This article will give you tips on how to analyze responses/data from a Fireside Chat Attendee survey about Topics of Interest using the latest AI tools for survey response analysis.
Choosing the right tools for analyzing survey responses
The best approach and tooling always depend on your data structure. If you have:
Quantitative data: Numbers—for example, how many people chose each topic—are easy to count in Excel or Google Sheets. These tools are great for charts and basic breakdowns.
Qualitative data: Open-ended survey responses or answers to AI follow-up questions are impossible to read through manually at scale. Here, you need AI tools to summarize, extract themes, and spot patterns fast.
There are two main approaches to choosing tooling for qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can export your survey responses as text and paste them into ChatGPT or a similar GPT tool. From there, you can chat with the AI, asking it to summarize the core topics, extract pain points, and more.
Inconvenient for large data: If you have a substantial amount of responses or complex data (such as follow-ups tied to each main answer), this method gets cumbersome quickly. You have to manually organize, split, and feed data into the chat—and AI context limits can quickly create problems.
Still, for small sets or quick insights, it’s a decent approach.
All-in-one tool like Specific
Purpose-built for survey analysis: Tools like Specific are designed from the ground up for collecting conversational survey data and instantly analyzing it with AI.
Deeper insights with follow-ups: As you collect data, Specific automatically asks smart follow-up questions, capturing rich, contextually relevant insights you can’t get in traditional forms. This leads to higher-quality data.
No manual work: Once responses are in, Specific’s AI summarizes everything, spots themes, quantifies how many people brought up each topic, and turns conversations into clear, actionable insights—without needing to export data or manage complicated spreadsheets.
Conversational analysis: You can chat directly with AI about your actual data, similar to ChatGPT, but with special features for survey context and filtering.
Other AI-powered tools exist too. For example, NVivo, MAXQDA, Delve, Canvs AI, and Quirkos all use advanced algorithms to automate coding, sentiment analysis, and theme extraction, making sense of open-ended survey data much more quickly than manual review ever could [1].
Useful prompts that you can use to analyze Fireside Chat Attendee Topics of Interest surveys
Getting powerful insights is all about asking your AI tool the right prompts. Here’s how I approach it when working with a large batch of fireside chat attendee feedback:
Prompt for core ideas: Use this prompt to quickly extract main discussion topics and see how common each is:
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
Give your AI context: To improve results, always tell the AI about your audience, goal, and situation. For example:
You are analyzing survey responses from attendees of a fireside chat event, and the main goal is to identify trending topics people most want to see in future sessions. Summarize the core themes and mention how many respondents mentioned each.
Dive deeper: Once the AI lists topics, follow with:
Tell me more about XYZ (core idea)
Validate interest in a specific topic: Use a targeted question like:
Did anyone talk about [ask about specific topic]? Include quotes.
Find distinct personas: To understand your diverse audience, try:
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.
Discover pain points and challenges: Essential if you want your next fireside chat to be even more relevant:
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.
Map the motivations of your audience: Great for event planning:
From the survey responses, extract the primary motivations, desires, or reasons participants express for their behaviors or topic choices. Group similar motivations together and provide supporting evidence from the data.
Check the mood with sentiment analysis:
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.
Extract suggestions and future ideas:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Spot unmet needs and opportunities:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you want even more ideas for crafting better questions, see this detailed guide on best questions for fireside chat attendee surveys about topics of interest.
How Specific analyzes qualitative responses by question type
Specific handles every kind of survey question for maximum insight:
Open-ended questions (with or without follow-ups): AI gives you a concise summary for all major themes—and for each follow-up, too. You get a big-picture view, plus details on clarifying questions asked during interviews.
Choices with follow-ups: Each choice gets its own summary of what people said in their follow-ups, exposing what drove their selection or hesitation.
NPS-style questions: The AI segments responses into detractors, passives, and promoters. Each set gets a tailored summary, showing you what each group values or dislikes.
You can mimic this process in ChatGPT by using filters, but it’s more labor-intensive—especially as your survey size grows. Specific automates all this so you can focus on action, not processing.
Want to craft a high-quality conversational survey with follow-ups built in? Head over to the AI survey generator for fireside chat attendees or learn how the automatic AI follow-up questions feature works.
Working around AI context limits
AI tools, including ChatGPT, have memory limits—called a "context window". If you try to stuff too many survey responses in, some will be cut off, and the model won’t see your full dataset.
You can work around this by:
Filtering: Only send conversations that meet specific criteria (for example, people who mentioned "networking" or answered a certain follow-up). This way, your questions to the AI are more targeted and fit within limits.
Cropping questions: Send just the most relevant survey questions and answers to the AI for each analysis session. This keeps context focused and lets the tool process more responses in higher fidelity.
Specific automates these for you, so you don’t have to worry about missing insights or wrestling with data exports. If you’re exploring other options, just remember: organizing your data well before sending it to any AI will almost always pay off.
Want to learn how to structure your survey for easier downstream analysis? Read more in our how-to guide for creating fireside chat attendee surveys.
Collaborative features for analyzing Fireside Chat Attendee survey responses
Collaboration is often the bottleneck in analyzing fireside chat attendee surveys about topics of interest—especially when multiple team members need to work together or want to check different angles.
Analyze together in real time: With Specific, you simply chat with the AI about your survey data. Multiple chats can be created—one per hypothesis or sub-project if needed.
Keep things organized: Each chat comes with its own filters and clearly shows who started the discussion. This helps prevent overlap and confusion, so you don’t lose track of insights or analysis direction.
Accountability and transparency: In AI Chat, every message displays the sender’s avatar. Now you can see instantly who asked a question or made a suggestion, making teamwork not just possible, but frictionless.
Share context easily: No more exporting data back and forth. Simply invite your stakeholder to the conversation and collaborate asynchronously or live, depending on your workflow.
Ready to start your own survey analysis project? Kick things off in the AI survey analysis workspace or learn how to get started from scratch with the AI survey builder.
Create your fireside chat attendee survey about topics of interest now
Start gathering richer feedback and unlock actionable insights in just minutes—create an engaging conversational survey that instantly adapts to your audience’s responses and gives you clear, AI-powered recommendations as soon as results start rolling in.