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Implicit Market Research Techniques: “What, Why, When, How?”

We’ve known for some years now that we don’t, or can’t, accurately express our motivations for doing the things we do. So what can we, and what should we, do about that?

What?

We’ve known for some years now that we don’t, or can’t, accurately express our motivations for doing the things we do. We inevitably process whatever it is in front of us via an automatic and emotionally charged filter – what Daniel Kahneman calls System 1 and others variously call our emotional brain or, more simply, our intuition (depending on who you read).

In essence, all the variations say the same: we always and necessarily react first (although this may not be noticeable to ourselves or others), and reflect later. 
The nature of this reaction is informed by an amalgam of mental shortcuts (“heuristics”) and preferences (“biases”) that accumulate in memory over time and are accessed / reconstructed in scintillatingly quick time when faced with a new situation.

The outcome of this process defines our “implicit” belief; the one that informs your ‘reflex’ response in any given situation. By contrast, the ‘reflect later’ part is our “explicit” thinking at work. I’ll use these two terms from hereon in.

Mostly, our implicit beliefs are formed below the level of conscious awareness, and therefore beyond our ability to articulate using language. And, yes, you guessed it, it turns out that our implicit beliefs are often different to those we declare explicitly. This suspicion was what inspired researchers at Harvard to create implicit association tests (IATs) on topics such as race, gender, and politics. It duly turned out that many of us hold implicit beliefs on issues such as racial equality that are inconsistent with the (more balanced) views that we tend to express explicitly. This dichotomy between implicit and explicit outcomes is at the heart of many of the behavioural economics and social psychology discoveries that emerged a few decades ago, and which have been growing in popularity and breadth ever since. Certainly, these findings are now feeding the deliberations of marketing and market research departments, alerted to the fact of their historically one-sided (explicit) questioning techniques! 

Why? 

Neuroscience can now prove implicit versus explicit brain activity.The science tell us that implicit beliefs are much more important in decision-making than we might previously have appreciated (even in what we might think of as rational and/or science based fields). Indeed, neuroscience has shown that when the part of our brain chiefly responsible for this automatic processing is damaged, people are rendered largely incapable of making any decision at all. In other words, humans are literally incapable of reasoning things out, we must have intuition alongside!

This is not to suggest that because explicit thinking is preceded in this way it is unimportant. In some instances such a case might be argued, but in others -for instance my ‘home’ sector of prescription medicine- we know that people also need reasons to believe. The problem, as hinted at above, is more that established market research practice has been exclusively explicit, until recently making little or no attempt to uncover the direction or flavour of our implicitly held beliefs.

At First Line we look for ways to gauge the strength and direction of our implicit as well as our explicit beliefs and associations. Only then we will have a proper basis on which to interpret and report our research findings.

When?

The Ad industry was I think the first to really pick up on the importance of implicitly held beliefs in a business context – there is an obvious relevance to the manner in which their product is consumed. But marketing and research is now sitting up and taking notice. Consequently we have a plethora of techniques, takes, and tools – not all of which agree with one another. That aside for now, here are three big areas of market research where implicit testing can come in to its own:
  • Alongside asking “Why?” or “Why not?” (to offer a much needed contrast to our post-hoc rationalisations)
  • Alongside asking brand or company “equity” or “image” questions (to reveal our intuitions about brands, and how implicit associations can differ from the explicit scale scores)
  • Alongside explicit measures of preference when testing promotional materials (add a layer of understanding by passively observing how we interact)
Fine, but why “alongside” rather than instead of? I think there is a slight danger here of preferring implicit techniques over explicit ones, because of the relative newness (in research terms at least). I’d argue that we should focus first on what it is that we are researching…

Let’s consider: <A> a new design for prescription-only medical device for use in children with Type II diabetes, and <B> a chocolate Easter egg. It’s pretty easy to envisage that a physician’s decision about whether to use <A> will be a more rational one than a Great Aunt making a decision with respect to <B>. Yet we know that medical decision-makers are still prone to the same heuristics and biases as the rest of us. We might also suppose that many a Great Aunt might take pleasure in pondering such a choice. Add in what we mentioned earlier about humans being unable to make a decision either way without at least a modicum of intuition and the question becomes how much weight to give to the implicit versus the explicit findings, rather than which of them to run with.


How?


Well, agencies are working on this stuff ever more seriously. Grab a free chat with someone in the know and talk through your objectives. As suggested in the bullets above, IATs are just one of a number of different types of of implicit research technique. Some, such as eye-tracking, have been around for a while. Many can be incorporated relatively easily into online surveys, and at no great expense. Others may, on closer inspection, turn out to be hybrids of implicit and explicit techniques rolled into one. Personally, I’m not sure this matters hugely so long as the implications are understood, and the method is fit for purpose. Research objectives obviously vary, and therefore so do the mix and weightings that one might want to apply to the implicit and explicit proposed. There are a few ‘off the shelf’ and easily adaptable techniques (like IATs) but for the most part design should be tailored to the project. I don’t think that necessarily means more difficult, more expensive, or more involved. Indeed many of the techniques themselves are shockingly intuitive to design and implement – a reflection, I think, of what it is we are measuring! 


John Aitchison, MD, First Line Research - www.firstlineresearch.com
Click here for the fully referenced blog entry: http://goo.gl/d078FZ

    10th May 2015

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