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Emily James

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Qualitative and quantitative research techniques are the staples of market research methodologies. With one concentrating on gleaning logical, statistical insights and the other specialising in diving deep into the emotional side of consumer behaviour, it isn’t too far to see why the insights industry has made its fortune by conducting both as standard.

But the one challenge this intense focus has thrown up time and time again is, how do insight teams get the most out of both methodologies? We asked a few of our in-house experts to help answer this question.

The Importance of Balancing Qual and Quant Data

Balancing both qual and quant data in a research experience is a great way of making sure insight teams get the most out of each methodology. Now this doesn’t mean splitting qual and quant fifty-fifty, but understanding the objectives of the research experience will help researchers to tailor this balance so that the insights generated are directly actionable.

Our in-house researchers have kindly lent their view on why it’s important to balance both quant and qual in market research:

Harriet Walton, FlexMR Research Associate, starts by saying that “whilst quant data can provide a numerical snapshot of people’s behaviour and decisions, if we are to understand any reasoning behind these decisions, we must consider utilising qual data. Essentially, quant data can explain ‘the what’ whilst qual data can reveal ‘the why’. As humans, our decisions are driven by emotion and experience so for research to provide businesses with insight into these processes, a more in-depth analysis is provided through qualitative data."

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It's important to balance the amount of quant and qual research conducted if insight teams are to get the most out of both - but the split doesn't have to be 50/50.

Another of our Research Associates, Dr Matthew Farmer, further explains “on their own, both qual and quant data can provide great insight into a question or issue, but each has particular strengths that complement the limitations of the other. Quant provides data with breadth, whereas qual provides data with depth. Balancing qual and quant data in your research allows for a more comprehensive and holistic approach to interrogating an issue, thereby generating more impactful insights.”

Providing an important glimpse into a stakeholder’s point of view, our CEO, Paul Hudson explains that “It’s important to combine both rational (quant) and emotional (qual) data to better influence decisions. Businesses typically attempt to be rational decision-making organisations and run on financial accounts, so numbers appear to drive a lot of decisions; however, it’s becoming more well-known that day-to-day decision making is also influenced by the full spectrum of human emotion - and getting people to actually act on data can be easier if we appeal to their emotional side too.”

Designing Balanced Research Experiences

To get the most out of qual and quant, insight teams need to think about how they can achieve this in the initial research design stage. This way, researchers and stakeholders can collaborate on balancing research and business objectives to make sure that the ratio of quant to qual is balanced enough to give them the data and insights needed to make the right decisions throughout the organisation on the inciting incident and other situations that arise. But how can researchers design projects that take advantage of both?

Matthew likes to take a tailored approach to designing research experiences, saying that “the order in which you build quant and qual methodologies into a research project will usually depend on the nature and objectives of the project and the strengths that each approach entails.

Starting a project with a quant element can help you identify any trends or common issues amongst a large sample, which you can then use qual methods to probe deeper into why those issues are occurring. Alternatively, you could lead with a qual approach to interrogate consumer priorities and interests in order to improve and refine a concept, and which you can then test the broader appeal of by using quant methods, validating the findings of the qual element of the research.”

Harriet adds that “when considering research design, researchers are able to take information from the aims of the project to understand where presenting statistical data can be of benefit and where a more detailed examination of decision making is required. For businesses to be able to utilise research findings to apply meaningful changes to their operations, it is simply not enough to know what decision consumers will make, but why and what powers it. By opening the floor to participants to provide open explanation behind their decisions, researchers can provide more valuable insights for the business.”

Paul mentions that he “used to think (as was taught) that qual led the project at the start so that we ensured we balanced our questions and designed surveys in line with the language and motivations and behaviours of consumers. However, as InsightHub and online techniques have developed and the use of emotion in driving actual decision making has evolved (i.e. using more video), I find myself recommending quant and then qual more often, so that we can then allow time to deep-dive the results and find the video/emotional evidence needed to explain results and drive decisions in a more efficient way. I guess the research purist approach is the original way, the latter more of a commercial approach.”

For Example...

With the amount of research conducted on a daily basis, there are many examples of how to balance quant and qual well in a research experience so that insight teams can take advantage of both methodologies.

Paul recalls a previous client of FlexMR’s Ennera who are a subsidiary of CAF group and are an established player in the Spanish sustainable energy market. Ennera used both quant and qual research to drive decisions through consumer intelligence gathering. This was a phased project that used both quant and qual tasks in parallel to business and competitive intelligence.

Another notable example Paul wanted to draw attention to was British Gas, who used an iterative, agile research experience that combined quant and qual research methodologies well in order to get some great insights into the experiences of the landlord segment of their customer base.

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With Quant and Qual research firmly entrenched as staples in the Insights Industry, there are many examples of how insight teams can take advantage of both methodologies.

Matthew recalled a research project he worked on “with a client to design some research that would help them refine their brand positioning. They had quant data from previous research that informed them what consumers generally considered important in this particular field, which gave them a starting point to develop some new concepts for brand positioning. We then designed and implemented a qual approach, specifically focus groups, to explore in more depth what were important considerations for different target consumers and to test some concept brand positioning statements.

This qual approach revealed some great insights into why consumers considered particular elements as more important than others and helped the client to refine the language they wanted to use in their brand positioning. We then returned to a quant approach, designing a survey that would help test and validate the refined brand positioning statement with a much larger sample size.”

FlexMR’s in-house experts are fluent in personalised research design and implementation, and they work with clients every day to make sure we create the best opportunity for key stakeholders to obtain accurate and relevant insights they can use to inform decisions of all scales throughout the organisation instantly. Each new unique research experience informs our evolving knowledge of market research, and thus our cumulative catalogue of tools, techniques, tactics and methodologies will grow and give us more opportunities to help clients get the most out of quant and qual research.

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