AI-Powered Survey Summaries: How Subo Reads 1,000 Responses in Seconds
Open-ended questions are where you learn the most. “What would you change about our events?” “Why did you join this server?” “What do you want to see more of?” These questions get you real answers instead of just checkbox data.
The problem is what happens after. If you get 200 responses to an open-ended question, reading through them all takes hours. And if you get 1,000? Most community managers simply don’t do it. The data exists, but it never gets used.
Subo’s Text Analysis feature is designed to fix that.
How It Works
Text Analysis is a server-level setting. Turn it on once, and from then on Subo automatically summarizes the open-ended (free-text) responses in your reports. There’s no per-question button to hunt for and no exporting.
- Turn it on in
/settings. Set the Text Analysis option toYes(and check you have AI credits available). You can turn it off any time, for example when an open question is collecting personal information in a form (names, emails) that you’d rather not send for analysis. - Run your survey as usual. Once it’s on, every summary report includes an AI summary of your open-text responses: the main themes, common requests, and notable outliers.
- Find the summary everywhere your results appear: in summary reports you request manually, in reports auto-posted to a channel via
Auto-Post Results, and in theSummarytab of the exported Excel Full Report. It’s also available in the results dashboard of the Subo web app.
You can generate a report at any time, even while the survey is still running. If more responses come in after you first summarize, hit Refresh Text Analysis to regenerate with the latest data.
What Makes This Different from Just Exporting to your favorite LLM
You could also export your responses to a CSV and paste them into a general AI tool like ChatGPT, Claude, Gemini, etc (in fact, feel free to do so if you prefer!).
Subo’s Text Analysis is different in a few ways:
It’s contextual. Subo knows what the question was, what survey it came from, and what your overall survey structure looks like. The summary is framed around your specific question, not just raw text.
It’s integrated. You don’t need to export, copy, paste, format, or do anything extra. The summary is generated inside your results, automatically.
It’s reliable for large datasets. Copy-pasting hundreds or thousands of responses into a general AI tool has practical limits. Subo handles this through its backend integration, so the data flows directly without you managing size limits.
It’s transparent about data handling. Text Analysis is powered by OpenAI (of ChatGPT fame). Open-text answers are sent to the OpenAI API to produce the summary. Per OpenAI’s policy, that data is not used to train their models and is retained only for abuse monitoring (up to 30 days, unless the law requires otherwise). For the full picture, see Subo’s Privacy Policy.
When to Use AI Summaries
After community feedback surveys
You ran a post-event survey with the question “What could we have done better?” You got 340 responses. Instead of reading every one, Subo gives you: “The most common themes were: (1) event timing, many members wanted earlier start times; (2) audio quality in voice channels; (3) requests for a post-event recording.” You now know exactly what to act on.
During onboarding surveys
“Why did you join this server?” from 500 new members tells you what’s driving discovery, but only if you read the responses. AI summary: “Members most commonly mentioned referrals from friends (in ~40% of responses), YouTube content, and Reddit threads. Several mentioned joining after seeing a specific video.” That’s your marketing data, instantly readable.
For product or feature feedback
“What feature do you most want Subo to add?” asked to your premium subscribers. The summary gives you a ranked list of requested features without manually categorizing 200 text responses.
For qualitative pulse checks
Regular pulse surveys with one open-ended question (“How’s the vibe in the server lately?”) can quickly tell you when something’s off. Summarizing over time helps you track qualitative shifts, not just quantitative averages.
AI Credits
Text Analysis and the /draft command both run on Subo’s AI credits. All new accounts in the Free plan come with enough credits to run analysis for a few surveys. If your server is big, you may need to upgrade to a paid plan to get more credits. Paid plans include a monthly allowance of AI credits that resets each billing period, with higher tiers getting a larger allowance. The credit cost scales with how much text is being analyzed, and the allowances are sized so most servers won’t come close to running out. See pricing for the current allowance on each plan.
The /draft Command: AI-Powered Survey Creation
While we’re on the topic of AI, Subo’s /draft command is the other side of the same coin. It uses the same AI credits, but instead of summarizing responses, it generates survey questions for you.
Tell Subo what you’re trying to find out, for example “I want to understand what keeps my members engaged and what’s causing them to leave,” and /draft generates a full set of suggested questions. You can edit, remove, or add to them, but the starting point is already built. It’s especially useful when you’re drawing a blank on how to structure a survey, or want to cover every angle on a topic without spending 30 minutes writing questions. See the survey how-to for where /draft fits in the build flow.
Limitations to Know About
AI summarization is powerful but not perfect. A few things to be aware of:
It’s not a replacement for reading key responses. The summary catches themes, but context, nuance, and specific standout responses sometimes get flattened. For important decisions, supplement the summary with a spot-check of individual responses.
It works best with substantial response sets. Summarizing 10 text responses isn’t meaningfully different from reading them yourself. The value really kicks in at 50 or more responses.
Sentiment is approximate. The summary gives you a good-enough read on whether responses skew positive, negative, or mixed, but it’s not a scientific sentiment-analysis tool.
Non-English responses. The AI handles mixed-language inputs reasonably well, but accuracy can dip with heavy use of uncommon languages or community-specific slang.
Getting Started
Text Analysis is available on all plans. Turn it on in /settings, run a survey with at least one open-text question, and your next report will include the summary. Need to regenerate after more responses arrive? Use Refresh Text Analysis.
It earns its keep on surveys that ask a real open question. The suggestion box and the community health survey are both built that way, and the pre and post assessment recipe pairs open answers with scores you can compare across two waves.
