Replicating actual market decisions in a survey choice task is an important design goal. However, given that survey respondents have moved to devices with smaller screens, it has become increasingly difficult to accomplish the goal of realism.

We need to design the choice exercise to allow respondents to make purchase decisions as they would in the marketplace

Choice modeling techniques should be recommended first to assess product pricing. When clients are budget-strapped and time-pressed though methods, such as the Gabor-Granger analysis, can provide sufficient guidance

Choice modeling makes it possible to simulate the decision-making process so a new product's sales revenue can be predicted. Choice modeling helps marketers understand the many variables that underlie that forecast.

Calibration of survey-based choice models can make a substantial difference in predicted demand and revenue resulting from price changes.

Choice models are valuable in assisting marketers in decision making. This article explains the benefits choice analysis: Improved experimental design algorithms; Latent Class and Hierarchical Bayes models; and Model calibration.

Messaging and positioning choice modeling is recommended when the primary research objective is to obtain information that would allow a company to develop the most effective communications message to consumers, maximizing attraction to its specific brand, product line, store, or department within the store.

The purpose of this article is to share some research and analytic ideas to improve the process of Category Management. The focus of this article is the automotive aftermarket.