The number one question researchers want answered is why. Why did you buy that? Why did you eat that? Why did you recommend that? Why did you vote for them? It’s an important question and it’s often where magical insight comes from.
So let’s test the why question. Tell me, which colour in this colour wheel is your favourite? I’ll wait right here while you choose…. And now, tell me WHY that is your favourite colour.
Do you like it because it’s bright? Because it’s calm? Because it’s bold? Happy? Peaceful? Cheerful?
My answer is easy. I like the purple slice. Why? Because it’s bright and I like bright colours. (Even if the only bright colours I wear are bright frogs and penguins on my socks.)
But that doesn’t answer the why question at all. Let’s switch over to the second colour wheel. On this wheel, I am still going to choose the purple because it’s bright. But, I could have easily picked any other colour in the same ring, colours that all fall into the same ‘brightness’ category. Colour experts will tell you that each of the colours in the rings has the exact same degree of brightness. So, the fact that I chose brightness as my reason explains nothing. I could have chosen the blue or green or yellow with the identical degree of brightness.
Alright, then let me try again. I like the purple slice because purple is a happy colour. Happy? How is it any happier than any of the other colours? Isn’t the bright green or the bright yellow just as happy? Can a colour really be happy?
If I use my best introspection techniques and ponder for an hour, I still have absolutely no idea why I like the purple slice.
I also have no idea why…
- I love nanaimo bars which are just as sweet and creamy and chocolatey as many other treats. Why nanaimo bars?
- I’m not a fan of modern art. But, of course, modern art is just as strange/pointless/dull as many other things that I DO like. Why don’t I like modern art?
It is pointless to ask why. People do not truly know why they like or dislike anything. And if you decide you’re going to find out why, prepare to be 5 year-old child who can ask why for 12 hours straight. Because once you get an answer to the question why, the only possible next step it to ask… why?
- What does plus or minus 3% 19 times out of 20 mean?
- Short answer lists inflate endorsement rates
- Precise or Casual: Which answer options work better?
- Open Ends: More than just an opinion
- Leading Questions are easy to avoid, aren’t they?
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.
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.
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