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1. Introduction
2. Research context: retail product categories
3. Inventory management
4. What items should retailers stock?
5. The impact of a SKU on a category
6. Background and existing research
7. Focus of this research
8. Why a Bayesian model?
9. The data overview
10. Average quarterly sales per store per item
11. The range and average percentage of stockouts
12. Basic model
13. Com-Poisson vs. Poisson
14. Data augmentation for stockouts
15. Item characteristics and store characteristics
16. Incorporating substitution effects
17. Accounting for item impact on category
18. Model summary
19. Sales are restricted by stockouts
20. Model estimates (1)
21. Model Estimates (2)
22. Sales share vs. demand share
23. Cross-effects: color
24. Cross-effects: size
25. Category effects
26. Category effect estimates
27. Simulation
28. Item share vs. net stockout impact on sales
29. Deconstruction of the net category impact
30. Summary of key findings
31. Two takeaways
32. Reference and acknowledgement

Topics Covered

• Product categories introduced
• How individual items influence category sales
• Overview of data structure as lead-in to model
• Model development
• Model estimates and results

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on February 16, 2010