How to Analyze Open-Ended Survey Responses: A Human Experiment

If you’ve ever opened a spreadsheet with thousands of open-ended survey responses and thought, “there goes my week,” you’re not alone.

But those answers hold real value. And yes, you can definitely get to it. Even for the most experienced insights teams and market research agencies, supported by enterprise-grade tools, turning free text into real insights is rarely a simple task. But luckily, there’s more than one way to do it right. Here’s how.

What is an Open-Ended Survey?

Open-ended surveys are used to gather customer and employee feedback, allowing people to answer questions in their own words rather than choosing from predefined options (e.g., 1–5 or Yes/No). It’s used to capture detailed, qualitative feedback — the kind that reveals deeper context and customer sentiment.

Open-ended responses is what we also call unstructured data, making up 80–90% of today’s global data. Analyzing it is tricky, but once you’ve learned how to do it — you’ll be happy you did (and so will your business).

Why Qualitative Analysis Matters

Quantitative metrics tell you what’s happening. Open-ended responses help explain why. They offer the nuance behind the score, the emotion behind the churn, the friction behind the drop-off — all in the respondent’s own words. But here’s the catch: while open-ends are often part of quantitative surveys, they come in unstructured, qualitative form. Analyzing them means converting that free text into structured, quantifiable insights — and without the right approach, you can be left with a biased, incomplete, or error-prone analysis. If you’re doing it right, though, this kind of analysis adds a ton of depth, clarity, and speed to decision-making.

Why You Should Quantify Your Open-Ended Data

Open-ended feedback is unbiased, rich, and unstructured. Without being confined to pre-defined options, respondents can share what's truly top of mind for them, often revealing nuanced perspectives that don't fit neatly into simple positive or negative categories. This makes it powerful, but also hard to use. That’s why quantifying it matters so much. Quantifying means assigning structure: tagging responses with topics, themes, and sentiment.

When done right, this lets you say how many people mentioned something, how they felt about it, and how that changed over time. It bridges the gap between messy comments and metrics your stakeholders can act on. In other words, you’re not just collecting feedback — you’re turning it into evidence. That’s the difference between a three-star review and “24% of passengers on Route A complained about flight punctuality in April.”

Open-ended responses don’t just give you quantified evidence but help you better understand your customers. While structured data tells you "what" happened, free-text responses tell you the "why" behind customer behaviors, preferences, and pain points — insights that might’ve never surfaced through multiple-choice questions alone.

7 Challenges with Open-Ended Responses

Turning open-ended feedback into insights sounds simple — until you’re doing it at scale. Between the volume, the variability, and the internal pressure to deliver results fast, a lot of teams (both in-house and through agencies) run into the same types of problems.

This becomes especially clear when looking at a real customer review. Just by analyzing this one example, we can see how challenging it can be to properly review and categorize open-ended responses:

Challenge #1 - Interpretation is subjective 🤔

Two different analysts can read the same comment e.g. “Landing was only an hour late. No worries” and tag it differently. That might be okay for small teams — but it doesn’t scale across markets, departments, cultures, or time zones.

Challenge #2 - Qualitative data is messy 🙃

Responses come in all forms: typos, slang, novels, sarcasm, emojis, long rants, or single-word answers. Even the best of models struggle to interpret meaning without context, e.g., “The best is the food.”

Challenge #3 - The process is time-consuming 🥵

Cleaning, tagging, and organizing thousands of comments takes serious effort. With large datasets, it’s a matter of days — not hours.

Challenge #4 - Manual tagging is inconsistent 😅

Reading and labeling by hand leads to fatigue, inconsistency, and error — especially when responses raise several conflicting points.

Challenge #5 - You risk missing what matters 🫠

Without structure, it’s easy to overlook key patterns. A product issue, a recurring frustration, a critical signal — all of it can get buried under generic summaries like “mostly positive.”

Challenge #6 - Trust in AI is still limited 🫤

Without structure, it’s easy to overlook key patterns. A product issue, a recurring frustration, a critical signal — all of it can get buried under generic summaries like “mostly positive.”

Challenge #7 - Most tools aren’t built for it 😢

Without structure, it’s easy to overlook key patterns. A product issue, a recurring frustration, a critical signal — all of it can get buried under generic summaries like “mostly positive.”

The method you choose in doing this makes all the difference in what you learn, and how fast you get there. That’s what we’ll unpack next — four ways to analyze open-ended survey responses, from fully manual to fully automated. You’ll see what each option looks like in practice, and where a platform like Caplena fits in.

4 Ways of Analyzing Open-Ended Survey Data

Across hundreds of teams — from fast-moving startups to global enterprises — we’ve seen four common approaches to analyzing open-ended feedback. These aren’t theoretical. They’re the methods real teams are using every day to extract insight from qualitative data.

Some are quick. Some are customizable. Some deliver impressive results. Others require a bit more pain tolerance. Let’s break them down.

Option 1: Manual Analysis

This is still the most common approach. Manual analysis means exporting responses to a spreadsheet and tagging them by hand — grouping themes, tagging sentiment, and building a taxonomy from scratch. It gives you full control and close contact with the data, which can be useful for small projects (~100–200 responses). But it doesn’t scale. It’s time-consuming, error-prone, and quickly becomes unsustainable as volumes grow.

Option 2: AI Assisted Analysis

Many teams now experiment with large language models (LLMs) like ChatGPT or Claude to summarize and tag responses. It’s fast and flexible — great for getting a high-level overview or exploring early-stage patterns. But without structure or oversight, the output can be misleading, and accuracy often drops off with larger (500+ responses) datasets. It’s a useful starting point — just not something you’d want to base major decisions on.

Option 3: Building Your Own Tool

For teams with strong internal AI capabilities, building a custom tool can offer complete ownership — tailored to your specific requirements, workflows, and integrations. But it’s a heavy lift. You’re not just building; you’re maintaining — which means ongoing updates, retraining, approvals, and technical debt. The trade-off: full control comes with high cost and complexity.

Option 4: AI-powered SaaS (Like Caplena)

Purpose-built platforms like Caplena offer a faster, more scalable path to insight. Designed specifically for the analysis of open-ended feedback, they combine automation with unparalleled speed of analysis — giving teams multilingual support, customizable topic categories, and advanced data visualization. It’s enterprise-ready without the overhead of building from scratch.

What Leading Brands Have Already Figured Out

Most companies, still to this day, choose the traditional way of doing it. That was also the case for multinational tech company, Bosch. They used to analyze their own open-ended responses this way, too. And just like for most of us, the challenge for them wasn’t really about collecting the feedback — but rather about making sense of it.

Nowadays, Bosch Comfort Home is using Caplena’s AI-powered feedback analytics (and have seen their NPS score improve from 18 to 52). And, sure, we obviously believe that we’ve got the best solution for analyzing open-ended survey responses. But it’s definitely not the only one.

Now let's compare the different ways of actually analyzing it (from spreadsheets to AI).

Lukas vs 9480 Rows of Data: A Human Experience

To test the four options, we had our good friend, Lukas (not a Caplena employee, but an unbiased person with a healthy curiosity for both data and AI), analyzed 9480 rows of real, open-ended customer feedback using our four options.

We looked at the approach Lukas took with each option, tracked how long each one took, where challenges appeared, and how reliable the results turned out to be — with the hope that Lukas’ experience will help you get a better idea of what option might be the right one for you.

About the Dataset

The dataset used in this experiment comes from real-world airline reviews submitted for the World Airline Awards, an annual ranking by Skytrax, based on global passenger feedback. It includes 9480 open-ended responses covering topics like service quality, in-flight experiences, and delays — making it a great test case for analyzing large-scale, unstructured survey data.

Option 1: Manual Analysis

Lukas began the same way many teams do — with a spreadsheet and a plan: to read the reviews, tag the themes, and structure the data. But the volume caught up fast, and after just 20 minutes, Lukas only made it through 15 entries. So instead, Lukas decided to try sampling the data.

How Lukas Approached this Option

The takeaway here was that sampling can be a smart shortcut — if you know what to sample and how to weigh it. But Lukas found that, without structure, the sampling itself became another problem to solve. And even with a well-designed sample, tagging almost 1000 rows by hand would still eat up two days minimum.

This approach gives you full control, yes — but it comes at a cost. It’s slow, repetitive, and prone to human error. And the more complex the feedback is, the less confident you become in your coding decisions.

Option 1: Manual Analysis is Good When ✅

• You’re working with fewer than 500 responses and want to stay close to customer language

• You don’t need to scale or repeat the analysis regularly

• You value depth over speed, and have the time to code responses manually

Option 1: Experiment Outcome 💡

• Time spent: 1 hour

• Progress: 40–50 rows meaningfully tagged

• Projected time for full dataset: 150+ hours

• Confidence in labels: Low — especially on multi-topic or mixed-sentiment responses

• Final verdict: Quit one hour in. Not scalable. Too error-prone to trust.

Lukas walked away from this round convinced of one thing: Even with clean data, manual analysis isn’t just inefficient — it puts insight at risk.

Option 2: AI-Assisted Analysis (ChatGPT + R)

Lukas turned to AI next. First with ChatGPT alone, then with a more structured approach combining GPT guidance and R — hoping to speed things up and avoid the grind of manual tagging.

At first, things looked promising. ChatGPT alone could handle the full dataset and returned results for every row. But those results weren’t great. When reviewing the data a little closer, sentiment was often wrong. Labels were inconsistent. And the model defaulted to “Mixed” far too often — even on clearly negative reviews.

With that in mind, Lukas quickly pivoted, using ChatGPT to instead help build an analysis workflow for R. This approach moved a lot faster than the manual work, but it came with risk. GPT was confident, even when it was wrong. R was flexible, but fiddly. Even viewing the outputs in R was clunky. It wasn’t clear where to focus, or what was worth visualizing. And neither delivered insights Lukas could fully stand behind.

How Lukas Approached this Option

Option 2: Analysing with ChatGPT + R is Good When ✅

• You’re experimenting with small datasets and looking for directional insight

• You’re technically comfortable with LLMs and scripting environments like R or Python

• You’re building a first-draft summary or exploring patterns — not presenting final insights

Option 2: Experiment Outcome 💡

• Time spent: ~1h 40min

• Progress: 1 graph, partial tagging, inconsistent output

• Label quality: Low to moderate — many issues with overuse of “Mixed” and incorrect topic tagging

• Confidence in insights: Low — needed verification before use

• Final verdict: Technically impressive, but not reliable. Useful for prototyping, not for reporting or making crucial business decisions.

Lukas ended this part of the experiment with one clear takeaway: AI can definitely help — but without structure and oversight, it could just as easily lead you astray.

Option 3: Building Your Own Tool (Hypothetically)

Let’s be clear: Lukas did not build a custom tool. Lukas is really great — but definitely doesn’t have the joint power of an entire IT department. Still, for the sake of comparison, we mapped out what it would take if you decided to go down the DIY route.

At first glance, building your own feedback analysis system sounds great. You get complete control — over features, interface, security, and the entire data pipeline. But people often forget that it’s a complex, long-term commitment that goes far beyond a one-time build.

How Lukas (Would Have) Approached this Option

The bottom line here is that you gain a lot of control — but at a pretty high price.

Option 3: Building your own tool is Good When ✅

• You have specific, repeatable needs that off-the-shelf tools can’t handle

• You’ve got an internal team with AI/ML and DevOps expertise, and the budget to support them

• You want full control over data, taxonomy, integrations, and compliance — and can handle the long-term overhead

Option 3: Experiment Outcome 💡

• Time to deploy: 12–24 months

• Effort required: High — multiple teams, long-term commitment

• Customizability: Total, but expensive

• Total cost of ownership: Often underestimated

• Final verdict: Only worth it if feedback analysis is central to your business IP

For most companies, the upfront investment is steep, the opportunity cost is real, and maintenance quickly becomes its own burden. For feedback analysis — where speed, flexibility, and reliability matter — the build route rarely wins on efficiency.

And for most teams, building in-house can be both slower and riskier. It can delay insight, add overhead, and pull valuable resources away from other priorities. That’s really why we built Caplena in the first place: to offer the control and power of a custom tool, without the wait or the complexity.

Option 4: AI-powered SaaS (Like Caplena)

After testing manual tagging, experimenting with generative AI, and fantasizing about being an entire department, Lukas moved on to Caplena — a dedicated platform for analyzing open-ended survey responses. With nothing but a 30-minute intro call under the belt, Lukas took the dataset and handled the entire process solo.

Despite being new to the tool, getting started was easy. Uploading the data was smooth (even after a small error, quickly fixed with a re-upload). Column selection and data type definition were intuitive, and in just a few clicks, Lukas had auto-generated topics ready to review.

How Lukas Approached this Option

Caplena stood out for its speed, structure, and usability. Setup took just minutes, with topics generated in seconds and only minor delays during processing. While the AI pre-tagged responses automatically, Lukas spent around 40 minutes refining labels — an investment that paid off as the model improved with each batch.

Unlike GPT or R, the results were consistently accurate and easy to explore through an interactive dashboard. By clicking into the data, anyone could see the original comments behind any trend or pattern. The AI's automatic summaries and recommendations made complex insights easy to understand and act on.

And with “Insight Chat,” Lukas could ask the tool questions and receive clear, trustworthy summaries without any extra scripting. These summaries combine quantitative metrics with qualitative insights, focusing on statistically significant patterns and trends that emerge from the data. The AI surfaces key findings that can be traced back to the source responses.

Option 4: an AI-powered SaaS tool is Good When ✅

• You need to process large volumes of text data accurately and efficiently

• You want a solution that balances automation with human-in-the-loop reviews and refinement

• You’re working across languages, teams, or markets — and need insights that scale without sacrificing clarity

Option 4: Experiment Outcome 💡

• Time spent: ~1h 15 min total, including manual review

• Progress: Full dataset processed, topics refined, dashboards live

• Label quality: High — with clear improvement over time

• Confidence in insights: High — due to traceable logic and manual validation

• Final verdict: Intuitive, fast, and reliable. A major step up in both speed and insight quality.

Compared to previous methods, Caplena didn’t just save time — it gave Lukas confidence. The analysis felt trustworthy, the outputs were stakeholder-ready, and most importantly, Lukas could focus on using the insights, not just digging for them.

Summary of the Live Experiment

Lukas tried all four approaches — from full manual tagging to AI-powered SaaS — and the differences were pretty clear. While each method has its place, only one delivered speed, structure, and results that Lukas felt confident sharing: Caplena.

Caplena is also the obvious choice for many of our clients, thanks to its efficiency and accuracy in handling open-ended feedback. That said, combining different approaches (like manual coding with AI analysis) can add value, as demonstrated by the market research agency Factworks. They chose to manually code answers in parallel with the Caplena software to fully understand the AI's behavior, identify strengths and weaknesses, and fine-tune the setup as needed.

To wrap things up, we asked Lukas to reflect on the process:

Which method gave the best balance of speed and insight?

“Caplena. It offered quick setup, accurate labeling, and easy-to-share outputs — all without compromising control or clarity.”

Which gave the most confidence in the results?

“Probably also Caplena. The traceability of labels, the ability to review and refine, and the transparency of the AI made it easy to trust what the data was saying.”

What was the most frustrating part of the whole experiment?

“Trying to clean and tag everything manually — or watching GPT confidently deliver wrong answers without realizing it. Both were exhausting in different ways.”

What would you tell someone else to avoid?

“Don’t rely on automation alone. And don’t try to do everything by hand unless you have weeks to spare. Accuracy requires structure — and structure takes more than a spreadsheet.”

How the 4 Options Compare

If you're serious about turning open-ended feedback into insights you can actually use — and share — here’s what Lukas' experience makes clear.

1. Don’t go fully manual

   Unless you’re working with a tiny dataset, tagging comments by hand will cost you time, consistency, and confidence. Sampling might help — but even that comes with its own overhead.

2. Don’t blindly trust generative AI

   LLMs like ChatGPT can be helpful for early exploration, but they’re not built for structured, large-scale feedback analysis. Their output sounds good — until it doesn’t.

3. Avoid building unless it’s your core product

   Yes, you could build your own tool. But unless your team has the bandwidth, budget, and long-term commitment to maintain it, it’s more likely to delay insights than deliver them.

4. Evaluate a purpose-built tool for your data

   Caplena was built specifically for analyzing open-ended feedback at scale. It’s fast to set up, easy to refine, and powerful enough to share insights your stakeholders can trust.

When done right, qualitative analysis can support your research and drive it forward. It gives you the “why” behind the metrics, the nuance behind the scores, and the clarity your stakeholders are looking for. Caplena is built for that — helping you go from raw feedback to actionable insight, faster and smarter.

If you’re curious about Caplena, feel free to book a call. We’d be happy to show you around.