Research Is Doomed! (Part 759)

Every few months one of the business sites publishes a piece having a go at market research. Usually these articles are a heady mix of dramatic predictions, weirdly dated assumptions about what researchers do, and – let’s be honest – a few sharp truths.

This one, in Forbes, is no exception. The gist is that “Big Research” is going to get kerb-stomped by “Big Data”, so the piece combines its downer on research with a bouncy optimism about the infinite powers of big data. I’m not going to go through it point-by-point, and anyhow plenty of other commentators have outlined where big data and research fit with each other. But this line stood out.

“What possible similarity is there between a person in 1972 and today in terms of how they respond to an ad?”

1972 fashion

1972 – too remote to matter?

What indeed? Let’s start with biology, psychology, emotions, how they make decisions, the fundamental needs they might be trying to fulfil… but no – in a world transformed by technology none of those things matter.

I can see what the writer was going for. Some things change slowly, but appreciably, over the course of 40 years, and if you’re measuring “brand image” the norms of 1972 may not be as relevant. But other things change very rapidly indeed, and still others don’t change at all. The question really is – which ones should market research worry most about?

Imagine a pyramid of change, with the slowest-moving factors acting on behaviour at the bottom, and the fastest ones at the top.

At the bottom you might have our evolutionary and psychological heritage – basic factors like how our brains work, how we take decisions, the universal emotions we all feel. There is plenty of disagreement on how these fundamental factors work, but changing them is probably out of scope for even the most powerful brands.

On the next layer up you’ll find social and cultural norms – which move slowly but do change. Brands can play a part in shifting these norms but it’s still a big ask. Demographics are generally the lens through which researchers understand these, which explains the current obsession with “millennials”.

Moving up, you find what we might call the “metrics layer”. This is where things like stated preference, brand image, brand ‘personality’ and so on might sit. This is assumed to change over time, and be within a brand’s remit to alter via marketing, communications, and so on.

And finally, right at the sharp end, you get the immediate contextual factors on behaviour itself – things like the way choices are framed, the environment, immediate social information, and the visceral state of the person deciding. These things – as behavioural economics tells us – can have an immense influence on behaviour.

Which layers should research care about? In general, it’s bothered most with the metrics layer. After all, metrics, like the Net Promoter Score, are often created by research – as proxies for some real attitude or behavioural baseline. As such, they are very easy to create norms around.

What’s happened recently – and Big Data, neuroscience and behavioural economics have all played a role – is that there’s been a shift in interest toward using the very bottom layer (how our brains and minds work) to understand the very top (how to influence decisions in the moment). It’s far more common now for researchers to look at real, not claimed, behaviour, and to study the levers which move implicit, rather than explicit, decision making.

Obviously – since our Behavioural Model is designed to do exactly this – we think this is a good trend! There is still plenty of room for metrics (as long as they are rooted in how people make decisions, not how we’d like to believe they do) but the closer you get to the moment of decision the more likely research is to turn into something applied and truly useful.

It also helps bridge the divide between “Big Data” and “Big Research”. Big Data is a record of outcomes – something research used to provide. In the future, research’s job will be more as a laboratory for working to change those outcomes.

For more on the changing face of research, tune into our free webinar on Tuesday 10th when Tom Ewing will present his double-award-winning “Research In A World Without Questions” paper.

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2 thoughts on “Research Is Doomed! (Part 759)

  1. Great way to articulate how the various consumer sciences can be structured and used in synergy. And great to see more engagement into all of the aspects of research, as opposed to predicting the victory of Big Data over everything else.
    I would even go a step further. And push research managers to make sure that deeper consumer sciences are used not just to make sense of behavioural data, but also to drive expertise in brand development where there is no observation or no data. The deep levers of human behaviours (e.g. the fundamental needs) are well- known and great brands are the ones which use those effectively. It is inefficient to keep rediscovering them …

  2. interesting thought: “Big Data is a record of outcomes – something research used to provide. In the future, research’s job will be more as a laboratory for working to change those outcomes.” .

    I wonder if you (or anyone) could provide an example of where Big Data was used to replace MRX data? I would be interested to hear about those, and understand how successful it was.

    I have been part of several of these efforts in different companies and I am sure it is happening all over, but I bet a lot of quality of the data and the depth of the analysis is being compromised.

    It may not be evident at first, and it will seem faster/easier with automation, but over time, a few things will happen which slow down big data, and require some serious thought:

    -concept of statistical significance will be almost meaningless with these data sets with millions of records. You will have a lot of people wasting time on type errors.

    -As a mrx guy, I love it when you can replace survey questions with systemically collected data, but more often than not I uncover major data issues. The data is simply not cared for in the same way. Often the data is coming for person X in dept Y which no longer exists.

    -the ability to slice and dice the data the fly will be limited. and what happens is people lower their standards for the analysis accommodate.

    -Data will not make sense, lead to poor decisions, and turn people off to using data.

    As MRX people I think we really have to push on the quality of the data and findings. It may not win friends in the short term, but it will be better for all on the long term. Just do it in a nice way.

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