Many microeconomic models are based on store-level data and can be biased when the same model is applied to retailer or market level data. This paper shows the fix we use to debias the estimates.

A common theme in the marketing literature is the acquisition and retention of customers as they trade-up from inexpensive, introductory offerings to those of higher quality. Standard models of choice, however, apply to narrowly defined categories for which assumptions of near-perfect-substitution are valid. We extend the non-homothetic choice model of Allenby and Rossi (1991) to accommodate effects of advertising, professional recommendation and other factors that facilitate the description and management of trade-up. The model is applied to a national study of an over-the-counter health product.

In this paper, we provide an economic model of demand for substitute brands that is flexible, parsimonious, and easy to implement. The methodology is demonstrated with a scanner panel data set of light-beer purchases. The model is used to explore the effects of price promotions on primary and secondary demand, and the utility of product assortment.

Maximum Difference scaling (MaxDiff), a technique for measuring the importance or preference of multiple items, is shown to provide results that have greater between-item and between-respondent discrimination, and greater predictive accuracy than either monadic ratings or paired comparisons. Steve Cohen describes the methodology and presents results for a methodological study comparing MaxDiff measurement with monadic ratings and paired comparisons, and also a case study focusing on using MaxDiff for segmentation work. Steve won the "best presentation" award with this paper at the 2003 Sawtooth Software Conference.

This academic paper was sponsored by Mark Garratt when he worked for Miller Brewing Company. The paper uses the Bayesian upper model to link motivations/need-states in various social occasions to the preference for different brands of beer.