Clustering Concepts
Abstract:
There is great interest in the accumulation of information concerning the performance of concepts. This information, typically in the form of some measure of purchase likelihood coupled with marketing information, is used as the basis for models which predict future product profitability from concept performance. Predictive ability improves considerably with the quality, breadth and relevance of the accumulated information. However, many researchers may have scant data that can be used with these models: potentially many recently surveyed concepts, but few brought to market, and little idea as to levels at which to set marketing variables. In lieu of these data, a simple approach to identifying successful concepts will be presented which relies only on some measure of purchase intent. The techniques employed here will not estimate sales or ultimate product profitability. Rather, the goal is to simply identify some subset or cluster of concepts which is "better" (evaluated more positively) than others. A statement of statistical significance of the distinctiveness of these clusters is also available.
