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How to use AI to analyze responses from hotel guest survey about fitness center experience

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Adam Sabla

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Aug 23, 2025

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This article will give you tips on how to analyze responses from hotel guest surveys about fitness center experience. If you want to learn practical ways to make sense of what your guests are really saying about your gym facilities, you’re in the right place.

Choosing the right tools for analysis

The best approach and tooling for analyzing feedback depends entirely on the structure of your guest survey data.

  • Quantitative data: If your data set mostly includes numbers, like how many guests chose “excellent” or “needs improvement,” the tried-and-true tools such as Excel or Google Sheets take you far. You can quickly tally up counts and build basic charts for reporting.

  • Qualitative data: Open-text fields and follow-up answers? That’s a different story. Reading hundreds of personal comments by hand is a fast track to burnout—and you’ll inevitably miss key signals. This is where AI-powered tools shine, helping make sense of vast qualitative feedback from your hotel guests about their fitness center experience.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

You can copy and paste your exported guest survey responses into ChatGPT (or similar GPT-powered tools) and "chat" about the results.

It works for simple cases, but it gets unwieldy as soon as you have more than a handful of conversations. Managing survey data this way isn’t convenient—there’s a lot of copy-pasting, you have to remember the context, and making sense of guest feedback with lots of details and follow-ups becomes time-consuming. This approach is best for those who have small batches of responses and just want to dip their toes into AI-assisted analysis.

All-in-one tool like Specific

Specific is an AI tool purpose-built for collecting and analyzing survey responses, so you can go from raw guest feedback to actionable insights fast. Unlike general-purpose AI tools, it’s designed for survey workflows from end to end.

Data collection is smarter—Specific surveys ask dynamic follow-up questions in the moment, which means you end up with much richer, more valuable feedback from each guest. If you’re curious how this works, read about the automatic AI follow-up questions feature.

AI-powered analysis on Specific summarizes all open-text responses for you, points out recurring themes, highlights standout suggestions, and even turns it into structured insights—no need to wrangle spreadsheets or read every comment yourself.

You can chat directly with AI about your survey results, just like in ChatGPT, but with extra tools for managing what’s being sent to the AI. This lets you cut out irrelevant content or drill into specifics quickly. For deep dives into AI-powered response summaries, take a look at how AI survey analysis works in Specific.

And if you’re still designing your survey, the AI survey generator can create a hotel guest survey about fitness center experience in minutes.

Smart tooling matters. With more than 80% of hotels now collecting feedback via digital surveys, and guest sentiment directly impacting service quality and revenue, being able to efficiently analyze both quantitative and qualitative data should be a top priority for any modern hospitality business. [1]

Useful prompts that you can use for analyzing hotel guest survey data about fitness center experience

Having the right prompts makes analyzing open responses easy and effective, especially when you’re dealing with feedback about fitness centers from hotel guests. Here are some practical prompts that work across ChatGPT, Specific, or similar AI tools. Use bold headings so you can quickly spot what each prompt does.

Prompt for core ideas: This is your Swiss Army knife prompt—use it when you want to see the top themes and what really stands out in your guests’ comments.

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

If you feed your AI more context—describe your survey’s purpose, what you want to improve, or give it hotel details—it will always generate more meaningful analysis. Here’s how you might add context:

Analyze comments from a hotel guest survey about the fitness center experience. The gym was recently renovated. Our goal is to understand what guests value and any opportunities for improvement before the next high season.

Prompt for specific topic: Use this prompt when you have a hypothesis: Did anyone mention the treadmills? The pool temperature? Just ask:

Did anyone talk about [topic]?

Tip: Add “Include quotes” if you want to see direct guest comments.

Prompt for personas: Characterize distinct types of guests—families, business travelers, fitness enthusiasts—who share feedback patterns. This helps with segmentation and targeted service upgrades.

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: Pinpoint frustrations so you know what’s holding back guest satisfaction.

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: What’s pushing guests to use (or skip) your hotel gym? This exposes their real reasons—and can guide better amenities.

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: Quickly get a sense of whether guests are raving, just okay, or frustrated with their gym experience.

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: Mine those useful guest suggestions for easy wins and big impact improvements.

Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.

Mix and match these prompts as needed. If you want to see the complete list of the best questions to ask in a hotel guest fitness center survey, check out this guide on best survey questions, or if you need help creating your own, here’s a quick how-to for building a hotel guest survey about fitness center experience.

How Specific analyzes guest feedback based on question type

With Specific, you get precise summaries tailored to how you structured your survey questions:

  • Open-ended questions (with or without follow-ups): The platform gives you an AI summary of all responses, plus any follow-up conversations linked to this question. This means you see not just what was said, but why guests felt the way they did.

  • Choices with follow-ups: For every choice (like “Equipped with weights”, “Needs more machines,” etc.), you get a separate summary of what people said as follow-up to that specific choice. You spot nuanced feedback on what works—or doesn’t—with each amenity.

  • NPS questions: Each guest type—detractor, passive, promoter—gets its own summary of follow-up answers. For example, you see all comments from those who would not recommend your fitness center in one view, making it easy to zero in on what you can improve to raise your score.

You can replicate this with ChatGPT, but it’s more labor-intensive and much less efficient when working with large or complex survey structures.

Tackling challenges with AI context limits

Every AI, including GPT models, has limits on how much data it can process at once—that’s called the context size. When your guest feedback survey amasses dozens or hundreds of conversations, you’ll hit that ceiling fast.

There are two proven strategies to sidestep this challenge. Both are built into Specific so you don’t have to worry:

  • Filtering: Instead of sending every single survey to AI, you filter by what matters. For example, only analyze conversations where guests answered the fitness center cleanliness question, or just those who gave a low score.

  • Cropping: Pick just a subset of questions to send to AI. This keeps each analysis focused, so you can dive deep into equipment quality, hours of operation, or any aspect you want—without blowing past context capacity.

This targeted approach means you always get relevant analysis, even as response volumes grow.

Collaborative features for analyzing hotel guest survey responses

Collaboration can be a headache when several team members need to work together to analyze feedback from hotel guests about their fitness center experiences. Notes, highlights, and insights get scattered, slowing down improvements and reducing accountability.

Specific solves this by enabling analysis via direct chat with AI. You and your colleagues can each set up multiple chat sessions—say, one focused on equipment complaints, another on spa-adjacent amenities. Each chat can have unique filters, so everyone can drill down or compare perspectives side by side. And because every chat session identifies who created it, teams stay in sync and never lose context.

Transparency in collaboration is built-in: In every AI chat, you see the sender’s avatar displayed on each message. It’s clear who said what when iterating on findings with colleagues. This minimizes duplication and makes it simple to build on each other’s insights instead of starting from scratch.

These features not only reduce friction but also add accountability, so actions actually get implemented—making a real difference in guest satisfaction and operational efficiency. Want to learn more about survey editing and design tweaks? See how the AI survey editor feature powers fast iteration.

Create your hotel guest fitness center experience survey now

It’s the perfect time to turn guest feedback into actionable insights—create a hotel guest survey about fitness center experience and see how effortless and actionable your analysis can be.

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Sources

  1. Smith Travel Research. 2023 Hotel Industry Trends: Guest Satisfaction and Feedback Programs

  2. Hotel Management Magazine. The Impact of Fitness Center Upgrades on Guest Retention

  3. Travel Weekly. Digital Survey Adoption in Hospitality 2023 Report

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.