Open-ended comments on surveys give respondents room to answer in their own words instead of picking from a list. A single text box can surface a complaint, a compliment, or an idea that a rating scale would never catch.
Most surveys lean on closed-ended questions because they are fast to analyze. But a score alone rarely explains why a customer rated an experience a 6 instead of a 9, or why an employee feels disengaged. Most online survey software supports both formats side by side.
This guide covers what open-ended comments are, how they differ from closed questions, real examples you can use, and how to turn free-text feedback into insight your team can act on.
What are open-ended comments on surveys?
Open-ended comments on surveys are free-text responses that respondents write in their own words instead of selecting from fixed answer choices. There is no predetermined list of options, so the respondent decides what to say and how much detail to include.
This format matters most when you need the reason behind a number. A satisfaction score tells you a customer is unhappy. An open-ended comment tells you the delivery arrived three days late, or that a support agent solved the issue in one call.
An open-ended question and an open-ended comment are closely related but not identical. The question is the prompt itself, such as “What could we improve?” The comment is the actual text the respondent submits in response.
Open-ended vs closed-ended survey comments
Confusing these two formats is common, and the choice changes what your data can tell you. Closed-ended questions restrict answers to a fixed set of choices, such as a rating scale or a multiple-choice list. Open-ended formats let the respondent write freely, with no boundaries on wording or length.
Here is how the two compare side by side.
| Aspect | Open-ended comments | Closed-ended questions |
|---|---|---|
| Response format | Free text, written by the respondent | Fixed choices, such as ratings or multiple choice |
| Data type | Qualitative, rich in detail | Quantitative, easy to tally |
| Analysis effort | Requires coding, sentiment analysis, or AI text analysis | Fast to chart and compare |
| Best use | Uncovering the reason behind a score | Measuring the score itself |
| Respondent effort | Higher, takes more time to answer | Lower, quick to complete |
Most well-built surveys use both formats together. A closed question sets the scale, and a linked open-ended comment explains the reasoning behind it. For a deeper breakdown of when to use each, see this comparison of open-ended vs. closed-ended questions.
Why open-ended comments matter for customer and employee feedback
Open-ended comments matter because they surface information you never thought to ask about. A single free-text field can reveal a bug report, a pricing objection, or a genuine compliment, all in the respondent’s own words.
Here is what open-ended feedback typically uncovers that closed questions miss:
- Root causes behind an unusually high or low score
- Unprompted product ideas and feature requests
- Specific praise you can share with the team responsible
- Early warning signs before a customer or employee disengages
- The exact language people use, which is useful for support scripts and marketing copy
These comments are also the raw material for customer sentiment analysis. Sentiment scoring depends on respondents writing freely, since a fixed rating scale gives the tool nothing to interpret.
Examples of open-ended comment questions
The strongest open-ended comment questions are specific enough to guide a useful answer, without boxing the respondent into a script. Vague prompts get vague replies.
Below are examples grouped by common use case.
| Use case | Example open-ended comment question |
|---|---|
| Customer experience | What is the main reason for your rating? |
| Employee feedback | What would make your work easier this quarter? |
| Product feedback | What almost stopped you from completing your purchase today? |
| Support interactions | What could the agent have done differently? |
| Catch-all comment box | Is there anything else you would like us to know? |
A catch-all comment box near the end of a survey is worth including even without a specific question in mind. It gives respondents a place to flag something you never thought to ask about, including feedback on the survey itself. This is one reason open-ended questions remain a standard part of most questionnaire designs.
Why open-ended comments are hard to analyze
Open-ended comments are harder to analyze because there is no fixed scale to average or chart. Every response has to be read, categorized, or processed with text analysis software before it becomes usable data.
Respondents also answer these questions less often than closed ones. According to Pew Research Center, nonresponse rates for open-ended questions on its American Trends Panel average around 18%, with some questions skipped by more than half of respondents, compared with just 1% to 2% for closed-ended items on the same surveys.
Volume compounds the problem. A survey with a few hundred closed-ended responses is simple to chart in minutes. A few hundred open-ended comments take hours to read manually, and a few thousand make manual review unrealistic without help.
How to analyze open-ended survey comments
There is no single correct way to analyze open-ended comments. The right method depends on your response volume, how urgently you need answers, and how much nuance the topic requires.
Here are four common approaches, ordered by increasing scale:
- Manual reading and tagging.
Works well for small samples, under a few hundred responses. A person reads each comment and notes recurring themes by hand.
- Verbatim coding.
Each response is assigned to a small set of predefined categories, turning free text into countable data you can chart like a closed-ended question.
- Sentiment analysis.
Software scores each comment as positive, negative, or neutral, which is useful for spotting shifts over time without reading every word.
- AI-powered topic modeling.
Machine learning clusters comments into themes automatically, which scales to thousands of responses without a manual coding pass.
Most teams start with manual reading and move toward AI-assisted methods as volume grows. General-purpose text analysis tools now combine several of these methods, from word clouds to automated theme clustering, in a single dashboard.
Common mistakes to avoid with open-ended comments
A few avoidable mistakes turn rich, open-ended data into wasted respondent effort. Watch for these before your next survey goes live.
- Asking too many open-ended questions in one survey, which raises abandonment
- Leaving the prompt too vague, such as “Any comments?” when a specific question works better
- Ignoring comments after collection because reading them feels time-consuming
- Sharing raw comments across the company without removing personally identifiable details
- Treating one loud comment as representative of every respondent
Good survey design practices, such as placing one open-ended question per section rather than clustering them, help avoid most of these problems before they start.
How QuestionPro helps you act on open-ended comments
QuestionPro brings free-text analysis into the same dashboard as closed-ended results, so a sentiment shift and a score movement show up together instead of in separate reports. From inside a survey’s results, a team can:
- Filter open-ended text responses by segment, such as region or customer tier
- Run AI-powered text analysis to group thousands of comments into themes without a manual coding pass
This matters most once comment volume grows past a few hundred responses, when manual reading stops being practical, and a coding backlog starts to form.
Getting more value from the comments you already collect
Most organizations already ask for open-ended feedback somewhere in their surveys. The gap is usually not collection, it is follow-through. Reviewing comments on a regular schedule, even monthly, turns a folder of text into a habit of small, steady improvements.
The businesses that get the most from this data treat it the same way they treat their quantitative metrics: reviewed on a cadence, shared across teams, and tied back to specific changes. A comment that never gets read is no better than a question that was never asked.
Frequently Asked Questions (FAQs)
Most survey researchers recommend one or two per survey. Adding more raises abandonment rates significantly, since free-text answers take longer to write than selecting a rating or a multiple-choice option.
It depends on how the survey is configured. Many platforms let you collect comments without linking them to a name or email, but internal identifiers like employee ID or account number can still make a respondent identifiable.
Not entirely. AI tools are strong at sorting volume and flagging sentiment shifts, but a person should still spot-check samples, since automated tools can miss sarcasm, context, or industry-specific phrasing.
There is no universal benchmark, but expect noticeably lower completion rates than for closed-ended items on the same survey. A well-worded, specific prompt placed early in the survey typically gets more complete answers than a vague one placed at the end.
Optional is generally the better choice. Forcing a response often produces low-effort filler text like “n/a” or a single word, which adds noise to your dataset without adding any real insight.
