This article will give you tips on how to analyze responses from a hotel guest survey about family friendliness using AI-powered survey response analysis and actionable prompts.
Choosing the right tools for survey response analysis
Before diving into survey data, it pays to choose the right tools for analyzing responses. The approach—and the best tools—depend on whether you’re working with quantitative or qualitative data from your hotel guest survey.
Quantitative data: If your survey includes counts—like how many guests prefer on-site children’s play areas—Excel or Google Sheets handle basic tallies and charts perfectly well. These tools help you spot trends at a glance.
Qualitative data: Open-ended responses, narrative feedback, and follow-up question answers are where AI really shines. Reading through dozens or hundreds of guest comments about family friendliness is overwhelming and inefficient. A well-trained AI tool can distill this ocean of feedback into meaningful themes and surface the real reasons behind guests’ experiences.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Using ChatGPT for survey analysis is flexible and easy to try. You can copy exported qualitative responses from your survey and paste them directly into ChatGPT. Then, you can use prompts—like the ones I share later in this article—to summarize, group, or analyze the text.
However, this approach has limitations. Handling data exports, splitting files when you have lots of responses, and giving ChatGPT proper context each time isn’t convenient. For a quick look, it works. For deeper, ongoing analysis, it quickly becomes a hassle.
All-in-one tool like Specific
Specific is built specifically for survey collection and AI-powered analysis. It streamlines the whole process: collecting conversational surveys, asking personalized follow-up questions to guests (which increases the quality of data), and then auto-summarizing responses with AI. The result? Insights that are ready to present or discuss, with no manual data wrangling.
When you analyze survey data in Specific, AI instantly summarizes guest feedback, identifies key themes around family friendliness, and finds actionable ideas—no spreadsheets or manual work needed. You can even chat with AI about your survey results—ask questions, dig into details, and manage which responses you want to send into the chat context for deeper discovery.
If you’re starting from scratch, the survey builder can generate questions tailored to hotel guests and family friendliness. You don’t need to be a research expert or learn coding—just describe your needs.
Want a ready-to-launch guest survey? Use the survey generator for hotel guest family friendliness or browse best questions for this survey type. This upfront investment pays off as clean, rich data that’s deeply insightful, especially in hospitality where 45% of families report difficulty finding family-friendly accommodations. [2]
Useful prompts that you can use to analyze hotel guest survey data on family friendliness
If you’re using AI—whether inside ChatGPT, GPT-4, or a tool like Specific—quality prompts are half the game. Good prompts lead to spot-on summaries and actionable results.
Prompt for core ideas from guest feedback: Use this for a quick, clean summary of what really stood out to hotel guests.
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 more context for better AI results: AI always delivers much stronger, more nuanced answers if you tell it more about your survey’s goal, situation, or audience:
You're analyzing responses from hotel guests about family friendliness. We're especially interested in what families with kids under 12 value, pain points around child amenities, and how on-site play areas affect overall experience. Summarize each theme with supporting evidence.
Drill into a theme: If you see an interesting core idea, ask AI: Tell me more about XYZ (core idea)
Validate specific topics: Did anyone talk about on-site play areas? (Add "Include quotes" for illustrative responses.) Tip: Great to check if those 38% of travelers with young children who highly value play areas [3] get vocal in your data.
Personas prompt: Want to profile respondent types? 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."
Prompt for pain points and challenges: Find out what really frustrates guests: "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 motivations & drivers: Surface what gets guests booking a room: "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."
Prompt for sentiment analysis: If you want an overview of how guests feel: "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."
You can also find prompts for analyzing NPS in hotel guest surveys in our guides: how to create the right questions or use the one-click NPS survey builder.
How Specific analyzes data from different question types
Specific’s survey response AI is purpose-built for the variety you get in hotel guest feedback, especially when it comes to family friendliness questions:
Open-ended questions with or without followups: For broad questions (e.g., “What could make your stay more family friendly?”), Specific summarizes all guest replies—including responses to automatic follow-ups—into a brief, actionable narrative. Follow-up questions driven by AI catch the “why” and “how” that basic forms miss.
Choices with followups: When a guest selects an option (say, “kids’ club” as their preferred amenity) and then answers a follow-up, Specific provides a separate thematic summary for each chosen option. This shows not just what’s picked, but why guests value it. Learn more about automated AI follow-ups here.
NPS questions: For Net Promoter Score, AI segments and summarizes feedback by category—detractors, passives, and promoters—letting you dig into what drives loyalty or what stops families from recommending your hotel. You can do all of this via ChatGPT or similar tools as well, but the manual effort increases dramatically as your data grows.
For more on editing and customizing surveys, see the AI survey editor guide.
How to tackle challenges with context limits in AI analysis
Managing AI context size: the core challenge. All large language models (LLMs), like GPT-4, have context limits—if you have too many guest responses, they just won’t all fit in the AI’s context window.
You have two practical solutions (both built into Specific):
Filtering: Narrow down which guest conversations are included—for example, just those where families mentioned amenities or replied to “family needs” questions. This keeps your data focused and manageable, and often leads to more actionable insights.
Cropping: Select the most relevant survey questions (or sections) before sending data to AI. By analyzing only targeted responses—such as just the follow-up answers about on-site child care—you sidestep context limits and get higher quality summaries.
The combination of filtering and cropping keeps your analysis deep even with a large volume of responses. This is especially handy in hospitality, where surveys can involve hundreds of guest comments and details.
Collaborative features for analyzing hotel guest survey responses
Analyzing family friendliness feedback is rarely a solo task—product teams, operations, marketing, and management all want a say in what matters most for guest experience. Coordination gets tricky fast.
Collaborative AI chat for hotel guest surveys: In Specific, you can analyze survey results just by chatting with AI—similar to ChatGPT, but designed for collaborative work. Spin up multiple chats, each focused on a different aspect of family friendliness (for example, “play areas” or “kid’s club satisfaction”). Each chat can have its own filter—say, only guests with children under 12 or only those mentioning meal options.
See who said what, work together in real time: Each chat displays the creator’s avatar, so you always know who asked which question or started a thread. As teams explore responses and hypotheses, it’s easy to track progress and share findings. This smooth collaboration also means one person can focus on analyzing NPS feedback, while another distills pain points around amenities.
If you want to see how this works, try the survey response analysis feature or browse interactive survey demos with your team.
Create your hotel guest survey about family friendliness now
Launch your own survey to understand what families want, improve loyalty, and boost satisfaction—Specific makes AI-powered survey creation and analysis effortless, so you get to focus on what matters most for hotel guests.