This article will give you tips on how to analyze responses from a Citizen survey about park maintenance using AI-powered survey analysis. If you want to uncover actionable insights, read on.
Choosing the right tools for survey response analysis
The best approach for analyzing citizen feedback depends on the structure of your survey data. Let’s break down the main types:
Quantitative data (numbers, ratings, choices): These responses—like "How satisfied are you with park maintenance?"—are straightforward. You can quickly tally results and build charts using Excel or Google Sheets.
Qualitative data (open-ended comments, written feedback): Here’s where things get challenging. Reading through hundreds of open-text answers or AI-probed follow-ups by hand isn’t practical if you want real insights. You need help from AI tools that can understand and summarize the content.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy and chat—simple but clunky. Export your survey data (usually as CSV or XLSX), then copy and paste sizable chunks into ChatGPT or another GPT-powered platform. You can ask questions like “What are the top issues mentioned by citizens about park maintenance?”
Not the smoothest workflow. You’ll bump into problems managing large or messy data, protecting privacy, and tracking context across multiple sessions. While it works in a pinch, scaling this approach for recurring surveys or large data sets isn’t ideal.
All-in-one tool like Specific
Purpose-built for survey collection and qualitative analysis. Tools like Specific handle both survey collection and AI-powered analysis seamlessly, whether it’s for in-depth citizen feedback or quick NPS checks.
Better data with follow-ups. When citizens answer questions, the AI interviewer can ask smart follow-up questions, capturing richer data automatically. See how this works in detail in our automatic AI follow-up questions feature guide.
Instant insights—no spreadsheet wrangling. Specific’s AI analyzes responses as they come in. It pulls out the top themes, summarizes conversations, and highlights trends—saving you heaps of manual labor.
Conversational AI to guide your analysis. You can chat with the AI directly about survey results—just like using ChatGPT, but with extra organizational features built for real survey data.
If you prefer to start from scratch, you can also check how to generate AI surveys for any topic and audience.
Useful prompts that you can use for analyzing citizen survey response data about park maintenance
Getting actionable findings from qualitative data often comes down to asking your AI the right questions. Here are prompts and strategies that work well for citizen surveys on park maintenance:
Prompt for core ideas. This cuts through the clutter and surfaces the key themes across your data. It’s what we use in Specific, but you can (and should!) copy it into ChatGPT or your preferred AI tool:
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
Improve results by giving the AI more context. Always add background: tell the AI that “these are responses from citizens about park maintenance in our city” and, if possible, what you’re hoping to learn. It improves accuracy and relevance. Here’s a sample prompt for context:
These are citizen survey answers about satisfaction and suggestions regarding park maintenance in [city]. I want a summary of the top issues affecting visitor experience, with supporting examples from the data.
Dive deeper into findings. Once you see core ideas, ask targeted follow-up prompts such as:
Tell me more about [core idea or problem]
Spot mentions of key issues. If you need to check for something specific (e.g., rubbish bins, playground upkeep):
Did anyone talk about [playground maintenance]? Include quotes.
Prompt for personas. To better understand differing perspectives, use:
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. To discover what citizens struggle with most:
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 sentiments and suggestions. To gauge feeling and crowdsource feedback:
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.
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
For more inspiration, see our step-by-step guide to creating a citizen survey about park maintenance, or review best questions to ask in these surveys.
How Specific analyzes qualitative survey data by question type
Specific is designed to break down citizen feedback efficiently, no matter how you craft your survey:
Open-ended questions (with or without follow-ups): Summaries are generated for all initial answers and follow-up responses linked to the original question, highlighting major themes and standout quotes.
Choices with follow-ups: Responses are grouped by each choice (e.g., “park is clean” vs “needs improvement”), then each group has its own summary of related follow-up answers.
NPS (Net Promoter Score): Analysis breaks down the conversation by promoters, passives, and detractors, summarizing all follow-ups under each score (for quick insight into why people are or aren’t recommending city parks).
You can replicate this with ChatGPT manually, but it’s more time consuming—you’ll likely need to manually tag responses and copy-paste data for each question or group.
If you want to try a ready-to-use NPS survey for citizens about park maintenance, explore the automated survey builder.
Solve the context size problem: working with lots of responses in AI
AI tools—including ChatGPT and expert survey platforms like Specific—have a limit on how much data you can send at once (called the context size).
Specific offers two ways to deal with this efficiently—no matter how many responses your citizen survey gets:
Filtering: Narrow your analysis to just the most relevant conversations—like only those where citizens reported dissatisfaction or discussed facility upkeep. This keeps the focus sharp and lets the AI dig deeper.
Cropping: Pick exactly which questions (or parts of conversations) you want to send to the AI at any time. This maximizes the volume of data you can process and ensures you don’t hit context limits.
More about managing context and AI-powered survey workflows can be found in this guide.
Collaborative features for analyzing citizen survey responses
Analyzing survey results is often a team sport—especially when city leaders, public works, and community engagement staff all want a say. But most platforms make it hard to see who asked what, or to keep track as teams dig into the feedback.
Chat-driven analysis for everyone. In Specific, anyone on your team can start a chat with the AI about the data. It’s as easy as texting, and there’s no learning curve.
Multiple chats and clear ownership. Each team member can set up their own chat view, with personal or shared filters. You’ll always see who created each chat for clarity—helping your parks team focus on “maintenance equipment” while communications tackles “community engagement.”
Face-to-face collaboration with avatars. When you’re working together in the Specific chat, every message shows who sent it. This keeps conversations transparent and actionable. It’s easy to build on each other’s insights—and to revisit any conversation later.
To see how this works in practice, check our how-to guide or explore interactive demo surveys for citizen engagement at Specific demos.
Create your citizen survey about park maintenance now
Unlock deeper insights from your community and turn feedback into action with an AI-based conversational survey that analyzes itself—faster, richer, and built for real public engagement.