This paper was first presented at the ESOMAR Congress in 2002.

The intent of this paper is to present practicing researchers with an innovative use of state-of-the-art tools to solve problems that are too often glossed-over. This paper critically examines the usual and standard tools and methods of benefit segmentation as practiced by most researchers and suggests new ways of renewing market segmentation through methods of measuring benefit importance and performing segmentation analysis on the resulting data.

The authors introduce Maximum Difference Scaling, a more powerful method for measuring benefit importance that is scale-free and thus very applicable to international segmentation research. The paper describes how Maximum Difference Scaling can be combined with Latent Class Analysis to obtain international benefit segments. Finally the paper describes an example of how these methods were used in a cross-national business-to-business study.

This paper won Best Paper at all ESOMAR Conferences in 2003.

Using Benefits Segmentation as our example, we compare two methods of measuring preferences for benefits and then extracting benefits segments. One method uses the traditional method of rating benefits on 5-point scales and then using Cluster Analysis to extract the segments. We also examine a newly introduced method, called Maximum Difference Scaling (MaxDiff), to provide the benefits ratings, and then we use a Latent Class Model to extract the segments.

Steve won the "best presentation" award with this paper at the 2003 Sawtooth Software Conference.

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.

This paper, co-authored by Steve and Bryan Orme of Sawtooth Software, won the David Hardin award for Best Paper in Marketing Research Magazine in 2004.

When it comes to scaling multiple items, researchers have several options. This article compares maximum difference scaling (MaxDiff) against monadic ratings and paired comparisons. Among other benefits, MaxDiff is flexible enough to be used with paper questionnaires and computerized interviewing pro­grams. MaxDiff also offers improved measures of discrimination across items over rating scales. The article discusses addi­tional benefits and weaknesses of all three methods.

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.

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.