This article will give you tips on how to analyze responses from a User Roundtable Attendee survey about Topics of Interest. If you ever wondered how to make sense of all those qualitative answers, you're in the right place.
Choosing the right tools for analyzing survey responses
The approach you take—and the best tool to use—depends on what kind of data you’ve got. If your survey asks both multiple choice and open-ended questions, you’ll need to analyze them differently:
Quantitative data: These are questions like “Which topic interested you most?” or multiple choice polls. You can easily count up responses with tools like Excel or Google Sheets, and even simple pie charts get you quick wins.
Qualitative data: This covers open-ended questions (“What topics do you wish to explore at future roundtables?”) and any follow-up, free-text responses. Reading these all by hand gets overwhelming fast—especially as your survey grows. For this, AI-powered solutions are a game changer.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you want to analyze qualitative data quickly, you can copy responses into ChatGPT or a similar GPT model. This lets you chat about your data and extract themes or insights. You’ll get instant summaries or even sentiment analysis if you prompt it right.
However, managing survey data this way is rarely efficient. Exporting from your survey platform, splitting it up to fit context limits, and pasting into the chat all take time. You may also need to handle sensitive data extra carefully.
All-in-one tool like Specific
Dedicated tools like Specific are built for survey collection and analysis. Specific lets you design the survey, ask smart AI-generated followups, and automatically analyze results—no spreadsheet wrestling required.
What’s unique is that Specific’s AI not only summarizes open-ended answers, but discovers core ideas, patterns, and actionable insights from the data. You can chat directly with the AI about results, just like with ChatGPT, but with extra features designed for working with survey context and large sets of feedback.
Response rates and data consistency seriously improve with this kind of AI-powered platform, cutting analysis time dramatically—some organizations even see up to a 70% reduction in time from survey to final report! [1]
Collectively, these options make analyzing complex roundtable surveys far less daunting. If you’re curious about building your own survey for this audience and topic, you’ll find templates for user roundtable attendee surveys that can help you get started, fast.
Useful prompts that you can use to analyze User Roundtable Attendee responses
If you’re using ChatGPT, Specific, or any AI tool to analyze your qualitative survey data, prompts make all the difference. Here are several you can try—especially relevant to your Topics of Interest survey:
Prompt for core ideas: Use this to pull out recurring themes from lots of open-ended answers. It’s the default analysis prompt in Specific, but works well in ChatGPT too. Paste your responses and use:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
Context boosts performance: Whenever possible, provide context about your survey audience, the goal, and what you want to learn. For example:
I conducted this survey with roundtable attendees to understand their main areas of interest for future discussions. My goal is to identify emerging topics and what motivates participation. Given this, extract the top trends, mention how many people spoke about each one, and flag any new or unexpected interests.
Dig deeper into a core idea: Once a trend or core idea emerges, ask: "Tell me more about XYZ (core idea)". This helps you surface specific details and perhaps even discover new sub-topics.
Prompt for specific topic: To validate a hunch or check if people discussed a particular trend: "Did anyone talk about [a particular topic]? Include quotes."
Discover segments (personas): To segment attendees by their interests or needs, use: "Based on survey responses, identify and describe a list of distinct personas, summarizing key characteristics, motivations, goals, and relevant quotes."
Surface pain points & challenges: Ask: "Analyze responses and list the most common pain points or challenges mentioned. Note frequency and summarize each."
Map out motivations & drivers: Use: "From the survey conversations, extract the primary motivations or reasons for attending, group similar ones, and provide supporting quotes."
Analyze sentiment: For overall mood: "Assess the overall sentiment of these responses, highlight positive and negative phrases."
Explore suggestions & unmet needs: To mine for actionable ideas: "List all suggestions or ideas, organize by topic or popularity, include direct quotes." and "Highlight any unmet needs or opportunities respondents mentioned."
Smart prompting turns AI into a true research partner, cutting down the time to actionable insights by up to 90% compared to manual methods. [2] For more inspiration, see our deep dive on AI-powered survey response analysis.
How AI handles different question types in Specific
Specific analyzes every survey response according to the structure of each question. Here’s how it works for the most common types you’ll find in a roundtable attendee survey:
Open-ended questions (with or without followups): The AI delivers a concise summary of all responses, plus a breakdown of followup answers associated with each initial question. That means you get both breadth and depth—every extra layer is included in one place.
Choice questions with followups: For each choice, Specific summarizes all relevant followup responses, so you see not just what option was picked but why. For example, you could easily compare what motivates attendees selecting "AI trends" versus "user research methods."
NPS: Detractors, passives, and promoters each get their own summary of related follow-up feedback, providing clarity around drivers of satisfaction or dissatisfaction.
You can achieve similar results with ChatGPT—just be ready for more copy-pasting and structuring on your part. Want to learn more about creating surveys with tailored follow-up logic? Our article on automatic AI followup questions takes you through what’s possible.
How to manage AI’s context limits when analyzing large surveys
One overlooked challenge when using AI to analyze survey data is the context window size—the limit on how much text the AI can handle at once. If you get lots of responses, you risk exceeding this limit.
Specific offers two built-in solutions for this:
Filtering: You can filter conversations, so only responses where users replied to certain questions or picked specific answers are sent to the AI for analysis. That means more focused, relevant insight without overwhelming the model.
Cropping: You can include just selected questions in the analysis, ignoring the rest. This keeps the data set manageable and ensures you get the deepest insights from the areas that matter most.
Both are essential for dealing with complex user roundtable attendee surveys. They help the AI stay sharp and the insights stay fast—something traditional survey tools just can’t match at scale.
For technical details or setting these features up, see our how-to guide for creating roundtable attendee surveys.
Collaborative features for analyzing user roundtable attendee survey responses
Working on survey analysis as a group can get messy, especially when feedback and discussions happen in scattered threads, docs, or spreadsheets.
Specific makes collaboration straightforward. You and your team can analyze User Roundtable Attendee survey data simply by chatting with the AI. Each chat is its own workspace, with custom filters reflecting what members care about most—say, only deep-diving into responses about “AI applications” or “industry pain points.”
Each chat shows who created it and who’s messaging within it. This transparency is surprisingly powerful: you see at a glance who is pushing which line of questioning, or who found that key insight.
Team avatars bring clarity to the conversation. When teammates ask questions in the AI Chat, each response displays the sender’s avatar next to their message. This simple feature means you never lose track of opinions, requests, or context—critical when digesting feedback from a Topics of Interest survey with multiple stakeholders.
Whether you’re dividing up analysis by theme, by persona, or by roundtable session, collaboration stays organized and visible. For more on building your own collaborative survey analysis workflow, our guides on survey editing with AI and crafting the best questions for roundtable surveys are great resources.
Create your User Roundtable Attendee survey about Topics of Interest now
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