This article will give you tips on how to analyze responses from an event attendee survey about likelihood to recommend. If you want to dig deeper and make your event even better, you’re in the right place.
Choosing the right tools for survey response analysis
The best approach to analyzing event attendee survey data depends on the types of responses you collect. Here’s a quick guide that’s helped me keep analysis efficient and valuable.
Quantitative data: When you ask simple questions, like rating your event from 1 to 10 or “Would you recommend us—yes or no?”, the results are straightforward. You can easily count, graph, and summarize the numbers with tools like Excel or Google Sheets. They’re handy for calculating things like Net Promoter Score (NPS) or charting ratings. Most successful events score between +30 and +50 on the NPS, with anything above +50 signaling outstanding performance. [5]
Qualitative data: Open-ended feedback—where attendees explain their scores or share stories—takes more effort to analyze. Reading through hundreds of responses isn’t practical. Here, AI tools come in handy: they can summarize answers, detect topics, and highlight patterns in minutes, not hours. If you use follow-up questions (like “What’s the main reason for your score?”), you usually get even richer data—but it makes manual analysis even harder.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you want a fast, hands-on approach, you can export your survey data and copy it directly into ChatGPT (or similar tools).
This method is convenient for smaller datasets or when you want to ask a few ad-hoc questions about your feedback. You can paste in the responses and chat about trends, pain points, or suggestions.
But: You’ll quickly run into problems for larger surveys. There are limits on how much text you can paste and context gets lost if you want to filter by specific attendee types or question paths. Plus, referencing specific answers, filtering follow-up data, or sharing your analysis gets clunky.
All-in-one tool like Specific
Specific is built exactly for AI survey creation, conversational analysis, and actionable insights—whether you’re running a small workshop or analyzing feedback from large conferences.
It’s not just about collecting responses. When you use a tool like Specific, you also get:
Conversational data collection—AI-powered, natural-language surveys with automatic follow-up questions to improve data quality (learn how it works).
Instant AI analysis—core insights, key themes, and actionable summaries right after your survey closes. No spreadsheets or manual sorting needed.
Conversational AI results chat—ask questions about your responses as you would in ChatGPT (e.g., “What’s most likely to make attendees recommend our event?”) but with features that help you manage context and filter your data live.
Team-friendly collaborative features—multiple people can analyze and discuss data in real time, each with their own focus and filters. This is a game changer when multiple teams are interested in different aspects of attendee feedback.
If you only need a simple NPS survey, Specific lets you launch one in minutes—see this ready-to-use NPS survey for event attendees about likelihood to recommend.
Useful prompts that you can use for analyzing event attendee likelihood to recommend survey data
Whether you’re using ChatGPT, Specific, or another AI survey response analysis tool, the quality of your prompts can make a big difference in the value of your analysis. Here are some practical prompts for exploring event attendee feedback about likelihood to recommend. I find these especially powerful when dealing with open-ended follow-ups.
Prompt for core ideas: This is my go-to for distilling what really matters to attendees. It’s also the default in Specific’s AI survey response analysis:
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 it more context. Include details such as: “This is survey data from attendees of [Your Event], focused on their likelihood to recommend the event, collected using both NPS questions and open-ended follow-ups. I want to understand the major drivers of recommendations and ways to improve attendee experience.”
Here are the survey responses from our annual product conference. We asked attendees how likely they are to recommend the event, why, and what could be improved. Summarize the main reasons for high or low likelihood to recommend.
Prompt for deeper insights on a theme:
Tell me more about XYZ (core idea)
Prompt for investigating specific topics:
Did anyone talk about XYZ? Include quotes.
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 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.
With these prompts, you can go beyond surface-level stats and connect feedback to actual event improvements. It’s no coincidence that 62% of attendees are more likely to recommend an event with a personalized experience [2], so understanding what resonates by digging into the “why” is gold.
For more prompt ideas, or to generate your survey from scratch, try the specific event attendee survey generator or check out ideas in this article on the best questions for an event attendee survey about likelihood to recommend.
How Specific (and AI) analyzes responses based on question type
Event surveys aren’t just about “Would you recommend us?”; you’ll usually mix question types to get the full story.
Open-ended questions (with or without follow-ups): Specific automatically summarizes all responses, plus any related follow-ups, into a crisp, high-signal summary. This saves tons of time compared to reading and coding every answer.
Multiple choice with follow-ups: Let’s say you ask what attendees liked best (“Keynote, Networking, Workshops…”) and then ask a follow-up like “Why did you choose that?”—Specific gives you a separate summary for each choice’s followup responses. It’s crystal clear what drives each preference.
NPS questions: Here’s where AI shines. Specific instantly splits the data: you get isolated summaries for Detractors, Passives, and Promoters, showing what each group said in their follow-ups. For reference, 72.43% of positive event reviews indicate a high likelihood (5/5) of recommending the event. [4] This separation makes it easy to improve in the right places.
You can replicate much of this in ChatGPT or other GPT-based tools, but it quickly becomes labor-intensive for large or structured surveys. Using tools designed for survey data will always reduce friction.
Working with AI’s context limits: how to tackle large survey data
AI tools are powerful, but there’s a catch: context size limits. If your event had hundreds or thousands of responses, you can’t paste them all into ChatGPT at once. You need smart workarounds.
Filtering: In Specific, you can filter responses before sending them to the AI. You might look at only respondents who rated the event 9 or 10, or segment by attendee type, session, or feedback theme. The AI only analyzes the filtered subset, making it efficient even for large surveys.
Cropping: Another option is to analyze answers to only certain questions. If you want to explore just the feedback for one workshop, crop everything else out—this saves space and keeps the analysis focused.
Both these features help you stay under AI context limits, providing high-quality insights even from massive events.
Collaborative features for analyzing event attendee survey responses
Collaborating on event attendee likelihood to recommend surveys can be tricky. Everybody—from marketing and product to event ops—wants actionable insights, but sharing spreadsheets, comments, and emails is messy.
Collaborative review is easy in Specific. You analyze survey data in an AI chat—no data wrangling or technical skills required. You can dig into attendee comments, filter by session, and share your findings instantly with anyone on your team.
Multi-threaded analysis lets you open different chats for different focus areas. Each chat can apply its own filters (e.g., “Promoters only,” “Networking feedback,” or “Pain points for first-time attendees”), and it’s crystal clear who started each thread. This makes it super easy to collaborate, document insights, and avoid stepping on each other’s toes.
Real team collaboration means you see who said what in every AI chat. Avatars and sender names are visible for every message. As a result, you avoid confusion and keep analysis organized—every team member’s angle is visible, contextual, and actionable for improving future events. If you want to explore more, check out the AI chat survey analysis feature in detail.
Create your event attendee survey about likelihood to recommend now
Start analyzing attendee feedback with AI-driven clarity. Get actionable insights, save hours on manual analysis, and empower your team—create your event attendee survey about likelihood to recommend now.