This article will give you tips on how to analyze responses from Ask Me Anything attendee survey about agenda preferences using AI. If you're running this kind of feedback survey, you’ll want actionable insights—fast.
Choosing the right tools for analyzing survey responses
The approach and choice of tooling for analysis depends on the form and structure of your survey data. Here’s a simple breakdown:
Quantitative data: Numbers and counts are easy to process with traditional tools like Excel or Google Sheets. For example, you might tally how many attendees ranked a particular session as their top agenda preference.
Qualitative data: Open-ended comments or chat-like follow-ups are where things get complicated. It’s not realistic to manually read through every single response, especially as the volume grows. You need AI-powered tools to reveal meaningful patterns, summaries, and themes without burning out your team. AI-driven surveys can boost your completion rates to 70-80% and drop abandonment to as low as 15-25%, compared to 45-50% and 40-55% for old-school surveys. [1]
When it comes to qualitative data, there are two main approaches for tooling:
ChatGPT or similar GPT tool for AI analysis
Quick and flexible: You can export your raw survey data and paste it into ChatGPT or another GPT model, then have a conversation with the AI to summarize or find trends.
Not ideal for scale: This gets unwieldy as data grows. You’re stuck prepping data, pasting, and scrolling through giant, messy conversations. Plus, context limits mean you can’t always upload every answer at once. It’s workable for small sets, but looking for nuance in larger or structured surveys? You’ll quickly hit the ceiling.
All-in-one tool like Specific
Purpose-built for survey feedback: Tools like Specific are designed from the ground up for collecting and analyzing qualitative survey data with AI. You can run AI-powered, conversational surveys and analyze everything in one place.
Automatic follow-ups and higher data quality: As Specific collects responses, it intelligently asks follow-up questions to clarify and deepen answers, resulting in a richer dataset. Curious about how AI-driven follow-up works? Check out our feature on automatic AI follow-up questions.
Real-time AI analysis: The platform summarizes attendee responses, highlights dominant topics, and pulls out actionable insights within minutes. You avoid the pain of exporting data, wrangling spreadsheets, or slicing mental energy. Response analysis is powered by AI and remains interactive—ask the AI anything about your data, similar to a ChatGPT chat, with context management built in. Want more on this workflow? Dive into AI survey response analysis.
Useful prompts that you can use to analyze Ask Me Anything attendee agenda preferences
When you want targeted insights from survey response data, clear AI prompts unlock the gold. Here’s how I approach it:
Prompt for core ideas: This is my go-to for boiling down lots of qualitative feedback to the essentials. It works well in both Specific and generic chatbots.
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
The results are concise and actionable. If you’re running an Ask Me Anything attendee survey, this surfaces what truly matters.
Context amplification: AI always performs better if you set the scene—describe your goals, any background, and what you want from the analysis. Try adding context before your main prompt. For example:
This survey is from an event where developers can ask experts anything. We’re deciding on the agenda for next year. Please extract themes, note emerging trends, and highlight suggestions specifically about technical workshops.
Dive deeper on key points: After you spot an interesting core idea, you can follow up with something like: “Tell me more about attendee feedback on panel discussions.”
Prompt for specific topic: Simple, but effective—for instance:
Did anyone talk about networking opportunities? Include quotes.
Prompt for personas: Want to understand segments within your respondent base? Here’s how you can get AI to surface potential personas:
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: Use this to find out what frustrates or blocks your attendees:
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: Figure out what makes your audience tick:
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 Suggestions & Ideas: Harvest innovation from your audience:
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: If you’re searching for hidden gaps or ways to improve next year’s program:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
How Specific analyzes qualitative data by question type
Specific’s AI analysis engine smartly segments and summarizes feedback based on how your survey was structured. Here are the main cases:
Open-ended questions (with or without follow-ups): Every attendee reply is summarized, and the follow-up questions probing deeper are included in the summary. You get the story behind the answers, not just surface comments. This works whether a respondent wrote a whole paragraph or just a few words.
Choices with follow-ups: Each selected option is paired with attendee feedback from related follow-up questions. For example, you’ll see not just which session type scored best, but why, and what attendees want to see improved for each option.
NPS-style questions: For Net Promoter Score surveys, feedback is organized by promoters, passives, and detractors, with summaries of follow-ups in each section. This segmentation lets you pinpoint pain points or ideas tied to loyalty and satisfaction metrics. You can create a dedicated NPS survey for Ask Me Anything attendees right from the Specific survey builder.
You could definitely do all of this with ChatGPT, but it means a lot more prep work, more copy/paste cycles, and way more time managing context.
Want to learn more about designing question types? There’s a full guide on the best questions for Ask Me Anything attendee surveys.
Strategies for tackling AI context size limits
One of the biggest technical challenges when working with AI tools is context size—you can only load so many responses at once before the AI loses the thread. To work effectively with hundreds of attendee survey comments, you’ll want to:
Filtering: Select just a slice of conversations based on which attendees replied to specific questions or picked certain answers. This lets the AI analysis focus on the most relevant segments and keeps the session within technical limits.
Cropping: Choose to analyze only certain survey questions. For example, send just agenda suggestions to the AI, ignoring demographic or satisfaction questions. This approach ensures richer, more focused outputs when you’re limited in space.
These two flows work out of the box in Specific, smoothing analysis even for sprawling feedback sets. If you’re going the generic ChatGPT or spreadsheet route, you’ll need to build these filters yourself—and wrangle the data every time.
Using NLP and machine learning means surveys can be processed in hours rather than days or weeks, giving you insights long before you’d finish manual coding. [2]
Collaborative features for analyzing ask me anything attendee survey responses
Collaboration friction: Analyzing qualitative feedback from attendee agenda preference surveys can be a headache when multiple stakeholders want in on the insights. Who drove which line of questioning? How do you keep discussions organized as you explore different topics or attendee segments?
Chat-based teamwork: In Specific, every team member can chat with AI to analyze survey results—no switching tools or exporting data. You can open multiple real-time chats, each with their own filters or analytic focus: for instance, one on “preferred keynote topics” and another on “pain points in previous formats.”
Accountability and traceability: Each chat analysis session is clearly labeled with the creator, and you see avatars alongside responses. This prevents confusion, encourages ownership, and helps your research or event-planning team stay aligned, especially for surveys like the Ask Me Anything agenda feedback, where nuances matter.
Shared context: The integrated chat makes it easy for event organizers, moderators, and analysts to discuss findings, reference attendee comments, and iterate on action items—all within the analysis tool. No extra spreadsheet versions, no scattered email threads. Read more about this workflow in AI survey response analysis.
Check out step-by-step instructions for survey creation at how to create Ask Me Anything attendee survey about agenda preferences.
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