This article will give you tips on how to analyze responses from Fireside Chat Attendee surveys about Agenda Preferences. If you’ve run a conversational survey and want actionable insights, here’s how I approach it using AI.
Choosing the right tools for analyzing survey responses
How you analyze your data depends on the structure and type of survey responses you’ve collected. You need to decide on your approach based on whether you have quantitative or qualitative feedback:
Quantitative data: Numbers and stats—like how many attendees chose particular agenda options—are straightforward. I use Excel, Google Sheets, or similar tools. You can create simple charts and pivot tables to spot trends quickly.
Qualitative data: Open-ended responses and detailed explanations are where things get sticky. Nobody has time to read every response manually—plus, people often share crucial context in these comments. This is the type of data where AI tools shine and, frankly, are now essential.
There are two approaches when you’re dealing with qualitative responses in your Fireside Chat Attendee agenda preference survey:
ChatGPT or similar GPT tool for AI analysis
Copying data into ChatGPT lets you discuss raw survey exports. You can paste open-ended responses and start prompting for key themes, trends, or summaries.
But… this gets unwieldy fast. Large surveys can’t fit into ChatGPT’s context window all at once. Formatting responses for copy-paste isn’t fun, and you lose valuable metadata or filtering abilities. Still, for smaller surveys, it’s a decent entry point into AI-powered analysis.
All-in-one tool like Specific
Specific is purpose-built for conversational survey analysis. It collects survey responses and automates follow-up questions to boost depth and data quality—a huge advantage when you need more than yes/no insights. If you’re curious about how our platform does this, check out this overview of AI survey response analysis.
The analysis is where it gets interesting:
AI-powered summaries highlight the big picture within seconds. Every theme, pain point, and trend is surfaced automatically—no spreadsheets required.
You can chat with AI directly about the results—“What was the top reason attendees requested this topic?”—getting immediate, context-rich answers. For deep dives, you can filter, crop, or manage what data is sent to AI contextually.
Bonus: Specific also manages the hassle of dealing with long-form qualitative data and keeps data organized, which is a lifesaver as your survey grows.
If you want to try Specific, here’s our AI-powered fireside chat attendee survey generator—it’s tuned for surveys like this. Other notable tools for text analysis in this space are NVivo, MAXQDA, and Canvs AI—all offering forms of AI-assisted coding, sentiment, and theme extraction [1].
Useful prompts that you can use to analyze Fireside Chat Attendee agenda preference survey data
AI analysis is only as good as your prompts. Here’s how I get the most out of survey response data—especially when working with Fireside Chat Attendee surveys:
Prompt for core ideas: To quickly extract the main themes and priorities expressed by attendees, use this in ChatGPT or with Specific. It is one of my go-to starting prompts. Paste your survey responses, then 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 is everything: Give AI more background about your survey. Tell it that responses are from Fireside Chat Attendees, that you care about their agenda preferences, and describe your event’s context. Here’s how to do it:
These responses are from Fireside Chat Attendees who shared their agenda preferences for our upcoming event. My goal is to understand the main topics and session types they want to see, as well as any unmet needs or pain points.
AI will always return better insights when you set the scene.
Follow-up prompt for details:
Tell me more about [core idea or topic]
This helps drill down into any specific theme or trend that caught your eye in the initial summary.
Specific topic validation:
Did anyone talk about [specific topic]? Include quotes.
Perfect for checking if a certain theme or suggested speaker came up—especially useful in agenda planning.
Prompt for pain points and challenges: Ask for recurring issues or frustrations, which often inform breakout topics or fireside questions.
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned by Fireside Chat Attendees about agenda preferences. Summarize each, and note any patterns or frequency of occurrence.
Prompt for personas: Useful if you want to understand the audience segments present in the room (e.g., “C-suite networkers” vs. “startup founders”).
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 Motivations & Drivers: Great for planning engagement strategies.
From the survey conversations, extract the primary motivations, desires, or reasons Fireside Chat Attendees express for their agenda choices. Group similar motivations together and provide supporting evidence from the data.
For more prompt ideas and question inspiration, you might find this guide to best questions for fireside chat attendee survey about agenda preferences super helpful.
How AI interprets different survey question types in Specific
Specific adapts its qualitative analysis depending on the structure of your survey:
Open-ended questions (with or without follow-ups): It summarizes all direct responses and any follow-up discussions. This way, deep qualitative insight isn’t lost in the noise.
Choices with follow-ups: Each agenda option (like “More Q&A time” or “Industry Trends”) gets its own summary—capturing all feedback relevant to that option’s specific follow-up discussions.
NPS questions: Each group (detractors, passives, promoters) receives a separate summary and analysis, making it easy to spot differences in attendee enthusiasm and priorities.
You can absolutely do this kind of analysis in ChatGPT, but it’s a more manual and repetitive process—especially when segmenting by category or choice. I prefer using a tool like Specific for the time savings.
If you want to learn how to quickly structure and build your next survey, I recommend this detailed walkthrough: how to create fireside chat attendee survey about agenda preferences.
Solving AI context limit challenges when analyzing big surveys
One of the most common hurdles when using GPT-based AI for survey analysis is context size limits. If you have hundreds of open-ended survey responses, the AI might not be able to “see” everything at once. Here’s my two-part approach, both of which Specific implements out of the box:
Filtering: Only analyze conversations where users replied to the selected questions or chose certain options—so you’re focused on the most relevant data, not every single response.
Cropping: Select which questions are sent to the AI for analysis. This is perfect when you want to limit the size of the input and make sure the AI focuses on your priority topics.
Combining these two methods helps me stay within context limits and still get high-quality AI insights—even from big surveys. Most dedicated AI survey tools and even advanced research platforms like NVivo and Thematic leverage similar “smart sampling” strategies for handling large text datasets [1][2].
Collaborative features for analyzing Fireside Chat Attendee survey responses
Collaborative analysis of Fireside Chat Attendee agenda preference surveys can get messy when teams are emailing spreadsheets or notes back and forth. Organizing consensus, seeing who suggested what, or following the thread of an idea—all of that’s tough in traditional tools.
Chat-driven collaboration: In Specific, you don’t have to fight with complex dashboards. I analyze survey responses simply by chatting with the AI, and I can invite teammates into the same workspace for real-time collaboration.
Multiple filtered chats: Each chat window can have its own filters—such as focusing only on responses about panel discussion topics. I can see who kicked off the chat, what questions they asked, and what conclusions the group landed on.
See who said what: Every message in the AI chat shows the sender’s avatar. Whether it’s me clarifying data or you asking for sentiment analysis, we keep track of who contributed each question, helping us build on each others' insights faster.
This keeps the analysis process transparent and collaborative, where teams can genuinely “think together.” If you're looking for more ways to customize your survey for better teamwork, check out our AI survey editor.
Create your Fireside Chat Attendee survey about Agenda Preferences now
Turn raw attendee feedback into clear, actionable agenda insights in minutes—no spreadsheet wrangling, no coding, just AI-powered clarity. Start now and see the difference a chat-based, collaborative survey tool can make.