Finding the balance: when qual meets quant

Finding the balance: when qual meets quant

We have written about finding the ‘why’ factor behind the purchase of a product or service in the past, and how a greater understanding of the ‘why’ will inform a client on how better to increase their business. Now we look at the issues with ‘how many’.

If there’s one thing we know, it’s that people can tell you one thing and do another. Nowhere is this more true than in parenting and market research. Here, however, we’re looking at the implications for market research, particularly in the current pandemic climate of remote interviews, panels and an increase in the use of questionnaires. I’m afraid it’s true: some of your interviewees will tell you what they think you want to hear, rather than what you need to hear, and the quantitative results will ultimately bear that out.

How you find the balance between the two is key and underlines the importance of using both qualitative and quantitative in equal measure, to ensure that you get to the real data nuggets that matter and provide insights.

Ron Sellers has written extensively on the subject of panels, questionnaires and skewed data, and has provided a number of crucial steps researchers can undertake to ensure data integrity. However, he’s quick to point out that there are no easy answers or quick fixes when it comes to insights. As he said on LinkedIn recently:

“In consumer insights, we don’t spend enough time recognizing what research CAN’T do effectively. For instance, people have great difficulty describing or even understanding what motivates them. They aren’t very good at predicting their future activities, and they often struggle with accurate recall of past activities.”

Warm data

It’s a theme that has come up several times in recent weeks, as some practitioners begin to look at the benefits of ‘warm data’, an idea pioneered by Nora Bateson of Warmdatalab.net. Bateson describes warm data as:

“…information about the interrelationships that connect elements of a complex system. Put another way, Warm Data is transcontextual information. Warm Data captures the qualitative dynamics and offers another dimension of understanding to what is learned through quantitative data, (cold data). The implications for the uses of Warm Data are staggering, and may offer a whole new dimension to the tools of information science we have to work with at present.”

The concept is gaining traction as marketers find themselves locked out of a lot of data systems due to the constraints of GDPR, consumer opt outs and privacy concerns and various legislative processes at a national level. All of these place barriers to data and the personalisation of the individual consumer; while some might feel this is a good thing, for market researchers, they pose a significant hurdle to achieving true insights into purchasing decisions and action. Warm data provides a new layer of data to mine and puts those data into context, which can be crucial.

As Bateson notes:

“In order to interface with any complex system without disrupting the circuitry of the interdependencies that give it its integrity, we must look at the spread of relationships that make the system robust. Using only analysis of statistical data will offer conclusions that can point to actions that are out of sync with the complexity of the situation. But information without context and interrelationality is likely to lead us toward actions that are misinformed, thereby creating further destructive patterns.”

Warm data could provide the ultimate in qualitative understanding for practitioners. To use the purchase of a lightbulb as an example, qual will tell you how many bulbs were purchased, quant will tell you why that happened. But with warm data, practitioners can discover why that brand was chosen, why that store was visited and connect all of the dots in between.

The trust gulf

As with all things, however, there are issues. How will companies access this information? How keen will consumers be to share even more with brands? Anecdotal evidence would suggest that some brands are now finding it harder to convince consumers to opt-in to various data sharing systems. This could be for a number of reasons, from recent high profile data breaches at large corporations which then lead to high profile hacking or ransomware attacks on customers, to simple push back over the amount of data being requested.

Companies can bridge the trust gulf by ensuring significantly better data security and by properly incentivising consumers to share more by offering worthwhile benefits in return. The better the rewards, the more likely customers will be to opt-in.

At the moment, capturing warm data remains a future goal for companies hoping to really see behind the customer’s curtain, as it were. When we may see it fully deployed in the field is open to speculation, as it will be the culmination of increased data gathering and improved customer relationship management.

The industry will always demand more data and, as new AI tools are adopted which simplify the process of sifting, greater data processing capacity will follow.

What this underscores is the need for both good quantitative and qualitative data in order to provide the best insights. Both systems have their proponents, but good practitioners appreciate that one hand shakes the other and that knowing the ‘why’ is just as important as the ‘how many’ to gain a true picture. The addition of warm data could offer further context to the specific project being undertaken by the practitioner, and offer further insight into consumer ‘Jobs to be Done’. The key, of course, is ensuring that equal weight is given to both sides of the equation in order to reach the right result for the client. Interpreting the data to see the stories behind it and capture greater insights, using both the numbers and the consumers’ own story; this is what makes market research so compelling and the insights it generates so important for business.

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