1. A Logit model of brand choice calibrated on scanner data
Peter M. Guadagni and John D.C. Little

2. Introduction
Investigate the effect of marketing variables (i.e., price, promotion) on consumer choice among product alternatives
Why do households purchase certain products?
Explanatory variables: brand loyalty, size loyalty, store promotion, regular/promotional price
Use scanner data on coffee purchases
Record individual level purchase
Low cost- store already collecting
Provide competitive environment of customer decision (vs. diary or warehouse data)
Multinomial logit choice model

3. Scanner Data
Relatively new at the time allowing for detailed understanding
Three types:
Groups of stores
National samples of stores
Instrumental markets
Current paper focuses on instrumental markets
Small/medium city with scanners in major grocery stores
Ability to split how cable company advertises
Full competitive picture: sales, prices, promotions, advertising, coupons, display, shelf- facings

4. Applied Research Question
Understand and predict consumer behavior with 100 households over a year with coffee brand

5. Multinomial Logit Choice Model
Computes probability of choosing an alternative of all available alternatives
Used previously in other marketing papers
Probability consumer buys a vehicle at certain dealership (Hlavac and Little, 1966)
Students’ choice of business schools (Punj and Staelin, 1978)
Predict mode of travel (Domencich and McFadden, 1975)
Basic Assumptions of Multinomial Logit
Individual has a set of alternatives (i.e., different products in a category)
Individual has a preference/utility (both containing observed/unobserved)
Individual chooses product with highest utility at that moment
Random component iid extreme value distributed

6. A few more details
This model will not be affected by constants, such as inflation
Figure 2 shows that very large/small values of utility make choice probability insensitive

7. Independence from irrelevant alternatives
Multinomial logit model satisfies this assumption
Violation will cause errors in probability of predicted choice
If you add an alternative, which is identical (almost) to an option already present in the set, they would be expected to affect only each other
Instead, it reduces probabilities of ALL alternatives
Tested in Appendix 1 and found not to be an issue here
In this case, the choice set alternatives were similar

8. Customer Utility Linear function
Contains both attributes of the product and customer/environment
Attributes unique to alternative k
Attributes common to all alternatives

9. Calibration Utilities and probabilities cannot be directly observed
Observe choice and attribute values
Choice is purchasing product at a specific time
Attributes have complete set of data and assigned a zero if not relevant
Calibrate both of these equations with maximum likelihood

10. Model Fit T- values for coefficients U2 for model
Logit computes probability only, need choice
“the fraction of uncertainty (entropy) empirically explained by the calibrated model relative to the prior distribution of choice probabilities” (p.33)
U2 =1, perfect
Chi-squared tests of model significance (add a parameter or no?)
Aggregate share (actual vs. expected share)

11. Data
Coffee frequently purchased, price change common, and has promotions
Four Kansas City supermarkets from September 14, 1978-March 12, 1980 (78 weeks)- about 2,000 households included
Household number, date of purchase, UPC, and price paid
Eliminated certain outliers such as light/nonusers of coffee
100 calibration, 100 test final model

12. Selection of Alternatives
Regular, caffeinated, ground coffee
Model brand-sizes (small vs. large by brand)

13. Apply the model Customer Utility Calibrate
Unique to alternative: Brand-size
Common across alternatives: regular price, promotional price, lagged promotion variables
Characteristics of customer: brand loyalty, size loyalty
Calibrate
Each purchase is an observation- cross-section and time-series data

15. Coefficients explanatory importance
Brand and size loyalty two most important variables
Promotion
Might not even need a price cut
Regular price, promotional price
Prior promotional purchase
More likely to purchase promoted brand-size because bought the first time BUT less likely than nonpromotional purchase

16. Testing Question #1
How well does the model predict brand-size share in the hold-out sample during the same time period used in the calibration?
Authors find the model performs well

17. Moves with the 90% confidence band, picks up upward and downward trend

18. Testing Question #2
How well does the model predict share during time periods subsequent to the calibration?
Authors must forecast
Loyalty variables an issue because depends on prior purchases to construct it- must construct a synthetic purchase history
Post-calibration tracking good

19. Moves with the 90% confidence band

20. Some issues though In the forecast period, actual purchases fall below
Appears model assumes customer loyalty persists after promotion but actual purchases do not
Directionality correct

21. Testing Question #3
Can the model, using constants derived from data that mixes together all stores, predict shares within individual stores?
Large supermarket, neighborhood market, warehouse store, conventional store
Calibration process did not take this into account
Model predicts well

23. Discussion
Multinomial logit model applied to the coffee market performed well at predicting market share
Model able to predict with the same variables across all brand-sizes and customers- good parsimonious theory
Model popular because of scanner data
Used successfully in real world application

24. Commentary Second highest citation prior to 1990 in Marketing Science
Impact on academia and practice
One of the first papers to use UPC scanner panel data
Commercial products developed and used based on paper