This article will give you tips on how to analyze responses from Conference Participants surveys about panel discussion quality using AI and modern analysis tools for actionable insights.
Choosing the right tools for panel discussion survey analysis
Your approach for analyzing Conference Participants survey data about panel discussion quality depends on the types of responses you collect. If you have numbers or clear-cut answers (like "rate from 1-5"), you’ll manage with spreadsheets. But once you dive into the rich stories and feedback from open-ended questions, you need smarter, AI-powered tools to process and interpret what people actually said.
Quantitative data: Responses like "How would you rate the panel discussion on a scale of 1 to 5?" are easy to summarize with tools such as Excel or Google Sheets. These let you quickly tally up averages and spot trends in participation or satisfaction.
Qualitative data: Open-ended answers—like what participants felt worked, suggestions for improvement, or key frustrations—can’t be handled easily by hand. Reading through dozens or hundreds of paragraphs is time-consuming and subjective. AI streamlines this by surfacing common themes and sentiments, especially important when you want to capture unmet needs or opportunities for improvement. The importance of analyzing such feedback is highlighted in studies showing that active audience engagement—like the number of questions asked—can be a key indicator of panel success [1].
When it comes to analyzing qualitative responses, there are two main tooling approaches:
ChatGPT or similar GPT tool for AI analysis
This approach works if you just want to experiment. You can simply export your open-ended survey responses and paste them into ChatGPT or another large language model. You then prompt the AI to summarize feedback, pull out key insights, or answer specific questions about the data.
The big catch: It isn’t built for survey analysis. Copying and pasting data can get unwieldy fast, especially with a lot of answers. You also have to figure out your own prompts, keep track of context, filter by demographics or question, and struggle with context size limits.
All-in-one tool like Specific
Specific is built for conversational survey analysis from the ground up. You can collect data—from open-ended to structured NPS questions—through its AI-driven surveys, which adapt mid-conversation to clarify or dig deeper with AI-generated follow-up questions (proven to increase both the quality and clarity of responses).
Instant, powerful AI analysis: When you analyze survey responses in Specific, the platform instantly summarizes feedback, explores key themes, and turns unstructured answers into ready-to-use insights. There’s no manual copy-paste, no cleaning data, and no struggling with custom prompts unless you want to dig further.
Ask and chat about your results: Like ChatGPT, but purpose-built for survey context—Specific lets you chat directly with the AI, narrow by segment, or drill down to follow-up responses tied to specific questions or choices. This is powerful for understanding not just the overall sentiment, but the ‘why’ and ‘who’ behind the data.
Manage data with more control: You can set filters, select questions, or view analysis by participant segments—and always see exactly which data points the AI is responding to. This is invaluable when comparing, for example, the effectiveness of moderators or the diversity of opinions panel by panel. For a broader look at how to create such surveys, try the AI survey generator for panel discussions or build your own with the conversational survey builder.
Useful prompts that you can use for Conference Participants panel discussion quality surveys
One of the best ways to unlock value from your survey data is by asking smart questions—both in your survey and during analysis. Here are the AI prompts that work especially well when analyzing Conference Participants’ feedback on panel discussions:
Prompt for core ideas: Use this to extract the big-picture themes from all participant feedback. This is Specific’s default, but it works in ChatGPT too:
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
To get the most accurate analysis, always share context—tell the AI what your survey is about, your goals, and what you care about when running the analysis:
Context: This survey is for Conference Participants and focuses on evaluating panel discussion quality at our annual tech summit. Our main goal is to understand strengths and weaknesses from diverse participant perspectives to improve future events.
Prompt: Extract the key themes from responses, grouped by audience type (panelists, academic researchers, first-time attendees, etc.).
Once you identify key themes, dig into specifics with follow-up prompts like:
"Tell me more about XYZ (core idea)" to expand on any major thread that emerges—such as clarity of the discussion, moderator skills, or level of engagement.
Prompt for specific topic: Use this to test a hypothesis or validate whether a detail was mentioned:
Did anyone talk about diversity of opinions? Include quotes.
Prompt for pain points and challenges: Use this to see where panels fell short in the eyes of participants:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned during the panel discussions. Summarize each and note frequency of occurrence.
Prompt for personas: Distill response segments by audience type. This uncovers if students, experienced professionals, or other groups offered different insights:
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 sentiment analysis: Quickly gauge mood and polarity in audience feedback:
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.
Prompt for suggestions & ideas: Highlight constructive feedback and fresh ideas—critical for future panel improvements:
Identify and list all suggestions, ideas, or requests provided by survey participants about the panel discussions. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: Find “white space” your panel didn’t address but your audience cares about:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you want more ideas for smart questions to ask, see the best questions for Conference Participants surveys on panel discussion quality.
How Specific summarizes panel discussion survey data by question type
Specific adapts its AI analysis based on how your survey was structured:
Open-ended questions (with or without follow-ups): Specific gives you a deep-dive summary for all responses, enriched by follow-up answers. This is ideal for understanding nuanced feedback about what worked or what didn’t in a panel discussion.
Choices with follow-ups: Each multiple choice option (say “Panel was engaging” vs. “Panel was too long”) receives its own summary, with all related follow-up feedback grouped underneath—making it easy to analyze preferences and reasons behind responses.
NPS: Every Net Promoter Score group—detractors, passives, promoters—gets a targeted summary of the opinions, pain points, and suggestions that drove their score. This surfaces what divides fans from critics.
You can replicate this in ChatGPT, but it’s a lot of manual copy/paste and tracking subtleties by hand. For a step-by-step on creating such a survey, read how to create a Conference Participants survey about panel discussion quality.
How to deal with AI context size limits in your analysis
AIs have a context limit: they can only “read” a certain amount of text (tokens) at once. With a large set of panel discussion survey responses, not all your data will fit at once. Here are two effective strategies (both available in Specific) to keep your analysis on track:
Filtering: Only analyze conversations where users replied to certain questions (like only those who commented on moderator performance) or chose specific answers (such as respondents who rated the panel below 4). This narrows the focus and fits more relevant data into the AI’s context window.
Cropping: Limit the AI’s view only to selected questions—such as analyzing just the open-ended questions about diversity or moderator effectiveness, while skipping demographic info or unrelated sections. This maximizes the number of full conversations you can analyze at once.
See how this works in practice in Specific’s survey analysis demo.
Collaborative features for analyzing Conference Participants survey responses
Collaboration is often the missing link when analyzing panel discussion survey feedback. Sharing a big spreadsheet (or worse, dumping qualitative feedback into email) leads to slow, fragmented insights. Teams need to work together—comparing findings, highlighting differences by job role or panel format, and iterating on what to ask the AI next.
In Specific, teamwork is at the core. You can analyze survey data directly by chatting with AI, and have multiple conversations open at once—each representing a different angle (for example: “Pain points by first-timers”, “Moderator effectiveness by demographic”, “Top quotes about diversity”). Each chat allows filters for specific questions or segments, and shows clearly who started the conversation and which filters are in effect.
Transparency and ownership is built in. Every message within a chat is labeled with the sender’s avatar, so as a team you always know whose insights you’re discussing. It’s the fastest way to turn raw feedback into group knowledge and action items.
If you want to tweak your survey for your next event, just use the AI survey editor to describe your changes and let the AI update your survey structure instantly.
Create your Conference Participants survey about panel discussion quality now
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