This article will give you tips on how to analyze responses from a civil servant survey about public feedback on new regulations. If you're trying to get insights out of these kinds of surveys, here's how I approach it using modern, AI-powered methods.
Choosing the right tools for analyzing survey responses
First, the approach and tooling really depend on what kind of data you get from your survey of civil servants about public feedback on new regulations.
Quantitative data – If your data is mostly counts (e.g., how many chose “agree” vs “disagree”), tools like Excel or Google Sheets work perfectly. You just tally the numbers, use filters, or whip up quick charts to see the big trends.
Qualitative data – If you have open-ended responses or lots of follow-ups, things change. You can’t realistically read through dozens or hundreds of conversations and reliably spot every theme or hidden insight. For that, you really need an AI-powered tool.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Option one is pasting your exported data into ChatGPT or another generative AI tool and chatting about it. This works, but let’s be honest—it’s awkward pasting lots of text, tricky to keep things organized, and if you have 100+ responses, you’ll quickly hit the context limit. It’s good for quick and dirty analysis, but not much else if you’re working with large-scale government outreach, like our topic here.
All-in-one tool like Specific
Option two is using an AI analysis tool that’s built for survey work. Tools like Specific let you both collect responses (as a conversational, chat-like survey) and analyze everything in one place. When you collect data, it automatically asks smart follow-up questions, so you get more depth and context with every answer—see how that works here.
On analysis: Specific summarizes responses instantly, finds themes, points out trends, and helps you spot what matters—without needing to open a spreadsheet or do word clouds by hand. I can even chat with the AI about my responses, asking things like “What’s frustrating civil servants about the latest policy proposal?” and get contextual, instantly updated answers. You get extra features that help you filter or crop which parts of your data the AI sees, instead of copying-and-pasting—the difference is huge, especially for analyzing feedback on regulatory changes. [1]
If you want a deep dive on making surveys like this, check out this guide to creating civil servant surveys for new regulations or try the AI survey generator for civil servants.
Useful prompts that you can use for analyzing civil servant survey data on public feedback
Now let’s talk prompts. Good prompts will drastically change the quality of your insight from any AI, whether it’s ChatGPT or a tool built around survey analysis.
Prompt for core ideas – Use this to quickly tease out major themes from big batches of regulations feedback. This is my favorite starting point—the same prompt fuels Specific’s built-in analysis engine, but it works in standalone GPTs too:
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
Extra tip: AI always gives you better analysis when you add more context. After pasting in your responses, set up your prompt with a quick note about “These are responses from a survey of civil servants on their experiences with recent regulatory changes. I want to understand common issues and suggestions.”
Analyze these responses from civil servants about new regulations. This survey is part of a feedback process for improving regulatory rollout. My goal is to identify frequent obstacles, suggested solutions, and general sentiment among participants.
Prompt for specific topic: When I want to validate if a particular idea or concern shows up, I go with something like:
Did anyone talk about transparency challenges in implementing the new regulations? Include quotes.
Prompt for personas: Understand who’s saying what:
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.
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 responses, 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.
If you want a comprehensive list of question ideas for civil servant surveys about public feedback, I recommend this article: best questions for civil servant surveys on public feedback.
How Specific analyzes qualitative data by question type
Getting actionable analysis depends on how your survey is structured. Here’s how Specific handles it (and what you can emulate in other tools):
Open-ended questions (with or without follow-ups): The tool summarizes all feedback tied to that question and any follow-up, giving you quick topic overviews and deeper sub-themes.
Choices with follow-ups: Each response option (e.g., "Strongly agree", "Neutral") is summarized separately across all its comments—so if civil servants pick different choices, I see the “why” behind each group.
NPS questions: Summaries are split for detractors, passives, and promoters, based on follow-up responses. If you run an NPS survey about regulatory changes, you won’t miss what matters for each sentiment group. If you’re curious, check out this NPS survey builder: create civil servant NPS survey.
You can replicate most of this in ChatGPT, but it'll take a lot more copying, pasting, sorting, and combining responses before it makes sense. Specific streamlines the workflow and shows how flexible analysis adapts to each survey structure. If you want to see what editing a survey looks like, try the AI survey editor—it’s as easy as chatting.
Handling context-limit challenges in AI survey response analysis
One big hurdle with AI is the limited “context window”—basically, you can only process so much survey data at once. If you hit this limit, you’ll either lose depth or need to break your data up creatively. Specific solves this in two smart ways:
Filtering: Only send responses to selected questions (or specific replies) to AI for analysis. That way, I don’t waste space on irrelevant comments—just the topics I want.
Cropping: Restrict the data to only certain questions (or groups of questions). So if I want to focus all my AI analysis on “feedback about consultation sessions,” I crop everything else out. Much less overwhelm.
Most survey tools don’t offer these built-in options, which means more manual prep if you’re using general AI tools. For a walkthrough of how AI analysis works specifically, check out AI survey response analysis in Specific.
Collaborative features for analyzing civil servant survey responses
Collaboration is a huge challenge when analyzing public feedback on regulatory changes—especially if policy, comms, and HR teams all need input.
Shared conversations: With Specific, you can analyze survey data together just by chatting with the AI. This means everyone can ask the AI different questions—each person or team can set up their own chat to filter responses by department, region, or policy area.
Multiple chats: Each thread can have its own search and filters, so you could be digging into sentiment around new data privacy guidelines while a teammate is focused on onboarding regulations. Every chat clearly shows who created it, cutting down confusion when collaborating across large teams.
See who says what: AI chat in Specific shows sender avatars for each message. It sounds minor, but knowing who brought up a theme or asked a smart follow-up can save so much time. Instead of sifting through giant Slack threads, everything’s right inside your survey analysis workspace—plus you get full traceability for every insight.
If you’re designing a workflow for collaborative analysis, these kinds of features are game-changing. You’ll never lose track of who identified a pattern or suggested a next step, and it’s easy to revisit previous analyses as new regulations come into play.
If you want to try creating one from scratch to see how it works, the AI survey builder is a unique way to get started.
Create your civil servant survey about public feedback on new regulations now
Capture rich, actionable insights from civil servants on new regulations—instantly analyze results with AI-powered summaries, theme detection, and collaborative features built for real-world teams. Start your survey today and elevate your policy feedback process.