This article will give you tips on how to analyze responses from an event attendee survey about value for money. By the end, you’ll know how to process qualitative and quantitative feedback efficiently using AI tools.
Choosing the right tools for analysis
The approach you take—and the tools you’ll need—depend on the form and structure of your survey data.
Quantitative data: If you have structured questions (“How satisfied are you?”) or multiple-choice answers, you can tally results quickly using Excel, Google Sheets or any reporting dashboard.
Qualitative data: For open-ended answers or text responses, things get complicated fast. Reading through dozens of attendee comments is tedious, and you’ll miss important themes or patterns unless you use AI-powered tools.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste, then chat: You can export your survey data as a CSV or plain text, and then copy it into ChatGPT or another AI chatbot. From there, you ask the AI to summarize themes or answer specific questions.
What’s the catch? This method gets you started fast, but it’s not ideal for big surveys. Large datasets may exceed the chatbot’s context window, and you’ll lose data structure (like which follow-ups go with which attendee) when pasting into ChatGPT.
All-in-one tool like Specific
Purpose-built for qualitative survey analysis: Tools built specifically for AI-driven surveys—like Specific—combine collection and analysis. You can run conversational, chat-like surveys, capture open-ended feedback (including automatic follow-up questions that go deeper), and analyze everything instantly.
Automated AI insights: Specific uses GPT-powered AI to summarize responses, highlight key themes, and deliver actionable insights almost immediately. No need for spreadsheets or painstaking manual review. The tool groups responses, shows trends, and lets you filter or segment data as needed.
Conversational AI analysis: With Specific, you can chat with AI about the results. Adjust the context, apply filters, and probe further—similar to ChatGPT, but with added features to manage the survey data and track each question’s responses.
This approach is backed by the growth in AI-driven feedback platforms. For example, Zonka Feedback, RainFocus, and Jotform are all leveraging AI for sentiment analysis or conversational feedback flows, proving how effective these tools have become for event organizers looking for actionable insight fast. [1]
Useful prompts that you can use to analyze event attendee value for money surveys
Prompts are your secret weapon when using an AI like ChatGPT or Specific to analyze survey results for value for money. Here are a few that have proven the most useful in my own work:
Prompt for core ideas: Use this if you want a bird’s eye summary of the big ideas or concerns voiced by event attendees. It works well on any large set of open-ended survey data—and it’s the default we use in Specific too.
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 works better when you provide good context. Tell the AI about your event, what you want to learn, or even share who the typical attendee is. Here’s how you can frame it:
I ran a feedback survey for attendees at our annual industry conference. Our goal was to understand how people perceived the value for money of the event—looking for both strengths and areas to improve. Focus your analysis on themes related to pricing, content quality, networking opportunities, food, logistics, and suggestions for next time.
Once you find an important topic—like “price justification”—ask the AI to dig deeper:
Dig deeper prompt:
Tell me more about price justification mentioned by attendees.
Prompt for specific topic: When you want to quickly check whether a topic came up:
Did anyone talk about VIP ticket value? Include quotes.
Prompt for personas: Useful when you want to segment feedback (often requested by organizers):
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: A classic for surfacing what went wrong or could have been better:
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: Great if you want to see overall mood (positive, negative, neutral):
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 unmet needs and opportunities: This is critical when planning future events:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For a comprehensive breakdown of how to formulate survey questions and analyze them for this audience and topic, read our guide on creating the best event attendee value for money survey questions.
How Specific analyzes responses based on question type
One of the strengths of Specific is how it organizes and summarizes feedback according to each question’s format. Here’s how it works:
Open-ended questions (with or without follow-ups): The AI produces a high-level summary, distills recurring topics, and identifies patterns within all responses. If there are follow-ups, it links these insights for richer context.
Choice questions with follow-ups: Each answer choice gets its own mini-report—a summary of all follow-up responses tied to that selection. This makes it easy to compare perceptions between different ticket types, for example.
NPS (Net Promoter Score) questions: NPS is broken out by detractors, passives, and promoters. For each group, Specific summarizes the follow-up responses, so you can see exactly why people gave the scores they did.
You can replicate this with ChatGPT, but it requires manual work—like segmenting responses by hand and running multiple prompts for each holder group. Specific automates this and keeps everything structured for you.
Want to see how this works in practice? Check out how Specific handles follow-up questions automatically and how the chat with AI about responses works.
Solving the context size limit challenge in AI survey analysis
Every AI—including ChatGPT, Claude, and the AI in Specific—has a context (memory) limit. When you hit it (which happens fast with lots of detailed attendee feedback), you risk missing parts of your data. There’s no magic fix, but two approaches help a lot:
Filtering: Only send the conversations that are relevant for your analysis (like those where people commented on price or networking, or those from VIP ticket holders). This narrows things down and stays within the AI’s input window.
Cropping: Instead of analyzing everything, include just the selected questions (for example, only the value for money section of the survey). This allows you to analyze more responses at once by focusing the AI only on what matters.
With Specific, both filtering and cropping are built-in options for AI analysis, saving you from complicated data management.
Collaborative features for analyzing event attendee survey responses
One of the biggest obstacles to making survey analysis actually lead to better events is teamwork—sharing findings, dividing up the work, and getting everyone on the same page, especially when dealing with complex topics like value for money.
Chat with AI, together: In Specific, you can collaborate by chatting with the AI about your survey data. Any team member can spin up a new chat and apply their own filters—meaning you can have a marketing chat, an operations chat, even a sponsors chat, each with tailored analysis perspectives.
See who’s saying what: When collaborating in the AI chat, each message has the sender’s avatar. You can always trace comments and follow up directly, making it easy for teams (or clients!) to understand who found what insight.
Organization made simple: Unlike juggling static reports, multiple dashboards, or long email threads, this chat-based workflow makes collaborative survey analysis both interactive and self-documenting.
If you want to learn more about survey-building and collaboration, or want to start from scratch, check out our AI survey editor or try the event attendee value for money survey generator.
Create your event attendee survey about value for money now
Unlock richer insights, act on attendee feedback in real time, and tailor your next event for true value—start creating your survey in minutes with Specific’s conversational AI analysis.