Monthly Archives: April 2014

Ooooh … It’s New and It’s Shiny


C-Suite folk and their minions, the marketing team, have always had a fascination with new and shiny things. This is especially true, it seems, when they look at marketing research technologies. A couple of years ago, I gave a talk at the MRMW/ US conference on the misuses and misrepresentations of eye-tracking, neuromarketing, and facial recognition. At MRIA and ESOMAR last year, I questioned whether Big Data is as big an issue as we seem to be making it. My goal in each of these talks was not to try and kill off these new tools. What I wanted to do was provide some guidelines for how we as an industry should be evaluating these new ideas. As innovation is the theme of a recent conference in Amsterdam and an upcoming one in Atlanta, it might be time to further explore these guidelines; especially because that’s what Lenny pays me to do (okay – he really doesn’t pay me much – an occasional promise of lunch).

The rallying cry that spurs this interest in new and shiny things is “Innovate or Die”. Here’s the problem – there’s no validation of this statement. While self-styled authorities such as Tom Peters have been saying “Innovate or Die” for years, Getz and Robinson (2003) actually studied the question of whether innovation leads to a long and happy business life. Their conclusion is simple – it’s not innovation that makes a company healthy, it’s the system they have for improving products. They point to Xerox, which may have been the most innovative company in the world in the 60s and 70s, and Alcatel, who was in the top three for communications technology in the 90s, as two of the great disasters of failed innovations. Their problem, according to the authors, was a set of innovations that nobody wanted.

Our tendency in marketing research is to take any new technology, hype its value beyond its original intent, then backpedal when we find out the emperor didn’t have as large a wardrobe as we thought. Neuromarketing has gone through this pattern in the last two years, with the extravagant claims of the charlatans largely disavowed by most in the business, even by those selling that stuff. We went through this last year with Big Data, when much of what was being promised turned out to be Big Hot Air. In both cases, we’re getting down to the real work of determining what we can learn from neuroscience and the analysis of large, semi-structured data sets and the perimeters around those tools. The problem remains though; marketers and executives seem to believe these are critical factors in their business and when we can’t deliver, it’s the research industry that takes the hit.

You can’t blame the executives for this [much]. They shouldn’t be expected to understand the intricacies of research in the same way we researchers often don’t understand the rules of accounting, the science behind logistics, or the legalities of human resource management.  Their job is to make decisions for improving their business; our job is to give them information that will inform those decisions. That information may be situational, as in, “here is where the marketplace is and where we are in that marketplace”; it may be proactive, as in, “we’ve identified an opportunity”; or it may be reactive, as in, “we’ve tested this marketing idea and here’s what we can expect consumers to do”.

So where’s the disconnect? Mostly it comes from the perception that we don’t do these things as well as they need or in a time frame that fits their perceived needs. This opens the door for anyone who promises to fill those needs, whether they can or not.  And this is where innovation often goes wrong and gives marketing research a bad name. It may be trite, but innovation needs to produce methodologies that are faster, better, or cheaper. Faster and cheaper need to be at least as good as what was there before.  Better needs to be demonstrably better, not just theoretically better. This is where I think we have missed the boat – we all too often sell what’s new and shiny rather than selling a better tool.

What makes a better tool? That’s easy – one that more accurately predicts what shoppers/consumers will do. The promise that many (not all) suppliers in the eye-tracking, neuromarketing, facial recognition, mobile research, and Big Data space make is that they have the technology to tell you what people will really do. The problem is mostly it’s not true. There is no data that says more attention or longer attention to a product on the shelf improves the probability of purchasing. There is little data to suggest that neurological or physiological patterns are better predictors of purchasing than well constructed survey or experimental techniques. The basic tenets of facial recognition are now coming under attack – they may not be as universal as we once thought and again, nothing has been published to say it is a better tool.

Just being new, just being different, just being sexy with lots of sizzle sells; it certainly has in the past and probably will in the future. But if MR is really swirling down the drain, then we may be cutting off our noses to spite our faces. Selling cotton-candy techniques is only hurting us. Considering them to be innovative just because they are new and shiny misleads those who we most need to trust us.  We can do a better job and we must do a better job of innovating.

Originally published at on 25 March 2014