Anyone who designs surveys is familiar with this situation: a customer gives a low rating, an employee leaves negative feedback, or a consumer says they are not completely satisfied. While the numerical score is important, it is often not enough.

A score tells you what happened, but it rarely explains why it happened.

This is exactly where open-ended questions come in. They are one of the most valuable tools in survey design.

Unlike closed-ended questions, which ask respondents to choose from predefined answer options, open-ended questions give people the freedom to express themselves in their own words. They allow respondents to share experiences, motivations, expectations, and pain points using natural language.

This is what makes open-ended questions so valuable. The most meaningful insights are often not the ones researchers anticipated, but the ones that emerge naturally from respondents’ answers.

A customer may rate their experience with a service as 6 out of 10, but the real strategic insight might be found in the comment that follows:

“The service was good, but I struggled because I couldn’t find clear information before making my purchase.”

That single comment provides a level of context that no numerical scale could ever capture.

In recent years, the role of open-ended questions has changed dramatically. For a long time, their use was limited by the challenge of analyzing large volumes of text. Surveys containing thousands of open-ended responses required lengthy and labor-intensive manual analysis.

Today, advances in artificial intelligence are transforming this landscape. Natural language processing (NLP) technologies can identify recurring themes, categorize comments, analyze sentiment, and most importantly—enable AI adaptive follow-up questions that automatically probe deeper based on respondents’ answers.

Open-ended questions are no longer just a traditional research tool. They are becoming one of the core components of the next generation of AI-powered surveys.

What are open-ended questions?

Open-ended questions are survey questions that allow respondents to provide a free-form answer, without being limited to a predefined list of options.

In a traditional survey, we can distinguish between two main categories:

  • Closed-ended questions, where participants select an answer from a set of predefined options.
  • Open-ended questions, where participants create their own response using their own words.

Example of a closed-ended question:
“How satisfied are you with the service you received?”

  • Very satisfied
  • Somewhat satisfied
  • Slightly satisfied
  • Not satisfied at all

This type of question makes it possible to measure satisfaction levels and generate data that can be easily compared and analyzed.

Example of an open-ended question:
“What is the main reason for the rating you provided?”

In this case, the numerical score is enriched by an explanation.

Respondents do not need to adapt their thoughts to predefined categories. Instead, they can freely describe what they consider most important.

This difference is essential in qualitative research because it makes it possible to collect information that often remains hidden when relying exclusively on quantitative methods.

Open-ended responses can reveal:

  • Rational motivations;
  • Emotions;
  • Expectations;
  • Suggestions and ideas;
  • Previously unidentified issues;
  • The exact language customers use.

This last point is especially important in marketing, customer experience, and market research. The words customers use spontaneously can provide valuable insights into how a product, service, or brand is truly perceived.

A common mistake in survey design is to think of open-ended questions and closed-ended questions as alternative tools.

In reality, they serve different purposes:

  • Closed-ended questions primarily answer the question: “How much?”.
  • Open-ended questions more often answer: “Why?” and “How?”.

This is why the most effective surveys combine both approaches, integrating quantitative metrics with qualitative insights to achieve a more complete understanding of customer opinions and experiences.

Why are open-ended questions important in surveys?

The main goal of a survey should not simply be to collect data, but to generate valuable knowledge that supports better decision-making.

Closed-ended questions are essential when the goal is to measure phenomena, compare results over time, or create standardized metrics. However, they have a limitation: they require respondents to choose within a framework that has already been designed by the researcher. This means they can only capture information that someone has already anticipated.

Open-ended questions, on the other hand, make it possible to uncover unexpected insights. Let’s imagine a study analyzing customer satisfaction for an e-commerce platform.

A closed-ended question might ask:
“How would you rate your online purchasing experience?” using a scale from 1 to 10. A customer might answer 7. But what does that 7 actually mean?

An open-ended response could reveal:
“The website was easy to use, but during checkout it was unclear when my order would be delivered.”

This operational detail can become a concrete opportunity to improve the customer journey.

The same principle applies across many other fields. For example:

  • In market research, an open-ended question can help identify emerging customer needs and trends.
  • In HR surveys, it can reveal organizational issues that were not previously considered.
  • In customer experience research, it can explain the reasons behind an NPS or CSAT score.

When should you use open-ended questions in a survey?

There is no ideal number of open-ended questions that works for every survey. The right choice depends on the research objective, the audience involved, and the level of detail required. However, there are specific moments when using open-ended questions can be particularly effective. Let’s explore the most common scenarios.

After a numerical rating

This is one of the most common uses of open-ended questions. After a question such as: “How likely are you to recommend this service to a colleague or friend?”, you can ask: “What is the main reason behind your rating?”. This transforms a simple numerical metric into a meaningful insight, helping you understand the reasons behind the score.

When exploring new customer needs

When a company wants to develop a new product or improve an existing service, it often does not yet have all the information needed. An open-ended question such as: “What problem would you like to solve better compared to the solutions you currently use?” can reveal unexpected opportunities and emerging needs.

In internal employee surveys

People within an organization can provide insights that are difficult to capture through numerical rating scales alone. Questions such as: “What change would most improve your work experience?” can provide valuable feedback and actionable insights for management teams.

During critical moments in the customer journey

After a complaint, customer churn event, or negative experience, an open-ended question can help uncover the real issue behind the problem. The response becomes more than just data: it becomes a direct account of the customer’s experience.

How to write an effective open-ended question

An effective open-ended question is not simply a question that allows respondents to answer freely. The quality of the information collected depends largely on how the question is designed.

A common mistake when creating surveys is to think that adding an empty text field is enough to generate valuable insights. In reality, a question that is unclear, too broad, or poorly formulated can lead to generic answers that are difficult to interpret.

Designing open-ended questions requires the same methodological attention used when developing quantitative survey questions.

A good open-ended question should have some essential characteristics: it should be clear, focused, neutral, and connected to a specific research objective.

The first rule is to avoid overly generic questions. A question such as: “What do you think about our service?” may seem simple and straightforward, but it often produces answers with limited value, such as: “It’s good”, “I had a good experience”, “Everything was fine”.

The problem is not the respondent’s answer, but the fact that the question did not provide enough direction.

A more effective version could be: “Which aspect of our service had the greatest impact on your experience?”. In this case, respondents are encouraged to reflect on a specific element while still having the freedom to express their own opinion without being guided toward a predefined answer.

Another important principle is neutrality. Open-ended questions should leave room for the respondent’s genuine opinion, avoiding language that implicitly suggests a positive or negative evaluation. For example: “How much did you appreciate the speed of our excellent delivery service?” already contains a judgment.

A more appropriate version would be: “How would you evaluate your experience with the product delivery process?”.

The language used in survey questions directly influences how people interpret them and can introduce bias into research results.

The most common mistakes when writing open-ended questions

Although they may seem simple, open-ended questions can present several challenges. One of the most common mistakes is trying to ask too much in a single question.

For example: “How would you evaluate the product, the price, the customer service, and the overall experience? Do you have any suggestions for improvement?”. This question actually contains four or five different requests. Respondents may focus only on the most noticeable aspect or provide an incomplete answer.

It is better to separate different topics into multiple questions or identify which information is truly the priority for the research objective.

Another common mistake is using questions that do not have a clear purpose in the following analysis phase. Every question included in a survey should serve a specific function. Before adding an open-ended question, it is useful to ask: what decision will I be able to make based on the answers collected?. If there is no clear answer, that question is probably unnecessary.

Another issue concerns the lack of context. A question such as: “What would you improve?” can be interpreted in many different ways. Respondents might refer to the product, the service, the price, the communication, or the entire relationship with the company. A more precise version could be: “Which aspect of the online purchasing process would you improve to make the experience easier?”.

Clear and precise wording directly improves the quality of the data collected.

From response collection to analysis: the biggest challenge of open-ended questions

For a long time, the main challenge of open-ended questions was not collecting responses, but analyzing them. A single open-ended question can generate hundreds, thousands, or even millions of comments. Transforming this unstructured text into actionable information requires structured qualitative analysis processes.

Traditionally, managing open-ended responses involved a manual coding phase. Researchers would read comments, identify recurring themes, and assign categories. For example, when analyzing thousands of responses to the question “What aspect of our service would you improve?”, researchers might identify categories such as:

  • Response times;
  • Ease of use;
  • Price;
  • Product quality;
  • Customer support.

This process makes it possible to transform unstructured text into analyzable data. However, it also has several limitations: it is time-consuming, requires specific expertise, and can be influenced by the individual analyst’s interpretation. Moreover, as the volume of responses increases, maintaining speed and consistency becomes increasingly difficult.

This is precisely one of the areas where artificial intelligence is introducing a significant transformation.

Natural Language Processing (NLP) technologies have made it possible to analyze large volumes of text much faster than in the past. Today, AI-powered systems can automatically identify concepts, emotions, recurring topics, and relationships between words.

This means that large amounts of customer feedback can be quickly transformed into strategic insights.

For example, a company might collect 5,000 responses to the question: “What could we improve in our customer experience?”. A traditional approach could require weeks or months of analysis. An AI-powered system, on the other hand, can quickly identify that:

  • 35% of comments relate to response times;
  • 20% relate to information clarity;
  • 15% relate to the purchasing process;
  • New and previously unidentified issues are emerging.

Artificial intelligence does not replace the researcher’s interpretation. Instead, it accelerates the transition from raw data to actionable insights. The greatest value is not simply saving time, but identifying weak signals and emerging patterns that could easily be missed through manual analysis alone.

From automated analysis to AI follow-up questions: surveys become conversations

The most interesting evolution, however, goes one step further. Until now, artificial intelligence has mainly been used after data collection, to analyze the responses gathered. The new frontier is using AI directly during the survey completion process.

This is where AI follow-up questions (or AI-driven follow-ups) come into play: dynamically generated follow-up questions based on the respondent’s previous answer.

The traditional model works like this:
Question → Answer → End.

The AI-powered model becomes:
Question → Answer → Understanding of the response → Personalized follow-up.

Let’s look at an example.
Initial question: “How would you evaluate your experience with our service?”
Response: “The service was good, but I had some issues with customer support.”

A traditional survey would simply record this sentence. An AI follow-up system could instead ask: “Which aspect of customer support created the greatest difficulty?” or “How much did this issue affect your overall experience?”.

The conversation becomes more intelligent and more similar to a qualitative interview.

The main advantage of AI follow-up questions is the ability to increase the depth of collected insights without making surveys longer or more complex.

Traditionally, achieving a high level of detail required designing many additional questions. However, this approach created several challenges:

  • Longer surveys;
  • Higher risk of respondent drop-off;
  • Greater complexity during survey design;
  • Difficulty anticipating every possible scenario.

With an adaptive approach, the system can ask different questions to different respondents based on what they have actually shared. A customer dissatisfied with delivery times will receive follow-up questions about delivery, while a customer frustrated by usability issues will receive a completely different set of questions.

The survey therefore becomes more relevant for each individual participant. This represents a significant shift in survey design logic: a questionnaire is no longer just a fixed sequence of questions, but a dynamic environment capable of adapting to the interaction in real time.

When AI follow-up questions can generate greater value

The applications of AI follow-up questions are extensive and cover virtually every field where questionnaires and surveys are used.

Customer experience

In customer satisfaction surveys, open-ended questions are essential for understanding the reasons behind a positive or negative evaluation. A low score indicates that there is a problem; an AI follow-up question can help identify what that problem actually is.

For example:

  • Rating: “3 out of 10”
  • Open-ended question: “What is the main reason behind your rating?”
  • Response: “I had to wait too long to receive support.”
  • Automatic follow-up: “How long did you have to wait?” / “What would have been an acceptable waiting time in your opinion?”

In just a few steps, it is possible to collect much more useful information and identify concrete actions for improvement.

Employee experience

In internal employee surveys, open-ended responses often contain sensitive and complex information. An employee might write: “I would like to have more clarity around the team’s priorities.”. An intelligent follow-up could ask: “In which situations has this lack of clarity had the greatest impact?”. This makes it possible to transform a generic comment into an actionable and analyzable insight.

Market research

During the development of new products or services, the ability to automatically explore user needs in greater depth can be particularly valuable. A response such as: “I am looking for a solution that is easier to use” can be explored further by asking: “Which aspect of current solutions do you find most complex?” or “Which activity would you like to make faster?”. The insights collected can directly support product decisions.

The role of more advanced survey software

The growing importance of open-ended questions and artificial intelligence is driving survey platforms to evolve rapidly.

In this context, IdSurvey also plays a role as a platform dedicated to collecting and managing data through professional questionnaires.

The evolution toward smarter surveys capable of integrating artificial intelligence features represents a natural step in responding to researchers’ new needs: collecting richer information, reducing manual analysis efforts, and obtaining insights faster.

However, the value of technology remains connected to the ability of professionals to use it within a sound research methodology. A successful survey is not created by the number of questions it contains, but by the quality of the insights it is able to generate.