This article will give you tips on how to analyze responses/data from a Community Call Attendee survey about Agenda Preferences. If you want to truly understand what your participants want, analyzing survey response data the right way is crucial.
Choosing the right tools for analysis
The approach and tooling you use always depends on the type and structure of your survey responses. Here’s a quick breakdown:
Quantitative data: When most questions are multiple choice (e.g., “Which topics interest you most?”), the data is easy to count. You can quickly analyze these responses with tools like Excel or Google Sheets.
Qualitative data: If you have open-ended responses or follow-up questions, things get trickier. Reading every answer is impossible at scale—especially for an engaged community call. To see the patterns, you’ll want to use AI-powered tools that can process and summarize complex qualitative data. This is where you unlock depth and nuance in attendees’ agenda preferences.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can export your survey responses and paste the data into ChatGPT, Claude, Gemini, or any similar GPT-powered tool for analysis. This lets you “chat” about your data—ask for summaries, patterns, or even sample quotes.
However, it’s not always convenient. You have to manually manage data format, chunk responses to fit context size, and repeat copy-paste cycles for new questions. There’s no built-in structure for survey logic (such as grouping follow-up responses), so you’ll do extra work organizing and filtering.
If you’re just dipping your toes into AI analysis, this approach gives you quick wins, but it won’t scale for bigger surveys or regular workflow.
All-in-one tool like Specific
An AI tool built for surveys like this simply works better. With a solution like Specific, you can both create and analyze conversational surveys—designed for deeper insights.
Specific does the hard work for you: When collecting responses, it asks smart AI-generated follow-up questions automatically. This boosts the quality of every answer, capturing richer detail about what makes a good community call agenda. Learn more on how it works in our automatic AI followup questions feature overview.
Analysis is instant: Specific summarizes all responses, finds main themes, and turns the mass of attendee feedback into clear, actionable insights. You won’t spend hours organizing spreadsheets or copying data. Instead, you just chat with the AI to ask any follow-ups (“What are key topics for next month?” or “Are there unmet needs?”)—just like you would with ChatGPT, but inside a survey context. You get extra controls, too: filter data, organize AI chats, and refine which responses get analyzed.
If you’re running recurring or high-volume surveys on agenda preferences, this kind of AI-powered workflow is a major timesaver—and leads to more informed, participant-driven calls.
Established solutions like NVivo, MAXQDA, QDA Miner, and Thematic are also out there—with capabilities for AI coding, advanced visualization, and theme extraction, but they may require steeper learning curves or more manual setup for typical community call use cases. [1][2][3][4]
Useful prompts that you can use to analyze community call attendee survey data about agenda preferences
When using AI to analyze Community Call Attendee Agenda Preferences responses, strong prompts make a world of difference. Here’s a set I reach for when chatting with Specific’s AI or using ChatGPT on exported data:
Prompt for core ideas: To quickly see main topics and how many people care about each, try this:
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
Always provide context—AI performs better with it. If you tell the AI about your survey goal and audience, it delivers more precise answers. Here’s how to set it up:
Analyze these responses from a Community Call Attendee survey about Agenda Preferences. My goal is to identify priorities for next month’s call. I want to know which agenda items are most desirable, any unmet needs, and what’s working well so far.
Dig deeper: After you find a core idea, zoom in with a targeted question: “Tell me more about XYZ (core idea)”—the AI will expand with supporting detail or quotes.
Prompt for a specific topic: Want to check if AI missed something?
Did anyone talk about expert guest speakers? Include quotes.
Prompt for pain points and challenges: Get straight to the problems in participants’ minds:
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: Understand what’s motivating your attendees:
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: Capture the mood so you spot enthusiasm, hesitation, or negativity fast:
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: Surface creative input to quickly spot new agenda topics:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Want more? See best questions for agenda preferences surveys or dive into how to create a community call attendee survey from scratch.
How Specific analyzes qualitative data (by question type)
Specific’s AI knows that not all survey questions are equal when it’s time to analyze agenda preferences.
Open-ended questions (with or without follow-ups): The AI gives you a summary based on all responses, including any follow-up conversations. You can see major themes, supporting details, and even suggested quotes—without sifting by hand.
Choices with follow-ups: Each choice (say, “panel” vs. “workshop”) gets its own summary of relevant follow-up responses. You’ll know exactly how attendees feel about each part of your agenda—and why.
NPS: Score breakdowns (detractors/passives/promoters) come with summaries of follow-up answers for each group. This way, you connect satisfaction level with what people actually say they need from the call.
You can absolutely mimic this approach using ChatGPT or similar AI tools, but be ready for more copy-paste and data wrangling to keep responses organized by question.
How to tackle AI context size limits
If you run a large or recurring agenda preferences survey for your community, you’ll quickly hit the context size limit of most AIs—GPT tools (and even strong AI survey platforms) can only handle so much data at once. Don’t let this block your insights.
There are two proven ways to deal with this—both built into Specific’s workflow:
Filtering: Filter conversations by user replies or specific answers. That means the AI only analyzes conversations where attendees replied to certain questions or gave a particular response (“only analyze people who said they want more Q&A”). You focus on what matters, and you never overload the AI.
Cropping: Crop questions for AI analysis. You can choose to send only selected survey questions (like, just open-ended responses about new topics), keeping analysis sharply focused and within the AI’s context window.
If you want a more technical solution or to build your own system, you’ll need to manually split the data before analysis—tedious, but possible.
Collaborative features for analyzing community call attendee survey responses
Anyone who’s tried to analyze Community Call Attendee survey responses on agenda preferences knows the challenge—collaboration is tough when feedback is scattered, spreadsheets get out of sync, or different team members ask the AI different things (with no record of who asked what).
In Specific, survey analysis is collaborative by design. You can analyze your agenda preferences data just by chatting with the AI. But that chat is not just for you—you can create multiple chats, each focused on a specific topic, filter (like “people who want breakout rooms”), or use case.
Each chat is attributed: You instantly see who created each analysis thread, with avatars shown right alongside the AI’s summaries. This makes it easy for product managers, facilitators, or organizers to divide up research areas, compare findings, and share relevant takeaways—without endless back-and-forth on Slack or email.
Chat histories are preserved: Whether you’re following up on new agenda ideas or revisiting sentiment from last month’s call, you can scroll back through all AI conversations. Changes and new chats are visible to your whole team, so insights are never lost or repeated.
Want to try it? If you haven’t yet, explore the collaborative AI survey response analysis tools in Specific, built exactly for this kind of team workflow.
Create your community call attendee survey about agenda preferences now
Start collecting rich, actionable insights from your community and turn every agenda into something your attendees genuinely want. With the right AI-powered survey analysis, you can deliver calls that stand out—this is your shortcut to understanding what matters next.