Designing Customer Surveys

✅ Survey Design Cheatsheet (PNG/PDF). With practical techniques to reduce bias, increase completion and get reliable insights ↓ 🚫 Most surveys are biased, misleading and not actionable. 🤔 People often don’t give true answers, or can’t answer truthfully. 🤔 What people answer, think and feel are often very different things. 🤔 Average scores don’t speak to individual differences. ✅ Good questions, scale and sample avoid poor insights at scale. ✅ Industry confidence level: 95%, margin of error 4–5%. ✅ With 10.000 users, you need ≥567 answers to reduce sample bias. ✅ Randomize the order of options to minimize primacy bias. ✅ Allow testers to skip questions, or save and exit to reduce noise. 🚫 Don’t ask multiple questions at once in one single question. 🤔 For long surveys, users regress to neutral or positive answers. 🚫 The more questions, the less time users spend answering them. ✅ Shorter is better: after 7–8 mins completion rates drop by 5–20%. ✅ Pre-test your survey in a pilot run with at least 3 customers. 🚫 Avoid 1–10 scales as there is more variance in larger scales. 🚫 Never ask people about their behavior: observe them. 🚫 Don’t ask what people like/dislike: it rarely matches behavior. 🚫 Asking a question directly is the worst way to get insights. 🚫 Don’t make key decisions based on survey results alone. Surveys aim to uncover what many people think or feel. But often it’s what many people *think* they think or feel. In practice, they aren’t very helpful to learn how users behave, what they actually do, if a product is usable or learn specific user needs. However, they do help to learn where users struggle, what user’s expectations are, if a feature is helpful and to better understand user’s perception or view. But: designing surveys is difficult. The results are often hard to interpret and we always need to verify them by listening to and observing users. Pre-test surveys before sending out. Check if users can answer truthfully. Review the sample size. Define what you want to know first. And, most importantly, what decisions you will and will not make based on the answers you receive. --- ✤ Useful resources: Survey Design Cheatsheet (PNG, PDF), by yours truly https://lnkd.in/ez9XQAk3 A Big Guide To Survey Design, by H Locke https://lnkd.in/eJWRnDRi How to Write (Better) Survey Questions, by Nikki Anderson, MA https://lnkd.in/eHpzr-Q6 Survey Design Guide, by Maze https://lnkd.in/e4cMp5g5 Why Surveys Are Problematic, by Erika Hall https://lnkd.in/eqTd-7xM --- ✤ Books ⦿ Just Enough Research, by Erika Hall ⦿ Designing Surveys That Work, by Caroline Jarrett ⦿ Designing Quality Survey Questions, by Sheila B. Robinson #ux #surveys

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

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.

Surveys are one of the most favoured research methods, not just among researchers, but among marketeers and PM’s as well. Creating effective surveys is crucial for collecting reliable data. Here are six essential tips from my experience in UX research: 1. Ask about the right things Only ask necessary questions. Keep your survey short and relevant. Don’t ask for information you can get elsewhere. 2. Use neutral, clear language Ensure questions are neutral and use simple language that is familiar to the user. 3. Don’t ask respondents to predict behaviour People are unreliable predictors. Ask about recent behaviours instead. 4.Focus on closed-ended questions Use closed-ended questions for quantitative data. End with one broad open-ended question for additional feedback. 5. Avoid double-barrelled questions Ensure each question addresses one concept. 6. Use balanced scales Make sure rating scales are balanced to prevent bias. 👍 Follow these tips to design surveys that respect your respondents' time and yield reliable data. How do you ensure your surveys are well-designed? Share your tips in the comments below! 👇

CSMs, are we asking the right questions? 🤔 Sometimes, we stick to surface-level questions that don’t really get to the heart of what our customers need. But small tweaks can lead to big insights. Here’s how to take your customer conversations from basic to brilliant: Go from: "Are you happy with the product?" ➡️ To: "Can you share a specific example of how our product helped you achieve a recent business goal?" Asking if someone is happy only scratches the surface. The better question digs into the value they get from the product and how it ties into their success metrics. Go from: "Do you have any issues with the product?" ➡️ To: "Can you walk me through a recent challenge you faced while using the product and how you worked around it?" A yes/no question limits feedback. Asking for a specific experience helps you understand user pain points and provides actionable data. Go from: "What features do you like?" ➡️ To: "Which feature did you use most often this past week, and how did it help your team?" It’s not just about what customers like; it's about what creates the biggest impact for their team. Go from: "What are you concerned about during your next board meeting?" ➡️ To: "What key metric are you most focused on reporting to your board next quarter?" Asking this question helps you understand your customer’s priorities and where your product can help them deliver on their goals. Go from: "What metrics are you held accountable to in your specific role?" ➡️ To: "Which metric has been most challenging for you to hit, and how can our product help improve it?" This question shifts the focus to their pain points, giving you a chance to help them leverage your product to overcome obstacles. Go from: "Are there aspects of our product that you feel you are not fully utilizing yet?" ➡️ To: "Is there a feature of our product that you haven’t fully explored but think could be valuable for your team?" This specific question gets customers thinking about how to get more value from your product and where they might need help to unlock new features. --- These updates give you more than answers. They push deeper talks that lead to useful ideas and better connections. What’s one question you plan to improve in your next customer conversation?

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 $$$.

After analysing 12 months of LTV data for a $7M+ DTC brand We found something disturbing… They were guessing who their best customers were. Here's the post-purchase quiz strategy we're implementing to solve this: So, after analysing LTV by product SKU for this arts & crafts brand. The data showed clear winners ↳ certain products had 90-day LTV growth of 40%+. But we were missing the "why." WHO buys these products? WHAT motivates their purchases? HOW do they actually use them? Without these answers, you're flying blind. And here’s how the post-purchase quiz solves this. Here's what we’re asking (and why): 1. Age range ↳ Why: Purchase behaviour, channel preferences, and messaging all shift by generation 2. Experience level with their category ↳ Why: Beginners need education. Experts want advanced techniques. One-size-fits-none. 3. Primary use case for the product ↳ Why: Same product, different jobs-to-be-done = different retention strategies 4. How they discovered your brand ↳ Why: Attribution data lies. Customers tell the truth. 5. Potential barriers to purchase ↳ Why: Surface hidden objections to address in future marketing This data doesn't just improve retention. It transforms your entire funnel. Email ↳ Segment flows by persona, not just purchase history Ad Creative ↳ Speak directly to your highest-LTV customer profiles ↳ Stop attracting the wrong customers with generic messaging Product ↳ Develop offerings for your most valuable segments

I analyzed 3,527,492 survey responses captured over the last year. Here's what the data shows... 1. Don't ask hard questions first ↳ Great surveys start with a VERY easy question ↳ Harder questions come later – once someone has "bought in" to your survey ↳ Consider starting with a Yes/No question ↳ The best surveys created on our platform have an 85%+ first answer completion rate. 2. "Choose one the following" > freeform inputs ↳ Freeform inputs are great for getting raw voice-of-customer language ↳ ...But they take effort to complete, and our monkey brains would rather just push buttons ↳ Freeform questions work best as contextual follow-ups to specific one-of-many questions, e.g. "Do you have a podcast? Yes/No" -> IF NO: "In a sentence or two, what's held you back from starting a podcast?" 3. Write conditional, "conversational" surveys ↳ Don't set up a survey that's just a flat list of one-size-fits-all questions ↳ The questions you ask should change based on previous answers ↳ ...And the question text itself should also change 4. Don't make it about you ↳ This is probably the most important point ↳ You're asking someone to give you time + personal data ↳ ...What's in it for them? ↳ Poor performing surveys don't make this obvious ↳ Great surveys make it clear that the data captured will help deliver better information, better recommendations, better everything – the questions are to help *them*, not *you* 5. No more than 4-5 answer options ↳ For choose one-of-the-following questions, limit your options to 4-5 ↳ If you need more options, show the top 4 first with a "Maybe something else?" option. If that option is selected, show other options. ↳ More options = more thinking = fewer completions 6. Short, punchy copy ↳ Poor performing surveys often have lengthy answer options ↳ Questions with high completion rates have simple, 1-2 word answer options ↳ More text = more thinking = fewer completions 7. How many questions doesn't generally matter ↳ Question #2 tends to have a 95% completion rate. Question #3 has a 96%. Everything beyond that has an 97%+ completion rate. ↳ If you're asking useful questions, people will keep answering ↳ Ideally use a survey tool, like RightMessage, that will capture data incrementally (rather than requiring the full survey to be completed) 8. Only ask what you really need ↳ Don't ask someone's gender unless it will help you give them better content ↳ Don't ask for someone's income unless this will help you qualify them or push them to the right offer ↳ Every question you ask should be framed as something that enable you to give them exactly what they need from you Which of these takeaways resonates best with you? Let me know in the comments 👇 And if you want to learn how to set up, write, and optimize great surveys, check out Segment With Surveys: https://lnkd.in/e9jdwfjn