Designing Customer Surveys

This teeny tiny survey in Figma is my favourite thing I've seen in a product this year. Here's why: Finding out why people don't buy is notoriously tricky. Largely because the cohort of people who don't buy are hard to talk to. And, if you ask them about why why didn't buy too late, they forget. So, when I saw Figma researching paywall abandonment last week, I hurried to screenshot & have a closer look. There I was in Figma creating a board with some paywall inspo for a client when I noticed a little free-text module crop up: 'This is your last free file' I'm gonna need some more, I thought. So I clicked 'upgrade now'. I get to a paywall with a plan comparison. I tap through to adjust the number of seats I need (just 1 for little ol' me). I get to payment and see £168/mo. I'm not ready yet. So I close the overlay, pressing 'cancel' thinking I can just delete some old boards for now. When I get back to my Figjam file I see a one-question survey bottom right: "Is there anything preventing you from upgrading your Figma plan?' With default text: "We'd love to know..." So, I screenshot it (of course). Type my response. Hit send and see a little 'thanks' pop up after submission. I reckon the trigger was exiting payment - unsure if they do it for exiting paywall. It was so quick, so easy, so well-placed. I love it. With these surveys, you want to: 💬 Place them right after the decision-making moment 💬 Make them so easy to answer it takes less than 5 secs 💬 Thank people for their time (and ask a follow up if you need) Super curious to know what they find out. I'm guessing: price concerns, not ready to buy, not sure if need both figma and figjam, not sure how many people they need, not sure if they can collaborate with just 1 seat or how that works. Who knows. hmu Figma 🤙 would love to see the results #ux #growth #product

Remember that bad survey you wrote? The one that resulted in responses filled with blatant bias and caused you to doubt whether your respondents even understood the questions? Creating a survey may seem like a simple task, but even minor errors can result in biased results and unreliable data. If this has happened to you before, it's likely due to one or more of these common mistakes in your survey design: 1. Ambiguous Questions: Vague wording like “often” or “regularly” leads to varied interpretations among respondents. Be specific—use clear options like “daily,” “weekly,” or “monthly” to ensure consistent and accurate responses. 2. Double-Barreled Questions: Combining two questions into one, such as “Do you find our website attractive and easy to navigate?” can confuse respondents and lead to unclear answers. Break these into separate questions to get precise, actionable feedback. 3. Leading/Loaded Questions: Questions that push respondents toward a specific answer, like “Do you agree that responsible citizens should support local businesses?” can introduce bias. Keep your questions neutral to gather unbiased, genuine opinions. 4. Assumptions: Assuming respondents have certain knowledge or opinions can skew results. For example, “Are you in favor of a balanced budget?” assumes understanding of its implications. Provide necessary context to ensure respondents fully grasp the question. 5. Burdensome Questions: Asking complex or detail-heavy questions, such as “How many times have you dined out in the last six months?” can overwhelm respondents and lead to inaccurate answers. Simplify these questions or offer multiple-choice options to make them easier to answer. 6. Handling Sensitive Topics: Sensitive questions, like those about personal habits or finances, need to be phrased carefully to avoid discomfort. Use neutral language, provide options to skip or anonymize answers, or employ tactics like Randomized Response Survey (RRS) to encourage honest, accurate responses. By being aware of and avoiding these potential mistakes, you can create surveys that produce precise, dependable, and useful information. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Imagine this: you’re filling out a survey and come across a question instructing you to answer 1 for Yes and 0 for No. As if that wasn't bad enough, the instructions are at the top of the page, and when you scroll to answer some of the questions, you’ve lost sight of what 1 and 0 means. Why is this an accessibility fail? Memory Burden: Not everyone can remember instructions after scrolling, especially those with cognitive disabilities or short-term memory challenges. Screen Readers: For people using assistive technologies, the separation between the instructions and the input field creates confusion. By the time they navigate to the input, the context might be lost. Universal Design: It’s frustrating and time-consuming to repeatedly scroll up and down to confirm what the numbers mean. You can improve this type of survey by: 1. Placing clear labels next to each input (e.g., "1 = Yes, 0 = No"). 2. Better yet, use intuitive design and replace numbers with a combo box or radio buttons labeled "Yes" and "No." 3. Group the questions by topic. 4. Use headers and field groups to break them up for screen reader users. 5. Only display five or six at a time so people don't get overwhelmed and bail out. 6. Ensure instructions remain visible or are repeated near the question for easy reference. Accessibility isn’t just a "nice to have." It’s critical to ensure everyone can participate. Don’t let bad design create barriers and invalidate your survey results. Alt: A screen shot of a survey containing numerous questions with an instructing you to answer 1 for Yes and 0 for No. The instruction is written at the top and it gets lost when you scroll down to answer other questions. #AccessibilityFailFriday #AccessibilityMatters #InclusiveDesign #UXBestPractices #DigitalAccessibility

Drawing from years of my experience designing surveys for my academic projects, clients, along with teaching research methods and Human-Computer Interaction, I've consolidated these insights into this comprehensive guideline. Introducing the Layered Survey Framework, designed to unlock richer, more actionable insights by respecting the nuances of human cognition. This framework (https://lnkd.in/enQCXXnb) re-imagines survey design as a therapeutic session: you don't start with profound truths, but gently guide the respondent through layers of their experience. This isn't just an analogy; it's a functional design model where each phase maps to a known stage of emotional readiness, mirroring how people naturally recall and articulate complex experiences. The journey begins by establishing context, grounding users in their specific experience with simple, memory-activating questions, recognizing that asking "why were you frustrated?" prematurely, without cognitive preparation, yields only vague or speculative responses. Next, the framework moves to surfacing emotions, gently probing feelings tied to those activated memories, tapping into emotional salience. Following that, it focuses on uncovering mental models, guiding users to interpret "what happened and why" and revealing their underlying assumptions. Only after this structured progression does it proceed to capturing actionable insights, where satisfaction ratings and prioritization tasks, asked at the right cognitive moment, yield data that's far more specific, grounded, and truly valuable. This holistic approach ensures you ask the right questions at the right cognitive moment, fundamentally transforming your ability to understand customer minds. Remember, even the most advanced analytics tools can't compensate for fundamentally misaligned questions. Ready to transform your survey design and unlock deeper customer understanding? Read the full guide here: https://lnkd.in/enQCXXnb #UXResearch #SurveyDesign #CognitivePsychology #CustomerInsights #UserExperience #DataQuality

User experience surveys are often underestimated. Too many teams reduce them to a checkbox exercise - a few questions thrown in post-launch, a quick look at average scores, and then back to development. But that approach leaves immense value on the table. A UX survey is not just a feedback form; it’s a structured method for learning what users think, feel, and need at scale- a design artifact in its own right. Designing an effective UX survey starts with a deeper commitment to methodology. Every question must serve a specific purpose aligned with research and product objectives. This means writing questions with cognitive clarity and neutrality, minimizing effort while maximizing insight. Whether you’re measuring satisfaction, engagement, feature prioritization, or behavioral intent, the wording, order, and format of your questions matter. Even small design choices, like using semantic differential scales instead of Likert items, can significantly reduce bias and enhance the authenticity of user responses. When we ask users, "How satisfied are you with this feature?" we might assume we're getting a clear answer. But subtle framing, mode of delivery, and even time of day can skew responses. Research shows that midweek deployment, especially on Wednesdays and Thursdays, significantly boosts both response rate and data quality. In-app micro-surveys work best for contextual feedback after specific actions, while email campaigns are better for longer, reflective questions-if properly timed and personalized. Sampling and segmentation are not just statistical details-they’re strategy. Voluntary surveys often over-represent highly engaged users, so proactively reaching less vocal segments is crucial. Carefully designed incentive structures (that don't distort motivation) and multi-modal distribution (like combining in-product, email, and social channels) offer more balanced and complete data. Survey analysis should also go beyond averages. Tracking distributions over time, comparing segments, and integrating open-ended insights lets you uncover both patterns and outliers that drive deeper understanding. One-off surveys are helpful, but longitudinal tracking and transactional pulse surveys provide trend data that allows teams to act on real user sentiment changes over time. The richest insights emerge when we synthesize qualitative and quantitative data. An open comment field that surfaces friction points, layered with behavioral analytics and sentiment analysis, can highlight not just what users feel, but why. Done well, UX surveys are not a support function - they are core to user-centered design. They can help prioritize features, flag usability breakdowns, and measure engagement in a way that's scalable and repeatable. But this only works when we elevate surveys from a technical task to a strategic discipline.

Designing a survey might seem straightforward, but there are some common pitfalls you need to avoid to ensure you get unbiased and accurate data. Let’s look at the top 6 survey design mistakes and how you can steer clear of them. 1. Ask about the right things: Don't bombard your respondents with too many questions. Keep it short and focused on what's crucial for your research. If you don’t absolutely need the information, leave it out. Avoid asking questions that respondents can’t answer accurately or that you could find the answers to elsewhere. 2. Use neutral, natural, and clear Language: Avoid biased or leading questions. Use straightforward, familiar terms. For example, instead of asking, “We are committed to achieving a 5-star satisfaction rating. How would you rate your satisfaction?” simply ask, “How would you rate your experience?” 3. Don’t ask respondents to predict behavior: People aren’t great at predicting their own future behavior. Rather than asking how likely they are to use a product, ask about their recent behavior. For instance, “Approximately how many times did you use this product in the past 7 days?” provides more reliable data. 4. Focus on closed-ended questions: Surveys are primarily for quantitative data, so rely on closed-ended questions. Open-ended questions can add qualitative insights but should be used sparingly to complement your quantitative data. 5. Avoid double-barreled questions: Double-barreled questions ask two things at once, which can confuse respondents. For example, instead of asking, “How easy and intuitive was this website to use?” split it into two questions: “How easy was this website to use?” and “How intuitive did you find this process?” 6. Use balanced scales: Make sure your rating scales are balanced to avoid bias. An unbalanced scale like “Excellent, Very Good, Good, Poor, Very Poor” can skew results. Instead, use an equal number of positive and negative options, such as “Excellent, Good, Neutral, Fair, Poor.” Have you encountered any of these survey mistakes before? Or maybe you have additional tips to share? Share your thoughts and experiences in the comments below! 👇

If one more person sends a survey with free-form questions, I might just scream. Keep free-form questions for customer interviews. Please. Surveys (and other quantitative research) are best when you need high quantity of responses. If you want quality, detailed responses, do a customer interview instead, where the person has agreed to give you 15-30 minutes of their time. If you're struggling to create multiple-choice options in your survey, then you might not know enough about your problem or target audience yet. And in that case, you should do a customer interview or market research first. You can then use the insights from those to create multiple-choice options so you can get larger sample size via surveys. Example👇🏽 Let's say I want to understand more about the problems that people face with content creation. The best solution isn't to create a survey with a free-form question like "What challenges do you face with content creation?" Instead, I'll speak with 5 people and do a search on Google and social media to see the common problems people mention. Then, I'll take the most repeated problems and put them in a survey as multiple-choice options. So when people fill the form, I can see which problems are most popular from a larger sample of maybe 100 people. Yes, it takes more time from you as a marketer. But it takes less time for your survey respondents. Which guarantees that more people fill your survey, which means you get better sample data.

Post purchase surveys are my FAV attribution But is this the BEST post purchase survey? 1. “Where did you FIRST hear about us?” - Your most valuable answer, so it goes first. - List your paid channels as the options. 2. “How long ago was that?” - Give time frame options - If you run specific creators or influencers, ask which one here. 3. “What made you buy today?” - Your best selling point, in the customer's own words. - Feed it straight into ad copy. 4. “What almost stopped you from buying?” - Gold for objection handling. - Every answer is a hook for your next ad. 5. “Who did you use before, or who else did you consider?” - Competitive intel you won't get anywhere else. 6. “What's one thing you wish we offered?” - Product and roadmap signal. BONUS: Matt Bahr, CEO of Fairing, gave me two of his favorites: 1. "How would you describe yourself" / "which of these best describes you". - It's a persona building question which when pivoted against attribution/LTV is super valuable 2. "Was there any part of our store that was difficult to use?" - Yes or no answer with follow-up. - His larger 100M+ customers LOVE this question and ends up being a core driver for their CRO efforts. You could also ask a different attribution question to returning users ie. "What led you back to Bombas?" Important benchmarks: - Aim for 30%+ answering the first question. - Expect to lose 50% of respondents at every question. E.g., Q1 = 30%, Q2 = 15%, Q3 = 7.5% etc Any post purchase survey / retention marketers out there who can share more data + learnings?

75% of post-purchase surveys WASTE their shot by leading with this one question... "How did you hear about us?" It is one of the most self-serving questions in ecommerce. It’s about fixing broken attribution models. It’s a marketer’s desperate attempt to plug gaps in GA4 and triangulate ROAS. It's a question designed to serve YOU, the marketer. Not the customer. Think about it... You finally got someone to buy. You earned their trust. You’ve got their attention at peak emotional engagement... They are literally and figuratively bought in. They're excited about what's coming. And the first thing you ask is: “How did you hear about us?” 🤔 What a waste. That post-purchase moment is sacred. It’s when people are most honest. Most open. It’s the perfect time to go deeper: - What are you hoping this product solves for you? - What convinced you this was the right fit? - What brands do you love (and why)? - What would make you come back again? Those answers don’t just fill a spreadsheet. They fuel retention, guide product strategy, and deepen customer relationships. Great brands don’t just collect data... they collect insight. They don’t just chase attribution... they chase understanding. So sure, ask about the channel if you must… But treat that thank you page like a trust moment. Because the goal isn’t just to know where they came from... it’s to know why they’ll come back.

Post-purchase surveys are being used wrong by 99% of DTC brands ↳ Here's how to actually make them valuable Everyone runs post-purchase surveys asking: → "Where did you hear about us?" → "How did you find our brand?" But the uncomfortable truth is: Your customers are GUESSING these answers. When was the last time YOU accurately remembered where you first saw a brand? Exactly. These surveys aren't the attribution solution everyone claims they are. But you could be mining them for creative insights. Here are the questions you should ask instead: 1. "What almost stopped you from buying today?"     → Reveals purchase objections → Shows what's missing from your ads → Identifies landing page weaknesses 2. "What's the main problem you're hoping this solves?"     → Reveals customer pain points → Gives you language for new hooks → Reveals benefits you're not highlighting 3. "What other brands/products did you consider?"     → Shows who your real competitors are → Highlights your unique advantages → Exposes gaps in your positioning Using surveys for attribution is good (better than relying blindly on attribution tools) But they can be used for something more powerful. Using them for creative insights is a goldmine. More informed creative = better performance = more $$$.