This article will give you tips on how to analyze responses from a hotel guest survey about pool experience, using AI tools and best practices for survey response analysis.
Choosing the right tools for analyzing your hotel guest pool experience survey
Your analysis approach—and the tools you’ll need—depend on the type of survey data you have.
Quantitative data: If you’re just counting how many guests selected certain options or ranked satisfaction (for example, rating the pool’s cleanliness), you can quickly crunch these numbers in tools like Excel or Google Sheets.
Qualitative data: When you’re dealing with open-ended responses—like written comments about the pool area, or answers to AI-generated follow-up questions—manual reading doesn’t cut it. You need AI survey response analysis tools to make sense of all that text at scale.
There are two main approaches for qualitative analysis tooling:
ChatGPT or similar GPT tool for AI analysis
You can export your survey data, copy it into ChatGPT, and have a conversation about your results. This approach gives you flexibility to ask questions in your own words, but it’s often clunky.
Managing spreadsheets and exported files feels old-school, especially if you need to rerun analyses or share results with teammates. Also, context size limits mean you can't just dump hundreds of survey replies into a single prompt—things get messy fast.
All-in-one tool like Specific
Designed for survey response analysis, Specific lets you both build surveys and automatically analyze the results using AI, in one place. When you run a hotel guest survey about pool experience with Specific, the survey’s conversational format can trigger smart, AI-powered follow-up questions—getting you richer, more actionable responses (learn how automatic AI follow-up questions work).
AI-powered analysis is built-in: Specific instantly summarizes responses, highlights key themes, and turns qualitative data into insights you can act on—no spreadsheets, data exports, or manual reading required. You can even chat with the AI about your results (see AI-powered survey response analysis), just like in ChatGPT, but tailored to your survey’s context. You have extra control over what parts of your data get sent to the AI, making it much easier to manage larger response sets.
Some leading AI survey tools for hospitality that combine these features include KePSLA, Sunbeam, and ReviewPro—all of which use AI-driven sentiment analysis to help hoteliers rapidly detect trends, improve guest experience, and act on guest feedback more efficiently. These tools have been shown to deliver faster insights than traditional survey analysis, with hotels seeing measurable improvements in guest satisfaction and operational decisions[1][2].
If you want to build a survey from scratch or get inspired, try this AI survey generator preset for hotel guest pool experience surveys or learn more about designing questions in this guide to the best hotel pool survey questions.
Useful prompts that you can use to analyze hotel guest pool experience survey responses
When you’re working with AI to analyze qualitative survey responses, smart prompting is key. Here are some prompt examples that work especially well for understanding guest feedback about pool experience.
Prompt for core ideas: Use this to get a quick list of main themes from your dataset—this is the backbone prompt used by Specific for any large set of text responses. Try this in ChatGPT if you’re exporting data:
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
Add your survey context for better AI results. If you share more about your hotel, pool facilities, guest profiles, or business goals, the AI will tailor the analysis to your needs. Try something like:
You’re analyzing guest surveys from a four-star city hotel focused on pool amenities. We want to improve overall guest pool experience and learn about pain points for families vs. solo travelers. Please summarize key findings and group results by guest type.
Prompt for follow up on core ideas: Once you’ve got a list of themes, dive deeper by asking:
Tell me more about “[core idea]”
Prompt for specific topics: Want to validate if anyone commented on something specific, like pool temperature or towel service? Use:
Did anyone talk about [pool temperature/towel service/lifeguards]? Include quotes.
Prompt for pain points and challenges: Find out what frustrated your guests, and spot patterns:
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 motivation and drivers: If you’re interested to know why guests used the pool or why some avoided it, use:
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: To check if the overall pool experience was positive, negative, or mixed:
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 and ideas: Get practical suggestions straight from your guests:
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 and opportunities: Discover what guests wanted but didn’t get, so you can prioritize improvements:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more tips on designing your survey, check this article on how to create a hotel pool experience survey.
How AI like Specific analyzes qualitative survey data by question type
Specific’s approach to AI analysis adapts to the structure of your survey, making it easier for you to dig into details by how the question was asked:
Open-ended questions (with or without follow-ups): You’ll get a summary for all guest responses, including each follow-up the AI asked. If your survey included “Tell us about your pool experience,” you can see what came up most and why.
Choices with follow-ups: Each choice (like “Loved the pool” vs. “Didn’t use the pool”) has a separate summary of follow-up responses. For example, if guests who said “Pool felt too crowded” gave details, you’ll see those grouped together.
NPS questions: The analysis breaks out promoters, passives, and detractors, giving you summaries of follow-ups for each group—helping you diagnose what drives loyalty and what needs work.
You can do similar breakdowns with ChatGPT by structuring your prompts well—but it does mean more copy-pasting, segmenting, and manual work. Using a platform like Specific or another AI survey response analysis tool just saves time, and it’s visual.
How to handle AI context limits when analyzing many survey responses
Anyone who’s dropped thousands of survey responses into an AI knows about context window size—the built-in memory limit for every AI prompt. If your pool experience survey gets hundreds of replies, you can hit those limits fast (even with upgraded GPT models).
Specific solves this by offering two essential features:
Filtering: You can filter conversations based on user replies—analyzing only guests who, for instance, mentioned the jacuzzi, or those who actually used the pool. This keeps things focused, and your AI prompt stays lean.
Cropping: You can crop the data sent to the AI by sending only responses to selected questions (e.g., just open-ended questions about pool cleanliness or staff interactions). This helps ensure your analysis covers as many conversations as possible—even if you have a large dataset.
These features are built-in with Specific, but you can also manually crop and filter as needed if you’re using ChatGPT or another generic AI tool. You just need more diligence around data prep with those.
Collaborative features for analyzing hotel guest survey responses
Collaboration is a common pain point when multiple teams want to analyze hotel pool experience surveys. You might have front desk leads, operations, or marketing all wanting to dig into the same data, but each has different priorities.
In Specific, sharing analysis is frictionless: Anyone on the team can analyze results just by chatting with the AI, making it easy for operations or CX managers to get to the heart of guest feedback, fast.
Multiple chats for deeper collaboration: You can create parallel analysis chats, each with its own set of filters and analysis focus—so one chat might focus on cleanliness, another on family comments, and another on NPS detractor feedback. You always know who made each chat (the creator is shown for every chat), keeping teamwork clear.
Know who said what: When you collaborate in AI Chat, every message clearly shows the sender’s avatar—making it easy to follow the flow of insights, whether you’re revisiting past conversations or working live as a team.
To see how these features work in practice, explore how Specific handles AI survey response analysis, or try the AI survey generator to kick off your own collaborative project.
Create your hotel guest survey about pool experience now
Start collecting smarter guest feedback—AI-powered surveys let you dig deeper, analyze responses faster, and surface actionable insights to improve your hotel’s pool experience today.