel methodology to estimate joint preference
• No need to collect joint data, individual data is enough
• Merges experimental design, statistical modeling and utility aggregation theories to measure joint preference
• Identifies serious limitations of individual preference as a surrogate for joint preference
• An empirical application involving cell phone purchase by parent teen dyads
• Applies to B2B domains

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010