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Don’t Make These Mistakes on Your Next Customer Satisfaction Questionnaire

The following seven mistakes show up all too often in a customer satisfaction questionnaire. In fact, they’re easy to avoid if you know what to look for.

Mistake #1. Fuzzy Business Objectives

Before commencing a customer survey you should establish clear business objectives. Those objectives will guide you through the multitude of trade-offs you’ll face while developing the questionnaire.

“But shouldn’t we also ask about [insert random question here]..?”

If your objectives are fuzzy, you’ll find yourself including questions about topics that may be nice to know but that will provide little direction to your subsequent marketing efforts. If you can’t write down two or three clear business objectives for your customer survey, you need to hit pause!

Mistake #2. Asking Too Many Questions

Remember the customer experience when thinking about a new questionnaire. If the satisfaction questionnaire goes on and on, with endless rating scales, you’re likely to incur a high abandon rate (participants who choose to exit the survey prior to completion) and/or questionable data from disinterested respondents who are simply trying to get through to the last question.

Keep your questionnaire short!

Your customers and clients will give your survey more attention, which in turn, will yield better data and more actionable results.

Mistake #3. Asking Questions the Respondent Can’t Answer

One of the temptations for novice survey writers is to ask for data or information that a respondent isn’t likely to know or have close at hand. Or to request information that might feel too personal or confidential to release in a survey – even from a known sender with promises of anonymity.

In the first case, the customer is forced to make up an answer in order to proceed, or to skip the question entirely. Either way, the ‘data’ you’ll receive will be suspect. Better to find a less taxing question that can be answered more easily – and reliably.

In the case of confidential information, a respondent may begin to lose trust in the researcher’s intention, which almost always results in a high abandon rate.

Mistake #4. Including Vague, Overlapping or Incomplete Response Categories

It’s common to find a customer satisfaction questionnaire that include classification questions (demographics, etc.) with vague or overlapping answer choices. Consider the following example.

Here’s are the possible answers for a question that asks respondents about their marital status:

A.) single

B.) not married, with kids

C.) married with kids

D.) never married

E.) divorced

In a situation like this, the respondent is left trying to interpret the intent of the question and to second-guess what was going on in the mind of the researcher who wrote the question. If you’re divorced, for example, should you answer ‘single’or’divorced’? On the other hand, if you are widowed parent, would you answer ‘single’ or ‘not married, with kids’? You’ll see several other equally troubling possibilities.

Here’s another example, this one from a question asking for respondents’ age.

A.) 18 – 20 years

B.) 20 – 30 years

C.) 30 – 40 years

D.) 40 – 50 years

E.) 50 – 60 years

F.) 60 + years

Two problems here. First, there’s no possible response for a 17-year-old. (Perhaps you intend to limit the survey to those 18 years or older, but with the present answer choices, you have no way to exclude a 17-year-old using a skip pattern.)

The second problem runs throughout the answer choices. Can you spot it? Everyone who’s age lands on a decade (20, 30, 40…) has two possible answers. I’m guessing that most of us would choose the younger bracket!

Mistake #5. Not Including Open-Ended Questions

In our haste to classify customers or prospects into specific groups and to keep the analytical task manageable, it’s easy to shy away from open-ended questions. That is, questions which ask the respondent to enter his or her opinions about a specific topic in a text box.

Such questions are notoriously difficult to analyze and often tend to be throwaways if you have a quantitative mindset. But if chosen correctly, they can provide extremely valuable insights that are rarely captured by closed-end questions.

The problem with closed-end questions is that we write them and we set up the answer choices. In turn, those answer choices are likely to reflect our organization’s internal view of the market — one that comes embedded with our existing paradigms.

Allow your customers or clients the opportunity to share their insights in open-ended questions. You’ll be surprised at how perceptive they can be and about how differently they see the world.

Mistake #6. Failing to Test the Questionnaire

Choosing not to test your questionnaire before releasing it to customers is asking for trouble. Each of the mistakes above can be caught with a thorough proofread and pretest of your customer satisfaction questionnaire.

Just as importantly, to ensure that your survey will perform as expected, all of the survey settings for online questionnaires must also be tested. That means testing the invitation link, any skip logic or piping, data collection, exit URLs and reporting output to be sure your data is stored as intended.

Once the questionnaire goes live, it is usually possible to make minor changes, but any major change, even if permitted by the survey tool, can result in a data set that becomes virtually unusable.

Remember the carpenters adage “measure twice, cut once.” You don’t get a second chance with a customer survey, so check it out twice before sending it the invitations.

Mistake #7. Forgetting to Thank the Customer

Last, but not least, remember that you are asking customers to help you improve your own business. Be sure to thank them for their time and effort and to keep their survey experience pleasant, brief and engaging. They’ll feel better about you asking their opinions if it’s clear you appreciate the time it takes them to answer.

Use this checklist as you develop your next customer satisfaction questionnaire. Avoiding these seven mistakes should improve your data quality, lead to more effective strategies and programs and result in more satisfied customers over the long run. Which in turn, should produce better business results.

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