This article will give you tips on how to analyze responses from a Community Call Attendee survey about Topics of Interest using the latest AI-powered survey analysis techniques and tools.
Choosing the right tools for analyzing survey data
The best approach for analyzing your Community Call Attendee survey depends on the type and structure of your data—whether you’re dealing with quantitative stats, open-ended replies, or blended formats.
Quantitative data — Results like “How many people selected X topic?” are straightforward. Tools such as Excel or Google Sheets make it fast to count, filter, and visualize numbers.
Qualitative data — When you have open-ended responses or rich follow-up answers, reading every reply becomes overwhelming. That’s the point where AI-powered tools step in: they spot topics, summarize themes, and surface hidden insights no human could process manually.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste exported responses into ChatGPT (or any large language model) and chat about your data there. You might start with prompts like “What are the main themes in these responses?”
This approach is helpful when you have a manageable number of replies and want to use advanced AI for custom questions. But it’s not always convenient: manually exporting responses, dealing with format limitations, and tracking your own analysis can quickly become a hassle.
All-in-one tool like Specific
Specific is built for this scenario—it both collects conversational survey data and analyzes the results using AI. When respondents answer, the AI can ask intelligent followup questions to dive deeper, boosting data quality and richness. Read more on the automatic followup questions feature.
AI-powered analysis in Specific instantly summarizes replies, reveals core themes, and transforms conversation logs into insights—without you needing to juggle spreadsheets or copy-paste data between tools. When you’re reviewing survey responses, you can have a chat with the AI about your results, just like you would with ChatGPT, but purpose-built for survey data. Plus, you get added control and features for managing what information is sent to the AI and how you collaborate on findings. Explore AI survey response analysis to see how it works.
If you want to see other industry-leading options, solutions like NVivo, MAXQDA, and Canvs AI offer advanced auto-coding, sentiment analysis, and pattern detection for qualitative surveys. These platforms leverage artificial intelligence to make sense of large, messy data—helping you save time and extract deeper meaning from your survey. [1]
For more on setup, browse our guide to creating a Community Call Attendee survey about Topics of Interest or jump straight to the AI survey generator.
Useful prompts that you can use for analyzing Community Call Attendee survey responses about topics of interest
Prompts are at the heart of AI-powered survey response analysis—especially for open-ended Community Call Attendee surveys focused on gathering a range of topics of interest. The right prompts can bring structure and clarity to vast, messy conversation logs. Here are proven prompts you can use in ChatGPT, Specific, or any AI tool:
Prompt for core ideas: When you want a quick summary of key themes and the number of attendees referencing each topic, use the following (this is also the default in Specific):
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 performs better if you brief it about your survey, your context, and your goals. For example:
This data comes from a pre-event survey for Community Call Attendees. Participants describe what topics they are most interested in discussing. My goal is to learn which subjects are most relevant, spot emerging trends, and identify subgroups with different needs.
Now, using these responses, extract the main core ideas and short descriptions.
Once you have the main topics, you can dig deeper by asking:
Tell me more about XYZ (core idea)
Prompt for specific topic: To check if any attendee mentioned a certain subject, simply ask:
Did anyone talk about XYZ? Include quotes.
A few more prompts tailored for Community Call Attendee surveys about Topics of Interest:
Prompt for personas: Ask the AI to synthesize personas, helping you group responses by attendee type:
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: Great for planning future event content:
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 suggestions & ideas: Capture all attendee-generated recommendations:
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 sentiment analysis: To get a sense of overall engagement or mood:
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 approaches help you turn messy survey data into clear, actionable reports—no matter if you’re prepping for a Community Call, building out your event agenda, or looking to measure impact after the session. For more inspiration, see our rundown on the best questions for Community Call Attendee surveys about topics of interest.
How Specific analyzes qualitative data by question type
With Specific, the way AI processes your data depends on question structure—optimizing how themes and patterns are surfaced:
Open-ended questions (with or without followups): AI summarizes all attendee replies and any followups, extracting the key ideas most relevant to your event’s topics of interest.
Choices with followups: Each choice (e.g., topic, format, tool) gets its own summary for related followup responses. This way, the reasons or context behind each choice aren’t lost in the aggregate.
NPS or scaled ratings: Instead of lumping everyone together, Specific builds separate summaries for detractors, passives, and promoters. This helps you see how different groups describe their needs and interests in their own words.
You get the same flexibility using ChatGPT or other LLMs—it just takes more manual work to keep context, aggregate responses, and stitch everything together. Here is where Specific's structure saves hours and prevents mistakes. For more on how conversational surveys capture richer feedback by following up, check the automatic followup questions feature or dive deeper with the AI survey editor.
How to overcome AI context limits with large Community Call Attendee survey data
When analyzing a big stack of open-ended responses (think: dozens or hundreds of attendees expanding on their topics of interest), you’ll eventually hit the limit of what AI models like ChatGPT can process in a “single shot”—the so-called context window. Overstuffing it causes errors and makes results less reliable.
Two best-practice strategies (which you get out of the box in Specific):
Filtering: Narrow the analysis to only include conversations where attendees answered certain questions or chose specific topics. That way, AI can focus on the most useful slices of the dataset (e.g., just those who want advanced technical discussions, or only those who submitted pain points).
Cropping: Instead of dumping the entire survey, select only the most relevant questions—or parts of conversations—to send to the AI for analysis. This keeps sessions within context limits and helps surface the targeted insights you actually care about.
If you want to try these in practice, head over to the AI survey response analysis tool, where you’ll see live filtering and cropping options designed for qualitative survey data (unlike typical spreadsheet exports).
Collaborative features for analyzing Community Call Attendee survey responses
Collaboration is often the hardest part of analyzing qualitative survey data, especially when multiple people need to review attendee topics, debate insights, or prepare content together.
Specific makes collaboration natural by enabling you and your teammates to analyze survey results by simply chatting with AI—no need for spreadsheet sharing or endless status meetings. Each teammate can run their own AI chats, apply custom filters, and see who contributed what, keeping all your discoveries organized and easy to find.
Multi-chat analysis means you can segment by cohort (e.g., newcomers vs. regulars, or tech focus vs. strategy focus) or even start a chat for each subgroup. Every conversation thread shows the creator, so when ideas or themes emerge, everyone knows who led the analysis.
When collaborating in AI chat, the sender’s avatar makes it simple to follow different perspectives—great for cross-team projects like community calls where organizers, subject experts, and facilitators each bring unique interests. Instead of conflicting spreadsheet versions, everyone’s findings live in context and can be referenced, exported, or built into your session agenda.
This collaborative workflow saves you hours, reduces duplicate effort, and lets every voice be heard (including your attendees’).
Create your Community Call Attendee survey about topics of interest now
Move from guesswork to clear, actionable insights—use AI to instantly analyze what matters most to your community call attendees, so you can deliver relevant, high-impact sessions every time.