This article will give you tips on how to analyze responses from conference participants surveys about accommodation recommendations using AI and smart tools for fast, high-quality insights.
Choosing the right tools for analysis
Your approach and tooling mostly come down to the structure of your survey data—quantitative or qualitative—and what you want to uncover.
Quantitative data: When you’re dealing with numbers, ratings, or tallying how many participants selected each accommodation option, tools like Excel or Google Sheets are all you need. These handle basic stats, graphs, and breakdowns easily.
Qualitative data: If your survey includes open-ended questions or collects rich, text-based feedback—like “Tell us about your stay” or detailed follow-up responses—it’s nearly impossible to read and synthesize everything by hand. This is where AI tools come into play, helping you sort, categorize, and extract actionable insights from the ocean of text responses.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy and paste exported survey data into ChatGPT or a comparable GPT-powered tool and use the chat interface for your analysis.
It’s direct and flexible—just chat about your data, ask follow-up questions, or request summaries. But handling large or complex datasets is not very convenient, as you need to clean up your data, cut it into chunks, and manage context limits yourself.
Manual context sharing is also required, meaning you have to be specific in your prompts and sometimes re-clarify what the data is about. This gets cumbersome, especially if you’re analyzing dozens or hundreds of participant responses or running regular survey projects across events.
All-in-one tool like Specific
Specific is purpose-built for AI-driven survey creation and response analysis. It combines conversational survey collection and automated, AI-powered analysis in the same workflow.
When you collect data with Specific, the survey engine automatically asks intelligent, context-relevant follow-up questions. This boosts the depth and clarity of the feedback you get, ensuring more useful accommodation recommendations from your conference participants. For more on this feature, see our writeup on automatic AI follow-up questions.
With Specific’s AI response analysis, you get instant summaries of all feedback, clear theme extraction, and conversion of free-text data into actionable insights—no exporting, no spreadsheets, no manual reading required. The chat-like interface lets you query the data as freely as with ChatGPT, but with added filters, controls, and context-awareness. See details on AI survey response analysis in Specific.
You’ll often get the best results if you start with a tailored, ready-made preset—like one from our AI survey generator for conference accommodation recommendations—but you can also craft your own in AI survey generator.
This integrated approach consistently improves efficiency and insight quality. According to research, “AI-powered survey analysis can reduce time-to-insight by over 70% compared to manual processing” [1].
Useful prompts that you can use to analyze conference participant accommodation survey responses
After collecting your survey data, you can accelerate your analysis using a lineup of effective AI prompts that work with Specific, ChatGPT, or other tools you like.
Prompt for core ideas: This is the default prompt I rely on to quickly extract patterns and key themes from accommodation feedback at conferences. Paste your data and use:
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 performs far better when you add more context. If you want richer, more tailored analysis, start your prompt by adding a description like:
This data comes from a survey of conference participants after a tech event. The survey asked for accommodation recommendations and feedback on different options near the venue. I want to find out attendees’ main preferences and pain points.
Once you spot a recurring idea, zoom in with: “Tell me more about XYZ (core idea)”—this reveals pearls in the details that can drive practical improvements for future events.
Prompt for specific topic: To confirm whether a topic appeared in the feedback, use:
Did anyone talk about hotel shuttle service? Include quotes.
Prompt for personas: Build a mental map of your attendees with:
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: Quick detection of friction points:
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 & drivers: Uncover the reasons behind choices:
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: High-level view of overall feedback mood:
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 suggestions & ideas: Gather improvement ideas and requests:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: Spot areas for future focus:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
Using these prompts and adapting them to your conference context helps you transform even a huge pool of open comments into concrete, actionable insights. For more prompt tips and survey question ideas, check our guide to the best questions for conference accommodation surveys.
How Specific handles analysis by question type
Specific recognizes that every question yields a different analysis challenge, so the platform tailors summaries depending on the type of question in your survey:
Open-ended questions with or without follow-ups: You get a concise, AI-generated summary of all responses, plus summaries of responses to each follow-up related to the main question. This gives a panoramic snapshot and lets you drill down as needed.
Choices with follow-ups: For questions like “Which accommodation did you choose?” where each choice kicks off custom follow-up questions, Specific produces a separate summary for participants who picked each option. This way, you see what matters to fans of each hotel, neighborhood, or Airbnb, and can compare audience segments at a glance.
NPS: When you ask “How likely are you to recommend our event?” using Net Promoter Score, Specific automatically groups responses by detractors, passives, and promoters—delivering distinct summaries for each group’s feedback. You see what annoys detractors, what delights promoters, and what might shift passives up the scale.
If you use ChatGPT for this kind of analysis, you can achieve similar results, but prepare for more manual copy-pasting, filtering, and prompting. Using integrated tools saves time and reduces the risk of missing nuances, especially as the volume grows.
If you want to launch a ready-to-use NPS survey tailored to conference participants and accommodation, jump straight to our NPS survey builder for conference accommodation.
Dealing with AI context size limits in survey analysis
The biggest obstacle in AI-based survey analysis? Context size limits. Even advanced models can’t process unlimited text at once, so if you have hundreds of feedback threads from conference participants, not all of it will fit in one go.
There are two simple workarounds—both built right into Specific, but you can adapt the concepts if using other tools as well:
Filtering: Analyze only the conversations or responses that matter most right now. For example, filter to just people recommending specific hotels, or focus solely on participants who reported issues with their rooms. This drastically reduces the amount of data being sent to the AI and helps you pinpoint clusters of similar feedback fast.
Cropping: Select only certain questions for analysis, such as responses tied to “biggest pain points” or “best-value accommodation.” Sending just the core questions means your AI has more room to analyze more participants in depth, without running out of space.
Using a combination of these methods, you can keep your analysis sharp and targeted, even as your dataset grows. According to industry reports, “over 60% of organizations struggle with context overload during qualitative analysis, making filtering and smart chunking essential” [2].
Collaborative features for analyzing conference participants survey responses
Analyzing accommodation recommendations as a team can quickly get messy if you’re working across files, long email chains, or complex spreadsheets. Collaboration is too often the bottleneck—especially when quick, actionable decisions are needed after a major conference.
In Specific, you analyze survey data just by chatting with AI. Your whole team can jump into the feedback together, discuss patterns out loud, and try different prompts without duplication or confusion.
Multiple chats for deep-dives: You can spin up several chat threads, each focused on a separate angle—pain points, event logistics, accommodation pros/cons—so everyone can work in parallel. Each chat can have its own filters applied (“let’s see just international attendees” or “show me feedback on hotel breakfasts only”), which makes it easier to slice the data in ways that are relevant for different stakeholders.
Attribution and transparency: In every chat, you can instantly see who asked what. Each message is labeled with the sender’s avatar, so collaboration is clear and traceable. No more guessing who surfaced the best attendee quote or flagged a trend worth sharing with event organizers.
Stakeholder involvement: Because chatting with AI feels as simple as messaging your colleagues, more people can get directly involved, including non-researchers, vendors, and decision makers. It’s a great way to collectively surface hidden gems in your accommodation data.
This collaborative approach not only speeds up your analysis, but also makes your survey work more transparent and inclusive—a key consideration for post-event debriefs and planning. If you’re new to designing this type of survey, our guide on how to create conference accommodation surveys is a great starting point.
Create your conference participants survey about accommodation recommendations now
Get richer feedback, smarter analysis, and better collaboration instantly—create your own conference participant survey for accommodation recommendations today and discover exactly what matters to your attendees, without the hassle.