It seems that more and more 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, two of which with a clear heritage in quant in my view. But all three would nonetheless 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 and quant and back, perhaps strengthened by the positive experience of online communities or proprietary panels that can be either scaled up or down at will.
Whatever the reasons, and whatever your opinion on whether this qual-quant “market view” is dated, a question worth asking is:
“Where should you start on the Insights Quest in a world where the qual-quant boundaries are blurring?”
I’ll come right off the fence and say “qual” should be the starting point in any MR project”, despite the fact that only roughly 15% of the world’s MR expenditure goes to qualitative research. Here’s why.
1. Hypotheses Should be Qualitatively Infused
Many (any?) MR projects begin with hypotheses. Often, these are rooted in “facts” – market/competitive/channel shares, movements – all quant stuff. But does qual play an equally important part? Qual research often gets downgraded – qual evidence is mistrusted, dismissed as not “a fact”.
Isn’t it time to re-think the widely held if not overtly articulated concept that qual is somehow flaky? Qual can be an immensely powerful source of hypothesis enrichment.
It also delves deep 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 scalability and digital technology are introducing into so many markets. Just think of AirBNB – the world’s largest provider of rooms for rent in a very short space of time.
2. Numbers “Numb the Brain”.
It’s one of the findings of behavioural economics that has stuck in my brain – that the aggregation of responses creates a distance between the brand owner and end-users, consumers, participants.
This paralyses us, effectively – we aren’t moved to act, which isn’t great for insights.
The percentage sign – that great herald of numbers – is a powerful re-assurer for quantitatively oriented decision makers – but the underlying assumptions have to be robust and actually rooted in insights and emotions that move and lead to action.
Only qual can do that. Wow – what a claim! I hope this will at least encourage people to respond with comments.
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 pressures. Timelines that contract, recruitment issues, quota fulfillment…
The rewards for getting up close are manifold and documented – for instance, mobile self-ethnography is there as a scalable (!) and affordable MR tool.
We also need to remind ourselves of how the “real world” of authors, journalists, musicians already operates with narratives, people understanding, the illumination of psychological complexity. It’s immensely sophisticated.
A couple of examples: BBC Music Magazine-when it interviews top artists such as soprano Carolyn Sampson in the June 2015 issue or the British novelist Hilary Mantel on her 2012 prize winning novel Bring up the Bodies. If our reports were like that, who knows, we might even be able to justify more research, and even higher prices.
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.
To strengthen the attack on insensitive quant methods, behavioural economics underlining of the complexities of our decision-making processes have gone mainstream – airport bookstore reading material. The “what” seems to be winning the MR battle – context enrichment and System 1 considerations for many a source of perplexing complexity.
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 un-influenceable, the role of meaningful or less meaningful 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 an MR world characterised 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-through rates…whatever KPIs are relevant. But they don’t explain the underlying dynamics – even competitive intelligence doesn’t help you answer why a Competitor B is doing this, for example.
It’s about people.
And there’s “nowt as weird as folk”, as the English expression goes. Capturing that “weirdness” in a percentage sign is a highly tricky business – whereas the pen-portrait, with many shades of grey, repeatedly captured over time is an extremely valuable exercise.
Now – how do we get that into the next RFP?
Curious, as ever, as to others’ views.
Edward Appleton is Global Marketing Director, Happy Thinking People.
* The article is re-published with the kind permission from RW Connect, ESOMAR's digital platform for sharing the best in research thinking and methodologies. You can read and comment on the original piece on their website.