"It's not terrible": Decoding what consumers really mean



Here's the situation. You've invested hundreds of hours in your new snacking product: its layers of flavor, its crunch, its nutritional profile, its lack of unwanted ingredients. Now the moment of truth: a consumer has tried it.

The verdict: "It's not terrible."

Ouch. Feedback is a gift, as they say in corporate-speak...just before you're fired.

But wait: What does that feedback actually mean? Is it bad? Or actually neutral? Or neither?

Decoding what consumers mean isn't easy. They are, after all, human...and humans are complicated. We say one thing and mean another. Our words and actions don't always line up. And yet, to understand your #innovation's full potential, understanding the consumer's voice is critical.

After 12 years of comparing consumer research to actual consumer behavior in market, I've concluded that context is king. What consumers mean depends on a host of factors: their mindset, their frame of reference, their expectations, the research environment itself.

In fact, if I were founding a new venture today, I would spend all of my early research resources on conversations with consumers -- as many of them as my budget and timeline could afford. No surveys. No quantitative concept testing. No big data (gasp!). Nothing other than conversations with real people about their unmet needs and how to address them.

Why so bold? Because it is critical -- especially early in an #innovation's life cycle -- to understand the problems it solves, as explained by consumers themselves, in a setting that gives you both verbal and non-verbal information. In other words, context. (For more on why understanding the problem matters, check this out.)

These kinds of qualitative learning opportunities are often denigrated as insufficient, because they lack predictive power. Nielsen and other companies have built very sophisticated (and often very expensive) research tools, used by the biggest consumer product companies in the world. Their argument: Statistical rigor is necessary for optimizing a product proposition and understanding demand. That means random sampling, large sample sizes, standardized questioning, sophisticated quantitative analysis, and so on.

And yet quantitative methods struggle to deliver the same depth as conversation. They can tell you what, but they often struggle to tell you why. Throughout my career, across several billion dollar brands, innovation failures often started with over-reliance on the what vs. the why.


So what makes context so valuable?

Let's return to the crunchy snack that earned a worrisome "not terrible" verdict. If you had been given that feedback in an online survey or quantitative study, you'd probably interpret it as negative, i.e., this consumer won't repeat his purchase, because no one pays money for "not terrible".

But here's more context. True story.

The consumer is a tweenaged boy -- my son, actually. We decided to give him a break from gluten for several weeks, as a sort of detox. Put another way: we tortured him. In his worldview, everything good in life has gluten, and #glutenfree substitutes are all terrible.

One day, I put a new kind of gluten-free cracker in his school lunch, and asked him to keep an open mind. I had been reading about Simple Mills, a fast-growing #Chicago-based #startup with a line of baked goods that are gluten-free, soy-free, and #paleo -- a combination that, frankly, usually leads to disaster. But Simple Mills had glowing reviews, and, importantly, distribution at #Costco. Costco's purchasing team genuinely cares about quality, and routinely rejects products that are just average.

So, when I asked my son what he thought of Simple Mills crackers, his mouth said, "It's not terrible"; however, his body language told a different story. Raised eyebrows, head nodding, lips bent in begrudging surprise. All of that meant, "The crackers were actually surprisingly good."

And he had eaten them all. Not a crumb left.

None of this context would have been available in a survey, quantitative study, or even a cursory conversation. I had to combine a wide variety of "data" to decode what he really meant and its implications for the product's potential. 

Far from negative, my son's comment made me wish that I owned equity in Simple Mills. They've built an exceptional product that defies expectations of even the stingiest consumer.


So what does this mean for #entrepreneurs and #innovators? Front-load your resource investment in context-rich methods that will deepen your understanding of the whys.

Does this mean there is no role for quantitative testing? Of course there is. But over-reliance is deadly, especially early in an innovation's life cycle. So, when every dollar counts, context matters most.