This article will give you tips on how to analyze responses from a citizen survey about community events and festivals. If you want actionable insights from your data, you’re in the right place.
Choosing the right tools for analyzing survey responses
The tools and approach you’ll use to analyze survey responses from citizens really depend on the type of data you collect. Here’s how to break it down:
Quantitative data: If your survey asks straightforward questions—like “Did you attend the last town festival?”—with set answer options, traditional tools like Excel or Google Sheets are often enough. Just filter, count, and you’re set.
Qualitative data: When you dive into open-ended questions (“What did you like about the festival?”), or collect follow-up stories, old-school methods break down quickly. Reading through hundreds of these responses is a slog. That’s where AI shines—specifically, large language models that can process and analyze massive volumes of written feedback automatically.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Manual copy-paste into ChatGPT: You can export your survey data (usually as CSV or spreadsheet), then copy the open-ended responses into ChatGPT. From there, you can start a conversation, asking for summaries or themes.
Not the most convenient: This can get messy quickly if your dataset is large—running into message size or formatting issues, losing track of follow-ups, etc. But for quick checks on a small sample, it’s accessible and cost-effective.
All-in-one tool like Specific
Purpose-built for surveys: Specific is built for both collecting and analyzing citizen survey responses about community events and festivals. As responses come in, the platform uses AI to dig deeper—asking real-time follow-up questions. That means you get richer, more complete insights than from classic forms.
Instant AI-powered analysis: The AI gets to work immediately—summarizing responses, pulling out key themes, and delivering a clear “story” behind the numbers. Instead of spreadsheets, you see insights right away, with no manual crunching. Curious about something? Just type your question to the AI chat, much like you would with ChatGPT, but with all the context built in. This is particularly powerful for collaborative analysis. Learn more about Specific’s AI response analysis.
Context control and management: You can easily choose which parts of your survey data are analyzed, and apply different filters for focused insights. The workflow is seamless for anyone running multiple citizen-focused surveys, or just looking to save a lot of time.
Useful prompts that you can use for analyzing citizen survey data
AI tools like Specific or ChatGPT need well-structured prompts to deliver valuable analysis. Here are some battle-tested ones to help you unlock deep insights from citizen responses about community events and festivals:
Prompt for core ideas: To get the most important themes from a large pool of responses, use this system prompt (which Specific also uses):
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
For best results, always give the AI more context about your survey—such as your target audience, survey goal, and background. Example:
I'm analyzing a citizen survey about community events and festivals in our city. Our goal is to understand what motivates citizens to attend, what they enjoy, and any barriers they face. Please extract the top-mentioned core ideas from these responses.
Prompt for more detail: Drill down on specific findings using:
"Tell me more about XYZ (core idea)"
Prompt for specific topic: To check if a topic (e.g., “accessibility” or “family activities”) got any mentions:
"Did anyone talk about XYZ?"
Tip: You can add “Include quotes.”
Prompt for personas: To uncover patterns across demographics or interests:
"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:
"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 and drivers:
"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:
"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:
"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:
"Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents."
If you’re looking for more inspiration or to craft the right questions to get this type of data, check our guide to the best survey questions for citizens about community events and festivals.
How analysis works for different question types in Specific
Specific handles qualitative response analysis with a system that fits the type of question:
Open-ended questions (with or without followups): You get a clear summary of all responses, plus summaries of any follow-ups related to each open-ended question.
Choices with followups: Each choice branches into its own summary, distilling feedback from all follow-up questions linked to that option. This makes comparing feedback across choices really simple.
NPS (Net Promoter Score): Responses are automatically grouped by category (detractors, passives, promoters). Each group gets its own summary of all follow-up comments, providing targeted insight into sentiment and motivations.
You can absolutely achieve similar results in ChatGPT, but it’s more manual work. With Specific, the flow is designed around survey logic, so the entire analysis stays organized as you switch between different question types.
If you want to create and customize surveys for this kind of analysis, try the AI survey generator for citizens about community events and festivals or explore the survey editor to tweak your questions easily.
Solving AI context limits for large surveys
One thing people run into with AI services is the “context size limit.” When you have hundreds—or thousands—of survey responses, even the most advanced AI models (like GPT-4) can process only so much at once.
To get around this, Specific bakes in two ways to zoom in on just the data you need:
Filtering: You can tell the AI to analyze only those conversations where citizens answered specific questions, or only those who picked a particular option (say, people who attended a particular event or reported dissatisfaction). This dramatically cuts down the data sent to the model, keeping everything within context limits.
Cropping: You can crop the data to just the questions relevant for your analysis. If you only want the AI to focus on follow-up responses to one question, you can select that—leaving all other responses out of that analysis pass. This maximizes the amount of feedback that fits into a single AI analysis session.
This means you never have to worry about “too much data”—whether you’re a festival organizer with thousands of participants, or a small municipality surveying a few dozen engaged citizens. (Pro tip: These strategies work for any AI solution, but having it baked into your tool saves hours.)
Given that 63% of event planners are already using AI tools to boost attendee engagement [1], these features are fast becoming non-negotiable for community program managers.
If you’re not sure which survey style or logic fits best, our how-to guide to creating citizen surveys for community events and festivals breaks it down, from logic to question flow.
Collaborative features for analyzing citizen survey responses
Collaboration can be the hardest part of analyzing citizen feedback on community events and festivals—especially if you have a team of stakeholders (public officials, event planners, volunteers) each bringing different perspectives.
Chat-driven collaboration: In Specific, you analyze data the way you’d talk with a colleague—just chat with the AI about the data. This enables real-time idea sharing, clarifies questions, and makes the analysis interactive, rather than a one-person slog.
Multi-chat environments: You can run multiple chats in parallel, each with its own set of filters (“show me only youth feedback,” “focus just on summer festivals,” etc.). Each chat is linked to its creator, so your team can see who started each thread and follow up directly. There’s no more confusion over what’s being discussed or who’s working on what.
Transparency on contributions: Chat messages display the sender’s avatar, so it’s always clear whose analysis or comment you’re reviewing. This feature removes ambiguity and builds trust among collaborators—essential when your survey touches broad community interests.
Streamlined workflow: For teams that need to coordinate fast, this setup cuts out countless emails and version headaches. Want to test it out yourself? You can go straight to the NPS survey builder for citizens about community events and festivals.
Create your citizen survey about community events and festivals now
Get fast, actionable insights from your community—use AI to gather better responses, analyze results instantly, and boost engagement for your next event.