This article will give you tips on how to analyze responses from a hotel guest survey about mobile app experience using AI survey analysis tools for better insights and faster decisions.
How to choose the right tools for analyzing hotel guest survey responses
Your approach depends a lot on the type of survey data you're dealing with. Here's what I suggest based on the structure of your responses:
Quantitative data: If you have multiple choice or rating questions (like “How likely are you to recommend our app?”), these are simple to tally. I often use Excel or Google Sheets to calculate percentages, averages, or run quick charts—for example, tracking how many guests found the check-in feature useful.
Qualitative data: The challenge ramps up with open-ended responses and follow-ups ("What would you improve in our app?"). Reading each reply doesn’t scale, especially if you have dozens or hundreds of guests. That’s where AI tools make a real difference—extracting themes, summarizing feedback, and surfacing what matters without scanning every line yourself.
There are two main approaches when it comes to AI tooling for qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy-paste exported survey data into ChatGPT to chat about responses. It’s a handy entry point—ask for top complaints or suggestions, and get a decent breakdown.
But here’s the drawback: Organizing, formatting, and chunking the raw text gets messy fast. Managing context limits (how much text you can paste in), separating guest replies from survey meta-data, and tracking which questions responses relate to all add friction. For small surveys it’s fine, but it gets unwieldy for scale or for team collaboration.
All-in-one tool like Specific
If you collect your survey data directly with a platform like Specific, analysis gets a lot smoother. Specific is purpose-built for this workflow, handling both data collection and AI-powered analysis in a single interface.
When collecting responses, its conversational AI asks smart, automatic follow-up questions—leading to richer, more detailed feedback versus standard forms.
Learn more about automatic AI followup questions here.
For analysis, Specific summarizes responses, pulls out core themes, and turns feedback into actionable insights instantly. You can ask questions about your data conversationally, like you do in ChatGPT, but it’s context-aware—meaning the AI knows which answer relates to which guest, question, or follow-up. No spreadsheets, no manual copy-paste.
Manage what data gets fed to AI, apply filters, and keep your whole team in the loop. If you want to see how to create or analyze this type of survey, check out this tailored survey generator for hotel guest app experience or go straight in-depth with AI survey response analysis.
Useful prompts you can use to analyze hotel guest responses about mobile app experience
Even with the best AI, giving it clear instructions—or prompts—matters. Here are my favorite AI prompts for analyzing mobile app feedback from hotel guests, with explanations for each.
Prompt for core ideas (great for getting a synthesized list of the biggest ideas or patterns in all guest feedback):
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
Tip: AI always performs better with detailed context. I usually provide more info in my prompt, such as the survey’s goal or background. For example:
Analyze the following responses from hotel guests who used our mobile app during their stay in 2023. Our main goals are to identify improvements, reduce friction, and discover what features guests value most.
After you get the core ideas, ask follow-up questions like: “Tell me more about XYZ (core idea)” if you want to dig deeper into a specific theme.
Prompt for specific topic: Use this if you want to check whether guests mentioned something in particular. For example:
“Did anyone talk about mobile check-in? Include quotes.”
Prompt for personas: If you want to segment guest feedback into types of users, try:
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 the most common guest frustrations with the mobile app, use:
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 suggestions & ideas: If you’re hunting for new features or hot 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: To find what’s missing or overlooked in your app, ask:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For a breakdown of the best questions to ask before running your survey, read this guide on best questions for hotel guest surveys about mobile apps.
How Specific analyzes responses by question type
One of the strengths of Specific is its context awareness with question types:
Open-ended questions and follow-ups: You get an instant, AI-powered summary of the core themes from all guest replies to each question—and if there are follow-ups (for example, clarifying “what did you mean?” or “can you give an example?”), those are organized together so it’s easy to see the full thread.
Multiple choice with follow-ups: For each choice (like “Which feature did you use most?”), the AI generates a focused summary from all related follow-up responses, helping you see not just quantitative results but the “why” behind each answer.
NPS: Each group—detractors, passives, promoters—gets its own summary drawn from all the qualitative feedback in that group’s follow-ups. You’ll quickly spot what your happiest or least satisfied guests have in common.
You can do all of this in ChatGPT if you batch and format your data correctly—it’s just more manual effort. If you want to automate or streamline this, check out how Specific handles AI survey response analysis.
How to stay within AI context limits when analyzing big survey data
AI tools, including ChatGPT and platforms like Specific, have limits on how much data they can process at once. If you have hundreds of responses, you’ll eventually hit a “context limit”—the AI can’t analyze everything in a single shot.
Filtering: I target smaller slices of conversations—filter for specific answers, key questions, or guest segments I care about most (like only guests who mentioned technical issues or used the check-out feature). This reduces the data size sent to the AI, making your analysis more focused and manageable.
Cropping: Sometimes I crop which questions or fields are sent to the AI for analysis—sending just open-text answers for “most liked feature” or “what frustrated you?”, instead of every question. That way, each batch stays inside the context window, and you get more targeted insights.
Specific has these features out of the box—just apply filters or crop fields right before analysis. For more custom control or for advanced surveys, try the AI survey editor.
Collaborative features for analyzing hotel guest survey responses
Collaborating on mobile app experience feedback can get messy, especially if you’re working across guest services, digital product, and marketing teams. Tracking who asked what, and sharing insights in real time, is key for keeping everyone aligned.
Chat-based analysis: In Specific, you can analyze hotel guest survey data by simply chatting with AI. This way, anyone on your team—regardless of technical skill—can ask, explore, and dig deeper into guest feedback.
Parallel analysis threads: Need to analyze feedback by guest segment, feature used, or any other filter? Specific lets you create multiple chats, each with its own filter set, topic, or analysis thread. For example, one chat could be focused on guests who tried mobile check-in, while another analyzes passives’ NPS follow-ups.
Collaboration transparency: Each chat shows who started it, and when collaborating, you always see the sender’s avatar beside every message. This keeps your research conversations organized—no more “who asked this?” or “where’s that insight from?” confusion.
For more tips on how to create these types of surveys (and why a conversational approach pulls richer feedback), check out this tutorial: how to create hotel guest surveys about mobile app experience.
Create your hotel guest survey about mobile app experience now
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