Which Tools Analyze Open-Ended Survey Responses at Scale?
Survey platforms, theme-coding tools, self-serve taggers, AI chat and analytics agents all code open-ended answers. Here is what each is built for, what 15,000 comments a year means on each plan, and why fixed themes mat…
If you run recurring surveys and need every open-ended answer coded the same way each wave, read next to tickets, reviews and calls, joined to scores, plan, region and revenue, and reported each quarter to other teams, the strongest choice is Dimension Labs: it applies one set of Dimensions to every response in every wave, reads them with your other channels, and writes the quarterly deck from them. For narrower jobs, the text analytics in survey platforms such as Qualtrics Text iQ and Medallia analyze answers where the survey lives, theme-coding tools such as Thematic, Kapiche and Keatext serve research teams, self-serve taggers such as ClientZen and FeedSense cost less, and general AI chat handles small batches.
For a quarterly survey with thousands of comments, the deciding questions are what unit the tool charges by, whether the themes stay the same from one wave to the next, and whether it can tell you which themes go with lower scores.
Below is what each kind of tool is built for, with published prices, what 15,000 comments a year means on each plan, and a worked example on labelled sample data that shows why fixed themes matter for trending.
What software can analyze open-ended survey responses at scale?
Published facts, read October 26, 2026
Five kinds of tools code open-ended answers, and most specialist tools start with a demo
What each kind is built for, with examples and the starting price each vendor publishes.
| Kind of tool | Best for | Published starting price |
|---|---|---|
| Analytics agent Dimension Labs | Every answer in every wave coded with the same Dimensions, read next to tickets and reviews, joined to scores and customer data, with reports every team uses on a schedule. | $299 a month (Explore); custom Dimensions from $999 (Discover); the join to customer data from $2,999 (Prove). 14-day free trial, no sales call. |
| Text analytics in your survey platform Qualtrics Text iQ, Medallia | Analyzing open text where the survey already lives. | Qualtrics: $420 a month for the online research plan ($5,040 billed annually), with 1,000 survey responses; CX products by quote. Medallia: not published. |
| Feedback and theme-coding tools Thematic, Kapiche, Keatext | Coding comments into themes, usually across surveys and other feedback. | Thematic: $25,000 a year for up to 25,000 comments. Kapiche: from $2,000 a month, 50,000 rows per project. Keatext: $550 a month. Each starts with a demo or call. |
| Self-serve feedback tools ClientZen, FeedSense | Lower-cost tagging you can start without a call. | ClientZen: EUR 240 a month. FeedSense: free plan, then $49 to $399 a month. |
| General AI chat ChatGPT, Claude | Reading a few hundred pasted answers and drafting themes. | A chat subscription |
The tools overlap, but they start from different places. Survey platforms analyze text next to the scores they already hold, which is convenient if all your feedback lives there. Theme-coding tools are built around turning comments into themes and usually read more than one source. Self-serve tools are cheaper and quicker to start. Chat tools need no setup but do not keep a codeframe from one session to the next. An analytics agent treats the codes as data: every answer gets the same fields every wave, and those fields can be joined to NPS, plan, region, renewal or revenue.
If you already run surveys in Qualtrics, Dimension Labs can read the open-ended responses, scores and embedded data from there; see the Qualtrics integration. For how Qualtrics and Thematic compare on price with other tools, see Qualtrics Text iQ alternatives and Thematic alternatives. If your open text also includes tickets and reviews, the best tools to analyze customer feedback compares tools across every source.
What is the best tool to code 15,000 open-ended comments without doing it by hand?
Any of the tools above will tag 15,000 comments faster than a person. The better question is what the volume costs on each plan, because vendors charge by different units: comments, rows, responses, seats or conversations. Take a quarterly survey with 3,750 comments a wave, 15,000 a year.
Dimension Labs analysis
At 15,000 comments a year, the unit a tool charges by matters more than its headline price
A quarterly survey with 3,750 comments a wave (15,000 a year), set against each plan's published volume.
| Plan | Published volume | Does a 3,750-comment wave fit? |
|---|---|---|
| Qualtrics online research plan, $420 a month | 1,000 survey responses | No; CX products are quoted |
| Thematic Foundation, $25,000 a year | Up to 25,000 comments | Yes, all four waves (15,000) |
| Kapiche Bronze, from $2,000 a month | 50,000 rows per project | Yes |
| Keatext Basic, $550 a month | Up to 10,000 records a year | Yes per wave, but not all four (15,000) |
| Keatext Pro, $999 a month | Up to 20,000 records a year | Yes, all four waves (15,000) |
| Dimension Labs Explore, $299 a month | 1,000 a month (3,000 in month one) | No |
| Dimension Labs Discover, $999 a month | 5,000 a month, custom Dimensions | Yes, each wave in its month |
Volume is only half of it. Ask each vendor three things about your own file: whether the themes come from your codeframe or from the tool's own clustering, whether the same answer gets the same code when you run it twice, and whether you can export the coded answers with their respondent IDs. Our post on how to check AI tagging accuracy covers the agreement test to run on a sample before you trust the counts.
At larger volumes the time saved is the main gain. The NBA moved its Voice of Fan surveys to Dimension Labs and now analyzes 100,000+ open-ended responses within hours instead of months.
From a published customer story
At the NBA, open-ended survey answers went from a two-month review cycle to hours
What the NBA's Voice of Fan program reports after moving its survey verbatims to Dimension Labs.
Before, manual annotation of verbatim comments took up to two months per review cycle; the story also reports up to a 50% reduction in manual analyst workload. The Seattle Mariners run the same kind of program across all 81 home games a season, with every open-text response coded the same way.
Which tool turns a quarterly "why" question into themes you can trend over time?
To trend themes, the definitions have to hold still. Tools that discover themes fresh on each batch are good for exploring a new question, but the theme names and boundaries can shift between runs, so a rise from one quarter to the next may be the tool redrawing the lines. A versioned codeframe, applied the same way to every wave, is what makes the quarter-on-quarter numbers comparable. Our posts on how to build a codeframe and topic modeling vs LLM classification cover both approaches and when to use each.
The example below is sample data, made up for this post. A fictional software company asks "What is the main reason for your score?" every quarter and gets 3,750 comments a wave. Each answer is coded with the six product and service codes from our illustrative codeframe, and an answer can carry more than one.
Sample data
With the same codeframe every quarter, one theme nearly doubles while the rest hold steady
Share of comments carrying each code, first wave against fourth wave. A fictional software company's quarterly NPS survey, 3,750 comments a wave; an answer can carry more than one code.
Wave 1Wave 4
Setup and onboarding goes from 9.1% of comments in the first wave to 10.5%, 13.9% and then 17.1% in the fourth, while every other code moves by 1.1 points or less. With themes rediscovered each quarter, that rise could have been split across "setup", "getting started" and "data import" in one wave and merged in the next, and nobody would see it as one trend.
Counting codes is the first step. The second is to ask which codes go with lower scores, because the most frequent reason is often not the one doing the most damage.
Sample data
The most-mentioned reason is not the one with the biggest score gap
Wave 4 of the same sample survey. Left: share of comments with each code. Right: NPS of respondents whose answer carries the code minus NPS of everyone else.
In the sample, price and plan is the most common reason at 24.0% of comments, but respondents who mention it score only 16.7 NPS points below everyone else. Bugs and reliability is in 11.5% of comments and carries a 73.2-point gap, and setup and onboarding, the rising code, a 53.2-point gap. A gap like this is an association. To test which codes actually move the score, with the customer's plan, tenure and segment held constant, see key driver analysis from text. We found the same pattern in real public reviews: in which complaints cause bad reviews, the most-mentioned hotel topic had almost no effect on bad ratings.
When is each kind of tool the better choice?
Your survey platform's text analytics is the better choice when all your feedback is in that platform, you already pay for the add-on, and a theme view next to the scores is enough.
A theme-coding tool such as Thematic, Kapiche or Keatext is the better choice when the main job is coding comments into themes for a research or insights team, and an annual contract or a demo-led start suits how you buy.
A self-serve tool such as ClientZen or FeedSense is the better choice for a smaller team that wants simple tagging at a low monthly price.
General AI chat is the better choice for a one-off read of a few hundred answers, or to draft a first codeframe from a sample.
An analytics agent is the better choice when you need the same codes on every answer in every wave, themes tied to scores and customer data, and the quarterly report built for you.
Which tool keeps survey codes stable from wave to wave?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
For a recurring survey, Dimensions work like a codeframe applied by a model to 100% of responses, the same way every wave, so research, product, CX and finance trend the same fields. Custom Dimensions are on Discover and up. Answers come in from Qualtrics or any export with their scores and embedded data, and are read next to tickets, chats, calls, emails and reviews. On Prove and up, they join to CRM, billing, orders and usage by respondent or account ID, and the codes are ranked by their measured effect on NPS, retention and revenue, with confidence intervals. The agent builds the quarterly deck and dashboards, so the next wave runs through the same Dimensions without starting over.
The Seattle Mariners, who code every open-text response across a full season, put it this way: "Ultimately, it allows us to quantify the qualitative and give context and reasoning to KPI movements." See customer intelligence.
FAQ
Can I use ChatGPT to code open-ended survey responses?
For a few hundred answers and a rough read, yes. It gets harder when the answers do not fit in one prompt, when you need the same codes next quarter, or when you need each code tied to the respondent's score and account. That is where a fixed codeframe and a repeatable pipeline matter.
How long does it take to code 15,000 comments?
With software, hours rather than weeks for the coding itself; the NBA reports insight review cycles cut from about 60 days to under 6 hours. Building and testing the codeframe on a sample takes longer than the run, and it is the part to do carefully.
Should I use AI-generated themes or my own codeframe?
Use generated themes to explore a new question, then write a codeframe from what you find and apply it the same way every wave. Fixed codes are what make quarter-on-quarter trends comparable.
How many open-ended responses do I need before themes are reliable?
It depends on how small the segments are that you want to compare. Code every response rather than a sample, so small segments still have enough answers, and report the count next to every share.
To try it on your own survey, export the open-ended answers with the respondent's score and ID and start with one file of survey responses. Plans start at $299 a month with a 14-day free trial, and there is no demo or sales call.