This article will give you tips on how to analyze responses from a hotel guest survey about value for money. Let’s break down the best ways to get actionable insights from your guests’ feedback—no guesswork, no hassle.
Choosing the right tools for analyzing hotel guest survey responses
How you analyze your survey results depends on whether your data is structured or open-ended. Here’s how I look at it:
Quantitative data: Think of things like rating scales, choices, or numeric values—basically, data you can count. I use tools like Excel or Google Sheets for these; they’re perfect for quick tallies, calculating averages, or running simple charts.
Qualitative data: Open-ended responses or detailed comments are a different animal. If you’ve ever tried to read dozens (or hundreds) of guest comments, you know it’s impossible to really “see the trends” with your own eyes. This is where AI tools shine. They turn walls of text into summaries, spot recurring topics, and help you act on what matters.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can take your exported guest comments and paste them straight into ChatGPT (or another GPT-based AI). From there, you can prompt the AI to find the main themes, summarize sentiment, or highlight standout quotes.
But here’s the catch: It’s not always convenient. You have to handle exports, format your data, and break it into pieces if you have many responses—GPT tools have context size limits. And managing analysis this way can get unwieldy for larger surveys.
All-in-one tool like Specific
Purpose-built analysis: Tools like Specific are built from the ground up for collecting and analyzing survey feedback. Unlike a general AI chatbot, Specific handles collection and analysis in a single flow. The platform asks follow-up questions in real time (using AI), meaning you get richer guest insights than you do from forms with simple comment boxes. Automatic follow-ups really dig for the “why” behind every answer.
Instant AI analysis: Once responses come in, Specific summarizes them, finds recurring themes, and distills the insights—all automatically, with no need for exporting data or wrangling spreadsheets. You can actually chat with the AI about your results, asking it specific questions or drilling down into subgroups (just like ChatGPT, but made for survey data). Managing what you send to the AI is simple—filters and context management features let you focus the analysis as needed.
Bottom line: If your hotel is serious about maximizing the value of guest feedback, using AI makes sense—especially since each point gained in guest satisfaction is tied to measurable increases in revenue and occupancy ([customer-alliance.com](https://www.customer-alliance.com/en/articles/guest-satisfaction-survey/))[1].
Useful prompts that you can use for hotel guest value for money survey analysis
You don’t need to be a data scientist to get actionable summaries from your survey responses. The trick is knowing what to ask your AI.
Prompt for core ideas: This is my go-to for discovering the main reasons behind guest perceptions of value for money. This works well with Specific or any GPT-powered tool like ChatGPT (just paste your guest comments and run this prompt):
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
Boost your results with context: Always give your AI more information about your survey. The more context, the better the analysis will match your goals:
Here is a set of open-ended comments from hotel guests about their experience with value for money at our property in May 2024. Most guests are leisure travelers staying for 2-3 nights. Our goal is to understand what drives both positive and negative perceptions of value. Please focus your analysis accordingly.
Prompt for deep dive: Once you spot a recurring topic (like “room cleanliness” or “breakfast quality”), ask: “Tell me more about [core idea].” This reveals the nuances—what guests actually said and why it matters.
Prompt for specific topic: Useful if you want to find out if, for example, anyone mentioned the spa or WiFi:
Did anyone talk about WiFi? Include quotes.
Prompts for broader insights:
Personas prompt: This extracts guest types, like business travelers prioritizing location, or families seeking amenities:
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.
Pain points and challenges: Ask your AI to surface recurring frustrations or needs:
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.
Motivations & drivers: To tap into what drives satisfaction or disappointment:
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.
Sentiment analysis: Get a feel for the overall mood—who’s happy, who’s neutral, and who’s annoyed:
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.
Suggestions & ideas: Quickly find improvements guests actually want:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Unmet needs & opportunities: This exposes what guests wish was better (great for competitive advantage):
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more real-world prompts and templates specific to hotel guests and value for money, have a look at the value for money survey generator or our guide to the best questions for guests on value for money.
How Specific analyzes qualitative data by question type
I like to break down survey analysis based on the kind of question asked. Specific does this automatically, but you can mirror this workflow in any AI tool, even if it means a little extra manual setup.
Open-ended questions (with or without follow-ups): You’ll get a summary across all guest responses to the main question, plus summaries for follow-ups—great for capturing the details that generic forms always miss.
Choice questions with follow-ups: Each choice (like rating “breakfast quality” or “staff friendliness”) gets its own summary of follow-up responses. It’s easy to spot which aspects drag down or boost value scores.
NPS (Net Promoter Score): Each group (detractors, passives, promoters) gets a distinct summary based on how they answered and their explanations. This is key if you want to see what keeps promoters coming back or drives detractors away.
You can replicate these breakdowns in ChatGPT, but it involves sorting and prepping your data manually. With Specific, these insights are surfaced automatically and organized for you.
If you’re still designing your survey, check out the how-to guide for hotel guest value for money surveys or play with the AI survey builder.
Managing AI context limits for large guest feedback sets
If you have a ton of guest responses, you’ll hit the limits of what AI can analyze at once (context size). Here’s how I handle this challenge, whether you’re working in Specific or another tool:
Filtering: Limit analysis to conversations where guests replied to a particular question (like only those who mentioned “poor breakfast” or gave low value scores). This focuses the AI, makes the results clearer, and fits within technical limits.
Cropping: Select only questions you want analyzed—say, just open-ended feedback about room value, not every question about check-in or the spa. This means more relevant results and more responses can be included per batch.
Specific makes applying both methods easy, so even big hotels can analyze guest sentiment at scale. If you hit limits in ChatGPT or another tool, just split your data using filters or only send the most important sections.
Collaborative features for analyzing hotel guest survey responses
Collaboration is often a sticking point with guest surveys—different teams want different insights, and data gets lost in long email threads or clunky spreadsheets.
In Specific, you can simply chat with AI about your survey data. This makes analysis accessible even to team members who aren’t comfortable with databases or spreadsheets.
Multiple AI chats: Need to check what families versus business travelers care about? Spin up different chats with their own filters. For each analysis conversation, you’ll see who started the chat—making it easy to trace findings and keep everyone on the same page.
Avatar-based collaboration: Whenever someone adds a message or question in AI chat, their avatar is displayed next to their input. This way, every insight or follow-up is clearly attributed, which really helps with larger teams or recurring discussions.
For hotels looking for frictionless collaboration and insight-sharing, this workflow saves time and keeps all voices included—especially when value for money perceptions impact so many stakeholders.
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