This article will give you tips on how to analyze responses from an online event attendee survey about expectations using AI—and why it matters if you want accurate insights fast.
Choosing the right tools for analyzing survey data
The approach and tooling you need for analyzing your survey responses depends largely on the structure of your data—whether it’s mostly numbers, open-ended feedback, or a bit of both.
Quantitative data: If your survey contained mostly structured responses (think “select all that apply” or scale ratings), these are straightforward to analyze with tools like Excel or Google Sheets. Just count how many people selected each option and you’ve got your spread.
Qualitative data: If you leaned into open-ended or follow-up questions, you’re dealing with unstructured data. Manually reading every answer isn’t feasible if you have more than a handful. In this case, using AI tools to summarize and surface key insights is essential—especially for larger surveys packed with attendee feedback about what they want from your event.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy and chat: You can export your qualitative data—those open-text answers—and paste them into ChatGPT or another generative AI tool. Then you can ask it to summarize or pull out key patterns.
Limitations: This approach comes with friction: handling copy-paste, staying within chat context size, and keeping track of versions or tweaks is labor-intensive. Managing hundreds of lines from an event survey quickly gets unwieldy, and you’ll need to engineer your prompts to get useful answers.
All-in-one tool like Specific
Purpose-built for qualitative surveys: A dedicated tool like Specific is designed for this situation. It handles both the collection (the survey itself) and the AI-powered analysis—bringing you instantly-generated summaries, themes, and insights. You skip spreadsheets and avoid manual sifting.
Increased data quality: Specific’s conversational surveys use AI to ask tailored follow-up questions, so you get detailed, context-rich attendee responses that reveal not just what participants expect, but why. (Learn more about the payoff from smart follow-ups here.)
Seamless AI-driven workflow: Once responses roll in, Specific summarizes, finds patterns, and lets you chat directly with the AI about results—like having a co-pilot researcher on demand. You get added features to manage, filter, and direct what data gets sent for analysis, which is especially helpful with large datasets.
Useful prompts that you can use for online event attendee expectations analysis
Whether you're using Specific or ChatGPT, having the right prompts can make a dramatic difference. Here are ways to get the most out of your AI:
Core ideas prompt: Use this to discover the main themes or topics running through your survey data. It’s the default in Specific and works with any GPT tool:
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 with more context. Add a short intro like this:
I ran a survey among attendees for an upcoming online event. The goal is to understand their expectations, what activities and session types they prefer, and what might make them more likely to recommend or attend similar events in the future. Can you analyze the responses below and summarize the main themes?
Explore core ideas further: After identifying a core idea, ask: “Tell me more about [core idea].” This reveals nuances, sub-themes, or specific issues—like why people crave certain networking options.
Ask about specific topics: Use the direct prompt: “Did anyone talk about [engagement, networking, etc.]?” To dig deeper, add: “Include quotes.”
Personas prompt: If you want a snapshot of different attendee types, ask:
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.
Pain points and challenges: Uncover what’s standing in the way of a great event with:
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.
Motivations & drivers: Find what draws people in with:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Sentiment analysis: Quickly surface the general mood using:
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.
Want even more prompt inspiration for this audience? Check out this guide on the best questions for online event attendee surveys about expectations.
How Specific analyzes qualitative data by question type
Specific is designed to adapt its AI analytics to the structure of your questions:
Open-ended questions (with or without follow-ups): You get a summary that covers all responses and dives into follow-ups, revealing the reasoning behind attendee answers—essential for understanding motivations, not just surface-level wants.
Multiple-choice with follow-ups: Each answer option gets its own tailored AI summary for any follow-up questions, so you can see what “networking” fans really want, compared separately from those who chose “learning sessions.”
NPS questions: Responses split into promoters, passives, and detractors—each with its own summary of why attendees scored your event expectations as they did. This helps you pinpoint what matters most to your most critical segments.
You can do the same thing using ChatGPT, but it’s slower and requires more manual sorting—especially as event surveys scale up.
Want to start a new survey and see these analytics in action? Use this tool to generate a new online event attendee survey about expectations with a single prompt.
Handling context limit challenges with AI when analyzing event survey responses
AI models can only process a certain amount of information (“context window”) at a time. If you’ve got loads of responses from enthusiastic attendees, you may hit this ceiling.
There are two reliable approaches to stay efficient (both built into Specific):
Filtering: Only analyze conversations where respondents answered specific questions or chose certain answers. This way, if you want to dive deep just on networking or Q&A feedback, only the relevant conversations go to AI. Precision saves time, memory, and headache.
Cropping: Select only the most relevant questions for AI analysis. For example, if your expectation survey had ten questions, but only two are critical, send just those for AI to chew on. This maximizes the number of conversations you can process—even with large datasets.
Specific makes these workflow steps simple, but you can adapt similar filtering or cropping techniques using an AI tool like ChatGPT (it just takes more setup work).
For more about how Specific AI manages context for deep survey analysis, read the handbook on AI-powered survey response analysis.
Collaborative features for analyzing online event attendee survey responses
Collaborative analysis can get messy fast—especially if you’re working across teams to make sense of mountains of attendee feedback about expectations for your event.
With Specific, you’re never stuck in solo mode. Instead of static dashboards or siloed CSVs, you and your coworkers can analyze responses by chatting with the AI. This is a big boost in fluidity for teams used to email threads or spreadsheet comments.
Multiple chats mean focus and flexibility: Each chat can have different filters—one conversation focused on engagement features, another on networking, and so on. You’ll always know who started each one, so it’s easy to track ownership and get the right people involved.
ID who said what: Avatars show who’s asking what in AI Chat. Feedback and insights are attributed, avoiding confusion when multiple team members are jumping in on the same project.
This modern take on collaboration—paired with AI that instantly summarizes qualitative responses—is a game-changer for fast-moving event teams.
Want to try a new workflow for collecting and analyzing attendee feedback? This AI survey editor lets you design and then adjust your event survey simply by chatting with the AI about changes.
Create your online event attendee survey about expectations now
Start collecting high-quality, actionable insights—with AI that helps you analyze everything—by creating your next online event attendee expectations survey with Specific’s conversational survey builder. Turn attendee feedback into clear event improvements in minutes, not weeks.