This article will give you tips on how to analyze responses from Conference Participants survey about Swag And Materials using AI survey analysis best practices and tools.
Picking the right tools for analyzing your survey data
The right approach depends on what kind of data you’ve collected from conference participants about swag and materials. Here’s how I break it down:
Quantitative data: When you want to analyze responses to closed questions (like multiple choice, ratings, or NPS), tools like Excel or Google Sheets do the trick. You can quickly tally up how many participants liked a specific item or ranked their satisfaction with branded notebooks.
Qualitative data: Open-ended questions—like “What swag did you actually enjoy using?” or “How could the materials be improved?”—generate lots of varied responses. Manually reading these isn’t scalable. You need AI-driven survey analysis tools to surface themes from large pools of participant feedback.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy and paste survey data into ChatGPT (or similar) and chat about it. This can work in a pinch—just export your results, drop them in, and start asking questions. But let’s be honest: it gets unwieldy fast with lots of responses, since you’re forced to manage copy-pasting, context limits, and formatting headaches. It’s functional, not seamless.
All-in-one tool like Specific
Specific is built exactly for this use case: you collect responses from conference participants about swag and materials, and instantly get GPT-based AI analysis that summarizes responses, extracts key themes, and allows you to interactively explore insights. Instead of wrestling with raw data, you chat with your results—just like you would with ChatGPT, but with additional filtering and context management features suited to survey analysis.
When you launch a survey in Specific, the AI automatically asks smart follow-up questions to dig deeper, improving response quality (learn more about AI follow-ups here).
Results appear as instant summaries, thematic extractions, and actionable highlights. You don’t need to spin up a spreadsheet or code your data manually.
If you’re curious, check out the AI survey response analysis feature for a walkthrough of how these AI-powered summaries and chats work.
For bigger surveys where you want to streamline the entire process, mixing survey collection and AI-driven analysis in the same tool is a total mental offload.
If you’re curious about what questions actually get the best insight from conference-goers, see this guide to the best survey questions for conference participants about swag and materials.
Not sure which AI-powered tool to use? There are some strong players out there: NVivo’s automatic coding, MAXQDA’s mixed methods, Atlas.ti for nuanced audiences, and Delve for tagging—all pack in robust AI features for qualitative survey analysis and can be worth exploring if your data needs are complex [1][2][3].
Useful prompts that you can use for analyzing feedback from Conference Participants about swag and materials
I’m a big believer in the power of good prompts. The better your questions to the AI, the more actionable your analysis. Here’s how you can steer your survey response analysis (these prompts work in Specific or ChatGPT):
Core ideas prompt — surfaces major themes and their explainers. This is my go-to for seeing what truly mattered to participants. Paste the prompt below into your AI 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 if you give more context about your survey. For example, you might add something like:
Here's context: This survey is from attendees of the 2024 TechConnect Conference about swag and registration materials. Our goal is to improve swag bag value and material usability in the next event.
Prompt for “zooming in” on a topic: After core ideas are listed, just follow up with:
Tell me more about [core idea]
This will give details and specific participant quotes.
Prompt for a specific topic (quick validation): If you want to check if anyone mentioned pens or tote bags (or any other swag item):
Did anyone talk about [specific swag item]? Include quotes.
Prompt for pain points and challenges: This helps you spot what didn’t work, so you know what to fix next year:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned about swag and materials. Summarize each and note any patterns or frequency of occurrence.
Prompt for suggestions and ideas: Crowdsourcing improvement ideas straight from your participants:
Identify and list all suggestions, ideas, or requests conference participants provided regarding swag and materials. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for sentiment analysis: Want to know if your swag was generally loved or panned?
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral) about conference swag and materials. Highlight key phrases or feedback for each sentiment category.
Prompt for personas: If you’re thinking bigger-picture—how different types of attendees interact with swag (e.g., students, professionals, speakers):
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.
Mix and match these prompts for a richer understanding. For even more inspiration, you might want to check out this practical guide to creating surveys for conference participants about swag and materials.
How Specific analyzes qualitative survey responses by question type
Specific segments its AI-powered survey analysis based on the structure of your survey. Here’s how that looks in practice:
Open-ended questions (with or without follow-ups): The AI summarizes all responses. If follow-ups were triggered (such as, “Can you tell me more about why you liked the water bottle?”), those deeper conversations are summarized separately alongside the main answer.
Choices with follow-ups: When attendees pick “yes/no” or select a swag item, each choice gets its own batch summary. For example, if someone chose “lanyards” and provided more details, you’ll see a dedicated summary of those follow-up answers.
NPS (Net Promoter Score): The system separates feedback by promoter, passive, and detractor groups—handy for quickly learning how high-scorers feel versus low-scorers, including their comment themes.
You can mirror this approach using ChatGPT, but it involves extra setup, data prep, and more manual steps compared to using Specific.
If you want to try creating such a survey, the AI survey generator for conference participants swag and materials is a fast starting point.
Dealing with context limits when using AI for survey analysis
One challenge you’ll face with AI tools is the context size limit: if you’ve run a big survey with hundreds of Conference Participants, you can’t fit every answer into a single AI prompt. Here’s how to work around this (and how Specific handles it out of the box):
Filtering: Filter conversations based on participant replies. You can narrow your analysis to only responses that mention certain swag items, or only look at conversations where attendees provided detailed thoughts on materials.
Cropping: Crop questions for AI analysis. Only send the most relevant questions (e.g., “What was your favorite item?”) to the AI, making it possible to analyze more conversations at once. This way, you avoid overloading the AI’s input window, but you still extract real, segmented insights.
This selective approach makes your analysis more manageable and focused, and you don’t waste valuable AI bandwidth on fluff.
If you want to customize your analysis or build more advanced question flows, the AI survey editor is a solid option—simply describe your changes and the AI updates your survey logic live.
Collaborative features for analyzing Conference Participants swag and materials survey responses
Collaboration is a common pain point: When several people need to analyze responses from a swag and materials survey, the process can get messy fast—endless email chains, lost insights, or confusion about who found what.
Analyze by chatting: In Specific, you (and any teammate you invite) can analyze survey results just by chatting with the AI. Each chat is individual—meaning it keeps its own filters, context, and prompts.
Multiple chats, multiple perspectives: Each analysis session shows who created it, so you can easily see which team member is responsible for which findings. This is great for breakout research, marketing, or event planning teams who want to slice the data their own way.
See who said what: Specific highlights contributor avatars beside each message. So if your marketing lead finds a killer insight, you know exactly where to look, making teamwork smoother.
Less friction, more collaboration: This multi-chat, visible-collaborator model helps reduce the back-and-forth that often happens when working across functions or teams, especially when prepping the next event’s swag plan or deciding what materials to print.
Create your conference participants survey about swag and materials now
Launch a smart, conversational survey that collects richer feedback from conference participants and unlocks instant AI-powered analysis—perfect for making your next swag and materials strategy a hit with your attendees.