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Is Qual “Scalable”?

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Is qual scalable? It’s a question I’ve heard a few times over the last 18 months from people in the broader Market Research community looking at how change is rumbling through our small industry, and puzzling over the qual. sector.

Qual is seemingly enjoying a renaissance. “Are we in a new golden age of qual?” asked the organisers of IIEX 2017 Europe. Qual360 APAC talks of a “huge revival of qual”. Individual qual practitioners have confirmed this in blog comments.

Is “qual” a potential investment opportunity? Can “small data” deliver globally, at speed, at lower cost, with potentially higher margins?

I wonder if a solid understanding of the nature of qual informs this line of enquiry, or if the run to the tech bus is crushing stuff underfoot. For example:

– Does asking thousands of people an open-ended question online and ranking the answers via real-time machine-intelligence, for a moderator to then probe further really “scale” qual?

– Does the use of mass mobile ethnography, inviting many thousands of people – “crowds” – across the world to share their thoughts and surroundings maybe via a mobile app on simple questions – do they drink tea for breakfast, for example – actually describe, let alone scale or even disrupt qual?

– What about allowing business managers direct access to their globally distributed consumers via video? To make no mention of confirmation bias, btw.

Do these tech-enabled data points equate to “qual being scaled”?

I’d say no. Qual by this definition morphes into a “first understandings” approach: a skin deep but globally accessible take on whatever the category may be – in effect little more than a multi-medially enriched open-ender in a quant survey.

The real “qual work” hasn’t even started at that stage – involving ethnographies, cultural and contextual interpretation, semiotic analysis perhaps, the exploration of emotions certainly, identifying and making sense of tensions and contradictions, teasing out meaning from the vast array of things unspoken, poorly articulated, maybe below the conscious level. All of which involves human interpretation.

Tech can potentially help qual get to the areas where deep-dives in whatever form are needed, but simply amassing qualitative-style, unstructured data isn’t really making qual scalable.

If I could dream up a tech-qual future, it would be one where significantly more time is spent on understanding business context and business outcomes, with machines/AI helping remove repetitive low-value tasks, allowing qual researchers to get to the part faster where we help brand owners make sense of things.

Let’s see how things evolve. My predictive algorithm is on energy-saving mode.



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