This article will give you tips on how to analyze responses from Conference Participants survey about Matchmaking Effectiveness. If you're looking to get actionable insights fast, especially with AI surveys, here’s what actually works.
Choosing the right tools for survey analysis
The way you analyze survey responses depends on the type and structure of the data you collect. Here’s how I approach this:
Quantitative data: For questions like, “How satisfied were you with matchmaking?” where people pick a number or option, I just count up the answers. Classic tools like Excel or Google Sheets work well for this—they let you run sums, averages, and filters in no time.
Qualitative data: For open-ended questions like, “What worked or didn’t work for you?” or deeper follow-up chats, reading everything becomes impossible when there are more than a handful of responses. This is where AI tools come in. They make sense of huge text dumps quickly by grouping key ideas and summarizing feedback.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Option one is to use ChatGPT or another general-purpose GPT tool. Just copy and paste your exported survey data into ChatGPT and start asking questions. This can work, especially for smaller data sets. You can ask for summaries, main themes, or even sentiment analysis.
But handling survey data this way is not very convenient. You need to organize your export carefully, and ChatGPT’s context limits make it tricky for larger surveys. There’s also no built-in support for segmenting by question or type of participant, so you wind up copying, pasting, and prompting over and over.
All-in-one tool like Specific
This is a purpose-built solution for survey analysis. Specific is designed for collecting conversational survey data and instantly analyzing it with AI. When you use Specific, the survey engine handles follow-up questions automatically, which means you get deeper and higher-quality responses right from the start.
The AI-powered analysis in Specific instantly summarizes responses, finds key themes, and turns data into actionable insights—no spreadsheets or manual work needed. You don’t have to worry about structuring exports or running repetitive prompts. Results are organized by question, segment, or NPS rating automatically, and you can filter or segment in a click. Chatting with the AI about your responses—just like with ChatGPT—gives you flexibility, but you also get helpful tools to manage context and keep things tidy.
If you want more details on how it works, check out AI survey response analysis in Specific.
AI tools are rapidly changing the game for event feedback. Recent studies show that AI-based attendee matchmaking has increased networking effectiveness by 40%, and 48% of organizers already use AI-driven sentiment analysis to gauge attendee reactions—because manual analysis just can't keep up. [1]
Useful prompts that you can use for analyzing Conference Participants survey responses about matchmaking effectiveness
Once you have your survey response data—especially if you used a conversational survey format with open-ended feedback—AI shines when you use the right prompts. Here are some proven methods:
Prompt for core ideas: This is the go-to for making sense of the main themes in a pile of feedback (it's how we run our own analysis, and you can use it in ChatGPT or a tool like Specific). Just paste this in:
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 when you provide more context about your survey, the situation, and what you want to know. For example, you can start with:
We ran a survey on the effectiveness of matchmaking at a professional conference. Most respondents are tech professionals attending their first event. Our main goal is to uncover what made connections successful or challenging, and what could improve matchmaking in the future. Distill the most repeated ideas.
Dive deeper with follow-up prompts: Once you identify a theme, just ask: "Tell me more about XYZ (core idea)" and the AI will expand on that point.
Prompt for specific topics: If you want to find out whether a common point (like app usability) was mentioned, use:
Did anyone talk about the matchmaking app’s ease of use? Include quotes.
Prompt for personas: This is helpful if you want to break down types of conference attendees based on survey feedback:
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:
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 motivations and drivers: Useful for understanding why participants engaged with matchmaking features at all:
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.
Prompt for sentiment analysis: For an emotional temperature check, especially useful given that AI-powered sentiment analysis now detects attendee satisfaction with 85% accuracy. [2]
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.
For more helpful question ideas specific to conference matchmaking, read this article on best survey questions for Conference Participants about matchmaking effectiveness.
How Specific analyzes qualitative survey responses by question type
Let’s break down how an AI survey platform like Specific helps you make sense of data, depending on your question type:
Open-ended questions with or without follow-ups: Specific automatically generates a concise summary for all responses, and you get second-level summaries for replies to follow-up questions. So, for a question like "What was your biggest challenge?" you see a high-level theme breakdown, and below that, summaries of what people said when probed further.
Choices with follow-ups: If someone picked "Networking sessions" as most effective, the tool compiles a separate summary for all responses that gave that answer, along with what those participants shared in their follow-ups. This makes it easy to compare, for example, what made networking work for some versus others.
NPS (Net Promoter Score): For NPS-style questions ("How likely are you to recommend this matchmaking experience?"), Specific summarizes open-ended feedback given by detractors, passives, and promoters, separately—so you instantly spot the difference in sentiment and suggestions across groups.
You can do the same kind of analysis with ChatGPT, but it’s more manual and takes extra prep and time, especially if there are many segments and long follow-up threads per answer.
For an overview of how automatic followups can work, see how AI follow-up questions enhance survey quality.
And if you want to build a Conference Participant survey from scratch, try the AI survey generator for matchmaking effectiveness.
How to handle context limit challenges in AI survey analysis
Large surveys often run into the problem of context size—AI tools can only “see” so much at a time. When you have hundreds of conversations, you’ll hit a wall.
There are two effective ways to make sure AI analysis still works:
Filtering: Analyze only those conversations where users replied to selected questions or made particular choices. This trims the data set to those with rich feedback, targeting analysis for higher relevance.
Cropping questions for AI analysis: Send only the responses to selected questions to the AI. This keeps everything under the maximum context size for processing. Both filtering and cropping are built into the workflow in Specific, with no extra steps.
This way you can handle even large response volumes without running into context size headaches.
(For an in-depth guide, check how to use an AI survey editor for efficient analysis workflows.)
Collaborative features for analyzing Conference Participants survey responses
Collaborating on analysis is a major challenge, especially for busy event teams dealing with matchmaking effectiveness surveys. When you need to synthesize attendee feedback as a group—whether it’s segmenting findings by session, or tracking what each analyst discovered—it’s easy for things to get messy.
With Specific, you can analyze survey data by simply chatting with AI, just like you would with a collaborator. Everyone on your team can open their own chat, with custom filters (such as feedback from first-timers, or only promoters), and see who created each chat. This helps parallelize the analysis and avoids stepping on each other’s toes.
In these collaborative AI chats, you instantly know who said what, because each message is tagged with the sender’s avatar. This makes it simple to divide work—one person can probe for personas, another for pain points, and you can cross-reference findings without duplicating efforts. It’s the fastest way to keep everyone aligned and organized.
For an example workflow or to get started with a tailored survey, open the NPS survey builder for Conference Participants about matchmaking effectiveness.
Create your Conference Participants survey about matchmaking effectiveness now
Start collecting better feedback and actionable insights today—capture the real voice of participants, uncover what makes for effective networking, and make your next event unforgettable with instant AI-powered survey analysis.