In the world of innovation, there’s a clear line of separation between a concept and a product. A concept represents what you plan to offer; it’s a helpful tool for prioritizing features and claims and for determining how to communicate the product’s benefits. It also informs ideal price points and which varieties will be needed to drive trial. On the other hand, a product is a tangible object that consumers purchase and use; its long-term success (i.e., repeated purchasing) relies heavily on the experience that consumers have with it.
When testing innovations, it’s risky to ask consumers to compare a new concept against an actual product that they currently purchase. This unbalances the entire evaluation by setting up an unfair comparison. As a new idea, a concept lacks the dimension of experience—and it doesn’t benefit from advertising or marketing in any way. An in-market product is already familiar to consumers, and it has built equity that lends to its clarity and credibility. By contrast, a concept has none of these advantages. Therefore, pitting existing products against concepts unfairly asks consumers to compare something with tangible, experienced benefits to the mere promise of a good experience. In new markets where there are not many concepts to compare against, it may be necessary to include a product benchmark; however, in most cases, this is not necessary or advisable.
Another problem with concept-to-product comparisons is that, by nature, concepts are “fuzzier” in consumers’ minds. New propositions can be misunderstood and lumped into categories were they don’t belong, and the actual competitive set may be drastically different than consumers anticipate. Additionally, research from the Ehrenberg-Bass Institute for Marketing Science supports the notion that brand loyalty is often low, so asking consumers to single out one specific product as a point of comparison does not reflect the reality of how people make purchasing decisions.
So how can companies evaluate their innovation initiatives without comparing concepts against products? That’s where the value of a concept database comes in—this is a collection of new product concepts that have been evaluated by consumers before the products were developed and eventually launched. By comparing the scores of new test concepts against those in the database, companies can eliminate biases related to product experience and marketing awareness that are problematic with concept-to-product comparisons. In short, concept databases provide a much more reliable benchmark for comparison.
That said, not all concept databases are created equal. They should contain many concepts to ensure that a brand’s particular category and market are well represented. (Nielsen’s database currently contains more than 200,000 concepts). However, even more important than the size of the database is what it measures. A database that measures concepts against purchase intent, for example, will be only loosely correlated with in-market success and therefore not a reliable gauge of how a new product will perform in market. To get a better read, companies should select databases that are calibrated to in market success, such as Nielsen’s concept database. Over a five-year period, we studied more than 600 successful new product initiatives and identified the attributes that contributed to consumer adoption. Then, we linked the scores of concepts in our database to these same attributes, enabling us to evaluate new concepts against in-market success factors. Currently, Nielsen’s database is the only one in the industry correlated with in-market outcomes. As a result, it’s the most predictive of success. A concept that performs well in Nielsen tests has a 75% chance of succeeding in market.
Of course, product testing is a critical step—but, until you get to that phase, it’s essential to let concepts compete fairly by testing them against other concepts. A well-calibrated comparison allows brands to get key trial-driving components down first before moving onto other elements. Moreover, when choosing a concept database, ensure that its metrics are strongly correlated with in-market success for the most accurate read.