Spin Cycle: The Symbiotic Relationship Between Quantitative and Qualitative Data
The numbers never lie; but sometimes, they don’t tell the whole story, either. The market research industry is often at odds when trying to decide whether to trust quantitative data (hard numbers and statistics) or qualitative data (which isn’t in numerical form, like survey answers). But the truth is that it shouldn’t be a question of quant versus qual, but how to use both types of data effectively for the clearest picture possible.
Quantitative data is the gold standard for market research analytics. With hard numbers, it’s easier to statistically prove a hypothesis, like how much, what percentage, or even who is doing what—and even when they’re doing it. Because of its highly predictable nature, quant is often seen as the default research method. Easy to explain, and even easier to prove, the facts and figures are clear.
But quantitative data has its limitations. It may be able to answer the questions of who, what, where, and when, but it falters when finding out the “why” of customer behavior. With quant, it can be difficult to tap into participant mindset, limiting the type of information that can be gathered. It may be logical, but it lacks a personal element.
All About Qual
Qualitative data refers to information that is more human. Instead of just who and what, quant asks “why” and “what’s your opinion?” Qual might not involve hard numbers, but its fuzzier nature is valuable when trying to tap into the minds of customers and discern attitudes toward products and issues. Qualitative usually means talking to fewer people, but for a longer amount of time when compared to a traditional quant-based survey.
Of course, with qual, you miss out on the sheer amount of data that you need for statistical significance. Since you spent more time with each subject, it’s impossible to get a large enough sample. It’s perfectly valid to talk to eight people in a 90-minute focus group to learn about their opinions, but it’s hard to decipher what those opinions mean until you have the opportunity measure at scale.
Best of Both Worlds
Quant data and qual data shouldn’t stand alone: Neither are enough to give the best insight. Instead, they should be used symbiotically as part of a cyclical process. Instead of a one-off survey for quant data or holding a focus group for qual, it’s more effective to start with qual as a method to frame your ideas and trends, but then use quant to see if those answers hold up to scale and prove your hypothesis. Doing so can even help you frame new questions and hypotheses (polls, statistics) to take back to a quant research method (in-home tools, diary studies) and repeat the process.
There’s no reason to stop at using just one market research method, especially if it only tells half of the story. A cyclical process is infinitely more effective as it gives you a better view of the current market, participant opinions, and ultimately, your next move.