This article will give you tips on how to analyze responses from a conference participants survey about health and safety, focusing on the best ways to use AI for survey response analysis.
Choosing the right tools for analysis
The tools and approach you use for analyzing responses from conference participants heavily depend on the form and structure of your data.
Quantitative data: When you have data like how many people selected each option or rated a health protocol on a scale, it’s straightforward to analyze these numbers using tools like Excel or Google Sheets. Crunching structured data is easy and mainstream.
Qualitative data: Things get trickier with open-ended survey questions and follow-ups. Manually reading through dozens or hundreds of text responses is simply not practical. This is where AI-powered tools become essential—the only realistic way to handle this scale and uncover actionable insights.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste analysis: You can export your survey responses and feed them into ChatGPT or a similar GPT-powered AI. Then, chat with the AI about your results.
Convenience versus scale: While this is a flexible, conversational way to interact with your data, it’s not very convenient with large datasets. Managing copy-paste, ensuring data privacy, and context limits can be a pain—especially as the number of responses grows.
All-in-one tool like Specific
Purpose-built analysis: A platform like Specific is designed for this situation. You both collect data with AI-powered conversational surveys (which ask smart follow-up questions—see how in this deep dive), and you analyze the responses with the built-in AI analysis tools.
Automatic follow-ups yield richer data: Specific improves the depth and quality of information you gather by automatically asking clarifying or digging questions, so respondents’ answers are never shallow or generic.
Zero manual analysis: The AI instantly summarizes long text responses, finds key themes, groups related insights, and turns conversations into crisp, actionable knowledge—no spreadsheets or manual copy-pasting required.
Chat with your data: You get a “ChatGPT for survey analysis” built in. Ask any question about your participant responses, analyze health and safety concerns, or drill down by segment, all in a few clicks—see how it works here.
Flexible AI context management: Features like filtering by question, cropping context, or drilling into individual themes give you direct control over what the AI analyzes, sidestepping common challenges with GPT-powered tools.
Useful prompts that you can use for analyzing conference participants health and safety surveys
Getting the most out of your survey data often comes down to knowing how to talk to your AI. Here are some field-tested prompts that help you uncover key patterns and actions from qualitative health and safety feedback.
Prompt for core ideas: This one just works. It’s great for extracting themes and key points from big datasets, and it’s the exact approach we use in Specific. It will work almost identically in ChatGPT:
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 when you provide more context about your survey and goals. For example:
Analyze these survey responses from 150 conference participants about health and safety in our recent event hall. We're specifically interested in concerns about venue layout, emergency preparedness, and food safety. The goal is to find what matters most and identify any actionable improvements.
To dive deeper, try:
Prompt for exploring a theme: After you identify a “core idea,” you can ask: “Tell me more about emergency exit concerns.” The AI will pull relevant mentions and patterns for you.
Prompt for specific topic: To validate assumptions or rumors, try: “Did anyone mention crowding in the exit areas? Include quotes.” This gets you instant facts rather than opinions.
Prompt for personas: Want to discover different types of conference participants? Try: “Based on the survey responses, identify and describe a list of distinct personas—similar to ‘personas’ in product management. For each persona, summarize key characteristics, motivations, goals, and include quotes.”
Prompt for pain points and challenges: Cut straight to the friction: “Analyze the survey responses and list the most common pain points or frustrations about health and safety. Summarize each and note how often each is mentioned.”
Prompt for motivations & drivers: If you want to understand what energizes people, ask: “From these conversations, extract the primary motivations or reasons participants give for their health and safety choices. Group similar motivations, and cite supporting evidence.”
Prompt for sentiment analysis: To get the vibe: “Assess the overall sentiment in these responses (positive, negative, neutral). Highlight key phrases that illustrate each group.”
Prompt for suggestions & ideas: To gather practical improvements: “Identify and list all suggestions or requests for better health and safety. Group by topic or frequency, with direct quotes when available.”
Prompt for unmet needs & opportunities: To spot hidden gaps: “Examine these responses to uncover any unmet health and safety needs or opportunities mentioned by participants.”
If you’re looking for more inspiration on what to ask, check out our guide to the best questions for conference participants health and safety surveys.
How Specific analyzes qualitative survey data by question type
Every question in your survey gets the right analysis treatment in Specific:
Open-ended questions (with or without follow-ups): Specific gives you a summary of all responses, plus analysis of any follow-up replies gathered by the AI during the conversation. This reveals both initial feedback and in-depth clarifications in one place.
Choices with follow-ups: For every choice, the AI groups and summarizes all related follow-up feedback. For example, if “food safety” was picked, you see a direct summary of everyone’s remarks and suggestions tied to that choice.
NPS (Net Promoter Score): Each group (detractors, passives, promoters) gets a focused summary of their feedback and comments. This way, you can zero in on what’s pushing scores up or down, and exactly what to fix or celebrate.
You can use ChatGPT or any GPT tool to do the same, but you’ll need to segment and prompt manually—which takes more effort and time.
If you want to see how to build a survey like this from scratch, here's a step-by-step guide with best practices.
How to tackle challenges with AI context limits
A challenge with all AI models—even the best ones like GPT-4—is the context limit. If your survey gets a lot of responses, the content might be too much for the AI to analyze in a single take.
Here’s how Specific handles this (and how you can tackle it manually):
Filtering: Analyze only those conversations where users answered certain questions or chose specific options. For example, just look at participants who flagged “venue layout” as a concern. This trims the size of chats and focuses your analysis.
Cropping: Select and send only certain questions (or sets of replies) to the AI for analysis. Instead of giving the full transcript, maybe you send only answers about “emergency exits.” This keeps each AI request compact but still focused.
By combining these two tactics, you can always squeeze maximum insight from even the biggest survey dataset—no need to break a sweat over AI limits. With Specific, this is built right in, but you can also manually filter and divide your data for ChatGPT analysis if you prefer.
For more technical information on context management, check out the Specific JavaScript SDK documentation or see how the conversational analysis feature works in practice.
Collaborative features for analyzing conference participants survey responses
Analyzing feedback from a conference participants health and safety survey is rarely a one-person job. Sharing work, comparing insights across teams, and keeping everyone on the same page are real challenges when you’re working with traditional survey tools.
AI chat collaboration: In Specific, you don’t have to juggle spreadsheets or long email threads. You can chat with AI about your data, and every team member can start their own chat or join ongoing ones focused on specific questions or themes.
Multiparty analysis: Each chat retains filters and context—so one colleague can deep-dive into “food safety,” while another explores “emergency preparedness.” You always see who started the chat and can follow their logic easily.
Clear accountability: Every question, answer, or insight is tagged with the contributor’s avatar. No more wondering who ran which analysis or made which suggestion.
Real-time or async: Because you chat directly with the AI, you can work in real-time as a team or asynchronously, picking up where others left off.
If you want to try creating a survey with collaborative analysis in mind, try this AI survey generator for conference participants health and safety.
Create your conference participants survey about health and safety now
Unlock actionable insights and boost event safety by creating a conference participant survey—instantly analyze responses with AI, identify key themes, and empower your team to make smarter decisions fast.