Blurring the lines between quant and qual

If recent articles in the trade press are anything to go by, we are witnessing the end of the Great Divide in market research. Qualitative and quantitative research methodology are beginning to merge, as practitioners begin to move away from a binary approach to research and all the limitations such an approach can bring. 

The pandemic has forced market research practitioners to embrace new tools and methods for research, with more work conducted online and, as a result, the arrival of ‘big qual’. In an article in Research Live, the managing director of Signoi, Andy Dexter, defined it as: 

Big qual on the horizon

“‘Big qual’ applies to anything that involves large-scale unstructured data, for example, conversations that people are having on social media around a particular topic. There’s the opportunity to scale it up to millions of tweets – something that you would never deploy in a mainstream research project…”

Quant has always had a greater appeal to ‘the numbers guys’ who buy research. Numbers are often seen as inherently more reliable, while qualitative research can still be seen as less so, because people can and will often tell you what they think you want to hear. This is more than a little unfair, particularly on research designers who go out of their way to plug any potential potholes which may skew results. 

Clients are generally less interested in the methodology and more interested in the insights the data can provide. How market research arrives at that point is less important to many clients than the cost-benefit analysis of the insights it produces. The combination of solid numerical data with consumer opinion has long been the preferred balance for many practitioners, but now market research is also beginning to blur the lines between qual and quant, with ‘big qual’ allowing for greater scale through sources such as social media, offering a far larger data set than traditional qualitative research may have previously worked with. At the same time, AI and machine learning mean that quantitative research can sift through huge data silos with ease. Assuming that the correct tools are employed, of course.

Tools and techniques

Ultimately, none of this matters to the client unless the advantages and insight benefits are clearly highlighted to them. Equally, where the market research team chooses to place emphasis has an impact on the way the results are presented, and the end result should remain the same, regardless of the blend of techniques. Or at least, that’s how it should work in principle.

A closer marriage between qual and quant could have an impact on insight agility, allowing faster responses by MR to specific client questions. At the same time, it will also see new disciplines become more commonplace, as discourse analysis begins to be used in conjunction with quant surveys, allowing language patterns to be more closely analysed to provide more evidence in a survey environment, where numerical data points may offer less clarity or evidence for motivation. This more empathetic approach to quantitative research could benefit researchers who favour one technique over another, allowing better interpretation of data in the long run.

As in-person interviews begin once again and workspaces reopen, there’s much talk of teams integrating and working more closely with each other. By suggesting a reduced reliance on one research discipline over another, MR may find initial hesitancy from clients who would rather ‘just see the numbers’. The key in smoothing that process out, of course, remains with the project leaders, who must ensure that any hand-holding at the initial stages of a project fully explains the tools and methodologies to be used, and what benefits they bring to the client and insights.

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