Writing Survey Questions That Inform Decision-Making

A good survey works like a therapy session. You don’t begin by asking for deep truths, you guide the person gently through context, emotion, and interpretation. When done in the right sequence, your questions help people articulate thoughts they didn’t even realize they had. Most UX surveys fall short not because users hold back, but because the design doesn’t help them get there. They capture behavior and preferences but often miss the emotional drivers, unmet expectations, and mental models behind them. In cognitive psychology, we understand that thoughts and feelings exist at different levels. Some answers come automatically, while others require reflection and reconstruction. If a survey jumps straight to asking why someone was frustrated, without first helping them recall the situation or how it felt, it skips essential cognitive steps. This often leads to vague or inconsistent data. When I design surveys, I use a layered approach grounded in models like Levels of Processing, schema activation, and emotional salience. It starts with simple, context-setting questions like “Which feature did you use most recently?” or “How often do you use this tool in a typical week?” These may seem basic, but they activate memory networks and help situate the participant in the experience. Visual prompts or brief scenarios can support this further. Once context is active, I move into emotional or evaluative questions (still gently) asking things like “How confident did you feel?” or “Was anything more difficult than expected?” These help surface emotional traces tied to memory. Using sliders or response ranges allows participants to express subtle variations in emotional intensity, which matters because emotion often turns small usability issues into lasting negative impressions. After emotional recall, we move into the interpretive layer, where users start making sense of what happened and why. I ask questions like “What did you expect to happen next?” or “Did the interface behave the way you assumed it would?” to uncover the mental models guiding their decisions. At this stage, responses become more thoughtful and reflective. While we sometimes use AI-powered sentiment analysis to identify patterns in open-ended responses, the real value comes from the survey’s structure, not the tool. Only after guiding users through context, emotion, and interpretation do we include satisfaction ratings, prioritization tasks, or broader reflections. When asked too early, these tend to produce vague answers. But after a structured cognitive journey, feedback becomes far more specific, grounded, and actionable. Adaptive paths or click-to-highlight elements often help deepen this final stage. So, if your survey results feel vague, the issue may lie in the pacing and flow of your questions. A great survey doesn’t just ask, it leads. And when done right, it can uncover insights as rich as any interview. *I’ve shared an example structure in the comment section.

After more than 25 years in market research, I’ve learned that a single poorly worded survey question can mislead teams and compromise decision-making. One of my most memorable examples of this was when I had a client that had built a prototype of a device to track and monitor driving and wanted to target parents with teenage drivers. This was their question: With 8% of all fatal crashes occurring among drivers ages 15 to 20, motor vehicle deaths are the second-leading cause of death for that age group. We know your child’s safety is of utmost importance, and you are willing to do whatever you can to keep them safe.  How likely would you be to install a device in your car to track and monitor your teenage driver? I told them that question would guilt a lot of the parents into selecting a positive rating, but it would not give them an accurate, unbiased estimate of market potential. Here's the wording they finally agreed to. A manufacturer has created a device that tracks a driver’s behavior (e.g., speeding, slamming on the brakes) and their location. It allows a user to set boundaries for where a car can be driven and be notified if the boundaries are crossed. It also allows a user to talk to the driver while they are on the road. How likely would you be to install a device with those capabilities to monitor your teenage driver? The results were not very favorable, which upset the client but also prevented them from making an expensive mistake. #MarketResearch #SurveyDesign #DataDrivenDecisions

It’s arguably never been easier to run surveys, but that doesn’t mean we’re getting better insights from them. Often this stems back to two main issues 1) lack of clear objectives and hypotheses upfront, and 2) poorly written questions. ✅ Start with a list of hypotheses you want to investigate. Think of these as statements you believe to be true and want to confirm. This should not be a list of stats you’d like to generate from the survey. Instead, what are the ideal “headlines” you’d love to report on? For example, rather than seeking a stat like “60% of Gen Z discover new products on social media compared to 20% of Gen X”, think of the overall insight you want to gain, like “the shopping experience has changed and brands need to adapt their marketing strategy: a majority of Gen Z now use social media to discover new products, while a minority of Gen X shoppers discover products this way”. ⁉️ Now, what questions help you get to these insights? One of the most frequent question pitfalls I see is asking two questions in one question. Don’t ask a question with “or” in the middle. Each question should have a single point to it. E.g. “Which of the below channels do you use for product discovery?” If you want to also learn about channels that they are more likely to convert from, ask it in a different question. Define all terms you are using. What do you mean by “discovery”? Are all the channels you list easily understood? Questions should be as simple and specific as possible: as few words as possible, no fancy vocab. Then test your questions with a few users. Do they all understand and interpret the questions in the same way? If people tell you multiple meanings, be more simple, and more specific. To put these points together, add a sentence to your survey draft above each question (or in some cases, a set of questions) with the headline you ideally want to share.    💡 To summarize, before running a survey, what insights do you want to take from it? And do you have the right question to get you there?