  The design of new products for consumer markets has undergone a major
transformation over the last 50 years. Traditionally, inventors would create a
new product that they thought might address a perceived need of consumers. Such
products tended to be developed to meet the inventors own perception and not
necessarily that of consumers. The social consequence of a top-down approach to
product development has been a large failure rate in new product introduction.
By surveying potential customers, a refined target is created that guides
developers and reduces the failure rate. Today, however, the proliferation of
products and the emergence of consumer choice has resulted in the
identification of segments within the market. Understanding your target market
typically involves conducting a product category assessment, where 12 to 30
commercial products are tested with consumers to create a preference map. Every
consumer gets to test every product in a complete-block design; however, many
classes of products do not lend themselves to such approaches because only a
few samples can be evaluated before `fatigue' sets in. We consider an analysis
of incomplete balanced-incomplete-block data on 12 different types of white
bread. A latent Gaussian mixture model is used for this analysis, with a
partial expectation-maximization (PEM) algorithm developed for parameter
estimation. This PEM algorithm circumvents the need for a traditional E-step,
by performing a partial E-step that reduces the Kullback-Leibler divergence
between the conditional distribution of the missing data and the distribution
of the missing data given the observed data. The results of the white bread
analysis are discussed and some mathematical details are given in an appendix.
