This article will give you tips on how to analyze responses from a Citizen survey about Public Spending Priorities using the best practices in AI survey response analysis.
Choosing the right tools for analyzing survey data
The strategy and tools you’ll use to analyze your citizen survey really depend on the mix of data—whether it’s mostly numbers or open-ended, in-depth responses.
Quantitative data: For questions like “How important is healthcare spending on a scale from 1 to 10?” or “Which of these should get the biggest budget increase?” Excel or Google Sheets can help you quickly count, graph, and filter responses.
Qualitative data: Open-ended answers—like why someone chose a particular priority—are richer but much trickier to wrangle. With even dozens of responses, it’s nearly impossible to read them all or extract trends by hand. Here’s where AI tools become essential for deeper qualitative analysis.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
This is the “copy-paste” route. Export your qualitative responses into a spreadsheet or text file, then paste chunks into ChatGPT or another GPT tool. From there, you can ask the AI to summarize, surface patterns, or answer follow-up questions.
It’s workable, but inconvenient. You have to deal with format conversions, context limits (ChatGPT can’t process very large datasets at once), and keeping track of which question or respondent you’re discussing. Plus, it’s easy to lose the structure and context of your original survey, which often dilutes insights.
All-in-one tool like Specific
Specific is purpose-built for this. It collects survey responses conversationally, asking smart, AI-driven follow-ups in real time. This boosts data quality significantly—respondents clarify answers as they go, without you lifting a finger.
Analysis is instant and tailored. When you’ve collected enough responses, AI-powered survey analysis in Specific gives you:
Automatic summaries for open-ended responses and follow-ups
Extraction of key themes, actionable insights, and trends—without manual sorting
The ability to chat with AI about your results directly (similar to ChatGPT, but tuned to surveys and with full context)
Management of filters for what’s sent into the AI chat context, so you can focus analysis on just the data segments you need
Pretty much: it handles messy data for you and saves hours per survey. You can see examples and work through sample analyses using AI survey generators preset for citizen surveys.
Useful prompts that you can use for citizen survey response analysis
The magic of AI tools like GPT lies in the prompts you use to direct the analysis. Even if you’ve used a conversational AI survey or exported your data and want to analyze in ChatGPT, certain prompts produce consistently robust, actionable insights.
Prompt for core ideas: Use this to get a summary of key topics and supporting detail—this is the same core analysis Specific delivers automatically:
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
Add context for better results: AI always performs better if it understands the background, goals, and any constraints of your survey. Try this:
“You're analyzing responses from citizens about public spending priorities after a major policy change. The goal is to understand where the public wants increased investment and why. Highlight the most discussed priorities and share explanations people gave.”
Prompt for deep dives on core ideas: After extracting the main topics, you can go deeper. For example:
Tell me more about [core idea], with supporting quotes.
Prompt for validation: Find out if citizens mentioned a particular theme or idea by asking:
Did anyone talk about [specific topic]? Include relevant quotes.
Prompt for personas: Uncover distinct segments in your audience:
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: Useful for public spending debates where needs are contested:
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: Get a sense of public mood around spending priorities:
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 unmet needs and opportunities: Identify what citizens say is missing in current government spending:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more inspiration, you can check out our AI survey generator or see how to create a survey for citizen spending priorities that sets you up for actionable AI-powered insights.
How Specific analyzes qualitative data by question type
Specific flexibly adapts its analysis based on the question’s structure, making it easy to extract insights regardless of your survey design. Here’s what happens:
Open-ended questions (with or without follow-ups): AI summarizes every citizen’s response, plus all follow-up answers tied to that specific question—surface-level and deeper context, neatly organized.
Choices with follow-ups: Each answer choice (say, “Increase education funding”) gets its own AI-generated summary. This means you see exactly why citizens chose a given priority, not just what they selected.
NPS questions: For Net Promoter Score-style questions, AI splits the analysis for detractors, passives, and promoters. Each group’s follow-up reasons are summarized separately—so you can pinpoint patterns and motivational factors by audience type.
You can mirror this process in ChatGPT or similar tools, but it’s definitely more manual—manage your follow-ups and segment data first, then copy each set into new AI prompts for separate analysis. In Specific, all this happens in a single workflow, so nothing slips through the cracks.
Solving challenges with AI context size limits
AI models have limits to how much text you can include at once (called “context size”). With hundreds or thousands of citizen survey responses, this gets tricky fast. There are two main solutions—both available in Specific out of the box:
Filtering: You can filter results—so, for example, only analyze conversations from respondents who answered a particular question about health spending. This keeps your dataset focused and manageable.
Cropping questions: Select only certain questions (like the open-ended “why?” answers) to send to AI for analysis. By trimming extraneous detail, you pack more value into each AI interaction and avoid hitting the context ceiling.
These tools ensure you get deep, representative analysis even on large, complex datasets—without random sampling or losing rare but important perspectives.
Collaborative features for analyzing citizen survey responses
Collaboration can quickly become chaotic when multiple people or teams analyze citizen survey results, especially on nuanced issues like public spending priorities. Who’s exploring which topic? Where are the insights coming from?
With Specific, you simply chat with AI about your survey data. Each team member or stakeholder can spin up their own AI chat focused on a particular segment (“Show me what low-income citizens said about infrastructure spending,” for example), with filters customized per chat.
Every chat is transparent and easy to attribute. Each chat shows who started the conversation, so you always know who’s working on what analysis. When several team members analyze survey findings in parallel, avatars display in the chat, showing who provided each prompt or follow-up.
This makes it easy to:
Investigate insights from different angles—for example, one teammate explores themes from education advocates, while another digs into healthcare priorities.
Share and debate findings with clear attributions, then export highlights or digests as needed for cross-team reports.
Read more on collaborative survey workflows and how to set up your citizen spending priorities survey for teamwork.
Create your citizen survey about public spending priorities now
Start analyzing your community’s needs with AI-powered insights—see instantly what citizens want, why it matters, and how to drive real impact with smart, rapid feedback.