Qual / Quant blurring in Market Research: Who's really good at What?
It seems that more and more Research Agencies are seeing the opportunity in positioning themselves as expert in both qual. and quant. fields.
Check out the websites of say Morpace, Brainjuicer, TNS (an arbitrary selection) - all three strong in quant.;with two of them their heritage would in my view be clearly quant. But all would likely claim qual. as a competence area, with teams of qual. specialists. All rounders in a world of specialisation - an "interesting" development.
Maybe this boundary-blurring is a reflection of evolving MR Customer wishes - "one-stop solutions", allowing a seamless movement between qual. to quant and back, perhaps strengthened by positive experience with Online Communities or proprietary Panels that can to be either scaled up or down at will.
Whatever the reasons, and whatever your view on the question if this qual-quant. "market view" is dated, one question that is definitely worth asking is: "where should you start in the Insights Quest a world where the qual. quant. boundaries are blurring?"
I'll come right off the fence and suggest that "qual." should be the starting point in any MR project, despite the fact that only roughly 15% of the world's MR expenditure goes on Qualitative Research. Here's why.
1. Hypotheses should be Qualitatively Infused
Many (all?) MR projects start with hypotheses. Often, these are rooted in "facts" - market/competitive/channel shares, movements - all quant. stuff. But does qual. play an equally important part? Often not - it gets downgraded - qual. evidence is mistrusted, it doesn't enjoy the status of being "a fact".
We would do well to re-think the widely held if not overtly articulated concept that qual. is somehow flaky - it can be an immensely powerful source of hypothesis enrichment.
It also goes into the area of market dynamics - what's going on, what's driving change?
These are cogent and business-critical questions. Ever more so given the pace of change that scaleability and digital technology are introducing into so many markets. Just think of AirBNB - the world's largest offerer of rooms for rent in a very short space of time (http://bit.ly/1vJy5qH).
2 . Numbers "Numb the Brain".
It's one of the findings of Behavioural Economics that has stuck with me - that the aggregation of responses creates a distance between the brand owner and end-users, consumers, participants. It paralyses us, effectively. (http://bit.ly/1FUmdRu) - we don't do anything.
The percentage sign is a great source of re-assurance for quantitatively oriented decision makers - but the underlying assumptions have to be robust and actually rooted in emotions that move and that lead to action. Only qual. can do that. Wow - what a claim!
3. Understanding means Getting Up Close
The drivers of MR business change - speed, impact, cost - are often at odds with the dynamics of powerful insights generation: in-depth understanding of individuals. Their personal history - their parents' vitae, their relationships with their siblings, their food preferences, their teenage years....
Often a project plan doesn't have the luxury to go into that level of ethnographic detail. Topline. Worries about timing, recruitment, quotas.
The rewards for getting up close are tangible - mobile self-ethnography is there as a scalable and affordable MR tool.
We also need to remind ourselves of how the "real world" already operates with narratives, people understanding, psychological complexity. It's immensely sophisticated.
Take a look at how eg how good novelists or good interviewers do it. BBC Music Magazine (for example - http://bit.ly/1ADtarm) when it interviews top artists such as Carolyn Sampson in the June 2015 issue. The British novelist Hilary Mantel - take her 2012 prize winning novel Bring up the Bodies (http://amzn.to/1f4CbTE). Both resonate deeply and broadly. Just examples.
Way to go for all of us in MR I would say.
4. Predictive Analytics and Psychology Don't Always Mix
Old-style quant, research is under attack - from all sides, whether it be Social Media Analytics, predictive analytics, Big Data, Internet of Things, wearables..... Behavioural-based data is beginning to out-muscle attitudinal data stuff, with Behavioural Economics underlining the complexities of our decision making processes. The "what" seems to be winning the MR battle - context enrichment coming a poor second perhaps.
But here's the thing: how predictably irrational are we, when, and why?
Is our behaviour only predictable to a degree - what about the unexpected, the uninflueacable, the role of meaningful or less meanigful others.... things that are difficult to anticipate and factor into an algorithm? Which means.....more qual. Non?
Back to my original question: where to "start" in world characteristed by the interplay between qual. and quant?
Well, data that informs quantitatively is always going to be of huge value - incorrigible facts such as market size, overall growth rates, open rates, click-throughs.....whatever KPIs are relevant.
But they don't explain the underlying dynamics - competitive intelligence doesn't help you answer why a Competitor B is doing this, for example.
It's about people understanding.
And there's "nowt as weird as folk", as the English expression goes. And capturing that "weirdness" in a percentage sign is a highly tricky business - whereas the pen portrait, with shades of grey, repeatedly captured over time is an extremly valuable exercise.
Now - how do we get that into the next RFP? ;)
Curious, as ever, as to others' views.