Check out this image (click to enlarge). This is from a questionnaire sent to Kathryn Korostoff from our web hosting company. So after nicely filling out the survey, what did she encounter? A bad field (zip code) and a bait-and-switch. The survey invitation had stated, “Once you’ve completed the survey, you’ll be entered to win a $1,000 Amazon Gift Card.” What do you notice in the text from the screenshot? That’s not cool. It’s a sure way to annoy the people nice enough to take time to answer your survey.
While we are sure the hosting company meant well, shabby surveys leave a bad brand perception and can result in poor data quality.
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3 comments
Sigh. This is why hiring a professional market researcher is so important. Leaving research participants with a good feeling in the end matters more than building an email list.
It’s also why any DIY market researcher had better be sure they’re testing their surveys before going live with them. At least that would address the bad zip code field (not so much the bait and switch — that’s just flat out not cool).
Unfortunately, I’ve seen MUCH worse than this one – poorly constructed scales, leading questions, questions where one has no idea what’s even being asked, order bias, missing common potential answer options without including “other”/ “NA”. I took a survey just last week like this – it was so bad that I was embarrassed for the firm in question (and it was a well known brand).
I’ve personally seen and heard of instances where legitimate design feedback from research professionals (in the rare instances where they were consulted), was explicitly ignored by junior staff with no research experience and/or limited business experience (the profile of who these sorts of efforts gets delegated to). They typically seem to naively assume they don’t need a research department/vendor, because how hard can it be to write a questionnaire, right? They wrote a questionnaire once for a class in college… I shudder to think of the number of bad business decisions being made as I write this based on the faulty data from studies performed by the untrained.