Open-ended survey questions reveal what people really think in a way predefined answer options can’t. But getting useful responses depends heavily on how those questions are written and where they appear in the survey.
Poorly worded questions can lead to vague answers, and respondent survey fatigue can leave you with rushed or low-effort responses. AI-generated and fraudulent answers can make it even harder to know which feedback you can trust. Then there’s the challenge of analyzing large volumes of open text without spending hours working through every response manually.
In this guide, you’ll learn when to use open-ended questions, how to write them effectively and how to improve the quality of the responses you collect. We’ll also cover practical ways to analyze open-text feedback at scale and use AI to speed up the process.
TL;DR
In this guide, you’ll learn:
- Open-ended questions let respondents answer in their own words, which can reveal context and ideas that fixed response options may miss.
- Strong responses start with clear question design. Keep prompts focused and specific, then use open-ended questions where a written answer will add meaningful value.
- Response quality can suffer when surveys are too long or questions are vague. AI-generated and fraudulent answers can also look convincing, so data quality checks are essential.
- Avoid forcing respondents to provide a written answer when they have nothing useful to add.
- AI can speed up open-text analysis by summarizing responses and surfacing common themes across larger datasets.
- Review individual responses alongside high-level patterns to understand what’s driving the themes you find.
Open-ended question examples
An open-ended question allows respondents to answer in their own words rather than choosing from predefined response options. They can be used across different types of research, from brand tracking to product development.
The best open-ended questions are specific enough to guide the respondent while still leaving room for a detailed answer in their own words. Here are a few examples:
- Brand awareness: Thinking about [category], what brands, if any, are you aware of?
- Brand perception: How would you describe [brand] to a friend?
- Customer experience: What did you like least about your experience with [brand]?
- Product improvement: If you could change one thing about [product], what would it be?
- Pricing: What factors do you consider when deciding how much you would pay for [product]?
- Purchase decisions: Why did you choose [brand] rather than another option?
- Customer pain points: What do you find most difficult when shopping for this type of product?
- Product development: Please describe your ideal product for this category.
The key characteristics of open ended questions
In addition to having no predefined answer options, open-ended questions share a few defining traits that set them apart from other survey question types:
- Answer length is flexible: Responses can range from a few words to a detailed explanation, depending on the question and how much the respondent has to say.
- Qualitative data: Responses come back as unstructured text, which needs to be analyzed to identify patterns and themes.
- Exploratory by nature: Because respondents are not limited by a set of choices, they can raise ideas, concerns or experiences you may not have thought to ask about.
- Higher respondent effort: Respondents need to think about their response and put it into words, so open-ended questions can be more demanding than selecting a predefined option
Open-ended vs close-ended questions: how are they different?
The main difference between open-ended and closed-ended questions is how people can respond. Open-ended questions let respondents answer freely in their own words, while closed-ended questions ask them to choose from a set of predefined options.
This means they produce different types of data. Closed-ended questions give you quantitative data that is easier to measure and compare. Open-ended questions give you qualitative feedback that can help you understand the reasons and experiences behind those results.
Here are a few examples of closed-ended questions and their open-ended equivalents:
| Close-ended question | Open-ended equivalent |
| How satisfied are you with our product? (Very satisfied/Satisfied/Neutral/Dissatisfied/Very dissatisfied) | What has your experience been like using our product? |
| Which brand do you prefer? (Brand A/Brand B/Brand C/Other) | When shopping for [product category], which brands come to mind and why? |
| Would you recommend our service to others? (Yes/No) | What would you tell a friend who was considering our service? |
| How would you rate our customer support? (1-10 scale) | Please describe your recent customer support experience. |
The advantages and disadvantages of open-ended questions
Open-ended questions can add depth to your research, but they also come with trade-offs. Here are the main advantages and disadvantages to consider before deciding where to use them in your survey.
Advantages
- Uncover unexpected insights: Respondents can raise needs or concerns you may not have thought to include as answer options.
- Add context to your findings: Open-text responses can help explain the reason behind a score or preference.
- Explore more nuanced topics: Respondents have space to explain experiences that are difficult to capture through fixed answer choices.
- Generate ideas: Open-ended questions can help you gather suggestions for improving a product or new concept. The responses can then give you ideas to explore through further research.
- Capture authentic customer language: Responses can reveal how people naturally describe a product or problem, which can inform messaging and future research.
Disadvantages
- Require more effort from respondents: Writing a thoughtful answer takes longer than selecting an option, which can lead to skipped questions or low-effort responses.
- Take more work to analyze: Responses need to be categorized and interpreted before researchers can identify broader patterns.
- Can vary in quality: Some respondents will provide detailed, useful feedback while others may give vague, irrelevant or rushed answers.
- Are harder to compare: Because responses are unstructured, it can be more difficult to measure differences between groups or track changes over time.
Best practices for asking open-ended questions
Asking open-ended questions sounds easy: just let respondents do the talking, right?
In practice, the way you phrase and position these questions can have a big impact on the answers you receive. Because respondents have no predefined options to guide them, a question that is too broad, unclear or poorly timed can lead to vague or low-effort responses.
That’s why we asked Nick White, Head of Research at Attest, to share his best practices for asking open-ended questions. His advice can help you write questions that are easier for respondents to answer and more likely to generate useful feedback.
Follow these best practices to improve the quality of your open-ended responses and make the results easier to analyze.
1. Keep the structure of your survey in mind
Nick recommends not kicking your survey off with an open-ended question. Normally it helps to start with some closed ended questions to qualify your survey respondents and as a way to ease your respondents into the context of your survey, before you ask those open-ended questions.
2. Ask only the essential open-ended questions
When you’re designing a survey, only include open-ended questions that will add something useful to your research. They take more effort for respondents to answer and more time for researchers to analyze, so asking too many can reduce response quality and create unnecessary work later.
As Nick puts it, “Nobody started your survey because they wanted to write an essay.” Use open-ended questions where the written response will genuinely help you understand something you could not capture as easily with a closed-ended question.
3. Ask one question at a time
We sometimes unintentionally cover multiple subjects in one sentence, but this can blur your answers and confuse respondents. So Nick advises to only ask one question at a time.
Don’t ask who their favorite skincare brand is and why in one question, but split questions up as much as you can. This will make it a lot easier to analyze the results in the end, and you will make sure every single question gets fully answered.
4. Be specific
When asking an open-ended question, Nick’s advice is to be super-specific.
Asking someone what they like about your product can give you a full range of answers that might be hard to categorize and analyze. Instead, split it up in smaller pieces. So instead of What do you like about our product?
Ask questions such as:
- What do you think about the design of our product?
- What do you think about the usability of our product?
- What do you think about the durability of our product?
5. Avoid leading questions
As Nick warns, “Leading questions push respondents in a certain direction, as they already contain information that you’re either trying to confirm or deny.” Biased questions in surveys won’t give you a true answer from most respondents.
Here are some examples of open-ended questions that are leading so you know to avoid them.
- How much did you enjoy our last event?
- Most people hate having to drive to the cinema for more than half an hour. What about you?
- What did you find most user-friendly in our new app?
The problem with leading questions is that they can shape the answer before the respondent has had a chance to give their own view. When reviewing your questions, consider what response each one seems to invite. If the wording points respondents toward a particular answer, rephrase it more neutrally.
6. Avoid closed-ended questions in disguise
Closed-ended questions can be answered with a simple “yes” or “no” and don’t provide respondents with an opportunity to speak up. But sometimes we ask closed-ended questions thinking they’re open-ended.
Having a text field ready for respondents doesn’t necessarily mean your question is open-ended. Take, for instance, the question: Were you happy with our latest product update?
A respondent might be able to say no and expand on that, but it would be better to ask a closed-ended question using the Likert scale and follow it up with an open-ended question for added context. For example:
Thinking about [brand]’s latest product update, which of the following statements applies to you?
- I loved it
- I quite liked it
- I neither liked or disliked it
- I didn’t like it
- I hated it
Then follow up with an open-ended question like: What made you feel this way?
How to improve open-ended response quality
There are several reasons why open-ended responses can be low quality. Sometimes the question is too vague or the respondent has little relevant experience to draw on. In other cases, survey fatigue leads people to rush their answers. And with AI tools and bots now able to generate plausible open text answers, a detailed response isn’t always a reliable sign of quality.
As Nick explains, “The biggest mistake I see is treating open-ended questions as an afterthought. The quality of the answers you get is heavily influenced by how you ask the question.”
Here’s how to improve the quality of the responses you collect.
Ask focused questions that respondents can answer
Each question should focus on one topic and give respondents enough context to know what you want them to talk about. Nick recommends asking about specific situations or recent experiences where possible.
For example, “Tell us about the last time you used our brand” is likely to generate more useful detail than “What do you think about our brand?”
Make sure the question is relevant to the respondent, too. If someone doesn’t have the experience needed to answer thoughtfully, even a well-written question is unlikely to produce useful feedback.
Use open-ended questions selectively
Open-ended questions take more effort to answer, so use them where the additional context will genuinely help your research. As Nick recommends, they can work particularly well after an important rating or choice question, giving respondents something concrete to react to.
Avoid filling your survey with open-ended questions. Too many text fields can contribute to survey fatigue, which may lead to shorter or lower-effort responses as respondents progress through the survey.
Avoid forcing answers where people have nothing to add
Making every open-ended question mandatory can encourage respondents to enter filler text simply so they can move on. Where a written response isn’t essential, give people a way to indicate that they have nothing to add.
This helps distinguish respondents who genuinely have no further feedback from those who provide a low-effort answer.
Use behavioural cues to encourage thoughtful responses
Small prompts can encourage respondents to be more specific. Asking them to explain their answer, describe a recent experience or share an example gives them a clearer sense of the detail you are looking for.
Be careful with minimum character counts, though. Requiring a longer response doesn’t necessarily produce a better one and may encourage people to add filler text so they can move on to the next question.
Check that responses are authentic
A well-written response isn’t necessarily a genuine one. AI tools and bots can produce plausible open text answers, so review for relevance and consistency with the rest of the respondent’s survey.
You can also look at information beyond the written answer, such as whether the survey was completed in a realistic amount of time and whether the respondent’s location or device data raises concerns. Use multiple checks together before deciding that a response is unreliable.
Build quality assurance into fieldwork
Don’t wait until fieldwork ends to find out that poor-quality responses have made it into your data. Reviewing response quality while a survey is live gives you a chance to identify problematic responses before they affect the final dataset.
When surveys are running, combine automated checks with AI-enabled review and human oversight to help identify low-quality responses early. Also ensure that open-ended responses are manually reviewed while research is still in progress.
How to analyze open-ended questions
Analyzing open-ended responses manually can take a lot of time, especially when you have hundreds or thousands of answers to work through. AI can help streamline the process by taking on some of that manual analysis and helping you get to the key themes faster.
Here’s how to use AI and other analytics tools to review open-ended responses more efficiently at scale.
Step 1: Start with a high-level summary
Before analyzing individual responses, first get a sense of what the full dataset is telling you. AI can surface the main themes across open-text or video responses to give you a useful starting point for your analysis.
Step 2: Group responses by themes or topics (thematic analysis)
Next, organize similar responses into broader themes. If you ask respondents what they like least about a product, for example, answers might cluster around usability, performance, missing features or price.
You can identify these themes manually or use keyword analysis tools to surface frequently mentioned words and topics. Look beyond exact word matches, though. Respondents may use different language to express the same underlying idea, so related terms should be grouped into themes that are meaningful for your research question.
AI-powered tools can help merge related keywords and surface common themes across larger response sets.
Step 3: Tag or categorize responses to quantify themes
Once you’ve identified your main themes, tag individual responses with the relevant categories. A comment such as “The app is slow and crashes frequently,” for example, might be tagged under performance and reliability.
Tagging gives some structure to qualitative data. You can see how often particular themes appear and use those categories in charts or crosstabs to explore the findings further. Some analysis tools also let you create custom tags, which makes it easier to track specific themes across different groups or over time.
Step 4: Look at sentiment where emotional tone matters
Sentiment analysis can help you understand the overall tone of open-ended feedback by classifying responses as positive, neutral or negative. This can be useful for questions about customer experiences or reactions to a product.
It won’t be useful for every research question, and sentiment alone can’t capture all the nuance in a response. Use it to identify broad patterns, then return to respondents’ comments when you need to understand what’s driving their sentiment.
Step 5: Compare responses across different groups
Analyzing all open-ended feedback together can hide important differences between respondents. Break the results down by relevant audience groups, such as age or purchase behavior, to see whether certain themes matter more to some people than others.
You can also compare themes across survey waves to understand how feedback changes over time. Using filters and crosstabs can make these comparisons easier and help you quickly identify differences between groups.
Step 6: Review individual responses for context and nuance
Once you know which themes appear most often, read a selection of the responses behind each one to understand what respondents are actually saying about the topic. You may find different reasons or experiences within the same broad theme.
It is also worth reviewing outliers that don’t fit your main themes. These can surface new issues or ideas that are less common but still relevant to your research.
For video responses, use transcripts to find relevant comments, then watch the original clips to hear the response in the respondent’s own words and select examples to support your findings.
Attest makes open-ended feedback easier to act on
Getting more value from open-ended questions starts before the responses come in. You need to ask clear, focused questions and make sure you’re hearing from genuine respondents. From there, you need an efficient way to analyze the feedback at scale.
Attest supports each stage of that process. Compass, Attest’s AI co-pilot, can suggest open-text questions and help place them where they will have the most impact. It can also review uploaded surveys before launch and flag potential issues that could affect response quality.
While your survey is running, Attest uses automated checks, AI-enabled review and human oversight to help identify low-quality responses before they reach your final dataset. This approach to data quality helps ensure the open-ended feedback you analyze comes from genuine, engaged respondents.
From there, our AI-powered analysis tools can help you get to the key insights faster. AI-generated summaries highlight the main themes across open-text and video responses. Keyword analysis helps surface recurring topics, while sentiment analysis shows whether feedback is broadly positive, neutral or negative.
Together, these features reduce the manual work involved in writing and analyzing open-ended questions, helping you get from survey design to useful insight faster.
