This article will give you tips on how to analyze responses from an event attendee survey about the check in experience using AI and other smart tools. If you run surveys like these, you want actionable insights, not hours sifting through spreadsheets.
Choosing the right tools for analyzing event attendee check in survey responses
The approach and tooling you use to analyze survey responses should match the type and structure of your data. If you’re dealing with quantitative or qualitative data, the best workflow will differ.
Quantitative data: These are straightforward numbers, like how many attendees selected “very satisfied” with check in. You can analyze these with conventional tools like Excel or Google Sheets—filter, count, and chart responses to see trends fast.
Qualitative data: Open-ended questions and detailed follow-ups hold the juiciest feedback but aren’t workable to read one by one, especially if you have hundreds of surveys. Here, AI makes life easier: it summarizes and spots patterns that you’d likely miss by combing through manually.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can export your open-text survey responses and copy them into ChatGPT, then ask questions or use analysis prompts. This works, but it’s not very convenient for bulk analysis. Large data dumps may hit context limits, and managing exported files gets messy.
Manual prep required: You have to figure out filtering, formatting, and prompts, and if you want someone to collaborate, you have to share exported data and chats separately. AI will surface trends or sentiment, but there’s more setup and less automation for surveys built this way.
All-in-one tool like Specific
Purpose-built for survey analysis: Platforms like Specific are built for conversational survey creation and AI-powered analysis. You collect responses in a natural, chat-style format, and the AI automatically follows up to get deeper answers—raising your data quality and completeness. See what makes automatic AI-driven followup questions so effective.
Instant AI insights: You get AI summaries and key themes as responses roll in. You don’t touch a spreadsheet. Just chat with AI about your results as you would in ChatGPT, but with event context always included. You also get filtering and data management features designed specifically for this use case.
No more data wrangling: Everything—from collection to insight—is under one roof. This is especially helpful for event feedback surveys where depth and speed matter. If you’re starting from scratch, you can use the event attendee survey generator for check in experience to design and launch your survey in moments.
Useful prompts that you can use to analyze event attendee survey responses about check in experience
When you analyze feedback, prompts matter a lot—especially for open-ended responses about check in. I’ve compiled working prompts that I rely on when digging into Event Attendees' thoughts. Use any of these in a tool like Specific or ChatGPT, and feel free to fine-tune for your survey:
Prompt for core ideas: Use this to extract the main themes from a large pool of 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
AI always performs better if you give more context around your survey, audience, and goals. Here’s an example:
"You are analyzing feedback from event attendees about their check in experience at a recent technology conference. The goal is to find actionable insights to improve registration and entry flow for next year."
Prompt for follow-up digging: After finding a main idea, prompt: "Tell me more about XYZ (core idea)". This pulls direct quotes and examples around each idea.
Prompt for specific topics: To check if anyone mentioned a topic: "Did anyone talk about XYZ? Include quotes."
Prompt for personas: When you want to understand event attendee segments: "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 quickly get a list of main friction points and frustrations, 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 sentiment analysis: If you want an emotion snapshot: "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 and ideas: Crowdsource quick wins: "Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant."
All of these prompts can be supercharged with solid context from your survey. Check out the best event attendee questions for check in experience surveys if you need inspiration for what to ask in the first place.
How Specific summarizes qualitative data by question type
Specific is built to reflect the structure of your survey when analyzing qualitative feedback. Here’s how it handles different question types for check in experience surveys:
Open-ended questions (with/without follow-ups): It generates a single, concise summary for all responses to the question as well as summaries for any follow-up answers—so you see the big picture and all the “whys.”
Choices with follow-ups: For a question like “How smooth was your check in?” (with choices), Specific summarizes the follow-up answers separately for each selected choice. This way, you get targeted feedback on what works (and what doesn’t) for each attendee segment.
NPS questions: Each NPS bucket (detractors, passives, promoters) gets its own summary, focused on what those groups said in their follow-up answers. It helps pinpoint what turns people into promoters—or what holds them back.
You can achieve the same using ChatGPT, but it’s a bit more manual—copying and filtering responses, prepping prompts, and reading through lots of output yourself. Specific just saves time and keeps everything organized by question type. There’s a detailed explainer of the process on the AI survey response analysis page.
How to deal with AI context limits in survey analysis
AI tools like GPT models have a context window, which means you can’t always analyze your entire survey dataset in one go—especially after a big event with hundreds of attendee responses. To handle this and keep your insights sharp, I recommend these two methods, both built into Specific:
Filtering: Filter conversations by user replies—so AI analyzes only those conversations where attendees answered a selected question or selected a specific response. That way, you stay focused and within technical limits. For example, you can look only at attendees who described a negative check in.
Cropping: Crop questions for AI analysis—AI receives just the questions and answers you select. This helps you keep the conversation concise for in-depth exploration, essential for large-scale surveys.
With these features, you don’t lose nuance or valuable feedback to technical constraints. If you’re using a general-purpose AI, you’ll have to do this filtering or cropping manually. More on this in our guide to AI survey response analysis.
Collaborative features for analyzing event attendee survey responses
Collaborative pain points: Analyzing event attendee survey responses about check in is often a team sport—and it’s easy to get lost in back-and-forths, duplicate efforts, or unclear ownership when you’re jumping between email threads and exported files.
Chat-first analysis: In Specific, analysis happens right inside secure, persistent AI Chat. You and your team can review summaries, dig into certain attendee segments, and ask follow-up questions—all in one thread, without switching tools.
Multiple chats per survey: You can open several chats on the same batch of survey results, each with its own filters and context—say one chat focused only on first-time attendees, another just on large group check-ins. Each chat shows who started it, so it’s clear who’s exploring what.
See who said what: When collaborating, each message sent in the AI chat shows the sender’s avatar. You’ll never lose track of feedback or which team member asked which follow-up.
Frictionless teamwork: These collaborative features save everyone from rework and make the analysis of event attendee surveys about the check in experience both faster and more transparent. If you’re customizing your survey or want to generate a tailored version for a new event, use the AI survey generator or see our walkthrough on how to create an event attendee check in experience survey.
Create your event attendee survey about check in experience now
Set up your conversational survey and get actionable AI-powered insights from your attendees—without manual number crunching, confusing exports, or missed follow-ups.