“Weighting” for the Right Attributes

Traditional penalty analysis enables you to more completely analyze a product’s attributes and provides a way to help guide the development of an optimum product. PenaltyPlus™ adds a new dimension to traditional penalty analysis (sometimes referred to as mean-drop analysis), and InsightsNow applies to all appropriate CLTs or HUTs using JAR scaling.

PenaltyPlus is an advanced method that incorporates a better filtering mechanism through the use of advanced significance testing and assigning a weight to the penalty.  Not only does it score for optimum attributes across all the respondents, it can determine niche products whose attributes that may appeaal to certain clusters in the population.

The method is enhanced through a user-friendly graphical presentation that makes it easy to interpret the findings.

Why “Weight”? 
Penalty analysis is used by market researchers and product developers to gain an understanding of the product attributes that most affect liking, purchase interest or any other product-related measure. Product attributes are measured using “just-about-right” or JAR scales. These scales distribute responses from “too little” and “too much” with “just-about-right” being the target. in the center.

Penalty analysis compares the overall mean liking score for any given product attribute to the “just-about-right” for the same attribute. The deviation between these two means is the penalty. With PenaltyPlus, significance testing is also incorporated and a penalty weight assigned. The weight provides a clearer distinction for which product attributes are significantly different and should be adjusted. 

Penalty Plus eBook

In our eBook, we explore two kinds of penalty testing: Grand Mean or JAR Mean Penalties, and discuss the pros and cons of each kind of testing. We also look at significance testing, and examine several methods that may be used to apply statistical testing to penalties. And it’s important to keep in mind that all these tests must have real-world significance—what are the safeguards you can employ to make sure your data is the best it can be? Read on to learn more about best practices and recommendations regarding penalty analysis, so you can understand how to apply it to your next project.