This article will give you tips on how to analyze responses from a hotel guest survey about net promoter score using the latest AI techniques and tools to turn feedback into actionable insights.
Choosing the best tools for analyzing hotel guest survey responses
The best approach for analyzing your data depends on what kind of responses you’ve collected. Here’s how to think about it:
Quantitative data: Things like how many guests chose each score or option are straightforward to analyze. Tools like Excel or Google Sheets make it easy to count, chart, and compare those numbers.
Qualitative data: Open-ended feedback and follow-up comments are a different beast. With dozens or hundreds of guest stories and explanations, no one wants to read through it all by hand. That’s where AI tools come in—they can help you sift through every response and quickly spot key themes or urgent issues.
There are two main approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you already have a batch of guest survey responses: You can copy the exported text (like .csv or Excel rows) into ChatGPT and start a conversation about what’s inside. While ChatGPT is powerful, it quickly gets clunky for large data sets—lots of scrolling, losing track of the chat context, and it’s not built for survey data specifically.
Bottom line: This works in a pinch but isn’t ideal for more than a handful of responses or if you want a repeatable, organized way to collaborate. Managing context and filtering data before analysis is all manual.
All-in-one tool like Specific
If you want something purpose-built: Specific is an AI survey platform that not only collects conversational data from hotel guests but also analyzes it using built-in AI. As guests answer questions, the AI can ask follow-ups (with features like automatic AI follow up questions)—this lifts the quality and depth of feedback.
After collecting data: Just hit “analyze”—the AI instantly summarizes open-ended responses, highlights main themes, and translates raw verbatims into easy-to-understand insights. You can even chat with the AI about your results, explore patterns, and tweak which data gets analyzed in real time, all in one place. (See how AI survey response analysis works.)
Advantages:
Purpose-built for structured and conversational surveys
Eliminates manual data wrangling
Supports collaboration—share results, run custom analyses, and chat threads with teammates
Directly filter and crop data before sending it to the AI for analysis
This approach aligns with a trend: the adoption of guest feedback technologies has resulted in a 145% increase in NPS survey volume from 2019 to 2023, showing how much hospitality relies on efficient and scalable response analysis now. [1]
Useful prompts you can use to analyze hotel guest Net Promoter Score survey data
Getting value out of AI tools (like GPT or built-in chat in Specific) starts with asking the right questions. Here are some proven prompts, along with suggestions on how to use them:
Prompt for core ideas: Use this to discover top topics guests mentioned, whether you’re using ChatGPT or Specific’s AI chat. Just paste in your responses and run the following:
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 provide additional context—such as who your guests are, what your hotel is like, or your analysis goals. Here’s an example of adding survey context:
This survey was conducted among leisure guests at a 4-star city hotel. We are most interested in feedback about room cleanliness, staff friendliness, and breakfast quality. Please focus on these areas in your analysis.
Dive deeper into an idea: After running the core ideas prompt, pick an insight and ask:
Tell me more about [core idea]
Check for a specific topic: For example, to see if guests commented on breakfast:
Did anyone talk about breakfast? Include quotes.
Analyze guest personas: Understand if there are distinct types of guests (e.g., business travelers, families):
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.
Uncover pain points and challenges: Find out what frustrates or challenges your guests the most:
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 and drivers for stays: To see why guests recommend—or don’t—your hotel:
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.
You’ll find these prompts give you a rock-solid base to start or deepen your analysis, especially when you iterate—combine and chain prompts as your guest stories unfold. Specific’s survey generator for hotel guest NPS surveys also makes it simple to build your research setup from scratch.
How Specific analyzes qualitative data from net promoter score surveys
Specific is built from the ground up for conversational, flexible surveys—and knows how to handle all question types:
Open-ended questions (with or without follow-ups): You get a summary of all responses, alongside summaries of each AI-generated follow-up for deeper insight.
Choices with follow-ups: Each answer choice is grouped, and the AI summarizes all its follow-up responses. This lets you quickly spot differences across options—like seeing what “budget travelers” vs. “luxury seekers” care about most.
NPS questions: Each response category—promoters, passives, detractors—gets its own AI summary for any follow-up feedback. For instance, you’ll see what promoters praise and what detractors find lacking in one glance.
Can you do this in ChatGPT? Yes, but you’ll need to manually group, copy, and re-prompt the AI for every segment and set of follow-ups. Here’s a guide to the best NPS questions for hotel guests to structure your data from the beginning.
Consistently segmenting your NPS survey data this way is key—especially since hotel guests who are “promoters” in hospitality have a remarkable 90% retention rate, compared to only 30% for detractors. [2] This makes it crucial to drill into each group’s feedback separately.
Dealing with AI’s context size limits for large hotel guest survey datasets
AI models like GPT are powerful, but there is always a context limit on how much data you can analyze at once—typically, you’ll hit a wall with big survey exports. Here’s how to work around that:
Filtering: In Specific (and you can do this manually in other tools), filter responses to analyze only conversations where guests answered a certain question or picked a particular option. You’ll send a smaller, more relevant set of data to the AI this way.
Cropping: Focus your AI analysis on selected questions only—exclude background or demographic questions, for example, to fit more meaningful conversations in one go.
This is especially important since survey volume keeps climbing: 2.6 million hotel guest surveys analyzed from 2019-2023 show more hotels doubling down on NPS surveys—and the need for smart, scalable analysis has never been greater. [1]
Specific’s AI survey editor also lets you redesign your surveys or edit questions for future analysis, simply by chatting in plain English.
Collaborative features for analyzing hotel guest survey responses
Collaborative analysis is a common challenge when reviewing NPS feedback from hotel guests. Spreadsheets and exported data can lead to confusion over whose insights or questions were driving decisions.
Chat-driven analysis in Specific changes the game. Just chat with the AI about your data—no spreadsheet nightmares needed. You can spin up multiple chat sessions, each with its own topic, filters, or angle (for example, separate the chat focusing on “families” versus “business travelers” or on “breakfast experience” versus “staff friendliness”).
Track discussions easily. Every chat thread shows who started it and who contributed, with avatars indicating every author message. It becomes easy to divide and conquer analysis among team members, compare perspectives, and maintain context on every insight generated.
Review findings together. Having analysis happen in a structured, persistent chat means you never lose track of observations, and anyone can pick up or extend a thread—whether it’s the hotel manager, the CX lead, or even external consultants. For deeper dives on how to design questions for this kind of team analysis, see this guide on creating NPS surveys for hotel guests or try the flexible AI survey generator to build a custom setup.
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