This article will give you tips on how to analyze responses from a Citizen survey about Trust In Local Government using AI tools and best practices for survey response analysis.
Choosing the right tools to analyze your survey data
How you analyze Citizen survey responses really depends on the type of questions you asked—specifically, whether the data is quantitative or qualitative.
Quantitative data: If your questions are about numbers or choices (like “How much do you trust your local government?” with answer options), these responses are easy to count or graph in classic tools like Excel or Google Sheets.
Qualitative data: For questions with open-ended answers (“Why do/don't you trust your local council?”), it gets trickier. If you have hundreds of Citizens leaving free-text responses, there’s no way to read them all efficiently. This is where AI tools shine: they quickly surface big themes, top issues, and surprising ideas, even from massive data sets.
When you’re dealing with qualitative survey responses, you’ve got two main approaches for tooling:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can export all your citizen survey responses and drop them into a GPT-powered tool like ChatGPT. Chatting with your data lets you ask smart questions (“What are the top themes?” or “Show me pain points around trust”).
Limitations: This method can feel clunky: managing large data exports, fitting within context size constraints, and sifting through responses in a general-purpose chatbot all mean more up-front effort and manual shuffling. If you’re just experimenting, it works. But it’s not ideal if you want fast turnaround, teamwork, or easy sharing.
All-in-one tool like Specific
Purpose-built AI survey platform: With Specific, you don’t need two separate tools. You collect Citizen survey responses (including rich follow-ups) and instantly analyze what people are saying—all in one place.
Follow-up questions boost quality: Specific’s conversational surveys ask AI-driven follow-ups (see how in our auto follow-ups feature guide), so you always get richer, more actionable data than a standard poll.
Instant AI-powered insights: The platform summarizes answers, highlights key themes in citizen trust, and pulls out quotes or suggestions—all with no messy data wrangling. You can chat directly with the AI about any slice of your survey results, similar to ChatGPT, but you also get advanced filters and easy context control.
Built-in collaboration and management: You and your colleagues can chat about findings, apply filters (e.g., only look at citizens who mistrust local government), and keep everyone on the same page—especially useful for large or sensitive data sets.
If you want to create a Citizen survey about trust in local government, platforms like Specific offer ready-to-deploy templates and automatic AI analysis. These tools are especially relevant as more local governments adopt AI for qualitative analysis to quickly respond to declining public trust. [1] [2] [3]
Useful prompts you can use to analyze Citizen trust surveys
Once you’ve gathered all your Citizen survey responses, the next challenge is asking your AI the right questions—AKA, prompts. Thoughtful prompts help you dig out key reasons Citizens feel the way they do about local government.
Prompt for core ideas: Use this to quickly uncover main topics and what matters most to people:
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 gets better the more context you provide. For example, you can add a little setup to help the model focus its analysis on your survey’s specific aim:
You are an expert in public policy research. Summarize core reasons citizens do or do not trust their local government, based on the open-ended answers from this survey. My main goal is to identify actionable insights for improving trust. Here’s the data:
Prompt for deeper exploration: After finding a key idea (“Transparency concerns”), ask: “Tell me more about transparency concerns.” The AI can dig into what people mean and give supporting evidence.
Prompt for specific topics: You might want clarity on a policy or issue. Try: “Did anyone talk about council tax?” This helps you check the frequency and content of specific topics. Add “Include quotes” for supporting examples.
Prompt for personas: Understand the kinds of citizens answering. Ask: "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 key characteristics, motivations, goals, and relevant quotes."
Prompt for pain points and challenges: Dig deeper with: "Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each and note any patterns or frequency."
Prompt for motivations and drivers: Pinpoint why people respond the way they do: "From the survey, extract the primary motivations or reasons citizens gave for their views on local government trust. Group similar motivations and provide supporting evidence."
Prompt for sentiment analysis: Get a sense of emotion: "Assess the overall sentiment in the responses (e.g., positive, negative, neutral). Highlight key feedback for each."
Prompt for suggestions & ideas: “Identify all suggestions or requests. Organize them by topic or frequency, and include direct quotes where relevant.”
If you want a deep dive into writing the best questions in the first place, check out our guide on best questions for citizen surveys about trust in local government.
How Specific analyzes qualitative data by question type
Specific’s analysis approach is all about context. It adapts to each question type in your Citizen survey:
Open-ended questions (with or without follow-ups): The AI summarizes all responses and any linked follow-ups, so you see the big picture for each topic and the stories behind them.
Multiple-choice with follow-ups: For each answer choice, you get a dedicated summary—making it easy to compare what people said and why they selected a given option.
NPS questions: The tool clusters responses into detractors, passives, and promoters, generating a summary for each group. That way, you don’t just see a number—you get the “why” behind the score.
You can replicate this with a general AI tool like ChatGPT, but you’ll spend more time prepping data and making sure you don’t miss patterns linked to each answer type.
For step-by-step tips on building and analyzing this type of survey, read our guide on how to create a citizen survey about trust in local government.
Dealing with AI context limits in big surveys
AI context limits are a real challenge—GPT tools can only review a fixed amount of data at a time. If your Citizen survey gets thousands of responses, you may hit that ceiling.
To manage this, there are two practical analysis strategies (both built into Specific):
Filtering: Limit which conversations get analyzed, such as only those where Citizens answered a specific question or picked an answer of interest (e.g., “people who rate trust below 5”). This slims the dataset to fit within AI context limits.
Cropping: Send only chosen questions to the AI for analysis. Focusing on the most revealing parts of your survey means you can analyze more responses without running over the token limit.
You can read more about how this works in practice—especially for NPS surveys—in our NPS survey builder for citizens about trust in local government.
Collaborative features for analyzing Citizen survey responses
When multiple researchers, policy teams, or council members need to pull out insights together, staying in sync is hard—especially if you’re working with a mix of spreadsheets, email comments, and exported data files.
Chat-driven analysis: In Specific, you collaborate by simply chatting with the AI about your data. No learning curve, no exporting files, just type your research questions directly and get structured findings back instantly.
Multi-chat workflow: Each research question (or analysis thread) can be a separate chat. You apply your own filters to focus on particular groups (like young citizens or first-time voters), and it’s clear who made each request—making team work seamless.
Transparent teamwork: Across all chats, you see who asked what, with avatars for every collaborator. It’s easy to review why someone drew a conclusion about a specific segment—or to add your own follow-up questions as you spot new patterns.
This level of collaborative clarity is tough to recreate with basic tools. Platforms like Specific have built-in features for teamwork, which is essential when interpreting complex trust data for government policy or community strategy.
Create your Citizen survey about trust in local government now
Get powerful, AI-driven insights from your Citizen trust survey in minutes—enjoy instant analysis, actionable summaries, and effortless collaboration with your team.