This article will give you tips on how to analyze responses from a Citizen survey about Local Government Performance using AI survey response analysis tools and techniques.
Choosing the right tools for analyzing survey responses
The tools and approach you pick depend on the structure and type of your survey data. Here’s what you need to know about Citizen feedback on local government:
Quantitative data (like single-choice, multiple-choice, ratings): These are easy to count and summarize with spreadsheet programs such as Excel or Google Sheets. You can quickly see trends, calculate percentages, and chart results.
Qualitative data (open-ended questions or follow-ups): This is where things get tricky. It’s impossible to read every detailed comment when you have lots of responses—especially on topics like local government performance, which prompt long, wide-ranging answers. That’s when you need AI tools for analysis.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Manual export and chat: You can copy and paste exported survey data into ChatGPT for analysis. This allows for natural, iterative questioning, but it’s a bit of a pain—managing the exported CSV or text file format can be clunky, especially if you need to split large data sets and keep track of context.
Limited workflow features: You don’t get extra tools for filtering, organizing, or managing context within ChatGPT; the workflow is pretty manual. Tracking which responses are grouped by question or theme requires copying, pasting, and a lot of patience.
All-in-one tool like Specific
Built-for-purpose AI platform: Specific combines data collection (through conversational AI surveys) and response analysis all in one place. Unlike generic tools, Specific can gather richer data—by letting the AI ask automatic follow-up questions, you capture more detail and context from every Citizen respondent. Learn more about specific AI followup questions at how automatic AI followup questions work.
AI-powered analysis: With the AI survey response analysis feature, responses are instantly summarized, and key themes emerge without you wrangling spreadsheets. You can chat directly with the AI about the feedback—like ChatGPT, but with extra controls for filtering and context management. The tool is built for analyzing feedback from open-ended and follow-up questions—a process that’s otherwise overwhelming.
Seamless actionability: Instead of just collecting Citizen complaints, you get concise insights and can identify trends in local government satisfaction or pain points in a fraction of the time. For a walk-through on creating your own survey, check this survey guide for Local Government Performance topics.
Useful prompts that you can use for Citizen survey data about local government performance
If you want to get serious value out of your citizen survey about local government performance, you need to ask the right questions—not just in the survey, but of your data. Here are example AI prompts that work with both Specific and general-purpose tools:
Prompt for core ideas: Want a quick, big-picture summary? Paste your data and use this prompt:
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
Provide context for best results: AI always performs better if you tell it about your survey’s purpose, background, or the kind of Citizen you’re surveying. For example:
This dataset contains open-ended responses from Citizens about their satisfaction with local government performance in areas such as municipal services, infrastructure, and communication. I want to understand the main concerns and suggestions.
Ask for detail on a key idea: After finding a recurring theme—like "lack of transparency"—ask, “Tell me more about lack of transparency.” This drills into specifics that matter for your follow-up actions.
Quickly validate a topic: “Did anyone talk about road maintenance?” (Tip: add “Include quotes.”) This checks if a topic surfaces and pulls sample comments for your report.
Prompt for pain points and challenges: Analyze the root frustrations that Citizens share with this prompt:
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: Gauge overall satisfaction—especially important, given how Citizen satisfaction with local governance is dropping globally [1][2]:
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 personas: Useful for understanding different Citizen experiences:
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 suggestions & ideas: To discover valuable suggestions from Citizens:
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 specific guidance on structuring your survey so you unlock the best prompts and analysis, read the best questions for a Citizen survey about local government performance.
How Specific analyzes qualitative survey data by question type
Specific’s AI gives you rich analysis for every kind of survey question:
Open-ended questions (with or without follow-ups): The tool summarizes all responses—plus any follow-up conversations attached to each main question. This gives you one aggregated, easy-to-read narrative for every open question.
Choice-based questions with follow-ups: For each answer choice, Specific produces a separate summary of the related follow-ups, helping you see not just which options Citizens picked, but why.
NPS (Net Promoter Score): The AI summarizes follow-up feedback for each segment—detractors, passives, and promoters—so you see what drives or hinders satisfaction for each cohort.
You can follow a similar process in ChatGPT by slicing your exported CSV by question/response and running prompts one section at a time. It’s doable, but a lot more effort.
If you prefer to edit and update your Citizen survey before analyzing, you can use the AI survey editor—just chat with AI to update questions as needed.
Solving the context limit problem in AI survey analysis
Large surveys can overwhelm AI tools that have limits on how much text you can analyze at once. Specific offers two ways to manage this:
Filtering: Instead of sending all conversations, filter by relevant replies. For example, analyze only conversations where Citizens complained about waste collection or praised public parks. This keeps the analysis focused and within limits.
Cropping: Select only the key questions for AI analysis. If you just want to examine open-ended comments about infrastructure, crop your dataset to include only those responses. This way, more survey conversations fit into the AI’s context window, and you avoid losing valuable data to truncation.
This approach ensures you get accurate, focused insights—without wrestling with technical hurdles or needing custom scripts.
Collaborative features for analyzing Citizen survey responses
Collaboration on survey analysis is one of the big pain points when teams are working together to process open-ended Citizen feedback about local government services. Juggling spreadsheet exports, comment threads, and lost email chains isn’t efficient.
Chat-driven analysis: In Specific, you can analyze survey responses by chatting with the AI—no complicated dashboards or specialist skills required.
Multiple parallel chats: Each chat can have its own filters applied, so one teammate can dig into Citizen sentiment about waste collection, while another looks at feedback on public safety. You’ll never overwrite or lose someone else’s work.
Clear authorship for collaboration: Each chat shows who started it, and messages are tagged with the sender’s avatar. This makes teamwork way simpler when summarizing findings for presentations or internal reports.
For simple step-by-step instructions on how to set up or share a Citizen survey, see this how-to guide.
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