This article will give you tips on how to analyze responses from a citizen survey about public transportation quality using AI survey analysis tools.
Choosing the right tools for analyzing survey responses
When you’re diving into survey data, the right approach and tooling depend entirely on the type and structure of your responses.
Quantitative data: If you're dealing with numbers or simple counts—like how many citizens chose “satisfaction” or “reliability”—Excel or Google Sheets work just fine. You can quickly calculate percentages, averages, or trends in a straightforward way.
Qualitative data: When your results are open-ended comments, or when you’ve collected a flood of feedback via follow-up questions, things get trickier. Reading every single reply is nearly impossible, especially at scale. For deep dives into these qualitative insights, AI-powered tools are a must—they instantly surface common themes and summarize feedback, revealing issues and opportunities that manual reading would miss.
There are two solid approaches for tooling when dealing with qualitative survey responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste for quick insights: You can export your survey responses and paste them directly into ChatGPT or a similar large language AI. This gives you fast, conversational feedback on your data. It’s especially useful if you just want to ask a few questions or get a rough summary.
What to watch out for: The process can get messy: formatting, context limitations, and privacy concerns might slow you down. You’ll need to keep track of your inputs and outputs, reformat responses, and sometimes clarify confusion manually in the chat.
All-in-one tool like Specific
Purpose-built for surveys and data analysis: An AI tool like Specific is designed for this exact job. It can collect survey data from citizens using conversational chat-based interviews, then analyze responses using advanced AI.
Captures deeper insights automatically: When collecting data, Specific’s AI asks targeted follow-up questions, producing clearer, richer answers about public transportation quality (learn more). The result? Higher quality data and more actionable findings.
One-click analysis: Specific then summarizes qualitative responses instantly: highlighting key themes, ranking what matters most (like safety, reliability, cleanliness), and producing actionable summaries. No spreadsheet wrangling needed.
Conversational data exploration: You can interact with the results by chatting directly with the AI—just like using ChatGPT, but with helpful features for managing survey data context, filtering by demographic, or examining specific question blocks. It streamlines the full analysis workflow.
Useful prompts that you can use to analyze Citizen public transportation survey data
The beauty of AI analysis is how you can guide it with prompts. Here are some of the best for public transportation quality surveys from citizens. Use these in ChatGPT, or leverage them in a platform like Specific.
Prompt for core ideas: This go-to prompt quickly extracts essential themes across open-ended responses:
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
Adding more context always boosts results. Include your survey goals, location, and anything unique about your citizen audience. A good example:
Here are the responses from a citizen feedback survey on public transportation quality in [your city]. Most citizens are daily commuters aged 18-45. I want to know the biggest challenges and aspects that need improvement, with examples if possible.
After extracting the most common themes, dig deeper with:
Prompt for follow-up: Ask the AI to “Tell me more about punctuality concerns (core idea)” or any core idea from your data to surface specific anecdotes, pain points, or ideas.
Prompt for a specific topic: To validate a hunch, ask:
Did anyone talk about accessibility or improvements for people with disabilities? Include quotes.
Prompt for personas: Understand citizen sub-groups with:
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: Zero in on what frustrates riders:
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 sentiment analysis: To gauge overall 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.
Prompt for suggestions and ideas: Surface actionable citizen 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.
You can always find more prompting inspiration and best practices in this article about best survey questions for citizen feedback on public transport.
How Specific analyzes qualitative survey data by question type
Specific shines when survey response analysis is tailored to the structure of the survey:
Open-ended questions (with or without follow-ups): Specific summarizes key themes and insights for all responses related to the main and follow-up questions. If citizens shared lengthy explanations, you get an instant, clear summary.
Choice-based questions with follow-ups: For each response option (say, “bus” vs. “metro”), every follow-up answer is summarized separately, so you can compare reasons behind each choice.
NPS questions: Specific breaks out summaries for each group: detractors, passives, and promoters. It highlights the most common feedback and suggestions for each group, making it easy to track what earns loyalty or causes distrust among citizens.
You can absolutely replicate this workflow in ChatGPT or another AI model—just be ready to do some manual sorting and re-formatting before and after each prompt.
Handling AI’s context limit: filtering and cropping
If your citizen public transport survey collected hundreds (or thousands) of responses, you’ll run into AI’s “context size” limit: too much data won’t fit for a single run. Specific tackles this challenge in two smart ways:
Filtering: Narrow down which responses go into AI analysis. Only include surveys where citizens replied to certain questions or selected certain options. This keeps analysis focused and efficient.
Cropping: Choose which question blocks actually get sent for AI analysis. This lets you handle bigger surveys without breaking context limits, and ensures that no key areas are left out.
When using general AI tools, you’ll need to manually break up and filter your data, then reassemble insights afterwards.
Collaborative features for analyzing citizen survey responses
Analyzing feedback from citizens about public transportation quality is rarely a solo mission. Collaboration usually means endless back-and-forth emails, sharing spreadsheets, and losing track of who said what—especially with a large, diverse team.
Chat as a workspace: With Specific, you chat with AI to analyze survey data just by asking questions—the same way you’d chat in ChatGPT, but purpose-built for citizen survey analysis.
Multiple chats, multiple angles: You can set up separate chats for different teammates, departments, or lines of inquiry. Each chat can have its own set of filters, letting a transit planner focus on bus data, while a communications manager zeroes in on accessibility mentions. The creator of each chat is visible, bringing transparency and accountability to every insight.
Clear visibility for teamwork: In every AI Chat, you can immediately see who contributed each message. Avatars next to messages make collaboration and handoffs effortless—no more digging through email threads to track input.
Instant sharing and updates: As your team uncovers new findings or theories about citizen needs and public transport satisfaction, you can share, update, or hand off chats easily within Specific—staying aligned and making decisions faster.
This is especially helpful when you’re handling data at scale or need to coordinate with urban planners, transportation officials, or external consultants—everyone gets visibility into the ongoing conversation, context, and conclusions.
If you're still building your survey, the guide to creating public transportation quality surveys for citizens is the best place to start.
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