This article will give you tips on how to analyze responses from a Conference Participants survey about Likelihood To Recommend using AI, making survey analysis faster, deeper, and more actionable.
Choosing the right tools for survey data analysis
The best approach and tools for analyzing survey results depend on your data’s structure and format. Here’s how I break it down:
Quantitative data: If you’re dealing with straightforward counts (like “How many people would recommend this conference?”), tools like Excel or Google Sheets make it easy to tally and chart the numbers. They excel at structured questions and can whip up quick visuals or summary stats.
Qualitative data: For open-ended survey questions (such as “What did you like most about this conference?”), manual reading and coding just aren’t practical. Once you have dozens or hundreds of responses or follow-up conversations, AI analysis tools become essential. They let you extract patterns and key themes from real participant language—something traditional tools just can’t do.
There are two main approaches when analyzing qualitative responses:
ChatGPT or similar GPT tool for AI analysis
AI chat tools like ChatGPT allow you to paste exported survey responses and have an open-ended conversation with the data. You can ask for themes, summarize feedback, or dig into specific topics.
But there’s a catch: Handling data in this way isn’t convenient for large or complex surveys. You end up copying, pasting, trimming context, and playing with prompt engineering to get your answers. This method works for occasional deep dives, but it’s not scalable for large-scale, multi-question Conference Participants surveys.
All-in-one tool like Specific
Specific is designed from the ground up to combine survey data collection and AI-powered analysis. When you run a Conference Participants survey about Likelihood To Recommend, Specific:
Asks smart follow-up questions automatically, which dramatically improves the quality and clarity of each response. You don’t have to figure out which key points are unclear—Specific’s AI probes for detail in real time. (see how AI follow-ups work).
Instantly summarizes all responses, identifies key themes, and translates data into actionable insights—no spreadsheets or manual tagging required.
Lets you chat directly with AI about your survey data, just like ChatGPT, but with features tailored for survey research: context-aware chat, filters, and dedicated survey threads (learn more about AI survey response analysis).
Gives you total control over data sent to the AI: filter by question, response, or respondent to focus your analysis.
If you want to start with a ready-to-use Conference Participants survey about Likelihood To Recommend, there’s even a generator with everything prepped (see the survey generator with preset).
According to PCMA’s December 2024 survey, over 90% of meeting planners are already leveraging AI tools for events and feedback analysis, proving how central these solutions have become in the event world. [1]
Useful prompts that you can use to analyze Conference Participants survey responses
When you’re analyzing open-ended feedback from a Conference Participants survey about Likelihood To Recommend, AI tools are only as good as the prompts you give them. Here are the most effective ones:
Prompt for core ideas and themes: This is my go-to for extracting main topics from responses, exactly as used in Specific. You can copy-paste this into ChatGPT or any major GPT:
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
Give more context for better AI results: AI analysis is stronger when it understands the situation. For example, describe the survey’s goal, conference setting, or what you want to improve. Try this:
You’re an analyst helping an events team. Here’s feedback from Conference Participants on Likelihood To Recommend. Extract recurring themes and let me know what drives positive or negative recommendations. Be concise.
Dive deeper into specific topics easily:
Tell me more about XYZ (core idea).
Validate if specific themes appeared: Great for pressure-testing assumptions or checking for pain points:
Did anyone talk about XYZ? Include quotes.
Find personas in your respondents: This is huge when conferences have different target audiences. Use this prompt:
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.
List pain points and challenges: Uncover what’s holding back high scores or recommendations:
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.
Map out motivations and drivers: Understand why your promoters are so excited—and why your detractors aren’t:
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.
Run a quick sentiment analysis: For a high-level emotional read:
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.
Catalog suggestions and improvement ideas:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Spot unmet participant needs or new opportunities:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
You can mix and match these prompts in AI tools, or let Specific do the heavy lifting for you across all your questions.
How Specific analyzes qualitative survey data by question type
Specific’s AI engine breaks down feedback based on how questions are structured—giving you more targeted, useful summaries.
Open-ended questions (with or without follow-ups): It generates a summary across all responses, plus individual follow-up responses (which the AI asks live, improving clarity and detail).
Choice questions with follow-ups: Each choice option gets its own focused summary from the resulting follow-up responses. This way you’re not generalizing but seeing exactly why people chose “Likely,” “Unlikely,” or anything in between.
NPS questions: Feedback from promoters, passives, and detractors is summarized separately, letting you target what motivates advocates or discourages detractors.
If you’re using ChatGPT or exporting your data, you can get similar results—but you’ll need extra manual effort to sort, segment, and re-prompt for each group. Want more detail? See this how-to guide to creating Conference Participants surveys about likelihood to recommend for more robust question design.
Working around AI context size limits in survey analysis
One of the biggest challenges with AI-driven survey analysis is context limits—essentially, how much text the AI can process at once. Especially with large Conference Participants surveys, it’s easy to exceed these boundaries. Based on research from SuperAGI, AI-based survey approaches can dramatically outperform traditional survey tools in completion and engagement, but only if we actively manage data scope [4].
There are two main solutions I use (both built into Specific):
Filtering: Filter conversations based on participant replies—focus the analysis on specific questions or answer choices, so only relevant conversations enter the AI’s context window. This is perfect for isolating promoters or detractors, or zeroing in on people who discussed a certain topic.
Cropping: Cropping lets you select just the specific questions to analyze—useful if only part of your survey or certain respondent segments matter (such as just the NPS follow-up questions). This keeps the dataset lean and the insights focused.
This targeted approach ensures you get the depth and specificity you need, without running into tech limitations or losing context.
Collaborative features for analyzing Conference Participants survey responses
Collaborating on Conference Participants survey analysis can be a minefield—version control issues, inconsistent tagging, and never-ending email chains. Here’s how I approach this, especially with Specific’s built-in features:
Seamless team analysis through AI chat: Simply chat with the AI about results; no need for separate exports, emails, or shared docs. Multiple team members can dive in together, make hypotheses, and get instant, shared answers.
Multiple dedicated chats with filters: In Specific, you can create multiple chats within a survey, each with custom filters or focal topics. This means your research lead can focus on overall sentiment, event operations on logistics feedback, and the marketing team on recommendations—all within one project, with clear ownership per chat.
Transparent collaboration: Each chat tracks who created it and every message includes the sender’s avatar, bringing much-needed transparency when teams analyze Likelihood To Recommend survey data together. It’s clear who’s asking what, which helps drive faster consensus and more reliable summaries.
This level of structured yet flexible collaboration makes it easier to keep everyone on the same page and reach meaningful insights while avoiding costly misunderstandings.
Create your Conference Participants survey about Likelihood To Recommend now
Start capturing rich feedback and actionable insights with an AI-driven survey that goes deeper, analyzes instantly, and keeps your team aligned.