SurveyGizmo Case Study: Take Advantage of Data You Already Have (Installment 3)

In the last installment we looked at ways to anticipate inferred differences through segmentation. But sometimes group differences are more explicit. For example, account levels are a natural grouping variable to include in the analysis. So that’s something we should include as a question in the survey, right? Wrong. We don’t have to ask the question because we already have the answer. We can look at customer data and then push those variables into the survey.

This feature comes up fairly frequently in support questions, and we have a three-part tutorial to walk you through the technical aspects of setting this. (Check out the tutorials here: Part 1, Part 2, Part 3)

So what I’d like to focus on as part of the SurveyGizmo case study, is an instance of how we’re implementing this feature to make the application more concrete. Account level is a great example of something you might want pushed into a survey (i.e. a question you don’t really need to ask). Other variables might include client start date, lifetime sales, account manager, region, last order date, or any other variable that you have in a database that would be useful for including in your analysis.

There are several good reasons for doing this. First and foremost, it’s courteous to respect respondents’ time. If we push in five variables, that’s five questions that respondents don’t have to answer. And perhaps just as important, this tactic helps you maintain professionalism. Your customers are important, and it really shows you know them and care about them when you don’t have to ask things like how long they have used your product or service.

In designing a survey, it’s easy to get carried away with adding separate questions for the variables you’re interested in using in your analysis. A good way to avoid asking unnecessary questions is to evaluate the survey from the perspective of the respondent, taking their perspective.

Ask yourself if there are any questions that seem like you “should know” without having to ask. Or think about what inferences you might make based on the question that appears.

For instance, what does it say about you as a non-profit if you have to ask me (the respondent) how much money I donated last year? It’s a fairly simple but effective way to break out of the mindset of the designer to make your survey as refined and polished as possible.

By pushing in variables automatically, we get richer data and save respondents’ time — not to mention, the data is 100% accurate (no respondent errors). It also avoids issues of survey participants getting frustrated from being forced to answer “obvious” questions that you as the company or non-profit “should know” which prevents distractions and potentially reduced response rates. By keeping things smooth and streamlined, we get data that will allow us to run the comparisons we want without sacrifice.

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