Finding the Why in Your Market Research Results #MRX

It’s probably safe to assume that every single research report you’ve ever written has been followed up with a single word – why.

It’s probably safe to assume that every single research report you’ve ever written has been followed up with a single word – why.

Why did this result happen? Why did people give this answer? Why is this the winning option?

It’s easy enough to read through any report and be faced with lots of interesting questions. I can usually think of three or four contradictory answers for every question coming out of a report. And I can usually make any of them match up with the data. Data in, preferred answer out. Want an insight? I’ll make one up for you.

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But which why is the right why? The problem is simple. Market research is rarely designed to answer the question why. Market research is usually designed to measure what. Surveys tell us what. Focus groups tell us what. Social media research tells you what. You see, even when you outright ask people to tell you why, you’re usually getting a why that has been massively skewed by deceiving memories and a variety of life experience. That’s not why.

Most market research is only designed to discover correlations which, I shouldn’t have to tell you, aren’t causation. Just because someone says they buy six cans of beans each week and they have six kids and they tell us they buy six cans because they have six kids does not mean that they buy six cans of beans because they have six kids.

The only way to measure why is with test control research. In the strictest sense, you must randomly create families with random numbers of random children. Randomly assign people to random families such that some of the families are two kid families while others are six or three kid families. Now you’ve got the correct conditions to observe whether families with more kids do indeed buy more beans. And then you’ll legitimately be able to say that having six kids causes families to buy six cans.

So until we can randomly assign people to families, to product offerings, to price differences, to political candidates, and more, we’re stuck with correlational results.

So keep on guessing why.