This article will give you tips on how to analyze responses from a hotel guest survey about accessibility for guests with disabilities. If you want actionable insights, the right AI analysis methods make a huge difference.
Choosing the right tools for survey response analysis
The approach and tooling you use should always fit the form and structure of your survey data. Here’s how I break it down:
Quantitative data: For metrics like “how many guests selected a particular answer,” you can count results quickly in Excel or Google Sheets. This works well for numbers and simple ratings.
Qualitative data: Open-ended or follow-up question responses need more than counting. When you have pages of guest stories or detailed feedback, it’s impossible to read and tag everything manually. This is where AI tools shine—they process volumes of text and pull out patterns or sentiments that would otherwise get lost.
There are two main approaches to tooling when you’re digging into qualitative (open-ended) responses:
ChatGPT or similar GPT tool for AI analysis
You can copy hotel guest response data into ChatGPT or another large language model and start asking it questions. This lets you chat about common themes or pain points right inside the interface.
The downside? It’s not very convenient. Formatting data for AI input, juggling context limits, cleaning up copy-pasted text, and keeping track of each analysis prompt (especially when you want to hand off findings to a team) can slow you down.
All-in-one tool like Specific
Specific is an AI-powered platform created for exactly this scenario. You build and deliver AI-driven surveys, collect richer open-ended feedback (with automated follow-up questions), and the platform instantly analyzes responses for you.
When collecting data, Specific dynamically asks smart follow-up questions. This boosts the quality and depth of your data—respondents don’t just check a box, they explain why.
Once responses come in, the platform uses AI to summarize all the text, surface key themes, and turn everything into actionable insights—without wrestling with spreadsheets. You can chat directly with AI about your survey results, analyze by custom filters, and even guide the AI conversation with your own prompts.
Bonus: You’re in control of exactly how much or which parts of your data you send to the AI for context, a big help when filtering for specific questions or guest types. Curious about all the survey-creation and analysis possibilities? Check Specific’s AI survey generator for hotel guests about accessibility and the article on the best practices for survey creation on this topic.
Useful prompts that you can use for analyzing hotel guest Accessibility For Guests With Disabilities survey data
AI analysis tools are only as effective as the prompts you give them. Good prompts help you get to the core of hotel guests’ accessibility experiences, frustrations, and needs. Here are several tried-and-tested ones:
Prompt for core ideas: If you want a clear, ranked summary of all the major things guests talk about, this one works every time—whether you’re using ChatGPT or an AI insight platform like Specific:
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 it richer survey background. For example:
You are analyzing open-ended survey responses from hotel guests, collected after their stay. The survey focuses on experiences with accessibility for guests with disabilities, including physical access, staff assistance, booking process, and amenities. My goal is to identify strengths, common issues, and priority areas for improvement.
Dive deeper: Want to see more detail behind a core idea or guest theme? Try:
Tell me more about physical access challenges mentioned by respondents.
Prompt for specific topic: When you need to quickly test a hypothesis (for example, whether anyone discussed accessible bathrooms or guide dog support):
Did anyone talk about accessible bathrooms? Include quotes.
Prompt for personas: To cluster different guest types (helpful for accessibility planning):
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: To surface frustrations suffered by guests with disabilities during their hotel stay:
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 Sentiment Analysis: To understand whether feedback trends positive, neutral, or negative, and highlight phrases driving these sentiments:
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: For quick collection of guest recommendations:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
If you’d like more prompt inspiration, Specific’s guide to best survey questions for this audience is a great resource that also covers powerful analysis tactics.
How Specific analyzes qualitative survey data from hotel guests
Let’s break down how Specific—an AI survey platform trusted by research and hospitality teams—handles analysis for each survey question type:
Open-ended questions (with or without follow-ups): You get a rich summary for all raw responses, plus any additional detail from the follow-up dialogue.
Choices with follow-ups: For each answer choice (e.g., “used wheelchair-accessible entrance”), there’s a dedicated summary based only on replies that chose that option—even capturing key themes from follow-up questions.
NPS: Specific automatically separates detractors, passives, and promoters. Each group’s written responses to their follow-up questions get their own summary, which makes it easy to see what influences ratings.
You can replicate this with ChatGPT, but you’ll need to do more work. It means filtering and exporting responses into separate chunks before prompting the AI for summaries, sentiment, or insights for each group.
AI context limits and how to get around them
Large-scale hotel guest accessibility surveys generate a ton of qualitative data—sometimes too much to fit into a single AI context window for analysis. I usually recommend two solutions, both built into Specific:
Filtering: Narrow down to just the conversations where guests replied to particular questions (like problems with check-in accessibility) or selected specific options (e.g., used a service animal). You can then analyze only those threads with AI, instead of the full data set.
Cropping: Select which survey questions to include as AI context. For example, focus just on answers to “Describe any challenges you faced during your stay,” leaving out demographic or rating questions that aren’t relevant for the current analysis.
These approaches keep quality high while staying inside AI limits, so you get fast, focused results even with dozens or hundreds of guest conversations.
Collaborative features for analyzing hotel guest survey responses
Collaborating on survey analysis is tough—especially when multiple departments care about guest accessibility, staff behavior, and facilities, but everyone’s working in silos. Coordinating on Google Sheets isn’t effective for high-quality outcomes on accessibility planning.
Chat-based AI analysis: With Specific, you can analyze hotel guest survey responses just by chatting with AI. There’s no need for everyone to be a “data person”—people across teams can jump into the chat, ask questions about the data, and immediately see structured answers supported by guest quotes.
Multiple parallel chats: You can set up separate AI chats—each with its own filters, questions, or focus (e.g., one chat for mobility-impaired guests, another for visual impairment). Each chat is labeled, easy to find, and shows who started it.
See who said what: Team collaboration is seamless because each message indicates who wrote it. Avatars appear next to each contribution, so you track exactly which team member asked which analysis question or found which insight. This transparency boosts accountability and speeds up cross-functional decision making for accessibility improvements.
Create your hotel guest survey about accessibility for guests with disabilities now
Start uncovering authentic guest experiences, address accessibility barriers, and drive inclusive improvements with structured insights from AI-powered analysis.