Open-Ended Questions in CX Surveys: How to Use and Analyze Customer Responses

Key takeaways from the article

Open-ended questions in CX surveys help move beyond simply knowing “what the score is” to understanding “why the customer rates the experience that way.” They work best as a supplement to metrics and closed-ended questions, not as a replacement for them.

• NPS, CSAT, and CES show the rating scale, but comments explain the reasons.
• Good open-ended questions are short, specific, and relevant to the stage of the customer journey.
• Analyzing open-ended responses requires a process: tagging, sentiment analysis, segmentation, and prioritization.
• Without designated owners and a closed-loop feedback system, it’s better to limit the number of such questions.
• Customers feel valued when they can express their opinions in their own words.

Introduction: Why Numbers Alone Are Not Enough in CX Research

In CX surveys, it’s easy to get stuck on the results: NPS dropped from 42 to 28, CSAT after contacting support is 3.7, and CES indicates growing difficulties in the process. This is important, but it’s not enough to make decisions.

The difference is practical: “We know that NPS has dropped” is a signal. “We know the drop is due to delivery issues and unclear order status communication” is already a starting point for action. Understanding consumers’ emotions and motivations helps avoid misinterpreting quantitative results.

Open-ended questions in CX surveys provide context and reveal the reasons behind customer feedback: a specific screen, unclear promotion terms, lack of information from the courier, or a form that’s too long. Feedback provides authentic quotes useful for marketing and customer service. In retail, it is often emphasized that 70% of the shopping experience is based on customer feedback—which is why it’s important to understand the content of that feedback, not just the rating.

What are open-ended questions in CX surveys (and how do they differ from closed-ended questions)?

Open-ended questions are a type of question where the respondent does not select a predefined answer but writes in their own words. They allow for the collection of detailed, qualitative data and in-depth information about experiences.

Closed-ended questions offer predefined response options: scales, rankings, single-choice, or multiple-choice questions. Closed-ended questions facilitate quantitative data analysis, statistical analysis, segment comparison, and rapid processing of results. They are faster to analyze and summarize.

Open-ended and closed-ended questions serve different purposes. Choosing between open-ended and closed-ended questions involves deciding on the type of data and the effort required from the respondent. In other words: closed-ended questions ask “how much,” while open-ended questions ask “why.”

Examples of open-ended questions:

• “What was the most difficult part for you when placing this order online?”
• “What was the main reason for your NPS score today?”
• “What could we improve in our interactions with our support team?”
• “What surprised you most positively about our complaint process?”

Examples of closed-ended questions: NPS 0–10, CSAT 1–5, CES 1–7. A well-designed CX survey combines both types to gather valuable insights: metrics and comments.

When to Use Open-Ended Questions in CX Surveys

Using open-ended questions is crucial when a company is looking for the reasons behind customer behavior, not just the outcome. They are essential in exploratory research and useful in exploratory studies when the topic being researched does not yet have a complete list of possible answers.

Best moments:

• after NPS: “What is the main reason for your score?”;
• after a low CSAT: “What made you dissatisfied with this stage?”;
• after a high CES: “What was the biggest obstacle to resolving your issue?”;
• after a purchase, delivery, complaint, return, SaaS onboarding, or contact via hotline, chat, or email;
• after cart abandonment, a loan application, or a form submission, if it’s technically possible to collect feedback.

Open-ended questions can reveal unexpected issues. For example, an online store might discover that the problem isn’t the delivery time itself, but the lack of precise information about the courier’s arrival time. In SaaS, post-onboarding feedback may show that users understand the product but get lost during the initial setup.

When it’s not worth overusing open-ended questions

Open-ended questions require more effort from respondents. Open-ended questions also demand greater cognitive effort from respondents: they need to recall the situation, think it through, and write a comment. Too many open-ended questions can lead to survey fatigue, short answers, and a drop in the completion rate.

It is not worth multiplying them if the company lacks the resources for analysis, a classification methodology, owners of the actions, or a process for implementing changes. Analyzing open-ended responses is more time-consuming than closed-ended ones, and open-ended questions are more time-consuming to analyze.

Common limitations include: “OK” responses, interpretive chaos, acting on the basis of individual opinions, and a loss of trust when nothing changes despite the surveys. In most satisfaction surveys, 1–3 good open-ended questions are sufficient.

How to Design Good Open-Ended Questions (with Examples)

Constructing a question starts with the objective. Questions should be tailored to the research objectives, and each question should address a single topic. Good questions should be clear and precise.

Rules:

• the question is short and unambiguous;
• it refers to a specific stage, e.g., “during online card payment”;
• it does not suggest an answer or an emotion;
• it is linked to a metric, screen, or context;
• it asks about a cause, a barrier, an emotion, or a suggestion.

Good examples:

• “What was the main reason for this rating?”
• “What made it most difficult for you to find the product?”
• “What was missing in your interaction with customer service?”
• “What could we improve to make future purchases easier?”

Weaker examples:

• “Describe your experience”—too general.
• “Why was our service bad?”—leading.
• “What do you think about the company, product, price, and service?” – too broad.

Before implementation, it’s worth conducting a pilot in a single channel to see if the responses provide material for interpretation.

How to combine open-ended questions with NPS, CSAT, and CES

An open-ended question works best as a qualitative layer following a closed-ended question.

• NPS 0–10: “What is the main reason for your rating?” You can tailor the version for critics and promoters.
• CSAT 1–5: “What most influenced your satisfaction or dissatisfaction?”
• CES 1–7: “What made resolving the issue easy or difficult?”

Survey outline: NPS question, open-ended question about the reason, closed-ended question about the stage of the experience: purchase, delivery, post-purchase service. Based on these, it’s easier to assess which topics drive the score.

Practical table: examples of open-ended questions for various situations

How to analyze open-ended responses step by step

The analysis of open-ended responses should not be based on intuition or a few quotes. Data analysis should focus on recurring issues rather than individual opinions.

Process:

1. Collect responses from CX surveys, emails, chat, apps, and the internet.
2. Data cleaning: remove duplicates, personal information, and obvious typos.
3. Read a sample to understand the customers’ language.
4. Creating a category glossary.
5. Coding comments; coding allows responses to be assigned to thematic categories.
6. Manual, semi-automatic, or automatic tagging.
7. Sentiment and emotion analysis.
8. Segmentation by channel, device, product, and NPS/CSAT/CES score.
9. Linking qualitative data with metrics and business variables.
10. Prioritization and forwarding of findings to process owners.

Manual analysis of open-ended responses is time-consuming and difficult, but it provides control. Automation using NLP or AI enables rapid processing of large volumes of comments. Data analysis tools are essential for effective interpretation, though their results should be periodically verified.

Tagging customer comments – the foundation of qualitative analysis

Comment tagging involves assigning one or more codes to each comment: “delivery,” “price,” “customer service,” “mobile app,” “payment,” “complaint,” “wait time,” “communication,” “lack of information,” “technical issue,” “UX,” “service quality.”

Tagging can be manual, semi-automatic, or automatic. The category system should be consistent, understandable, and comparable over time. A glossary is a best practice: a brief guide for analysts to ensure that each category means the same thing.

It’s also worth detecting unreliable responses, as this helps verify the reliability of customer ratings.

Sentiment and emotion analysis in open-ended responses

The topic alone is not enough. A comment about delivery can be positive, neutral, or negative. Sentiment analysis classifies statements, e.g., “delivery was express,” “delivery on schedule,” “package was 3 days late.”

AI-powered sentiment analysis accelerates qualitative data analysis. It can also detect frustration, disappointment, uncertainty, trust, or delight. This is crucial in the event of a payment failure or after a shopping cart update, when the number of negative comments for the “payment” tag suddenly spikes.

Segmenting open-ended responses – don’t analyze all comments as a single mass

Segmentation addresses the needs of different customer groups. The same topic may carry different weight in B2C, B2B, mobile, brick-and-mortar stores, call centers, regions, products, or high-value segments.

Example: “lack of delivery status information” may account for 5% of total comments, but 25% of premium customer feedback. In that case, it has strategic importance. Segmentation also makes it easier to align the product roadmap, service, and communication with real-world experiences.

How to Turn Customer Comments into Insights and Actions

A quote is a single statement: “I didn’t know when I’d receive the package.” An observation is a pattern: many comments mention a lack of information. An insight is a conclusion: the lack of clear delivery status increases uncertainty and lowers CSAT.

A good insight connects comments, metrics, and a possible solution. UX: Hiding delivery costs until the final step increases cart abandonment. Customer service: A lack of information about response times leads to repeat contacts. Every insight should end with a recommended action.

How to prioritize actions based on open-ended responses

Not every issue has the same impact. Prioritize by: frequency, sentiment, impact on NPS/CSAT/CES, journey stage, segment value, implementation cost, and the risk of churn, complaints, or escalation to social media.

A simple matrix: problem frequency × impact on experience × cost of resolution. Reviewing priorities quarterly helps align findings with the roadmap and CX goals.

Common mistakes in analyzing open-ended questions

The most common mistakes are:

• reading only the most emotional quotes;
• lack of consistent tags;
• lack of segmentation;
• lack of connection to NPS, CSAT, and CES;
• lack of accountability;
• a one-off report instead of a process;
• collecting feedback without a closed-loop feedback system.

The minimum standard before scaling is: tags, segments, a reporting cycle, owners, and regular reviews.

The role of a CX platform in analyzing open-ended responses

A CX platform, such as Voice of Customer solutions, can support the collection of feedback from websites, apps, stores, and hotlines in one place. The software helps with automatic tagging, sentiment analysis, trend detection, segmentation, dashboards, alerts, and linking comments to metrics.

The goal isn’t just to count opinions, but to quickly identify patterns, root causes of issues, and topics critical to competitiveness and experience quality. This is a key element of closing the feedback loop.

Checklist: A Good Open-Ended Question in a CX Survey

Before publishing the questionnaire, check:

• Is the question short?
• Does it pertain to a single stage?
• Does it suggest an answer?
• Is it clear how the analysis will be conducted?
• Does the question support decision-making?
• Does the respondent have a reason to answer?
• Does the question duplicate closed-ended questions?
• Could the answer change the process, product, or service?

FAQ: Practical Questions About Open-Ended Questions in CX Surveys

How many open-ended questions should be included in a single CX survey?

Typically, 1–3 open-ended questions in a single survey questionnaire are sufficient. Including more makes sense mainly in exploratory research, when an organization wants to delve deeper into the topic and has the resources to analyze the data.

Do open-ended questions lower the survey response rate?

A single open-ended question after the demographic section is usually not a problem. The risk increases when the interviewer or system asks too many questions requiring a long written response.

Can open-ended responses be analyzed automatically?

Yes. Modern Voice of Customer tools support tagging, classification of customer feedback, sentiment analysis, and topic detection. The best results come from combining automation with manual verification of the results’ reliability.

Is it worth analyzing responses like “ok” or “everything’s fine”?

Yes, but as a separate category: “no substantive content.” Individually, they don’t add much, but they largely indicate whether customers with high ratings don’t feel the need to elaborate on their comments.

How often should you analyze customer comments?

In e-commerce and SaaS, it’s worth monitoring them constantly, while discussing deeper insights on a monthly or quarterly basis. Regular “voice of the customer” reports increase the importance of data within the organization.

Summary: Open-ended questions in CX surveys as a source of the real “why”

Open-ended questions in CX surveys are one of the best ways to understand the real reasons behind ratings. Numbers show the scale of success or a problem, but customers’ text responses indicate what actually needs to change.

The greatest value comes from a combination of well-designed questions, systematic analysis, segmentation, sentiment analysis, quantitative data, and a closed-loop feedback system. There is no point in asking customers open-ended questions if the organization is not ready to listen, analyze, and implement changes based on their responses.