This article will give you tips on how to analyze responses from a user survey about support experience using AI-powered methods and best practices for extracting meaningful insights.
Choosing the right tools for analyzing survey data
When it comes to analyzing user support experience surveys, the approach—and especially the tools you choose—depend on whether your data is mostly quantitative or qualitative. Ideally, you want to maximize the value from every response.
Quantitative data: Numbers, ratings, and select-one answers (like “How satisfied are you?”) are easy to count and chart. For this, Excel or Google Sheets will do the job—quick sums, averages, and pivots get you instant answers.
Qualitative data: Open-ended responses—“Describe what didn’t work,” stories, detailed suggestions—are a goldmine for insight. But reading every line isn’t scalable. Here, you really want AI-driven tools that distill all that rich input into core themes automatically. Manual review just isn’t feasible if you have a lot of users.
There are two approaches for tooling when dealing with qualitative (open-ended) survey responses:
ChatGPT or similar GPT tool for AI analysis
Quick start, but manual: You can copy/export survey data and paste it into ChatGPT (or another GPT-based tool) to chat about your responses. It's simple, but it's not exactly convenient—juggling text files, breaking up large response sets, and manually slicing data to fit chatbot limits can slow you down fast.
Context is limited: If you have a lot of responses, ChatGPT may hit context cap, and you’ll need to filter/crop input yourself. Asking the right prompts is also all on you, with no built-in guidance on survey nuances.
All-in-one tool like Specific
Purpose-built for survey analysis: Specific is designed for this entire workflow—you can build the survey, collect responses (even asking follow-ups automatically, boosting quality), then analyze them in one place.
No exporting or spreadsheets: With Specific, AI summarizes support experience feedback, finds the strongest themes, and delivers insights in seconds. No more copying and pasting between tools or manually filtering.
Chat-driven analysis: Like with ChatGPT, you can ask questions conversationally about your data (“What do users say about response times?”), but now you have more granular control, extra filters, and visibility into how your data is fed to AI. The AI context management is handled for you. [1]
Automatic follow-ups: Surveys can ask users clarifying questions mid-interview, which increases the amount of actionable detail your analysis starts with. See how automatic probing works in practice on our AI follow-up questions page.
Useful prompts you can use for analyzing user support experience survey responses
AI gets you quick answers, but having the right prompts to guide it is what unlocks the gold—especially with nuanced support experience feedback. Here are a few tried-and-tested prompts I recommend (these work whether you use ChatGPT or AI survey tools like Specific):
Prompt for core ideas - find the themes that matter most: This one is perfect for surfacing the main takeaways from a pile of qualitative feedback (it’s the prompt Specific uses behind the scenes):
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 performs much better if you include context about your survey and goals. For example, add a preface such as:
We're analyzing user survey responses about our support experience in a SaaS product. The goal is to understand recurring pain points, what delights or frustrates users, and to identify improvement opportunities.
Then ask follow-up prompts like:
Dive deeper into key topics: “Tell me more about [core idea].”
Spot specific mentions: “Did anyone talk about response time?” (Add “Include quotes” to get actual replies.)
Find the patterns - personas, pain points, and more:
Prompt for personas: Useful if you want to see who your customers really are in their own words.
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 & challenges:
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:
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 unmet needs and opportunities:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more examples of targeted questions and survey setup for this audience and topic, check out how to design your user support experience survey and the best survey questions for user support experience resources.
How Specific analyzes qualitative data based on question type
Specific brings structure to your support experience analysis by handling data differently based on question types. This lets you drill into what users actually say, across any style of question—even nuanced follow-ups.
Open-ended questions (with or without follow-ups): You get concise summaries of all responses grouped by main topic or theme, plus breakdowns of what users said in follow-up conversations.
Choices with follow-ups: Every answer option gets its own mini-report; for example, if users pick “Live chat,” you see what all “Live chat” fans specifically liked or disliked, as revealed by their follow-up answers.
NPS (Net Promoter Score): The system gives you a separate summary for detractors, passives, and promoters. Quickly see, for instance, what’s frustrating to detractors and what promoters rave about. That’s immediate, focused insight into advocacy drivers and pain points.
You could do the same thing with ChatGPT by slicing and analyzing your exports per group, but that’s a lot more labor-intensive.
Working with AI’s context limits: cropping and filtering survey data
AI can only process so much text at once—the context size limit is real, especially with hundreds of survey responses. Here’s how to keep analysis sharp and efficient:
Filtering: Analyze only a selected subset—such as users who commented on “response time” or gave a low satisfaction score. This keeps the analysis focused and fits within context boundaries.
Cropping: Instead of sending entire transcripts, you can send just the chunks you care about—such as only open-ended comments on “quality of support.” This approach also ensures you can analyze more conversations at once without hitting the wall.
Specific offers both of these approaches straight out of the box, so you don’t have to manually pre-process your data every time.
Collaborative features for analyzing user survey responses
Digging into user support experience survey analysis is rarely a solo sport—you need to share findings, brainstorm with teammates, and measure impact together.
Chat-based AI analysis: In Specific, you can analyze data simply by chatting with AI. No more writing up reports in isolation—just share the chat.
Multiparty collaboration: You can spin up multiple chats, each with its own filters (for example, one chat analyzing only promoters, another focused on users who mention “slow response times”). Every chat clearly shows who started it, making cross-team work transparent and organized.
Team visibility: While collaborating, you always see who said what—each message in AI chat is attributed by avatar. That gives clear audit trails, avoids confusion, and turns every analysis into a team workshop.
Turn insights into action faster: These features were designed to let product, support, and ops teams converge on next steps, rather than piecemeal individual exports.
Create your user survey about support experience now
Get a pulse on how users view your support by building your own survey and instantly analyzing results with AI—unlock deeper insights, spot improvement opportunities, and empower your whole team to act on real feedback, faster.