This article will give you tips on how to analyze responses from a citizen survey about tax fairness perception. Let’s dive right in to make your survey analysis process smarter, faster, and more insightful using AI.
How to choose the right tools for analyzing survey responses
The approach and tools you use really depend on the form and structure of your citizen survey data. The main split is between quantitative and qualitative responses.
Quantitative data: This is the numbers game—like how many people selected a particular response. Tools such as Excel or Google Sheets are straightforward and get the job done quickly.
Qualitative data: This is where things get interesting (or overwhelming). Open-ended answers, stories, complaints, motivations—this is the goldmine. But with dozens or hundreds of paragraphs, it’s impossible to read and accurately summarize without smart help. This is where AI analysis comes in—you'll need something beyond old-school spreadsheets, because you want to analyze themes, ideas, and feelings at scale.
When dealing with qualitative responses, there are two general approaches to tooling:
ChatGPT or similar GPT tool for AI analysis
Direct use of GPT-based tools: You can copy and paste your exported survey data into ChatGPT (or another large language model) and chat about the data. This is handy for a quick read or brainstorming, but:
- Handling large datasets gets clunky. The context window (maximum data size) limits how much you can paste and analyze at once.
- Limited structure. You don’t get summaries by question, themes by category, or automatic filtering—unless you tediously prompt the AI for each one.
- Manual prep needed. You have to clean, format, and copy-paste data, making this feasible only for smaller surveys.
That said, you’ll get value with careful prompting (see next section for prompt ideas), especially if you guide ChatGPT with survey context.
All-in-one tool like Specific
Purpose-built AI analysis platforms: With Specific, you can both collect survey data and instantly analyze it with built-in AI tools—no exports or convoluted prompt engineering.
- Better data collection. The AI asks intelligent follow-up questions during the survey, so responses are richer and easier to analyze. Read more about our automatic follow-ups here.
- Instant AI summaries and insights. The AI summarizes responses, lists key themes, and surfaces actionable findings—right out of the box.
- Conversational analysis, tailored for surveys. Chat with your survey results directly, similar to ChatGPT, but with features like filtering, context management, and multi-survey support.
- No spreadsheet wrangling. Results are organized, filterable, and ready to discuss with colleagues.
- Other leading tools like NVivo, ATLAS.ti, or MAXQDA provide similar AI-driven coding or sentiment features, but often require more manual work and cost than a fully integrated solution like Specific [1][2][3].
Many leading researchers use these kinds of platforms to extract deeper insights without drowning in raw data. If you’re ready to start, check out our citizen tax fairness perception survey generator or learn more about top survey questions to ask.
Useful prompts that you can use to analyze citizen survey data about tax fairness perception
Getting good insight from your qualitative citizen survey data comes down to asking the right questions—literally. The more precise your prompt, the more useful your findings. Here are some go-to prompts that work with Specific’s AI chat or in ChatGPT:
Prompt for core ideas: Use this for distilling the main themes or topics from a big set of tax fairness perception responses. Paste your data and use:
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 performs better if you give it more context, such as the purpose of your survey, what you hope to learn, and any specific worries. Here’s an example of adding context to your prompt:
The following responses come from a citizen survey about perceptions of tax fairness in our city. We want to understand core concerns and opportunities for better communication with citizens. Please extract key themes as above.
Dive deeper into a core idea: If a pattern emerges, probe further. Try:
Tell me more about XYZ (core idea)
Direct prompt for specific topics: Check if anyone brought up a key topic, like progressive tax or public services. Try:
Did anyone talk about XYZ?
Include quotes.
Prompt for pain points and challenges: To surface what's most frustrating for citizens, ask:
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 personas: Find out if there are clusters of similar-minded citizens:
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 motivations and drivers: Uncover why citizens feel the way they do or what prompts certain attitudes:
From the survey conversations, 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.
Want to see more example prompts or tips? Our in-depth guide on how to create a citizen tax fairness perception survey can help you get the most out of your next survey.
How Specific analyzes qualitative data based on question type
Open-ended questions, with or without follow-ups, are handled by generating a summary for all respondent answers. For follow-ups, you’ll see summaries of every extra reply, giving depth to each main answer.
Choices with follow-ups: Each choice creates its own “bucket.” You get a separate summary of all responses to follow-up questions for that specific selection. For example, if someone thinks taxes are fair but worries about corporate loopholes, those comments appear together.
NPS (Net Promoter Score) questions: Responses are grouped by category—detractors, passives, and promoters. Each group gets its own summary of follow-up comments. This makes it simple to spot what's different about each segment.
You can absolutely do all of this using ChatGPT—it just requires more manual prompting, splitting up data by question/choice, and some copy-pasting. Specific makes this process seamless.
Working with AI context limits in qualitative survey analysis
Context size matters. AI tools can only “see” so much data at once. If your citizen survey receives hundreds of responses, you might blow past those limits and need to choose what data to analyze.
There are two smart ways to manage this with tools like Specific (and you can do similar steps with other tools, but with more effort):
Filtering: Only send conversations with replies to select questions, or just conversations where people gave specific answers. You can focus on citizens who feel most strongly about tax fairness, for example, or those who skipped a key question.
Cropping: Select just the question(s) you want to analyze and ignore the rest. This helps the AI process more conversations at once. You see what matters, not a wall of irrelevant text.
Both approaches dramatically improve the relevance (and usefulness) of your insight, especially if you use them together.
Collaborative features for analyzing citizen survey responses
Collaboration is hard when email threads and spreadsheets fly back and forth between team members. When analyzing citizen surveys about tax fairness perception, multiple people might want to explore different themes, apply custom filters, or dig into unique subgroups.
In Specific, analysis can be collaborative and transparent. You can open multiple chats about your survey data, each with its own custom filters, core ideas, or focus. Colleagues can set up their own chat threads, and every conversation clearly shows who started it—ideal for teams with different goals (like policy versus communications).
See who said what. Inside AI chats, messages include avatars, so you always know which insight comes from which teammate. No more version control headaches or lost context from forwarding emails.
Filter, segment, and focus together. Apply filters (like “only look at negative sentiment on fairness”) and collaboratively build insights—this greatly accelerates institutional learning on complex, sensitive citizen data.
Want to try this? Building your survey is just a few clicks away with the AI survey builder or you can edit and iterate using the AI survey editor.
Create your citizen survey about tax fairness perception now
Unlock richer insights and make smarter policy decisions—create your own citizen tax fairness perception survey with Specific and get actionable, AI-powered analysis instantly.