This article will give you tips on how to analyze responses from a Citizen survey about Public Safety And Policing. I'll focus on practical ways to turn survey analysis into actionable insights—using AI, proven approaches, and the latest tooling in survey response analysis.
Choosing the right tools for survey response analysis
How you analyze survey responses depends a lot on the mix of data you get back. Are your Citizen surveys leaning heavily on yes/no or ratings questions—or do you have lots of open-ended feedback about public safety and policing?
Quantitative data: When you're dealing with numbers—like how many Citizens selected "increase police presence" or rated trust in policing as "high"—tools like Excel or Google Sheets do the job fast. You can count, chart, and visualize clear trends with basic formulas.
Qualitative data: This is where Citizens pour their thoughts into text boxes. If responses cover topics like policing fairness, safety issues, or lived experiences, reading each comment is impossible at scale. Here, AI tools become essential. They help you summarize open-ended responses, extract key themes, and identify important trends without endless copy-pasting.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy your exported survey data and paste it into ChatGPT or another GPT-based chatbot to kick off your analysis. This flexible approach lets you ask nearly any question about your data. For instance, you might say, "What are the most common safety concerns Citizens mentioned?" or "Did anyone express mistrust in the police?"
The downside: Handling unsolicited, messy data in ChatGPT isn’t always convenient. Large text dumps can hit context limits, and keeping track of conversation topics—or returning to previous analysis—is tricky. You'll need to clean your data and guide the conversation, which can be both time-consuming and cumbersome if you want to slice data by demographic or survey logic.
All-in-one tool like Specific
Platforms like Specific are purpose-built for survey work. Specific can collect data (by running AI-driven Citizen surveys about public safety and policing) and analyze results instantly.
Better data at collection: Specific’s surveys don’t stop at your initial question: the AI asks targeted follow-up questions, which increases the quality and depth of responses. Respondents’ thoughts about public safety topics are automatically expanded, meaning you get richer insights without extra work.
Fully integrated analysis: When you’re ready to analyze, Specific summarizes results using AI—finding key themes, trends, and actionable points in seconds. You don’t need to copy-paste anything, wrangle spreadsheets, or set up complex analysis flows.
Conversational querying: Just like ChatGPT, you can chat with AI about your results: “Tell me about how Citizens feel regarding police presence,” or “Highlight concerns about fairness in policing.” But you also get features like context management, and the ability to filter and chat only about specific demographics, topics, or types of responses.
If you want to see it firsthand, try making your own Citizen survey using this survey generator with prebuilt template or explore our AI survey builder from scratch. For question ideas and survey design tips, check out our guide to the best survey questions for public safety and policing.
Industry context: Recent studies on public safety and policing support the need for qualitative analysis. For example, the Denver Community Survey (2024) showed that while 44% of residents felt safe, concerns around police presence and property crime were highly nuanced and varied by neighborhood [1]. Relying entirely on numbers misses these crucial layers.
Useful prompts that you can use for Citizen survey analysis on public safety and policing
Let’s get practical. Analyzing qualitative feedback from Citizens about public safety takes more than just “summarize.” You need targeted prompts—whether you’re using ChatGPT, Specific, or another AI—so you extract the gold buried in raw responses. Here are some go-to prompts for uncovering real insights:
Prompt for core ideas: This works great for getting themes from big data sets and is the backbone of Specific's own analysis. Try it as-is:
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 want even sharper results, give AI more context about your Citizen survey, your goals, or any hypotheses. Example:
This survey was sent to Citizens in Denver in 2024. Our goal is to understand their experiences and opinions regarding local public safety and policing. Focus on identifying main safety concerns, attitudes toward police, and suggestions for improvement in city policies.
Dive deeper with: “Tell me more about XYZ (core idea)”. This is your secret weapon for doubling down—use it after you get main themes.
Prompt for specific topic: “Did anyone talk about XYZ?” (e.g., homelessness, discrimination, trust in police). Add “Include quotes” to pull relevant passages directly from responses.
Prompt for personas: Useful if you want to sketch out who is answering and why. Try: “Based on the survey responses, identify and describe a list of distinct personas—similar to how 'personas' are used in product management.”
Prompt for pain points and challenges: Especially powerful for policy: “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 motivations and drivers: Useful for understanding roots of behaviors or concerns: “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.”
Prompt for sentiment analysis: Get a snapshot vibe: “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: Spot areas for improvement: “Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
If you want even more tips, check out our how-to article on building Citizen surveys about public safety and policing or our page on automatic AI follow-up questions—the backbone for deeper data.
How Specific analyzes different types of survey questions
How do you actually “see” what Citizens are saying—across all survey types?
Open-ended questions (with or without followups): Specific summarizes all responses, including related follow-ups. Example: If you ask, “How safe do you feel in your neighborhood?” and add, “Why?”—you’ll get a concise summary that captures both.
Choices with followups: For multiple choice (“What should police focus on?”), Specific summarizes the follow-up thoughts for each option. That means you see why different Citizens chose each response, not just the counts.
NPS (Net Promoter Score): Every group—detractors, passives, promoters—gets its own AI summary of their open-ended feedback. So you instantly know what’s driving different types of ratings.
In classic ChatGPT, you can do most of this—but it’s slower and involves more manual data copying. Specific automates these breakdowns, giving you faster access to themes and evidence for your policy or program changes.
Tackling AI context size limits in response analysis
Big Citizen surveys often produce more data than an AI model’s “context size” can handle at once. When you have hundreds or thousands of open-ended responses about public safety and policing, you need a way to avoid losing key information.
Here are two proven approaches (and yes, Specific handles both out of the box):
Filtering: Instead of analyzing every response at once, filter conversations to include only those where the Citizens answered certain questions or chose specific options. That way, the AI focuses on the data you care about most—like just comments about police presence, or only feedback from those who reported feeling unsafe.
Cropping: Send only the relevant questions (and their responses) to the AI for analysis. This helps you stay within context limits and makes sure you get thorough insights on your highest-priority survey topics.
These approaches aren’t unique to Specific—but having them integrated makes scale analysis far less daunting for Citizen survey projects.
For a detailed breakdown of AI-driven analysis and tips on designing robust surveys, dive into our AI survey editor guide.
Collaborative features for analyzing Citizen survey responses
Collaboration challenges: Analyzing public safety and policing survey data rarely happens in isolation—it’s a team sport. Police departments, policy makers, city offices, and third-party analysts often need to weigh in.
Multiple chat threads: In Specific, you can run multiple chats with AI—each tailored to a particular question, topic, or data filter. You always see who created a chat, making it easier to track context and collaborate with teammates (and the whole process is auditable).
Seamless collaboration: When teams review Citizen survey feedback together, you can see who said what in chat, complete with avatars. It’s easy to split up analysis tasks, review findings, and revisit specific lines of inquiry—without exporting data or waiting for someone else to draft a summary.
Live AI chat: Want to look at public perceptions by demographic, or compare themes by neighborhood? Just start a new chat, apply a filter, and pull in stakeholders. Everyone can contribute and see context instantly.
This collaborative workflow is a big leap compared to old-school spreadsheets and static dashboards—especially when it comes to policy-sensitive issues like public safety and policing.
Create your Citizen survey about public safety and policing now
Kickstart your survey project, engage Citizens in natural conversations, and instantly discover what really matters—complete with actionable AI-powered insights.