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5 MR Fundamentals For Superior Product & Concept Testing

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Concept testing is nearly always part of a new product launch initiative—an important part, to be sure—but not the end all be all.

When done correctly, product or concept testing can provide incredibly helpful insights into what your customers want.  Proper testing can anticipate what customers are likely to purchase, whether it happens to be some version of the new product or the same old one in a new, shiny wrapper. 

Concept testing is a foundation that you can build on with the rest of the product development you’re doing.

Here are five fundamentals to keep in mind when you next embark on product and concept testing research.

1 - Monadic Testing
 
If you have 5 new concepts, each respondent in the study should be asked to evaluate only one of them.  There should also be a control concept: typically the current product or a competitor’s version.
 
When you show a respondent more than one concept, the comparisons become a beauty contest and not a science. Going to market on which product people like better is a mistake. The degree to which something is liked is a good metric (also known as hedonic scaling), but that’s only one of the elements that we suggest you measure. Further, by showing the respondent a single concept, you get more precise information about how people are reacting to that specific concept (and can compare this to your control) without introducing any bias or noise associated with subjective comparisons to a sometimes confusing array of choices.   Individual concept performance against the control is a very solid, predictable analysis in a monadic environment.
 
2 - Measure Purchase Intent with Caution
 
This is pretty much always overstated by respondents.
 
There are some steps we can take to mitigate this overstatement of purchase intent through a modeling algorithm based on multivariate regression.  You can have some refined purchase intent information, comprising the key metrics used in a concept test, to come up with a predictive composite value.  The highest composite score relative to the control wins.
 
3 - Measure the Attributes that Drive Trial
 
When a consumer tries something for the first time, it’s not an accident.
 
Take into account high believability of product claims and its uniqueness. To establish a basis of comparison, use competitor concepts or the existing product on the market as a control.  Pick the design that has the best chance of first-purchase success and, at the same time, alienates the fewest current customers who currently buy the existing product.
 
4 - Measure the Appropriate Product Attributes
 
Use statements couched in a Likert-scaled phrase like “On a scale of 1 to 7, how much do you agree with the following statements…”
The statements should be articulated directly toward the product (e.g. freshness, whether family wants to eat it, brand I trust, good value for the money, etc.).  The proper statements will help differentiate between the concepts and will ultimately drive your marketing message.
 
5 - Compare the Data
 
Report individual pieces of data for comparison.
 
Your analysis will be often less about the absolute value of the scores of the products than it is the deltas (differences) between the scores.  Don’t be surprised when the existing product (your control)—which has been in consumers’ consciousness for a much longer period of time than the 10 minutes of this survey—wins; this is very common in concept testing.  We’re looking for the best new concept performance which will approximate that of the control and importantly, outperform competing products.

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Dino Fire
Dino Fire
Dino Fire

Dino serves as President, Market Research & Data Science. Dino seeks the answers to questions and predictions of consumer behavior. Previously, Dino served as Chief Science Officer at FGI Research and Analytics. He is our version of Curious George; constantly seeking a different perspective on a business opportunity — new product design, needs-based segmentation. If you can write an algorithm for it, Dino will become engaged. Dino spent almost a decade at Arbitron/Nielsen in his formative years. Dino holds a BA from Kent State and an MS from Northwestern. Dino seems to have a passion for all numeric expressions.