This article will give you tips on how to analyze responses from a tenants survey about building safety. When you're dealing with this kind of feedback, picking the right AI tools and prompts is crucial if you want actionable results fast. Let's break down the process from start to finish.
Choosing the right tools for analyzing survey data
The approach and tooling you use to analyze your survey responses depends on the kind of data you have. The more structured your data is, the easier it becomes to analyze—while open-ended comments push you toward AI solutions.
Quantitative data: For numbers—like how many tenants selected a particular safety concern—traditional tools like Google Sheets or Excel work perfectly. These are tried-and-true for tallying up counts, calculating percentages, or making simple charts.
Qualitative data: If your survey includes open-ended responses or follow-up questions, just reading the answers one by one quickly becomes impossible. When the volume grows, you'll want AI-powered tools to find patterns, extract themes, and summarize the tenants’ real concerns. This matters, especially when safety issues impact so many people's well-being: the UK Government’s National Tenant Survey shows **13% of tenants are dissatisfied with their home’s safety**, citing delays in repairs (26%) and building security issues (17%) as top culprits. Trying to read and manually cluster all of these concerns? Forget it. [1]
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
This is the DIY route. You can export your tenant survey results as a spreadsheet or text file, and then copy-paste batches of responses into ChatGPT (or Claude, Gemini, etc.). From there, you can chat about the results, ask for summaries, or get the AI to spot trends or key pain points.
But handling data this way is rarely seamless. Limited input size means chunking up responses. Formatting gets fiddly. Context can be lost between batches—the deeper your survey, the more manual labor required. For a one-off, it's doable, but for recurring safety surveys where insights actually drive improvements, you’ll want something purpose-built.
All-in-one tool like Specific
This approach is built for this exact need. Instead of exporting between tools, Specific covers both steps: you collect the data using an AI-driven tenants building safety survey, and analysis happens in the same space.
Specific goes further by asking follow-up probing questions automatically, making sure you capture high quality, detailed responses right from tenants—even about tricky safety or maintenance concerns. When it’s time to analyze, AI summarization instantly extracts core themes, giving you clear, actionable insights with zero manual sorting.
You can chat directly with the AI about your results—just like in ChatGPT—but with survey context built in. Features like filtering, answer segmentation, and chat histories make group work and deep dives much easier than juggling spreadsheets.
If you want to try creating a survey setup for this audience and topic, check out our tenant building safety survey generator. Or, explore more in the guide to best questions for a tenants building safety survey.
Useful prompts that you can use to analyze tenants building safety surveys
AI survey response analysis only works as well as the prompts you use. The right question lets the AI surface the “real story” hiding in tenant feedback.
Prompt for core ideas: To get the main themes from long lists of open-ended responses, this “core ideas” prompt is the foundation I use when analyzing tenant surveys. Paste a batch of responses and use this:
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 does better if you supply context about the survey’s audience, goal, or background. For example, you could start your prompt with:
This data is from a tenants survey about building safety in UK apartment complexes. Our goal is to identify safety weaknesses and priorities for improvement, so please focus the analysis on actionable themes that affect tenant wellbeing.
Prompt for exploring ideas further: When you see a recurring topic, ask the AI: "Tell me more about building security concerns"—this will drill into all the details about that theme, including related quotes.
Prompt for specific topic: If you want to know, “Did anyone mention fire safety?” ask the AI directly. Try: "Did anyone talk about fire safety? Include quotes." It’s a quick way to back up hunches with real feedback, or spot urgent weak signals before they become bigger problems.
Prompt for personas: To understand if you have different “types” of tenant (families vs students, frequent reporters vs silent majorities), use this:
"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: To surface what frustrates tenants most:
"Analyze these 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: To gauge the overall emotional tone (safety, trust, anxiety):
"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 & ideas: If you want improvement ideas direct from tenants:
"Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant."
How Specific handles qualitative analysis by question type
In Specific, survey response analysis adapts automatically to how you structured your building safety survey. Here's what happens for each question type:
Open-ended questions (with or without follow-ups): The AI summarizes all responses, and groups or highlights any follow-up exchanges for deeper detail. This helps uncover “hidden” themes—even when tenants ramble or mention multiple issues in one go.
Choice questions with follow-ups: For each choice (for example, “Which safety issue concerns you most?”), the AI bundles together all follow-up responses for that category. You get a focused summary for each, letting you compare worries about fire safety, repairs, or neighbor security side by side.
NPS (Net Promoter Score) with follow-ups: The AI creates separate summaries for detractors, passives, and promoters—so you instantly see what frustrates the least satisfied, and what your happiest tenants love most. This links directly to reasons behind your NPS trends.
You could technically do the same thing with ChatGPT, but it’s slower and much more manual—especially as your survey grows.
Dealing with AI context limits for bigger data sets
When you get hundreds of responses (or more), AI tools like ChatGPT can hit a “context limit”—the maximum amount you can analyze in a single shot. Specific solves this with two key features:
Filtering: Narrow your analysis to just the conversations where tenants answered a specific question, or picked a certain option. This ensures only the most relevant responses go into the AI, so you don’t waste precious context on empty or off-topic answers.
Cropping: Send only the selected questions to the AI for analysis. For example, you might want to focus on maintenance concerns rather than general feedback—cropping makes this fast and keeps within AI context limits so you can analyze more responses at once.
Both filter and crop are available in Specific out of the box, so you don’t have to slice and dice data manually.
Collaborative features for analyzing tenants survey responses
When analyzing building safety surveys, collaboration is a real challenge. It’s easy to step on each other’s toes when data is scattered, or when different team members are pulling insights in their own tabs.
In Specific, you can analyze tenant feedback just by chatting with AI—and do this together. You get multiple independent chats, each with its own filters (say, repairs vs. security issues), and you see who created each thread. This keeps projects organized, and ensures team progress isn’t lost in the shuffle.
Chats are “person-tagged,” so you know who said what. In group analysis, each message is labeled with your avatar, making it instantly clear who’s suggesting which follow-up question or insight. This reduces confusion and helps teams summarize findings much faster.
AI-powered discussion encourages deeper investigation, not just data crunching. By asking the AI new questions live (“What’s driving negative sentiment about repairs?”), everyone can chase hunches, share discoveries, and iterate faster—often surfacing new insights that might’ve been overlooked in static spreadsheets.
If you’re curious about building a truly collaborative process for AI survey response analysis, or want tips on creating a workflow for this audience, there are helpful guides such as how to create a tenants survey about building safety.
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