This article will give you tips on how to analyze responses from a citizen survey about cost of living concerns. If you're looking to turn survey feedback into clear insights, you’re in the right place.
Choosing the right tools for survey response analysis
How you analyze survey data from citizens about the cost of living depends on your data’s structure. If your responses are mostly simple choices or ratings, you’ve got it easy. Open-ended statements or stories? That’s where things get interesting—and where AI shines.
Quantitative data: If you asked citizens to select from predefined answers (like "very worried" or "not worried"), any spreadsheet tool (Google Sheets, Excel) can tally up results quickly. A pivot table or a simple bar chart is often all you need to see trends.
Qualitative data: Open-ended answers explaining "why" people feel the way they do, or what’s most stressful about the rising cost of living, are far more complex. Manually reading through hundreds of responses is just not practical, especially as surveys grow and more voices are added. This is exactly the case with citizen concerns worldwide, where survey after survey underscores that the overwhelming majority are deeply worried about cost of living increases—93% in the EU, for example, rate it their top issue. [2] Let’s face it: reading and coding hundreds of mini-essays by hand is no one’s idea of efficiency.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can export your citizens’ responses and put them directly into ChatGPT. Then, simply chat with the AI—ask for summaries, top themes, or explanations in plain language.
However, copying and pasting chunks of survey data can get unwieldy. As your dataset grows (pretty common in national or city-wide research), you’ll bump against context limits. Tracking which prompt produced which result and slicing the dataset for different questions or personas is trickier than it sounds.
All-in-one tool like Specific
Specific is built from the ground up for this workflow. It’s designed to both collect survey data in a conversational, chat-like way and analyze it instantly using AI, without the manual overhead. If you use Specific, the survey AI automatically asks follow-up questions, improving both the quality and the depth of citizens’ responses. For example, if someone says “it’s hard to pay for groceries,” AI can probe gently: “What’s changed about your shopping habits over the past year?”
When it’s time to analyze:
The AI summarizes every open-ended answer and highlights big themes.
You can chat directly with AI about survey results in Specific without leaving the platform.
You control which data or follow-ups are included in each chat.
No copy-pasting, and no spreadsheets needed. If you want to see the specific survey templates or prompts for citizen cost of living issues, visit this preset survey generator for cost of living concerns.
Useful prompts that you can use to analyze citizen cost of living concerns survey responses
When you analyze open-ended survey data, your results are only as good as your prompts. Here are practical, tested prompts that work whether you’re using ChatGPT, Specific, or another AI-powered tool.
Prompt for core ideas: Use this when you want to distill the “big picture” from a pile of citizen comments about rising costs.
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
If you give AI more context about your survey, it will always perform better. For instance, tell it what country you surveyed, or that your goal is to prepare insights for local policy makers. Example:
Analyze these responses from a 2024 survey on cost of living concerns among urban residents in the EU. The goal is to help city council understand which specific cost factors matter most and how they affect different demographic groups.
After you extract key themes, dig deeper by asking: "Tell me more about [specific core idea]".
Prompt for specific topic: To check if citizens mentioned a concern about, say, food prices, use:
Did anyone talk about food prices? Include quotes.
Prompt for personas: This prompt is invaluable for understanding who your respondents are—urban renters, families, retirees, young professionals, etc. Each persona may have unique cost of living anxieties.
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: Get a straightforward list of what’s hurting the most (rent, food, fuel, childcare, etc.).
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: Knowing the emotional charge behind comments on inflation or bills helps prioritize response. Use:
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 suggestions and ideas: Gather citizen-proposed solutions.
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs and opportunities: Critical for finding blind spots in government support or community programs.
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more detail on prompt engineering or AI-driven survey strategies, check our deep dives on AI survey response analysis and AI follow-up question workflows.
How Specific analyzes qualitative data by question type
The way you analyze qualitative citizen data will change based on your question structure, and Specific handles each case for you:
Open-ended questions (with or without follow-ups): The AI summarizes all citizen comments, including follow-up responses, giving you a snapshot and a deeper dive (e.g. “groceries are expensive because…”).
Choice-based questions with follow-ups: For every choice, Specific offers a separate theme summary grounded in the matching follow-up answers. This is huge for dissecting, for example, why "fuel costs" resonate with certain subgroups.
NPS survey questions: Each group (detractors, passives, promoters) gets their own summary, spotlighting what makes different citizens hopeful or worried about their economic future.
You can replicate this in ChatGPT, but it’ll take manual filtering, copying, and sorting on your end. Specific does the heavy lifting for you, but if you want to hack your own workflow, try segmenting exports in Excel before uploading to your AI tool of choice. If you need inspiration on question design, check out this guide to the best questions for cost of living concern surveys.
Managing AI context limits with large survey datasets
AI tools like GPT have limits. If you paste too much data at once (common in nationwide citizen surveys), the analysis will get cut off, or performance nosedives. Specific solves this out-of-the-box with two techniques:
Filtering: You can analyze only conversations where citizens replied to specific questions or made particular choices. This focuses the AI on relevant data, fitting more in without loss of nuance.
Cropping: Select which survey questions to send into AI for analysis. Fewer questions per conversation means you squeeze more full journeys into one analysis batch, making sure the most crucial topics (like urgent worries about inflation) don’t get lost.
This approach makes it manageable to analyze even huge, multi-hundred-response citizen surveys while avoiding the dreaded "context window" wall. If you’re curious about best practices, see this how-to on building and handling large citizen feedback surveys.
Collaborative features for analyzing citizen survey responses
Collaboration on citizen cost of living surveys is always tricky. You might deal with multiple teams—policy analysts, researchers, community engagement specialists—all working on the same dataset, often in different places and at different times.
In Specific, you can collaborate in real time by chatting directly with the AI about your citizen data. Multiple chats can run side by side, each with its own filters or summaries.
Each chat shows its creator. You see who made what observation, or who is following up on a specific trend in housing costs (or whatever subtopic matters). Feedback is never lost in email chains—a huge win for collective insight generation.
Avatars next to every chat message make team contributions instantly clear. Analysts, policy leads, or internal stakeholders can all participate, ensuring diverse points of view are heard as you interpret cost of living worries and ideas.
If building surveys from scratch is on your mind, or you want to see how collaboration can work in practice, try the AI survey generator for collaborative team surveys or play with the AI-powered survey editor. You can even auto-generate a citizen cost of living NPS survey instantly for your next round of research.
Create your citizen survey about cost of living concerns now
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