This article will give you tips on how to analyze responses from a civil servant survey about cost of living concerns, using AI survey response analysis and best practices.
Choosing the right tools for analyzing civil servant survey responses
How you analyze the results of a civil servant survey about cost of living concerns depends a lot on what form your data takes. Survey responses generally fall into two categories:
Quantitative data: When you have structured data—like how many civil servants chose a specific answer or agreed/disagreed with a statement—traditional tools such as Excel or Google Sheets let you easily count and chart responses.
Qualitative data: Open-ended questions and follow-ups contain narrative feedback. Reading every answer by hand is overwhelming (and unscalable!) when you have hundreds, or even thousands, of responses. Here’s where AI-powered tools are invaluable—they let you extract patterns, themes, and insights from unstructured feedback that would otherwise get lost.
When you’re tackling qualitative feedback specifically, there are two main approaches:
ChatGPT or similar GPT tool for AI analysis
Copy-paste workflow: You can export your survey responses as text or in a spreadsheet, and paste them into ChatGPT or a similar large language model. Then, you can “chat” about the data by giving the AI specific instructions or prompts.
Limitations: While this approach works, it’s not seamless. Formatting data for export, chunking responses into the AI’s limited context window, and keeping track of prompt history all slow you down. Exploring nuanced themes or tying insights back to specific survey questions requires lots of manual legwork.
All-in-one tool like Specific
Purpose-built AI survey platform: With Specific, you get a tool made for collecting and analyzing civil servant surveys about cost of living concerns from the ground up. Specific can run your entire survey—asking follow-up questions automatically to improve the richness and relevance of the data you collect. (For more, see this guide on automatic AI follow-up questions.)
Instant analysis and chat: When responses are in, Specific uses advanced AI to instantly summarize open-ended and follow-up questions, flag core themes, and turn large response sets into practical insights—no spreadsheet wrangling needed. You can chat directly with the AI about the results, dive into subgroups, and segment by response or filter by question. The extra features for managing what data gets sent to the AI context make the workflow much smoother than using generic AI chat tools. Learn more about AI survey response analysis in Specific here.
Useful prompts that you can use for civil servant cost of living surveys
Prompts are the backbone of effective AI survey response analysis. Whether you use Specific or chat with ChatGPT, prompts determine how useful and actionable your findings will be.
Prompt for core ideas: Perfect for uncovering the main themes mentioned by civil servants. (This is Specific’s default for deep analysis of open-text responses.)
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
You’ll always get better results if you provide the AI additional context—explain who responded, what your goal is, or what you’re looking for. Here’s an example:
I ran this survey among UK civil servants in early 2024, aiming to understand how rising cost of living is affecting their well-being and workplace performance. Please focus your analysis on practical, work-related impacts and challenges.
Prompt for deeper explanations: After you’ve extracted a core idea, you can follow up with:
Tell me more about [core idea]
Prompt for specific topic: Quickly validate hunches or stakeholder questions. For example:
Did anyone talk about wage freezes? Include quotes.
Personas prompt: Ideal if you’re looking to identify subgroups within the civil servant 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.
Pain points and challenges 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.
Motivations & drivers prompt:
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.
Sentiment analysis prompt:
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.
For more tailored questions and best practices, check out this guide on the best questions for civil servant cost of living surveys.
How Specific analyzes qualitative survey data by question type
Open-ended questions with or without followups: Specific summarizes all responses as well as all replies to any follow-up questions that came from a particular main question. This gives you a layered understanding of surface feedback and the nuanced context behind it.
Choices with followups: When respondents select choices with follow-up questions attached, each choice receives a focused summary for its related open-text answers. This lets you compare, for example, why those selecting “dissatisfied” with their salary cite different issues from those who are “satisfied.”
NPS (Net Promoter Score): For NPS-style questions, you’ll get segmented summaries for each category—detractors, passives, and promoters. Each summary shows what triggers these attitudes in relation to cost of living concerns. Same workflow applies if you’re building an NPS survey from scratch in Specific (see our dedicated NPS survey generator for civil servants).
You can use a similar approach with ChatGPT, but you’ll have to organize and resend the follow-up batches by hand—it’s more laborious and less traceable than the automated workflow in Specific.
Working around AI context limitations: dealing with large response volumes
AI models like GPT have context size limits—that is, there’s only so much text you can paste in for analysis at one time. If your civil servant cost of living survey generated hundreds or thousands of responses, you could easily overwhelm what the AI can process in a single pass.
There are two proven ways to handle this challenge—both built into Specific for convenience:
Filtering: Filter responses by certain questions, specific respondent segments (e.g., only those who mentioned “transportation” or skipped meals), or answers to particular questions. Then, only this subset is analyzed by AI, saving context space and giving sharper results.
Cropping: Choose specific questions (not the entire survey) to include for the current analysis. This targeted approach lets you work around context limits and still extract meaningful insights from high-priority data.
This type of focused, iterative analysis is especially valuable when you want to compare regional responses or look at global patterns—something that’s become urgent, given that civil servants in places like Kenya have faced a 15.8% decline in real wages since 2020, and 8% of UK civil servants have used food banks in the past year [1][2].
If you’re setting up a new survey, you can also control for survey length from the start—learn more in our how-to guide on civil servant cost of living survey creation.
Collaborative features for analyzing civil servant survey responses
Civil servant cost of living surveys often cross teams, departments, and even policy boundaries. Collaboration on analysis can be painful: people lose track of who prompted which analysis, or copy-paste findings from inbox to inbox.
AI chat for survey data: With Specific, stakeholders can analyze survey data just by chatting with the built-in AI. No need to wait for an analyst to run off a static chart—you can ask, “What’s making civil servants feel most financially insecure in 2024?” and get an answer that’s context-aware and deeply sourced in your own data.
Multiple chats and filters: You can spin up as many AI chat threads as you want, each with its own question filters and context. You’ll always see who started each chat, streamlining collaboration—critical when multiple researchers or departments are reviewing the same cost of living survey results.
Identity and transparency: In group discussions, every message in the chat shows the sender’s avatar, so ownership is always clear. This makes passing findings, raising follow-ups, or reanalyzing specific subgroup feedback more straightforward. It’s a smarter, more transparent way to analyze data than sending spreadsheets or email threads back and forth.
If you want to create or edit a survey collaboratively, the AI survey editor lets you edit surveys in natural language, so everyone stays on the same page all the way from question creation to insight extraction.
Create your civil servant survey about cost of living concerns now
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