This article will give you tips on how to analyze responses/data from Ask Me Anything Attendee survey about Discussion Topics. Whether you’ve run a conversational or traditional survey, choosing the right approach for analysis can save hours and surface insights you’d never spot manually.
Choose the right tools for analyzing survey responses
How you approach your analysis depends on the form and structure of the data you’ve received. Here’s how I break it down:
Quantitative data: Things like “Which topics interest you most?” or rating questions are easy to handle. Just pop them into Excel or Google Sheets—simple counting, pivot tables, and charts usually do the trick for structured, close-ended responses.
Qualitative data: The real challenge is open-ended questions, follow-ups, and free-text feedback. These responses are too numerous (and nuanced) to read one by one. AI tools are a game changer here, letting us group responses, spot trends, and get to the “why” faster than any spreadsheet. Powerful platforms like NVivo, MAXQDA, and Thematic use AI to automate coding and sentiment analysis, making it practical to analyze thousands of open responses quickly. [1][2]
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you’ve exported data, you can copy your survey responses into ChatGPT and start chatting about them. You can paste in responses and use prompts to find patterns or summarize key themes. This method is dead simple and flexible.
But here’s the catch: Handling the data this way gets messy fast, especially for large datasets. There are limits on how much text you can process at once, there’s no built-in structure, and complex filtering or follow-up questions take a lot of manual setup. Still, for a small set of responses, it gets the job done.
All-in-one tool like Specific
Purpose-built solutions have a big edge for qualitative analysis. I often use Specific: Surveys are conversational (think “AI chat interview”), and the tool automatically collects, structures, and analyzes open-ended responses.
Rich data collection: The AI asks follow-up questions during the survey, not only boosting response depth but also connecting ideas. The result? Higher-quality, context-rich data.
Instant AI summaries: As responses come in, Specific’s AI summarizes them, finds recurring themes, and instantly highlights actionable insights—no exporting, no wrangling spreadsheets, no manual coding.
Conversational insights: You (and your team) can chat with the AI about your results, ask it to analyze any segment, and use rich filters or persona tools to go deeper. All this happens in one place, purpose-built for AI survey analysis.
For an in-depth comparison of survey creation options, you’ll find more ideas in this How to create Ask Me Anything Attendee survey about Discussion Topics guide.
Useful prompts that you can use to analyze Ask Me Anything Attendee Discussion Topics responses
Prompts are the secret sauce for making AI tools (like ChatGPT or Specific) truly helpful. The right prompt reveals actionable insights—no matter how massive your dataset. Here are some of my favorites:
Prompt for core ideas: This is my go-to for extracting themes from large sets of open-text responses. It’s built into Specific, and here’s how you can use it elsewhere:
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
AI always gets better when you give it more context—about your AMA session, discussion topics, or research goal. For example:
Here are 120 responses from attendees who participated in our Ask Me Anything event about new product features. My goal is to identify key topics of interest and any major uncertainties attendees expressed, so I can plan future sessions more effectively. What are the major themes?
Once you have the key topics, go deeper:
Prompt to drill down: “Tell me more about XYZ (core idea)”
Prompt for a specific topic: If you want to check whether anyone mentioned something (“Did anyone talk about Q&A format?”), just ask: “Did anyone talk about XYZ?” You can add: “Include quotes.”
Prompt for personas: 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.”
Prompt for pain points and challenges: Ask, “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.” This is gold for improving topic relevance.
Prompt for motivations & drivers: Use, “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 a high-level vibe check: “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: Try, “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: Use, “Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
For even more ideas on what to ask or how to design good questions for your target group, check out this list of best questions for Ask Me Anything Attendee surveys about Discussion Topics.
How Specific summarizes qualitative data by question type
Specific makes sense of all response types—from free text to structured choices—by matching the analysis to the question, so nothing gets lost.
Open-ended questions (with/without follow-ups): You’ll get a full AI summary of all responses, including any follow-ups related to that question. Themes, key points, and recurring phrases are clearly surfaced, giving you usable high-level insights.
Choices with follow-ups: For each choice (say, “Preferred session topic”), there’s a dedicated summary of all open-text follow-up answers tied to that choice. This way, you see WHY a specific topic was picked, not just how many picked it.
NPS (Net Promoter Score): Detractors, passives, and promoters each get their own summary for the follow-up responses connected to them, highlighting what drives enthusiasm or dissatisfaction.
You can do all this with ChatGPT or similar tools—it’s just a bit more hands-on work to organize, especially if you have lots of questions and varied formats.
If you want to experiment with NPS surveys tailored for AMA attendees, you can try the NPS survey for Ask Me Anything Attendees about Discussion Topics builder, or start from scratch with the AI survey generator.
How to work around AI context limits in survey analysis
AI models like GPT have a context size limit. If your survey gets hundreds (or thousands) of responses, you won’t be able to parse everything at once in ChatGPT, Claude, or similar tools. How do you get around this?
There are two practical approaches (which Specific offers out-of-the-box):
Filtering: Before sending anything to the AI, filter out only those conversations where people replied to relevant questions or chose specific answers. This trims down your dataset and targets analysis to what matters most.
Cropping: Limit what you send by question—just select the questions you want to analyze, and only those parts of the conversation go to the AI. You’ll fit more into the AI’s context and can focus the analysis on particular topics or themes.
These approaches also help keep your queries manageable and your insights focused, rather than getting lost in too much data. For details on how Specific handles this, take a look at AI survey response analysis.
Collaborative features for analyzing Ask Me Anything Attendee survey responses
When I work with teams on Ask Me Anything Attendee surveys about Discussion Topics, a big challenge is keeping analysis collaborative and organized—especially as ideas, insights, and new questions start flying around.
Analyze survey data by chatting with AI: In Specific, anyone can initiate an AI chat about the survey results. You can ask questions, dig into themes, or run specific prompts—all backed by the tool’s underlying structure.
Multiple chats for different threads: You can create as many chats as you need, each with its own filters or focus areas. Every chat shows who started it and who’s participating, making it easy for teams to organize their analysis around interest areas or stakeholder needs.
Track collaboration visually: In each collaborative chat, user avatars are shown next to every message, so you always know who contributed which idea or question. This is huge for context—no more mystery about who asked what or offered which interpretation.
Seamless across roles: When you’re running a feedback session or an AMA for product, research, or community teams, you want all your colleagues to see, filter, and segment findings in whatever way makes sense for them. These collaborative features make it easy to drill down on niche topics (“Did anyone mention accessibility?”), surface pain points, or prep for your next AMA session together.
If you want to try this out or see how it plays, you might check out how automatic AI follow-up questions work in Specific or try building a survey in the AI survey editor.
Create your Ask Me Anything Attendee survey about Discussion Topics now
Jumpstart your survey analysis: collect richer data, instantly uncover key discussion themes, and make each AMA session better with actionable insights—without the spreadsheet grind.