This article will give you tips on how to analyze responses from your hotel guest survey about check in experience using AI and modern tools for survey response analysis.
Choosing the right tools for analyzing hotel guest survey responses
The approach and tools you use depend heavily on the type and format of your survey response data. Let's break it down:
Quantitative data: For structured, countable data—like “How many guests chose mobile check-in?”—tools such as Excel or Google Sheets work perfectly. You can quickly see that 70% of American travelers prefer self-service check-in via apps or kiosks, so tracking those numbers in your survey dataset is straightforward and actionable. [1]
Qualitative data: When you’re dealing with open-ended answers, conversational feedback, or multi-layered stories from guests, manual reading is not scalable. AI tools let you quickly scan hundreds of responses to extract hidden patterns, pain points, and meaningful suggestions, which is critical when guests tell you why the process worked or where it failed.
There are two main approaches for handling qualitative response analysis:
ChatGPT or similar GPT tool for AI analysis
Export and paste your data. You can take your exported survey responses and paste them into ChatGPT or comparable GPT-based tools.
Chat interactively about your results. Get summaries, look for trends, dive into outliers—almost like talking to a researcher.
Convenience is a challenge. Handling big data sets, formatting, and keeping the context organized can be tricky and time-consuming. Paste limits may force multiple rounds, and you risk missing the structure of follow-up questions.
All-in-one tool like Specific
Purpose-built for surveys. Specific is designed to take you from collecting feedback to a summary of findings in one place. You create surveys, collect rich conversational responses (including automatic AI follow-up questions—read more about that here), and then analyze all the data immediately inside the platform.
Instant, actionable insights. The analysis engine finds main themes, summarizes core ideas, and even links follow-up answers to each choice or NPS score. No more spreadsheets or manual counting! You can see the feature breakdown at AI-powered survey response analysis.
Conversational analysis. You chat directly with AI about your results—just like with ChatGPT but tailored for survey data, with the structure and filtering built-in.
Control and flexibility. You choose which questions or segments to analyze, making it easy to dig into what matters most—whether that’s mobile check-in, wait times, or guest frustrations.
Want to start from scratch? Try the AI survey generator for any topic, or directly create a hotel guest survey about check in experience.
Useful prompts that you can use for analyzing hotel guest check in experience survey responses
Prompts are key for unlocking value when chatting with AI about survey data. Here’s a rundown of practical prompts tailored for hotel guest check in experience feedback.
Prompt for core ideas: Start with this to distill the main themes from a large batch of 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
Give context for best results. AI gives you better analysis if you let it know what you’re after. Here’s an example of giving context:
The responses are from hotel guests after their recent check in. We’re trying to improve the digital check-in experience and want to uncover what works well and what frustrates guests most.
Dive deeper into any idea. Use follow-up prompts like:
Tell me more about “wait time at front desk”
AI will zoom in on exactly that topic, breaking down guest sentiment, specific stories, or suggestions.
Prompt for specific topic: Pinpoint mentions on a particular theme.
Did anyone talk about mobile check-in? Include quotes.
Prompt for personas: Segment your guests into types—great for deeper customer understanding.
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: Uncover common friction points in the check in experience.
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: Gauge the mood and how it shifts between digital and traditional check-in.
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: Harvest actionable guest advice for improvement.
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: Spot what guests wish hotels would do differently at check in.
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more inspiration, see this guide on best questions for hotel guest check in experience surveys or check the how-to on survey creation.
How Specific analyzes qualitative data by question type
Not all survey data is the same—and Specific handles them differently for better results:
Open-ended questions (with or without follow-ups): You get a summary for all responses along with any detailed follow-ups, so you see both the “headline” ideas and deep, story-driven insight.
Choices with follow-ups: Each choice (like “mobile check-in” or “kiosk”) gets its own focused summary. This helps you see why 71% of guests now prefer digital check-in, and what made them happy or frustrated with each path. [2]
NPS: Specific breaks it down by detractors, passives, and promoters—each group gets its own summary for any follow-up answers (like “Why did you give that score?”). You quickly see what delights high scorers and what turns others away.
You can extract the same insights in ChatGPT or similar tools—it just takes more setup, copying, and manual summarizing.
Working with AI’s context limits in survey analysis
AI models, including ones in ChatGPT and Specific, have a limit to how much data they can process at one time. If you have hundreds or thousands of guest responses, it might not all fit. Here’s how to tackle this:
Filtering: Focus on conversations that include specific replies—like only responses from guests who mentioned frustrations with the app, or only “passive” NPS responses. This narrows the dataset and gets you richer answers.
Cropping: Select just the questions you want the AI to analyze. For a check-in experience survey, you might only want to send open-ended questions relating to digital check-in. This ensures the best use of your AI’s attention span.
Specific offers these options out of the box, helping you stay within AI context limits and keep analysis accurate and focused. For step-by-step on building your survey, check the AI survey editor guide.
Collaborative features for analyzing hotel guest survey responses
Teaming up to analyze hotel guest check in experience surveys can get messy if everyone works from separate spreadsheets or shared docs.
Analyze directly in AI chat. In Specific, you don’t need to export or paste survey results elsewhere—just chat with AI about the data, and you’ll collaborate in real time.
Multiple analysis chats. You can set up several analysis threads at once (say, one focused on digital check-in feedback and another on traditional front desk). Each chat can have custom filters—by user type, question, or time period—and it’s clear who started or contributed to each thread.
See who said what. With avatars and clear user IDs in every chat, team members always know who contributed each insight, prompt, or suggestion.
That makes it much easier to coordinate on large feedback projects—no more versioning headaches or losing track of the latest results.
Create your hotel guest check in experience survey now
Don’t wait to uncover what matters to guests at check-in—leverage AI for actionable insights, seamless collaboration, and rapid improvement.