I Went Fishing and All I Caught Was a Red Herring

Innovation needs to improve the predictability and reliability of our research – it does not have to be new, flashy, sexy, or disruptive.

By Dr. Stephen Needel

The annual GRIT report should be required reading for marketing researchers; it provides a current snapshot of what senior players in our business are feeling about things. That said the reader should take a big grain of salt to go along with the red herring that winds its way through the report. The red herring is innovation, described as a holy grail with magical powers that should be self-evident. It is my contention that innovation for the sake of innovation is foolish. It only appears to be effective because so many buyers are enthralled with all things new and shiny, rarely digging beyond the surface validity of a lot of new techniques. Innovation needs to improve the predictability and reliability of our research – it does not have to be new, flashy, sexy, or disruptive.

The “innovate or die” movement persists, even though research has shown this to be untrue (e.g. Getz and Robinson, 2003). Yes, I know about the buggy whip company who didn’t see cars coming and Netflix changed the game much to Blockbuster’s consternation. Amazon brought us a new way to shop and one day their financial performance might justify what they do and how they do it – or not. Walmart’s latest challenge will certainly put a dent in Amazon’s growth estimates. Now take a look at all the “innovative” tools marketing researchers have developed. Are there any that blow you away?

In the GRIT report, John McGarr makes a critical point. He says, “MR providers need to keep in mind that no client owes the industry a trial of new methods for the sake of being innovative.”  Some will argue that we should expect buyers to try new things when they are (sorry about this) faster, better, or cheaper. I’d take the position that faster is fine, as long as it is at least as good and has a similar value proposition. I’d take the position that better is always better, even if it is not as fast and not as cheap – you should pay more for a better answer. I’d take the position that cheaper alone almost always has a hidden cost, usually in bad design, sampling, or survey construction.

The marketing research industry needs innovation, but the innovation needs to be directed at solving research problems. Here’s an example – we have a problem with predicting new product performance, as good as many models are. When our new product failure rates are over [70%, 80%, 90% – pick your number] we clearly do not understand the shopper dynamics beyond basic trial and repeat analyses. When was the last time we saw a new or better way to do our new product forecasting that was validated?

Instead of solving research problems, we pretend that automation and DIY are the innovations most needed because they make the research process less expensive. These types of innovations are technical innovations, but not problem-solving innovations. We miss the point – it’s not the price tag that matters, it’s the quality of answers we are able to give our clients. Of course budget limitations play a part in purchasing decisions. But, and it’s a big but, a methodology that is meaningfully more accurate will always be worth the cost. Greg Archibald, in summing up the report, says, “Over the next few years, we are going to see a continued focus on improving tools and methodologies…” I’d respectfully disagree – I don’t think we, as an industry, are very focused on improving our tool kit but rather we are trying to come up with the next new sexy thing to sell.

Innovations such as automation and AI are great for the business of marketing research, but that only provides a trivial benefit for our end-client. They need us to do our job better, they will pay us to do that, and we need to innovate with that in mind.

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4 Responses to “I Went Fishing and All I Caught Was a Red Herring”

  1. Ray Poynter says:

    February 9th, 2017 at 9:46 am

    Hi Steve, are there any MR innovations that blow you away? How about the use of the internet for data collection? That is new (at least to people as old as you and me), it has changed the cost of research, it has changed how research is priced, it has changed the speed of research, it has changed who is researched (we used to research people who very rarely completed surveys and who were contacted in ways that very loosely aped random probability sampling – now we use panels or customer lists/communities for most research). Talk to people heavily invested in call centres if you want to see the cost of missing an innovation. Talk to all the people investing in geolocaiton apps for a warning about investing in the wrong innovation 🙂

  2. Steve Needel says:

    February 9th, 2017 at 4:20 pm

    Ray – yes, the telephone, the mall, and the internet, and the mobile device, in succession, are all important to our ability to do our work better because, in theory, it gives us access to better samples at lower prices (we can debate whether the samples really ARE better, but they are in theory). Speed is less of an issue to me – I don’t think most of our work needs to be done as fast as we often think it does.

    My issue in the above is that what is hyped to our clients is research that is often unvalidated and provides little benefit to them. Indeed, much of what is hot these days is more expensive (think facial coding, because clearly we’re too stupid to be able to ask the right questions to determine affect, or fMRIs at $2000 or so a pop). I think we’d be better served focusing our innovation efforts on better predicting what shoppers will do. That might be better sampling, better analytics, and/or better methodology.

  3. Kevin Gray says:

    February 15th, 2017 at 5:16 pm

    Thanks for this, Steve!

    To me the most revolutionary and impactful innovation would be in the field of Decision Science. Much of what the decision makers of the future seem to be taught today is better characterized as Decision Scientism, IMO. MR can only influence this development on the margins, if at all. Nonetheless, if decision makers still do not understand how to integrate data and analytics into decision making then the incremental value of MR will be reduced. Like some of my colleagues and contacts, I feel that Decision Science, once matured, should be taught beginning in elementary school.

  4. Jeff Adler says:

    February 15th, 2017 at 5:25 pm

    Steve – I very much agree with most of your observations, especially that innovation needs to be directed at solving research problems. However, I don’t know that I would pick a better way to do new product forecasting as the main focus. Rather, I think the main focus should be on helping clients make better business decisions. I believe that requires improving our tool kit rather than trying to come up with the next new sexy thing to sell – and in many cases, “improving our tool kit” translates to making better use of tools we already have.

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