Published: Jan 2009
| ||Format||Pages||Price|| |
|PDF (284K)||4||$25||  ADD TO CART|
PRIMO (Product Improvement Opportunity) analysis is a method whose initial step is penalty analysis (see Appendix C), followed by the application of Bayesian theory to identify desired changes in JAR attribute ratings in order to maximize the probability of potential product improvement, according to a given criterion. The criterion can be either categorical, such as Top or Top Two Box scale ratings, or continuous, such as mean Overall Liking. For each attribute, this analysis provides a confidence level that the product can be significantly improved with respect to the chosen criterion, by altering the current level of the attribute. The resulting output is a list of JAR attributes and their respective confidence levels, in descending order, corresponding to the potential product improvement. As a result of PRIMO analysis, researchers gain an understanding on how to alter the product on the attributes that promise a high confidence level for improvement. This procedure puts PRIMO Analysis in the class of Bayesian choice models.
PERT Survey Research, Bloomfield, CT