This article will give you tips on how to analyze responses and data from a User Roundtable Attendee survey about Expectations using AI and proven techniques for survey response analysis.
Choosing the right tools for analysis
Your approach and choice of tooling depend on the structure of the data you collect in your survey responses. Here’s what I’d focus on for each main type:
Quantitative data: If you’re looking at numbers—like how many picked each option—tools like Excel or Google Sheets are all you need. They’re great for counting, charting, and spotting quick patterns.
Qualitative data: When you’re dealing with open-ended responses or long, text-heavy follow-ups, things get trickier. Manually reading and making sense of lots of free-text answers is overwhelming. Here, AI-powered tools shine. They help you extract themes, summarize feedback, and avoid drowning in text.
There are two main approaches for analyzing qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy-and-paste exported data from your survey into ChatGPT or a similar GPT-style interface and chat about your responses. The beauty is that you can ask highly custom questions and get fast results.
However, handling your data this way isn’t exactly user-friendly, especially with lots of responses. You’ll spend time prepping the data, breaking it up to fit context limits, and copying questions back and forth. As your dataset grows, this method gets pretty inconvenient.
All-in-one tool like Specific
With an AI-first survey platform such as Specific, the whole process is streamlined. You collect your data (the survey) and analyze the responses in one place.
The magic is in the follow-ups: When collecting answers, the tool asks intelligent follow-up questions, which means the feedback you get is way richer and more actionable than typical static surveys (see how automatic follow-up questions work).
Instant AI-powered analysis: Instead of combing through long transcripts, Specific instantly summarizes responses, highlights major themes, and identifies trends. You can also chat directly with AI about your results—just ask questions like you would in ChatGPT. For power users, there are features to manage how much context gets sent to AI, so you keep things relevant and focused.
If you ever want to start from scratch, try their AI survey generator for User Roundtable Attendee surveys about expectations.
Specialized qualitative tools: Many researchers still use tools like NVivo, MAXQDA, QDA Miner, and KH Coder for AI-assisted text coding, categorization, and visualization. These automate theme extraction and reduce manual work, but aren’t purpose-built for conversational survey data, so the learning curve is steeper if you just need insights fast. [1]
Useful prompts that you can use for analyzing User Roundtable Attendee survey responses about expectations
When you’re analyzing feedback from User Roundtable Attendees, giving the AI the right prompt is everything. Here are some proven starting points (you can use these in Specific’s AI chat, ChatGPT, or any GPT-based tool):
Prompt for core ideas: This one works great for boiling long-winded responses down to major topics.
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
Boost results with survey context: Always give AI more background—what your event is, what you’re hoping to achieve, who responded, your goals for the analysis. It really sharpens the outcome.
I am analyzing responses from a pre-event survey for user roundtable attendees. The event is focused on product strategy and attendees were asked about their expectations, pain points, and goals. My goal is to extract clear themes that can help us tailor the session to audience needs.
“Tell me more about X”: After you find a core idea, just ask, “Tell me more about mainstream adoption concerns (or any other theme you spotted).”
Prompt for specific topic: If you want to see whether anyone mentioned a topic, ask:
Did anyone talk about XYZ? Include quotes.
Prompt for personas: Want to segment your attendees into types? Use:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: Calls out core frustrations.
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for Motivations & Drivers: This unlocks what’s really behind their expectations:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Prompt for Sentiment Analysis: Spots the mood in the room:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
For more prompt ideas and deep dives, see our article on how to create a user roundtable attendee survey about expectations.
How Specific analyzes survey data by question type
Specific is purpose-built for making sense of a range of question types. Here’s how:
Open-ended questions with or without followups: You get a summary covering every response as well as a focused summary for each follow-up. Perfect for capturing subtle expectations or unusual ideas.
Choices with followups: For every answer option, you’ll see a separate summary—a fast way to spot why some attendees picked option A versus B.
NPS: If you use a Net Promoter Score question, you get not only the standard score calculation but also AI-generated summaries for each segment: detractors, passives, and promoters, based on what each group said in their follow-up responses.
You can replicate this in ChatGPT, but it’s more hands-on; you’ll have to chunk data and keep track of which responses relate to which question.
If you’re looking for tips on designing those questions in the first place, check out our article on the best questions for user roundtable attendee surveys about expectations.
Overcoming AI context size limits in survey analysis
Anyone dealing with lots of survey data runs into AI “context limits”—most large language models can only look at a certain amount of text at a time. If you have a big batch of attendee responses, here are two practical solutions (both offered in Specific as standard):
Filtering: Narrow down analysis to just those conversations where users replied to the questions you care about, or picked specific options. This way, the AI only chews on what matters.
Cropping: Instead of sending the AI all questions, just pick the key ones you want analyzed. This lets you go deeper on the high-value bits while staying within the context size.
If you want more control over survey design—what questions to include, how much probing—it’s worth checking Specific’s AI survey editor, where you can easily update surveys in plain language.
Collaborative features for analyzing User Roundtable Attendee survey responses
Collaboration can be messy when you have a team reviewing expectations from a user roundtable attendee survey. If you’re passing around spreadsheets or sharing ChatGPT transcripts, it’s hard to keep track of what insights came from who or what was already discussed.
Specific makes collaboration seamless. With AI chat built into the results view, you can invite teammates to ask their own questions—each conversation gets its own thread, and it’s clear who’s leading which line of inquiry. No more stepped-on toes or duplicated work.
Chat visibility, with context: Each analysis chat shows who started the discussion and lets collaborators see every follow-up or filter applied. When you’re co-analyzing, there’s no confusion about who said what, or what was already covered.
Juggling multiple viewpoints: Your UX researcher can dig into pain points, your event lead can focus on logistics, and your CX person can analyze sentiment—each in separate threads, all in the same workspace. This sharpens everyone’s conclusions but keeps the conversation unified.
Ready to move your next survey’s analysis out of the email chain? Specific’s collaborative review tools help teams unlock insights together, not in silos.
Create your User Roundtable Attendee survey about expectations now
Start gathering actionable attendee insights in minutes, leverage AI-powered follow-ups for richer data, and enjoy instant, collaborative analysis to improve your upcoming roundtable.