This article will give you tips on how to analyze responses from a citizen survey about road maintenance using AI-powered survey response analysis. Jump in to learn what tools and prompts work best for making sense of your survey data.
Choosing the right tools for analyzing survey responses
The tools and approach you use to analyze survey responses depend on the type and complexity of your data. Here’s how to break things down:
Quantitative data: Numbers are your friend here. Questions like “How satisfied are you with road maintenance?” (rated 1–5, or as a choice) are simple to analyze. Count the responses in Excel or Google Sheets to get instant charts or averages.
Qualitative data: These are open-text answers (“What would you fix on your street?”), or detailed stories gathered through follow-up questions. This is where things get messy fast—the volume and depth of responses make manual analysis unmanageable. AI tools are a must!
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copying data into ChatGPT: If you export your survey results as text or CSV, you can paste them into ChatGPT or another GPT-powered AI and start chatting about the results.
Not very convenient: While this approach is accessible, it doesn’t scale well. Formatting issues, data limits, and a lack of structure make it a hassle if you’re juggling hundreds of citizen responses or want to filter by demographic or question type. Still, for a quick-and-dirty analysis on small data sets, it gets the basics done.
All-in-one tool like Specific
Specific brings everything under one roof. It combines survey collection and deep AI-powered analysis. You launch your conversational citizen survey about road maintenance, and the platform takes care of follow-up probing automatically—boosting the depth and context of each response.
Instant summaries and insights: With AI survey response analysis in Specific, your qualitative answers are summarized instantly. Key themes, core ideas, and actionable suggestions pop out—no sifting through spreadsheets.
Chat with your data, plus advanced controls: You can ask Specific’s AI anything about your survey outcomes, just like chatting with ChatGPT, but with features tailored for survey data: filters by question, conversation, or respondent, and contextual controls for managing what’s sent to the AI.
Other notable AI tools: MAXQDA, Atlas.ti, Looppanel, InfraNodus, and Qualz.ai all offer robust AI-powered features for qualitative survey analysis. Each has its own strengths—automated coding, transcription, sentiment analysis, or visualization—which can add speed and structure to your road maintenance research, especially if you handle more than just survey data. [1][2][3]
Useful prompts that you can use for analyzing citizen survey responses about road maintenance
When working with AI for survey analysis (whether it’s ChatGPT, Specific, or another tool), prompts shape your results. Here are tried-and-true examples adapted for citizen road maintenance surveys:
Prompt for core ideas: Use this to extract the big themes from dozens or hundreds of answers at once. It’s Specific’s default, but works well anywhere. Paste your text data with this instruction:
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 provide more context. Include details like your city, what you want to learn from citizens, or your goals for road improvement:
Here's the context for these answers: We conducted this survey in Springfield. The goal is to find out why residents are unhappy with current road maintenance efforts, and what would make a difference for them. Please use this background to guide your summary.
Dive deeper into specific topics. When something interesting pops up, ask:
Tell me more about road safety concerns mentioned by respondents.
Prompt for specific topic: Want to see if anyone mentioned a certain issue (like potholes or snow removal)? Use this:
Did anyone talk about pothole repair? Include quotes.
Prompt for personas: To identify who’s responding (commuters, cyclists, parents):
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 & 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 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.
How Specific analyzes citizen survey responses based on question type
When you collect feedback in Specific, every type of survey question is analyzed with an approach that fits its structure. Here’s what happens:
Open-ended questions (with or without follow-ups): You get an AI summary for all the responses, and—if follow-up questions were asked—separate summaries for those deeper conversations. That’s useful for “What should the city fix next?” types of questions where context and reasoning matter.
Choices with follow-ups: Each choice (e.g., “Snow removal,” “Pothole repair,” etc.) gets a dedicated summary of any follow-up feedback related to it. You instantly see themes behind why people made specific selections.
NPS questions: Responses are split into promoters, passives, and detractors. Each group’s follow-ups are analyzed and summarized separately—making it easy to pinpoint what’s driving satisfaction or dissatisfaction with road work.
You can do all this in ChatGPT too, but you need to prep and organize your data into logical groups for each question and category—a bit more manual labor!
How to handle challenges with AI context size limits
AI models (like GPT) have strict context size limits. If your citizen survey about road maintenance gathers a flood of responses, you might not be able to analyze everything at once. Here’s what works (and what Specific offers out of the box):
Filtering: Only include responses to selected questions or choices, narrowing your data set before sending it to the AI. Analyze what matters most without drowning in irrelevant answers.
Cropping: Send only certain questions (or segments of your survey) to the AI. This keeps within context limits and focuses analysis on high-priority insights.
This way, you get deep, structured analysis of the most relevant citizen feedback—regardless of total survey volume.
Collaborative features for analyzing citizen survey responses
Real-world challenge: Analyzing citizen surveys about road maintenance is rarely a solo sport—you want colleagues, city planners, engineers, or research partners involved in the insights process.
In Specific, analysis is a true team sport. You can investigate survey data by chatting with AI, and every collaborator on your team can have their own separate chats with different filters and focuses. Each chat is clearly labeled with the creator, so everyone knows who’s working on what.
See who said what. When collaborating in AI Chat, each message carries the sender’s avatar, making teamwork seamless. You’ll never get lost in a sea of anonymous suggestions—every insight and opinion is attributed and transparent.
Flexible collaboration: Tackle NPS feedback, open-ends, or specific road maintenance issues together. You can all view, filter, and summarize different slices of your survey—get more perspectives, minimize bias, and make higher-quality decisions for your community.
Create your citizen survey about road maintenance now
Get better insights, faster: launch a conversational AI survey, uncover citizen priorities, and drive real improvement in your city’s infrastructure with the intelligence of Specific.