Almost Half of Market Researchers are doing Market Research Wrong! - My Interview with the QRCA (And a Quiet New Trend - Science Based Qualitative).
Two years ago I shared some research on research about how market researchers view Quantitative and Qualitative research. I stated that almost half of researchers don’t understand what good data is. Some ‘Quallies’ tend to rely and work almost exclusively with comment data from extremely small samples (about 25% of market researchers surveyed), conversely there is a large group of ‘Quant Jockey’s’ who while working with larger more representative sample sizes, purposefully avoid any unstructured data such as open ended comments because they don’t want to deal with coding and analyzing it or don’t believe in it’s accuracy and ability to add to the research objectives. In my opinion both researcher groups have it totally wrong, and are doing a tremendous disservice to their companies and clients. Today, I’ll be focusing on just the first group above, those who tend to rely primarily on qualitative research for decisions.
Note that today’s blog post is related to a recent interview, which I was asked to take part in by the QRCA’s (Qualitative Research Consultant’s Association) Views Magazine. When they contacted me I told them that in most cases (with some exceptions), Text Analytics really isn’t a good fit for Qualitative Researchers, and asked if they were sure they wanted to include someone with that opinion in their magazine? I was told that yes, they were ok with sharing different viewpoints.
I’ll share a link to the full interview in the online version of the magazine at the bottom of this post. But before that, a few thoughts to explain my issues with qualitative data and how it’s often applied as well as some of my recent experiences with qualitative researchers licensing our text analytics software, OdinText.
The Problem with Qualitative Research
IF Qual research was really used in the way it’s often positioned, ‘as a way to inform quant research’, that would be ok. The fact of the matter is though, Qual often isn’t being used that way, but instead as an end in and of itself. Let me explain.
First, there is one exception to this rule of only using Qual as pilot feedback for Quant. If you had a product for instance which was specifically made only for US State Governors, then your total population is only N=50. And of course it is highly unlikely that you would ever get all the Governors of each and every US State to participate in any research (which would be a census of all governors), and so if you were fortunate enough to have a group of say 5 Governors whom were willing to give you feedback on your product or service, you would and should obviously hang on to and over analyze every single comment they gave you.
IF however you have even a slightly more common mainstream product, I’ll take a very common product like hamburgers as an example, and you are relying on 5-10 focus groups of n=12 to determine how different parts of the USA (North East, Mid-West, South and West) like their burgers, and rather than feeding directly into some quantitative research instrument with a greater sample, you issue a ‘Report’ that you share with management; well then you’ve probably just wasted a lot of time and money for some extremely inaccurate and dangerous findings. Yet surprisingly, this happens far more often than one would imagine.
Cognitive Dissonance Among Qual Researchers when Using OdinText
How do I know this you may ask? Good Text Analytics software is really about data mining and pattern recognition. When I first launched OdinText we had a lot of inquiries from Qualitative researchers who wanted some way to make their lives easier. After all, they had “a lot” of unstructured/text comment data which was time consuming for them to process, read, organize and analyze. Certainly, software made to “Analyze Text” must therefore be the answer to their problems.
The problem was that the majority of Qual researchers work with tiny projects/sample, interviews and groups between n=1 and n=12. Even if they do a couple of groups like in the hamburger example I gave above, we’re still taking about a total of just around n=100 representing four or more regional groups of interest, and therefore fewer than n=25 per group. It is impossible to get meaningful/statistically comparable findings and identify real patterns between the key groups of interest in this case.
The Little Noticed Trend In Qual (Qual Data is Getting Bigger)
However, slowly across the past couple of years or so, for the first time I’ve seen a movement of some ‘Qualitative’ shops and researchers, toward Quant. They have started working with larger data sets than before. In some cases, it has been because they have been pulled in to manage larger ongoing community/boards, in some cases larger social media projects, and in others, they have started using survey data mixed with qual, or even better, employing qualitative techniques in quant research (think better open-ends in survey research).
For this reason, we now have a small but growing group of ‘former’ Qual researchers using OdinText. These researchers aren’t our typical mixed data or quantitative researchers, but qualitative researchers that are working with larger samples.
And guess what, “Qualitative” has nothing to do with whether data is in text or numeric format, instead it has everything to so with sample size. And so perhaps unknowingly, these ‘Qualitative Researchers’ have taken the step across the line into Quantitative territory, where often for the first time in their career, statistics can actually be used. – And it can be shocking!
My Experience with ‘Qualitative’ Researchers going Quant/using Text Analytics
Let me explain what I mean. Recently several researchers that come from a clear ‘Qual’ background have become users of our software OdinText. The reason is that the amount of data they had was quickly getting “bigger than they were able to handle”. They believe they are still dealing with “Qualitative” data because most of it is text based, but actually because of the volume, they are now Quant researchers whether they know it or not (text or numeric data is irrelevant).
Ironically, for this reason, we also see much smaller data sizes/projects than ever before being uploaded to the OdinText servers. No, not typically single focus groups with n=12 respondents, but still projects that are often right on the line between quant and qual (n=100+).
The discussions we’re having with these researchers as they begin to understand the quantitative implications of what they have been doing for years are interesting.
Let me preface this with the fact that I have a great amount of respect for the ‘Qualitative’ researchers that begin using OdinText. Ironically, the simple fact that we have mutually determined that an OdinText license is appropriate for them means that they are no longer ‘Qualitative’ researchers (as I explained earlier). They are in fact crossing the line into Quant territory, often for the first time in their careers.
The data may be primarily text based, though usually mixed, but there’s no doubt in their mind nor ours, that one of the most valuable aspects of the data is the customer commentary in the text, and this can be a strength
The challenge lies in getting them to quickly accept and come to terms with quantitative/statistical analysis, and thereby also the importance of sample size.
What do you mean my sample is too small?
When you have licensed OdinText you can upload pretty much any data set you have. So even though they may have initially licensed OdinText to analyze some projects with say 3,000+ comments, there’s nothing to stop them from uploading that survey or set of focus groups with just n=150 or so.
Here’s where it sometimes gets interesting. A sample size of n=150 is right on the borderline. It depends on what you are trying to do with it of course. If half of your respondents are doctors (n=75) and half are nurses (n=75), then you may indeed be able to see some meaningful differences between these two groups in your data.
But what if these n=150 respondents are hamburger customers, and your objective was to understand the difference between the 4 US regions in the I referenced earlier? Then you have about n=37 in each subgroup of interest, and you are likely to have very few, IF ANY, meaningful patterns or differences.
Here’s where that cognitive dissonance can happen --- and the breakthroughs if we are lucky.
A former ‘Qual Researcher’ who has spent the last 15 years of their career making ‘management level recommendations’ on how to market burgers differently in different regions based on data like this, for the first time is looking at software which says that there are maybe just two to 3 small differences, or even worse, NO MEANINGFUL PATTERNS OR DIFFERENCES WHATSOEVER, in their data, may be in shock!
How can this be? They’ve analyzed data like this many times before, and they were always able to write a good report with lots of rich detailed examples of how North Eastern Hamburger consumers preferred this or that because of this and that. And here we are, looking at the same kind of data, and we realize, there is very little here other than completely subjective thoughts and quotes.
Opportunity for Change
This is where, to their credit, most of our users start to understand the quantitative nature of data analysis. They, unlike the few ‘Quant Only Jockie’s’ I referenced at the beginning of the article already understand that many of the best insights come from text data in free form unaided, non-leading, yet creative questions.
They only need to start thinking about their sample sizes before fielding a project. To understand the quantitative nature of sampling. To think about the handful of structured data points that they perhaps hadn’t thought much about in previous projects and how they can be leveraged together with the unstructured data. They realize they need to start thinking about this first, before the data has all been collected and the project is nearly over and ready for the most important step, the analysis, where rubber hits the road and garbage in really should mean garbage out.
If we’re lucky, they quickly understand, its not about Quant and Qual any more. It’s about Mixed Data, it’s about having the right data, it’s about having enough data to generate robust findings and then superior insights!
Final Thoughts on the Two Meaningless Nearly Terms of ‘Quant and Qual’
As I’ve said many times before here and on the NGMR blog, the terms “Qualitative” and “Quantitative” at least the way they are commonly used in marketing research, is already passé.
The future is Mixed Data. I’ve known this to be true for years, and almost all our patent claims involve this important concept. Our research shows time and time again, that when we use both structured and unstructured data in our analysis, models and predictions, the results are far more accurate.
For this reason we’ve been hard at work developing the first ever truly Mixed Data Analytics Platform, we’ll be officially launching it three months from now, but many of our current customers already have access. [For those who are interested in learning more or would like early access you can inquire here: OdinText.com/Predict-What-Matters].
In the meantime, if you’re wondering whether you have enough data to warrant advanced mixed data and text annalysis, check out the online version of article in QRCA Views magazine here. Robin Wedewer at QRCA really did an excellent job in asking some really pointed questions that forced me too answer more honestly and clearly than I might otherwise have.
I realize not everyone will agree with today’s post nor my interview with QRCA, and I welcome your comments here. I just please ask that you read both the post above, as well as the interview in QRCA before commenting solely based on the title of this post.