This article will give you tips on how to analyze responses from an event attendee survey about AV quality using AI-powered approaches and practical prompts.
Choosing the right tools for analysis
The approach and tooling you pick depend on the form and structure of your data. If you have:
Quantitative data: Numbers, ratings, or counts (like "how many people picked this option") are straightforward to work with. Just drop your data in Excel or Google Sheets, and you'll uncover patterns, tallies, and averages without much hassle.
Qualitative data: Think open-ended responses or follow-up replies. Reading this manually gets overwhelming fast—and nuanced feedback often slips through the cracks. That’s where AI tools shine: they parse large volumes of text, surface themes, summarize key points, and even detect underlying sentiment and emotion. Modern AI survey analysis tools reliably reveal what attendees truly felt about AV quality, including subtleties easily missed by the human eye. [1]
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you have exported your survey data, you can copy and paste these responses into ChatGPT or another general-purpose GPT tool—and chat about them, just like you would with a research assistant.
It's not always convenient. For smaller sets of feedback, this works. But once the data grows, you spend more time wrangling exports, chunking by conversation, and nudging the AI back on track. There’s no smart way to manage what’s sent to the AI context, so important nuances can get lost or cut off.
These tools aren’t purpose-built for survey data. You’ll need to engineer your prompts and structure your data yourself. The process can become tedious, and it’s easy to overlook patterns or to misinterpret feedback.
All-in-one tool like Specific
Specific was designed for this—you can both collect conversational survey data, including AV quality feedback, and analyze it instantly with AI. Learn more about AI-powered survey response analysis in Specific.
Better data, right from the start. By asking follow-up questions automatically, Specific gets richer responses than a simple form. This boosts the quality of the conversations—so when it’s time to analyze, you get deeper, more actionable insights. Read more about how automatic AI follow-up questions work.
Instant, actionable analysis. The AI summarizes feedback, identifies patterns, and distills everything into clear, actionable insight—without manual copying or pasting. The chat-based analysis experience feels just like interacting with ChatGPT, except it’s purpose-built for survey data. You can ask follow-up questions, segment your data, and fine-tune the AI context to focus on what matters.
All-in-one workflow. Specific’s unified platform handles creation, follow-ups, analysis, and discussion with your team—no more juggling exports, emails, or spreadsheets. If you want to design your own survey from scratch, try the AI survey generator.
Useful prompts that you can use for analyzing event attendee AV quality feedback
Well-crafted prompts help you get more value from your AI analysis—whether you’re using Specific or a tool like ChatGPT.
Prompt for core ideas: Use this to extract the main themes from your AV quality survey responses. This is the core prompt that powers Specific’s insights, but it works well anywhere:
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 context, get better results. The more you tell the AI about your survey, event, and what you care about, the more targeted its answers become. For example:
My survey was sent to event attendees at a hybrid tech conference to understand which aspects of AV quality most impacted their overall experience. Please prioritize technical issues, overall clarity, and attendee suggestions for improvement.
Prompt for more details: To deepen your understanding about a core topic (for example, audio issues), follow up with:
Tell me more about XYZ (core idea)
Prompt for specific topics: To check if anyone mentioned a particular AV element—say, microphone feedback:
Did anyone talk about microphone feedback? Include quotes.
Prompt for pain points and challenges: Great for uncovering frustrations or frequent complaints about AV quality:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned regarding AV quality. Summarize each and note any patterns or frequency of occurrence.
Prompt for suggestions and ideas: Use this to surface attendee suggestions for improving AV at future events:
Identify and list all suggestions, ideas, or requests provided by survey participants to improve AV quality. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for sentiment analysis: This is handy for seeing at a glance how people felt overall:
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 tips on question design and prompts, check out our breakdown of the best questions for event attendee surveys about AV quality.
How AI (and Specific) handles different types of survey questions
What you get from survey analysis depends heavily on how your questions were structured. Here’s how analysis breaks down by type:
Open-ended questions (with or without follow-ups): AI will summarize all responses, along with deeper insights captured from any follow-up questions. In Specific, you see a concise theme summary for each open text prompt—plus side-by-side breakdowns of all follow-up replies.
Choices with follow-ups: Each option (for example: "Audio quality was poor", "Video quality was acceptable") gets its own summary of follow-up responses. This lets you see, in one glance, not just what people picked, but why.
NPS: The AI separates feedback from detractors, passives, and promoters so you can see what distinguishes your supporters from your critics.
You can do the same type of theme analysis in general-purpose AI tools by copying chunks of filtered data from your exports (for example, just responses tied to a particular choice), but you’ll need to invest more manual sorting and setup.
For a step-by-step guide on survey creation, see how to create an event attendee survey about AV quality.
How to tackle challenges with AI’s context limit
AI models—whether you use ChatGPT or an in-platform solution such as Specific—have limits on how much data you can “send in” at once for analysis. If your AV quality survey has lots of responses, here’s how you can avoid running into a wall:
Filtering: Select just the conversations where users replied to certain key questions (for example, only those who flagged audio issues), so only relevant data is analyzed.
Cropping: Choose which questions to send to the AI for review. You might only analyze answers to "What was the biggest AV challenge you encountered?" and skip the rest, to fit more conversations into one analysis batch.
Specific offers these features out of the box, making it seamless—so you don’t have to manually chunk up your data to squeeze it into context limits.
Collaborative features for analyzing event attendee survey responses
Collaboration is often a pain when it comes to analyzing event attendee AV quality survey feedback. Files multiply, context is lost, and important findings get buried in email threads or spreadsheet tabs.
In Specific, anyone can analyze survey data simply by chatting with AI. But there’s more—if you have a team, you can work together in real time. Each chat has its own filter set (maybe you’re focused on hybrid session attendees, a colleague is digging into in-person issues), and you always see who created a particular chat, streamlining collaboration among event staff or AV partners.
Transparency is built in. Every chat message shows the sender’s avatar, making it clear who asked what. This helps when reviewing insights as a team, since it's easy to pick up from where colleagues left off—and no feedback or interpretation gets lost in translation.
Need inspiration for setting up your next survey? Play with the dedicated AV quality survey generator for event attendees, or fine-tune with the AI survey editor for a completely custom approach.
Create your event attendee survey about AV quality now
Ready to transform AV feedback into actionable insights? Create your event attendee survey about AV quality now and experience instant, AI-powered analysis and collaboration, all in one place with Specific.