This article will give you tips on how to analyze responses from an event attendee survey about event app usability. I’ll outline the best tools, prompts, and workflow to turn qualitative and quantitative feedback into actionable insights, fast.
Choosing the right tools for survey response analysis
Your approach—and the tools you’ll use—really depends on the type of data you collected from event attendees. Let’s break it down:
Quantitative data: When you’re working with response counts (such as which features attendees used, ratings, or “yes/no” votes), traditional spreadsheets like Excel or Google Sheets are still your friends. They make it easy to group, count, and visualize data trends without much fuss.
Qualitative data: If your survey included open-ended questions (or follow-up questions), it gets much trickier. Manually reading every attendee’s response is slow and easy to misinterpret at scale—especially when looking for common themes or undercurrents. You need help from AI tools to make sense of all that free-text feedback.
When you’re working with qualitative responses, there are two main toolsets to consider:
ChatGPT or similar GPT tool for AI analysis
You can copy and paste your exported responses into ChatGPT, Claude, or similar GPT-based platforms, and “chat” about the data. This is doable for small datasets, but it’s often clunky—managing context, pasting long transcripts, or keeping track of the thread can get tedious fast. Formatting constraints and context length limits can prevent you from getting a true bird’s-eye view of all responses at once.
All-in-one tool like Specific
Specific is purpose-built for this scenario. It handles both collecting conversational survey responses and then analyzing them with AI. Specific’s unique twist: It automatically asks follow-up questions during data collection, which results in deeper and clearer answers. When it’s time to analyze, the built-in AI:
Instantly summarizes responses, distills key themes, and surfaces core ideas—no spreadsheets, no copy-pasting, and no manual digging needed
Lets you chat with the AI directly about your event app usability survey data (just like you would with ChatGPT), but with options to set context, filters, and questions for richer insights
You can learn more about Specific’s AI-powered survey response analysis and see how it cuts research time dramatically.
Useful prompts that you can use to analyze event attendee Event App Usability survey data
You can get much more from your AI analysis if you use great prompts tailored to your event attendee responses. Here are several proven ideas—just swap in your own survey text as needed.
Prompt for core ideas: Use this prompt to extract summary topics and see what matters to most attendees. It shines when you have lots of free 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
Context matters: The more the AI knows about your survey, the smarter it gets. Providing a “system prompt” about your goal, the survey’s audience (event attendees), and what you want to improve will get you more relevant insights. For example:
You are an expert survey analyst. The data comes from event attendees who used our event app at a recent conference; our goal is to improve app usability, participant engagement, and overall satisfaction.
Want to dig deeper into specifics?
Use: “Tell me more about XYZ (core idea)” to have the AI break down a specific theme or pain point.
For validation, try: “Did anyone talk about in-app notifications?” This helps you spot discussion of features or problems and can even work better if you tack on “Include quotes” for more context.
Other prompts that make sense for event app usability surveys:
Prompt for pain points and challenges:
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 personas:
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 suggestions & ideas:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for sentiment analysis:
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 even more targeted question ideas to ask your event attendee audience, see our guide on crafting the best questions for event app usability surveys.
How Specific handles different question types in your survey
I like being systematic about this. Here’s how Specific tackles analyzing qualitative feedback by survey question type:
Open-ended questions (with follow-ups): You’ll get a rich, GPT-powered summary that accounts for every attendee’s original answer plus all AI-generated follow-ups. This way, the summary reflects true context and not just the first thought that popped into someone’s head.
Choices with follow-ups: Each pick (like “navigation,” “live chat,” or “event agenda”) gets its own focused summary of all attendee comments and follow-ups that relate to it. This highlights what works—or doesn’t—about each feature individually.
NPS questions: The platform separates out feedback from promoters, passives, and detractors, summarizing what’s unique about each group’s experience and suggestions for improvement.
You can absolutely use ChatGPT to do this manually—but honestly, handling branching follow-ups and matching them to the right core question takes a lot more copy-pasting and context management. It’s one place where an expert survey tool pays off.
For more details, see how to automate your event app usability survey analysis with Specific.
Working around context limitations in AI survey analysis
Here’s a practical tip: all AI chat tools (including ChatGPT, Claude, and even Specific’s AI) have context size limits, meaning you can’t feed thousands of survey responses in at once. How do you make sure your analysis still gets done?
Filtering: Analyze only the conversations where event attendees replied to your most important questions, or selected a specific answer choice. By narrowing your input, you get more focused themes.
Cropping: Instead of sending the entire survey transcript, crop out just the most relevant question(s) and pass those to the AI. This not only fits comfortably within context limits, but it also sharpens the insights you’ll get back.
Specific provides filtering and cropping out of the box as you prepare your dataset for analysis. No need for third-party data wrangling.
Need to build a survey that asks better follow-ups? Learn about automatic AI follow-up questions for richer feedback.
Collaborative features for analyzing event attendee survey responses
Collaborative analysis is usually a nightmare—everyone ends up emailing exported spreadsheets or debating interpretations in Slack. When you need to get multiple team members (event organizers, product teams, or sponsors) involved in evaluating the data, you want everyone working from the same context.
Analyze survey data in real time with AI chat: With Specific, you can open an AI chat about your survey—and any collaborator can join in. Each conversation is instantly up to date, with the same GPT-powered knowledge of all responses.
Multiple, shareable chats for different lines of inquiry: Maybe marketing wants to look for engagement insights, while UX is all about navigation pain points. Each person can open their own AI chat with custom filters, so nobody is stepping on anyone else’s toes. It shows who created each conversation, making teamwork visible.
Clear attribution in conversations: When collaborating with colleagues in Specific, everyone’s AI chat responses and follow-up questions are attributed with their avatar. It’s easy to see who made which observation or kicked off which research thread.
These features make collaborative survey analysis less about “who said what in which messy doc” and more about insight discovery.
For a hands-on guide to setting up surveys like this, check out how to create an event attendee survey about event app usability.
Create your event attendee survey about event app usability now
Take the pain out of survey analysis—get actionable insights instantly, ask smarter follow-up questions, and share results seamlessly with your team. Create your event attendee survey about event app usability using AI to boost response quality and get more from your event feedback, right from the start.