This article will give you tips on how to analyze responses from an event attendee survey about accessibility. If you're aiming to unlock real insights, using the right survey analysis process (and AI tools) will save you hours and give you clarity.
Choosing the right tools for analyzing event attendee accessibility survey responses
What analysis method and tool you pick depends on the form and structure of your responses. I always start by breaking down my data into quantitative (numbers, counts) and qualitative (text, stories) chunks:
Quantitative data: These are your straightforward tally-up results—like how many attendees chose a certain accessibility barrier or rated the event as accessible. I quickly sum and chart these in Excel or Google Sheets. It's instant insight with basic formulas.
Qualitative data: This is everything written out in attendee replies: answers to open-ended questions, stories, suggestions, or explanations. Reading through even 50 replies can take forever. Manually tagging themes here isn't practical, so it's essential to use AI tools to help process and summarize this data.
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 into ChatGPT, Claude, or another GPT tool, then use prompts to discuss or analyze your event accessibility survey results.
It's simple, but not convenient: Exporting data, cleaning it up, pasting it to ChatGPT, and rerunning prompts when something changes can be clunky. It also means you have to manually manage context size limits and re-paste fresh data with every edit. Still, this setup gives you flexibility and works if you have a manageable number of replies.
All-in-one tool like Specific
Specific is an AI tool designed for survey response analysis—perfect when you need to both collect and analyze responses from attendees in one place. It’s built for conversational surveys, so you get more, higher-quality data (the AI asks smart follow-ups based on each reply).
Specific takes care of everything: It automatically summarizes attendee responses, surfaces key themes from qualitative answers, and even lets you chat directly with the AI about your findings in a way that’s tailored to survey analysis. No spreadsheets, no manual sorting—just actionable insights in minutes.
You get more control and features: You can manage which data is sent to the AI, filter responses on the fly, and collaborate across your team. If you're looking to focus on survey data about accessibility—especially from event attendees—this workflow gives you a cleaner, deeper understanding. Want to see how it works for yourself? Here's a detailed breakdown: AI survey analysis for qualitative event feedback.
Useful prompts that you can use for analyzing event attendee accessibility survey responses
Whether you’re pasting your event attendee accessibility survey data into ChatGPT, Claude, or working with a built-in survey analysis tool, good prompts unlock real insights. Here are a few that I rely on:
Prompt for core ideas: Start here to get the main pain points, needs, or ideas attendees mention. This prompt is the backbone of Specific’s theme-finding system, and works great on its own (just copy-paste it into your AI):
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
Add survey context for better results: AI performs much better if you give it extra detail—describe your survey, your event, or what you’re hoping to uncover. For example:
This is a set of responses from event attendees after the Accessibility conference. The survey had both open-ended and multiple choice questions. I’m interested in the top accessibility barriers people experienced, suggestions for improvement, and overall sentiment.
Prompt to dig deeper: Once you spot a core idea—"lack of wheelchair access" or "unclear signage"—ask the AI: Tell me more about XYZ (core idea). This fetches all details and nuances about that specific point.
Prompt for a specific topic: If you want to check if anyone mentioned a given accessibility challenge, use: Did anyone talk about XYZ? For extra clarity, add: "Include quotes" to see original attendee remarks.
Prompt for personas: Want to segment your accessibility feedback? Try:
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 recurring struggles, use:
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 suggestions & ideas: This is great for event improvement:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Let AI do the heavy lifting—you’ll move from scattered raw feedback to a structured list of prioritized themes, concrete ideas, pain points, and next steps. If you want more ideas on the best questions to ask, check out this guide: best questions for event attendee survey about accessibility.
How Specific analyzes qualitative data from each question type
Specific handles a range of survey question types, each needing a different approach to analysis. Here’s what happens under the hood (and you can mirror the workflow manually with ChatGPT if needed):
Open-ended questions (with or without follow-ups): The AI summarizes all responses to the original question, plus the detailed follow-ups for even richer insight. It draws relationships between comments to create a theme-based summary.
Choices with follow-ups: For every choice (i.e., “what was your biggest barrier?”), you get a dedicated summary of all written feedback and follow-up answers tied to that choice, so you can directly compare attendee perspectives from each angle.
NPS (Net Promoter Score): Each respondent category—detractors, passives, promoters—gets its own summary, focusing on all relevant follow-up feedback from within their group, so you don’t lose track of what drives satisfaction or dissatisfaction.
You can absolutely get similar results in ChatGPT by copying in the relevant text blocks per question category—but expect to spend more time managing the process. Want to automate this? Use Specific’s AI-powered response analysis designed for survey feedback.
How to avoid AI's context window limit when analyzing survey responses
Every AI tool, including ChatGPT and Claude, has a context size limit—that is, a maximum amount of text it can process at once. With surveys that go deep (several hundred attendee responses about accessibility at a conference is common), you’ll hit this limit fast. There are two practical ways to tackle this problem (both are built into Specific):
Filtering: Narrow analysis by filtering on questions or answers—like only showing conversations where attendees commented on sign language interpretation. This reduces the number of responses fed into the AI, helping you stay within the limit while still surfacing the insights most relevant to you.
Cropping questions: Focus analysis only on selected survey items (e.g., the main open-ended question about event accessibility). This boosts efficiency and lets you explore one topic at a time without overwhelming the AI.
Managing your context lets you dig deep without losing sight of nuance—just another reason to use dedicated tools built for survey response analysis like Specific's AI survey response analysis feature.
Collaborative features for analyzing event attendee survey responses
Collaboration is often messy when multiple team members need to review and comment on accessibility feedback from attendees. Do you ever worry about who’s working on which themes, whose insights are up to date, or how to share findings across the wider team?
Analyze data by chatting with AI together: In Specific, every team member can have their own chat sessions, each with custom filters, questions, and perspectives. This means a designer can focus on physical access issues, while a comms lead explores inclusive language—a separate chat for each area, all within the same dataset.
Every conversation is attributed: It’s clear who created each AI chat, and in collaborative sessions, each person’s avatar is shown beside their question or comment. No more guessing whose ideas are on the board.
Share context instantly: Anyone can jump into a chat, apply their own filters (like limiting data to attendees with disabilities, or only reviewing feedback on digital access), and instantly see fresh summaries and extracted ideas. This makes cross-functional teamwork much smoother for accessibility events.
Create your event attendee survey about accessibility now
Capture the real accessibility needs of your event attendees using actionable surveys—Specific helps you collect, analyze, and act on feedback, with insights tailored to your team’s needs.