Brands armed with new products have always rushed to be first to market, as first movers often establish a stronghold that can be difficult for later entrants to break into. But being “first mover” at the expense of being “best mover” can often lead brands to competitive disadvantage.
For example, if you asked Americans today to name the first Greek yogurt brand that comes to mind, most would say Chobani—despite the fact that Fage beat Chobani to U.S. shelves by nearly a decade. Whereas Fage had established itself as a Greek yogurt for niche, health-conscious consumers, Chobani targeted the masses. Instead of focusing on plain yogurt or family-sized containers, Chobani offered single-serve yogurts in mainstream flavors—a familiar offering in a familiar package, but with the added protein and thicker texture of Greek yogurt.
Another example is Tagamet, an over-the-counter anti-acid medication that enjoyed considerable success before Zantac was launched. However, Zantac caused fewer side effects and could be taken less frequently—and, given these advantages, it eventually eclipsed its predecessor.
While speed to market certainly has its benefits, it clearly isn’t everything. As futurist Joel Barker quipped, “Speed is useful only if you are running in the right direction.”
CPG brands must strike a balance between being “first mover” and “best mover.” They must move fast, but not so fast that they fail to refine their innovation adequately prior to launch. Brands need predictive and actionable consumer analytics at various stages of product development. In other words, brands need to know if their product is good enough to meet the standards required for in-market success (trial, repeat, etc.) and, if it isn’t good enough, what levers they can and should pull to make it more likely to succeed in market. But they need it more quickly than they used to. We at Nielsen have introduced an accelerated version of our established concept-testing business to provide just that.
First, how do you know if your product is good enough? Since there are many contributors to product success, you need a measurement standard that is both predictive and precise. Nielsen’s “Factors for Success,” a long-established offering, scores innovations on twelve key dimensions such as consumer need/desire, distinctiveness of the proposition, advantage over existing products, etc., all of which are necessary to succeed in market, and all of which can be improved in one way or another(1). With this information, brands know exactly what and how to maximize their innovation’s potential prior to launch. Innovations that meet Factors for Success criteria have a 75% chance of succeeding in market. By comparison, most innovation testing models only require that an innovation score highly on consumer purchase intent to be deemed launch-ready—but this criterion yields only a 46% chance of success.
Arming marketers with precise refinement insights pays off. For example, a large food manufacturer had been struggling with low innovation success and growth rates. By embedding predictive analytics across key stages of their innovation process (including concept and product testing), they were able to establish consistent standards for success so as to screen out bad ideas early. But, just as importantly, they armed marketers with better and clearer direction for improvement for ideas that showed high potential. As a result, the manufacturer moved from being ranked No. 13 in revenue from innovation across the industry to No. 2.
Of course being good enough isn’t good if you get beaten to market. More than ever, brands need to get to good, fast. Today, there are more competitors in the market, and there is also great pressure to be more “agile,” releasing products sooner and refining them through “versioning,” rather like companies in Silicon Valley. How do you get quality and speed together?
Actually, it’s been a challenge. Many fast innovation testing models have popped up in recent years to help researchers—with 40% of market researchers reporting that they’ve used them. But the data produced is often unreliable or fails to provide the depth of insight marketers need to make informed decisions. In fact, 69% of researchers who had used fast concept testing solutions were unsatisfied with the quality and predictive reliability of the data they received. And most respondents agreed that a significant level of reliability is essential; 75% said that the predictive accuracy of the data they receive is the top reason to choose a concept testing vendor(2).
To address the speed challenge without sacrificing quality analytics. Nielsen recently applied a number of speedier approaches to our concept testing solutions, improving three time-consuming aspects of concept testing, and reducing the overall time required by two-thirds, to a matter of days:
More effective collaboration and faster input: From our research, we know that marketers spend a lot of time building consensus on what exactly to test when it comes to a new product’s value proposition, including: core consumer insights, product features, claims, benefits, ingredients, varieties and more. For a high profile initiative, everyone from the president to finance to R&D might weigh in. In fact, market researchers report spending 43% of their time preparing and aligning on study inputs(3). Not surprisingly, 29% feel that getting internal alignment on the concept and study inputs is the number one task that should be easier than it currently is. (68% of researchers ranked it in the top three)(4). Software can speed up this collaboration process by getting everyone to provide feedback in one central place online—instead of across disparate email chains and documents—which makes it easier to synthesize and curate the comments.
But it’s not just about input or signoff. Real collaboration has also been shown to greatly enhance innovation success—yet another reason to make the process easier and less time-consuming with technology. In our study of CPG initiatives, we found that concepts created by teams with six or more members were significantly preferred by consumers to concepts created by teams with two or fewer members. Teams that included functions beyond marketing and insights—such as R&D and finance—also generated concepts with greater appeal than those with less functional diversity.
Consistent data collection and analysis: Software performs the job of standardizing and automating many aspects of data processing. Once you’ve established the metrics and data outputs required to guide good decisions—and the process to get these insights produced at quality standards—software can minimize the manual intervention that often slows the process down and leads to human error.
Dynamic data analysis and reporting: The purpose of testing and forecasting a product concept is to see if there is a financial case for investment. If there is, the brand manager needs to prove to the President or Finance that an innovation will yield positive returns that justify the cost of manufacturing and marketing. In the process, the brand manager will typically spend 20% of their time on analyzing, interpreting, charting and reporting on data—on top of what they have already paid the research vendor, who has been doing the same thing before handing a deliverable off to its client (5). And yet the brand manager still rarely finds herself with the exact analysis that would best make the case. New, dynamic, digital reporting tools make it possible for the brand manager herself to query the data iteratively in order to get to the best possible case, and to present the data in a format that summarizes key findings at the highest level, while also enabling any party to click through to more in-depth analyses. In this way, all the relevant stakeholders are able to access the information they need, and decisions are made much more quickly.