This article will give you tips on how to analyze responses from a Fireside Chat Attendee survey about Discussion Topics. Whether you’ve collected dozens or hundreds of replies, the right approach will save you hours and help you get more out of your data.
Choose the right tools for analyzing responses
The way you analyze your Fireside Chat Attendee survey depends on the kind of data you have. Most surveys have a mix of structured quantitative data (for example, how many people said “yes” or “no”) and more nuanced qualitative responses (such as open-ended thoughts or ideas about Discussion Topics).
Quantitative data: Numbers, choice counts, and ratings are a breeze to analyze. Spreadsheets like Excel or Google Sheets work perfectly for counting and spotting patterns.
Qualitative data: Open-ended responses, especially when paired with follow-up questions, quickly get overwhelming. Reading every comment is impossible once you’ve gathered a decent sample. That’s why AI tools are key for sorting through these responses and pulling out the best insights.
There are two approaches for tooling when dealing with qualitative responses. Here’s how each works in practice and where they shine.
ChatGPT or similar GPT tool for AI analysis
You can copy and paste exported survey data into ChatGPT or a comparable GPT-based tool, then prompt it for a summary or analysis. If you’re comfortable with the workflow, it’s a low-barrier option for smaller datasets.
However, it isn’t the most convenient method. You’ll juggle spreadsheets, worry about formatting, and hit context limits with longer answer sets. You also miss features tailored to feedback analysis, like follow-up summaries, segmenting by question, or intuitive filtering.
All-in-one tool like Specific
An AI tool such as Specific is built from the ground up for survey collection and response analysis. With Specific, you create conversational surveys that ask natural, personalized follow-up questions—resulting in richer, higher-quality responses right from the start.
AI-powered analysis instantly summarizes responses, identifies trends, and highlights actionable themes. That means no spreadsheets, no endless scrolling—just insights you can immediately use.
Dedicated chat with AI lets you explore results conversationally, like you would in ChatGPT, but with data context and focus tools unavailable in generic models. You can filter which parts of the survey or responses the AI “sees” for more precise analysis and less noise.
On top of these perks, research shows that AI-powered survey platforms like Specific can slash analysis time by up to 90% compared to manual workflows, with marked boosts to data quality and response rates. [1]
Useful prompts that you can use for Fireside Chat Attendee survey response analysis
To get the most out of AI survey analysis, it helps to know which prompts work best—especially for Discussion Topics. Here are several that consistently unlock better insights, whether you’re using Specific or any other GPT-based tool.
Prompt for core ideas: Use this to automatically extract recurring topics or themes from the attendee responses. Paste all responses and use the following prompt:
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 AI more context: AI always delivers stronger analysis if you share additional context about the survey, your target audience, and your goals. For example:
These survey responses are from Fireside Chat Attendees. The main goal is to identify the most requested Discussion Topics and understand any patterns or emerging trends participants care about.
Dive deeper: To get nuance around a specific core idea or pattern, ask:
“Tell me more about XYZ (core idea)”
Prompt for specific topic: To see if anyone mentioned a particular subject, try:
“Did anyone talk about XYZ?”
Include “Include quotes” in your prompt to get verbatim comments.
Prompt for personas: Get a sense of recurring attendee types and segment 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 pain points and challenges: If you want to surface the top attendee 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: To unlock what draws people to different Discussion Topics:
“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: For an overview of the mood behind comments:
“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: When feedback includes requests or tips:
“Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.”
Prompt for unmet needs & opportunities: To pinpoint areas for new Discussion Topics or improvements:
“Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
For more advice on great questions to ask in Fireside Chat Attendee surveys or how to set up your own survey about Discussion Topics, check these guides.
How Specific analyzes qualitative data from each type of question
The type of question changes how the AI processes and summarizes results:
Open-ended questions (with or without follow-ups): Specific gives you an instant summary of every reply as well as any extra details captured via follow-up prompts. The AI clusters similar phrases so key themes float to the top.
Choices with follow-ups: For every choice, you get a summary just of the follow-up responses that relate to it—super handy for mapping Discussion Topic preferences to underlying reasons.
NPS-style questions: Responses are automatically grouped into promoters, passives, or detractors, and you get summaries for each segment along with their follow-up details.
You can do all of this manually with ChatGPT or similar tools, but it takes a lot more effort—requires copying, formatting, and keeping notes by hand. Specific handles this seamlessly from start to finish.
If you’re interested in how AI follow-up questions improve the depth and clarity of attendee feedback, there’s more info on the automatic follow-up questions feature.
How to tackle issues with AI context limits in survey analysis
When you’re analyzing responses from a large Fireside Chat Attendee survey, you’ll quickly run into what’s known as the “context limit.” GPT-based tools can only process so much text at once before the analysis quality suffers or it stops working altogether.
There are two proven ways to work around this, both offered natively in Specific:
Filtering: Analyze only a subset of conversations—say, just those where respondents answered a certain question or picked a specific option. This keeps the dataset manageable and laser-focused.
Cropping: Rather than sending the entire survey for every respondent, select just the questions you care about most. That way, you maximize the number of full responses sent to the AI while staying within its memory constraints.
Adopting these strategies can also cut the total time from data to insight by 60-70%, especially as your number of responses climbs. [2]
Collaborative features for analyzing Fireside Chat Attendee survey responses
Collaborating on survey insights often gets messy with Discussion Topics—for example, who asked a question, who made a particular observation, or how to keep track of everyone’s threads of analysis.
With Specific, AI-powered survey analysis is collaborative by design. You and your colleagues can analyze data together by simply chatting with the AI. Each chat lets you apply different filters, dig into unique perspectives, or ask the AI questions about whatever aspect interests your team.
Chat visibility and ownership are baked in: It’s always clear who started each chat, which filters they set, and which conclusions they reached. When several people are involved, avatars next to every AI or human message show at a glance who drove the line of inquiry.
Faster, deeper analysis together also means less duplicated effort. One team member might focus on segmenting feedback by discussion category, while another explores attendee motivations. No more digging through endless spreadsheets or toggling between different files.
For more hands-on ideas or to start your own survey, check the Fireside Chat Attendee survey generator for Discussion Topics or the AI survey generator preset templates.
Create your Fireside Chat Attendee survey about Discussion Topics now
Start your journey with AI-powered insights in minutes—capture meaningful feedback, analyze it instantly, and unlock Discussion Topics that truly resonate with your audience.