• 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. Overview of the study
3. Contents
4. Consumers purchase more than one product
5. The need for variety - explanations
6. Discrete choice model
7. Desirable utility model
8. Model of product offering
9. Utility specification
10. The shape of the utility function
11. Satiation parameters
12. Displacement parameters
13. Likelihood function - Kuhn-Tucker conditions
14. Likelihood function - summary
15. Data - yogurt category
16. Frequency of corner and interior solutions
17. Estimation (1)
18. Estimation (2)
19. Estimates of common parameters
20. Heterogeneity
21. Policy experiment
22. Compensating value
23. Implications for pricing policy
24. Model extension
25. Model of product characteristics
26. Utility specification - characteristic level
27. Salty snack category data
28. Data Collection
29. Estimation: heterogeneity
30. Results: characteristics parameter estimates
31. Elasticity of demand to characteristics
32. Conclusions
33. Applications of the proposed models for variety
34. Bibliography

Topics Covered

• Issue of multi-item buying behavior in scanner-panel data
• Preference and satiation in consumer behavior
• Economic theory for multiple-discreteness and challenges in model building
• How to build models at product offering level
• How to build models at product characteristic level
• Application of models to marketing decision
• Discussion on future direction for model extension

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010