This article will give you tips on how to analyze responses from a SaaS customer survey about support response time using AI-powered survey response analysis tools and conversational survey best practices.
Choosing the right tools for AI survey analysis
The approach and tooling you use depend on the type and structure of the survey data you’ve collected. Let’s quickly break it down:
Quantitative data: If you’ve got clear numbers—like “how many customers chose 1 hour vs. 24 hours for support response time?”—traditional tools like Excel or Google Sheets do the job. It’s a simple count and aggregate, perfect for bar charts and quick stats.
Qualitative data: For open-ended responses, nuanced feedback, or follow-up questions—think “describe how you felt about our response speed”—manual reading just doesn’t scale. Even with 50 responses, reading through the reasoning, detail, and emotion gets overwhelming. That’s where AI tools become necessary, rapidly finding patterns, themes, and actionable insights in a sea of text.
When you’re dealing with qualitative responses, there are two main approaches for tooling:
ChatGPT or similar GPT tool for AI analysis
Copy and paste your exported survey data into ChatGPT (or any GPT-4-powered tool) to analyze themes or summarize responses. You can ask questions like, “What are the top complaints about our support response time?” and iterate from there.
However, exporting and structuring the data to work in ChatGPT isn’t very convenient. You need to clean your data, watch out for formatting errors, and manage context limits by chunking responses. Results can be powerful but take more manual effort and time to manage effectively.
All-in-one tool like Specific
A purpose-built AI solution like Specific offers an integrated experience for both survey collection and analysis. The biggest advantage? When you collect feedback using conversational surveys, Specific’s AI automatically asks intelligent follow-up questions, dramatically improving the quality of your response data. Learn more about this in our detailed guide to automatic AI follow-up questions.
Specific instantly analyzes qualitative data without spreadsheets or extra work:
Summarizes all responses and highlights key themes or patterns
Visualizes frequency of the most mentioned ideas
Lets you have a chat with AI about your results: no manual prompt-building or data transfer needed
Additional control: You can manage exactly which responses and which questions go to the AI chat for focused discussion
It’s a full-cycle solution from data collection to analysis. If you’re interested in creating your own, check out our AI survey generator for support response time.
Takeaway: For simple number crunching, old-school tools still work. For deep qualitative survey insight, AI-powered platforms or a manual workflow using GPT-models are now essential to save time and uncover value hidden in customer conversation data. According to industry data, 88% of customers expect a response to their inquiry within 60 minutes—yet the average first response time is 12 hours. Addressing this insight is critical for customer loyalty. [1]
Useful prompts that you can use to analyze SaaS customer survey responses about support times
AI is only as good as the prompt you give it. Here are tried-and-tested prompts to get valuable insights from your support response time survey, whether you’re using Specific, ChatGPT, or any other AI survey maker.
Prompt for core ideas: Use this prompt when you want a quick readout of main topics or pain points straight from your raw responses data (works for hundreds of entries):
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
Add context for stronger insights: The more you explain about your survey—who answered, the questions you asked, your business goal—the better AI will perform. For example:
We surveyed 300 SaaS customers about their recent support experience. Our main goal is to improve first response times and identify pain points. Responses include open-ended feedback as well as follow-ups if users rated us below 7/10.
Follow up on core ideas: Once the AI identifies core topics, drill deeper by prompting: “Tell me more about response speed concerns”. The AI can extract which feedback is associated with that issue or even highlight direct customer quotes.
Prompt for specific topic: To spot specific signals or issues (like “mentions of chat support delays”), use:
Did anyone talk about chat support delays? Include quotes.
Prompt for personas: When you want to understand segments among your audience—great for identifying subgroups like power users versus new customers:
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: Find the most common areas where user experience fell short.
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: Understand why customers value fast support—or not.
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: Learn how users felt overall, and which issues drove negative/positive emotion.
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 & ideas: Great for collecting actionable fixes or feature 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: Uncover hidden growth levers your team may not have noticed.
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you want more on crafting good questions for support experience, see our guide: best survey questions for SaaS customers about support response time.
How Specific analyzes each qualitative survey question type
Different question formats in your survey get summarized and analyzed in different ways in Specific—and these strategies translate for any AI-driven survey platform.
Open-ended questions (with or without followups): You’ll get an AI summary covering all answers plus a rolled-up summary of every related follow-up, capturing deeper context and root causes. You see what customers really mean—not just their first answer.
Choices with followups: Each choice (e.g. “Response in under 1 hour” or “Took more than a day”) gets its own mini-report. The AI looks for patterns and verbatims in the open-text followups linked to each selection to uncover why users chose what they did.
NPS questions: For detractors, passives, and promoters, you’ll get focused summaries showing what drove high scores, complaints behind low scores, and ideas for turning passives or detractors into loyal supporters.
You can extract similar insights by using GPT models manually, but it’s more labor-intensive—lots of copying, cleaning, and context switching. With Specific, qualitative survey response analysis is automatic, with actionable results from the start. Get hands-on with our interactive AI survey response analysis demo.
Managing large survey datasets and AI context limits
A big challenge with AI-based analysis is the context size limit: Large surveys with hundreds of open-text responses may surpass what AI like ChatGPT can process at once. But there are proven solutions:
Filtering: Only analyze conversations where users replied to specific questions or selected certain choices—e.g., looking only at feedback from customers who waited more than 12 hours for a reply. This makes the data set smaller and analysis more focused.
Cropping questions: Limit what goes to AI by sending just a subset of questions or even one question at a time for deeper analysis. This keeps you under context size limits and ensures AI focuses on what matters most.
Specific has these approaches built in, so you never have to worry about hitting a ceiling. Filtering and cropping allow you to deliver high-quality analysis, even with thousands of responses. These same approaches can be managed with manual workflows in ChatGPT, but it’s much less efficient.
For more, check out our in-depth on analyzing survey data with AI.
Collaborative features for analyzing SaaS customer survey responses
Getting everyone onto the same page when analyzing SaaS customer support response time surveys can be a real challenge. Teams often juggle different spreadsheets, email threads, and feedback docs—which risks losing valuable user insight or doubling up on work.
With Specific, survey data analysis is conversational and collaborative by design. Anyone on your team can chat directly with AI to query, summarize, or explore survey results without leaving the dashboard. Multiple users can have their own simultaneous AI chats, each with its own context filters (e.g., focusing only on late responses or critical NPS comments).
Each collaborative chat clearly shows who asked what and when. When you’re working in AI chat with co-workers, everyone’s avatars are visible—making team collaboration smoother and more transparent. Ideas, findings, or insights are captured in context, speeding up meetings and boosting alignment.
Specific also lets you share survey results and insights with key collaborators or stakeholders instantly. Whether you need quick summaries for leadership or want to compare findings across different teams, sharing is a click away.
If you’re looking for step-by-step advice on building your survey workflow, see our “how to create a SaaS customer survey about support response time” or start from a blank AI survey preset.
Create your SaaS customer survey about support response time now
Act quickly and uncover what really matters to your SaaS customers—generate actionable support response time insights with AI-driven analysis in minutes.