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How to use AI to analyze responses from fireside chat attendee survey about expectations

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Adam Sabla

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Aug 22, 2025

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This article will give you tips on how to analyze responses/data from Fireside Chat Attendee surveys about Expectations. If you want actionable, AI-powered insights from conversational surveys, keep reading.

Choosing the right tools for analyzing survey data

Choosing the right approach and tooling depends on the type and structure of your Fireside Chat Attendee Expectations survey data.

  • Quantitative data: If you ask attendees to rate expectations on a scale or pick from pre-set options, these numbers are straightforward. You’ll easily tally and visualize this kind of survey feedback using familiar tools like Excel, Google Sheets, or even a regular survey platform. That works for questions like “Which topic interests you most?”—just count up the votes.

  • Qualitative data: Open-ended questions or AI-powered conversational follow-ups? Those responses are gold for depth but can be a nightmare to digest manually. Reading dozens (or hundreds) of long answers is impractical—especially if you want to spot trends quickly. This is where you’ll want AI-enabled tools to do the heavy lifting, because AI can analyze large volumes of text up to 70% faster than traditional methods, with impressive accuracy for sentiment classification and theme detection. [3]

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

Copy and paste your exported data into ChatGPT or a comparable tool. You can ask the AI to summarize, extract themes, or spot sentiment. This approach works, but:

  • It’s not very convenient. Formatting your exported survey data into something a general-purpose AI tool can digest often takes more time and effort than you might expect.

  • Handling large datasets is clunky. Most of these tools weren’t designed for big survey data—they’ll hit context size limits quickly, so you’ll end up trimming or splitting your data.

For quick, one-off summaries or lightweight qualitative analysis, this is doable. But for more structured, ongoing survey analysis, it can slow you down.

All-in-one tool like Specific

Purpose-built for conversational survey analysis, Specific combines data collection and AI-powered insights in one platform. Here’s how it’s different:

  • It collects richer data by asking real-time AI followup questions, which means you get deeper, more focused attendee insights about their expectations. (See more about automatic followup questions.)

  • Instant AI summaries: Once responses are in, the platform distills key themes in seconds—no spreadsheets, no copy-pasting. It also visualizes sentiment and groups expectations by prominence.

  • You can chat with AI about your results, just like ChatGPT, but with survey-specific controls. Assign filters, manage which data feeds into the analysis, and collaborate across your team. (Learn how this works: AI survey response analysis.)

  • The tool is structured for survey workflows: Handle NPS questions, multi-choice follow-ups, and open-ended prompts, all with tailored summaries for each format—no manual wrangling required.

If you want a simple survey builder, try this AI survey generator for Fireside Chat Attendee Expectations. For more flexibility, try the general AI survey generator—or edit your survey the smart way with AI-powered survey editing.

AI-powered tools like this really shine for qualitative research. According to industry benchmarks, leveraging such platforms can cut down manual analysis time drastically while improving the consistency of discovered insights across large datasets. [1][3]

Useful prompts that you can use to analyze Fireside Chat Attendee Expectations survey results

AI is only as good as the prompts you give it. Here are some go-to prompts you can use with either a general AI tool or inside Specific’s chat for survey response analysis.

Prompt for core ideas: Use this to extract the key topics from a pile of Expectations survey responses and quickly see what’s most important to your attendees.

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

Pro tip: Always give AI more context—describe your event, objectives, or any attendee segments you care about. For example:

I'm analyzing responses from Fireside Chat Attendee surveys about Expectations for an upcoming SaaS industry panel. My goal is to understand key attendee priorities and validate our session topics. Please group and explain themes in their responses.

Prompt to “go deeper”: Follow up with: "Tell me more about XYZ (core idea)" to zoom in on a specific expectation or trend. You’ll get more examples, direct quotes, or see how nuanced a trend is.

Prompt for specific topics: To check if anybody mentioned a particular topic, try: "Did anyone talk about networking opportunities? Include quotes."

Prompt for personas: Want to understand your Fireside Chat Attendees better? 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: To uncover what worries or frustrates your attendees, use:

"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 see what’s driving people to attend or what they hope to get out of the fireside chat:

"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: Get the overall mood about Expectations:

"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."


These prompts help you make AI your co-analyst—digging deeper into every survey and saving hours over traditional manual methods.

Looking for inspiration on what to ask in the first place? Check out the best questions for Fireside Chat Attendee Expectations surveys.

How Specific analyzes qualitative survey responses, by question type

Once your Fireside Chat Attendee Expectations survey responses are in, Specific organizes and analyzes them differently depending on the question format:

  • Open-ended questions (with or without follow-ups): You get a summary of all responses, plus summaries for any follow-up replies linked to each main answer. This lets you see both the breadth (overall trends) and depth (why someone answered as they did) of attendee expectations.

  • Multiple choice with follow-ups: Each option gets its own breakdown. If an attendee picks “Networking” and answers a follow-up, you get a tailored summary of why that mattered to them, making it easy to say “40% picked networking, and here’s what really matters about it.”

  • NPS (Net Promoter Score): Responses are grouped by promoters, passives, and detractors, with a summary of their follow-up explanations. Each group’s expectations and reasoning are separated, allowing for targeted improvements ahead of your event.

You can mimic this in ChatGPT manually, but keeping track of threads and data structure becomes a chore quickly when dealing with even modestly sized surveys. Specific automates it—all with survey-aware context so nothing gets lost or mixed up.

If you want a hands-on walkthrough for survey setup, check this detailed guide to creating Fireside Chat Attendee Expectations surveys.

Handling AI context limits for large survey data sets

AI has a powerful brain—but even GPT hits limits on how much data it can process at once. When analyzing dozens or hundreds of attendee responses, you need ways to keep your analysis within those boundaries. Here’s what works:

  • Filtering: Only analyze conversations that include relevant answers—like those where attendees replied to a certain expectation question, or picked a specific session topic. This focuses the AI on what matters to your event.

  • Cropping questions: Restrict what goes into the AI’s context window. Send only the questions and answers you want the AI to read; that way, even large surveys can be digested, summarized, and visualized efficiently.

These methods are built-in with Specific, but you can simulate them by manually slicing and filtering your spreadsheet or exported results before sending smaller chunks to ChatGPT or another AI tool.

For details about building AI-ready surveys from scratch, see this AI survey builder guide.

Collaborative features for analyzing Fireside Chat Attendee survey responses

Aligning a whole team around Fireside Chat Attendee Expectations insights is hard when everyone is passing around static spreadsheets or disconnected dashboards.

Analyze conversationally, together: In Specific, you can chat with the AI directly about your survey data—as a team. Conversations happen in parallel, letting multiple stakeholders (e.g., event organizers, moderators, CX pros) dive into the data from different angles at once.

Multipurpose chats, filtered by interest: Each chat can have its own filters—like “let’s focus on attendees interested in product launches” or “show responses about Q&A formats.” Each chat is attributed to its creator, keeping collaboration organized.

Real accountability and easy tracking: As you work with colleagues in AI Chat, every message is labeled with the sender's avatar. It’s easy to see who asked what, comment on emerging insights, or continue another person’s analysis instead of duplicating work.

This structure makes cross-team event planning and real-time attendee insight much simpler and more transparent.

Create your Fireside Chat Attendee survey about Expectations now

Quickly collect and analyze what matters to your audience, with AI-powered followups and instant insights—so your next fireside chat exceeds every attendee’s expectations.

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Sources

  1. TechRadar. Best survey tools for quantitative analysis.

  2. Thematic. Using AI and large language models for qualitative survey analysis.

  3. Insightlab. AI automation for faster, more accurate survey insights.

  4. Insight7. Tools for advanced qualitative survey analysis.

  5. Jean Twizeyimana Blog. AI tools for survey data analysis.

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.