Richard M. Johnson is the Founder and Chairman of Sawtooth Software, Inc. Rich has spent the greater part of the last four decades working in the field of marketing research. He is widely cited in the conjoint literature, and is credited with the development of trade-off matrices and the ACA System for Adaptive Conjoint Analysis.
What is your opinion about the current relationship between academics and practitioners in marketing research?
We are all aware that there is quite a gulf between academics and practitioners. Most practitioners are simply unable to understand the contents of most of the relevant journals today, and, sadly, they don't believe they're missing much.
In one sense, Sawtooth Software has profited from this gulf. We stand with one foot on each side, and one of our roles is to translate and convey information from the academic world to the practical world. To a lesser extent, we also transfer information in the other direction, by bringing academics and practitioners together with our Sawtooth Software Conferences.
I think there is an unsatisfied market for journals with important and useful articles, written in a way that seems relevant to practitioners. At our conferences we observe the dictum that every presentation must show promise of providing benefit to the least sophisticated listener, and yet contain something of interest to the most sophisticated. The journals would profit from observing that principle as well.
I think this is an important problem, not only because a lot of good academic work is not finding its way into practice, but also because academics need help from practitioners in identifying problems that are really important, as well as interesting to work on.
What do you feel are the most exciting new developments in conjoint analysis?
Without a doubt, the most interesting current developments are ways to estimate individual utilities from choice studies. Many researchers think choice questions mimic product decisions that respondents make in real life, so there has been a rapid growth in the use of choice designs. However, since choice data are less efficient than other conjoint methods, it hasn't been possible to estimate utilities for individuals. Until recently, it has been necessary to do aggregate analyses, either by combining all respondents or by combining respondents into segments, as in latent class analysis. Of course, such aggregation necessarily assumes that individuals in a group are identical, which is almost certainly not true. Aggregate analyses always entail a serious risk of obscuring important differences among respondents.
Fortunately, there have been three promising developments recently. Huber and Zwerina found that they could estimate individual utilities from efficient, individually customized choice questionnaires. Lenk, DeSarbo, Green and Young found that individual utilities could be estimated by a hierarchical Bayes method, as did Allenby, Ginter, and Arora. And we've been testing still another method of estimating utilities for individuals. Our method starts with a segmentation, such as produced by latent class, and then finds the unique weighted combination of groups' utilities for each individual that best fits his or her choice data. All of these new approaches find more heterogeneity among individuals than is commonly captured by recognizing market segments, and they can use this additional information to improve predictions.