This article will give you tips on how to analyze responses from a Customer survey about Website Usability using AI survey analysis tools and proven prompts.
Choosing the right tools for survey response analysis
The right approach and tooling for analyzing survey responses depends on whether your data is quantitative or qualitative. Let’s break this down:
Quantitative data: If your customer survey asked simple choice questions (e.g., “How satisfied are you with our website?”), tools like Excel or Google Sheets work great. You can quickly count answers, visualize trends, and share the numbers with your team.
Qualitative data: If you used open-ended questions or follow-ups (“What’s the one thing you wish our website did better?”), the responses are often long and varied. Manually reading everything isn’t practical—even for 20-30 customers. Here’s where AI tools change the game, processing rich feedback in seconds and surfacing the main ideas. AI is now considered essential for handling such data, as it can spot trends, highlight problems, and group similar feedback efficiently. AI-driven analysis can process large volumes of qualitative data, identify patterns, and generate actionable insights more rapidly than traditional methods [2].
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy your survey data into ChatGPT or a similar language model and start chatting about your results. For example, you can paste in the open-ended responses and prompt it to find themes or summarize feedback.
However, this approach is often clunky. You’ll need to manually export and clean your data, break large data sets into pieces (because of context size limitations), and structure your prompts carefully. It can get repetitive, and you may end up spending a lot of time on manual prep work instead of actual analysis.
All-in-one tool like Specific
Specific is designed exactly for these customer survey workflows. It lets you create a conversational survey, launch it to your audience, and instantly analyze all responses—without spreadsheets.
As you collect data, Specific’s AI asks custom follow-up questions in real time, getting much richer feedback than a standard survey. (See more on AI follow-ups here.)
Once you have responses, analysis is one click away: AI summarizes every answer, finds recurring themes, and turns all your open-ended feedback into actionable insights—no manual effort required. You can chat directly with the AI about your data, like you would in ChatGPT, but with added power: filter by customer segment, zoom in on problem areas, or export summaries for your team report. Learn more about AI survey response analysis here.
Useful prompts that you can use for analyzing customer website usability survey responses
Prompts unlock the power of AI analysis. Use these in ChatGPT, Specific, or any GPT-powered analysis tool to dig deep into your data. I’m sharing real prompts I use when working with customer website usability surveys.
Prompt for core ideas: This one pulls out the biggest topics or themes across all feedback. It’s battle-tested and used in Specific by default.
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
For better results, give AI more context about your survey. Tell it what type of customers answered, what you hope to learn, or what changes you’re considering for your website. Here’s an example:
Here’s context: We are a SaaS company running an ongoing customer survey about website usability. Our main goal is to improve mobile navigation and increase conversion from product pages. Here are the responses. Please extract the core themes as above.
Prompt for explaining a specific idea: Once you see a core idea (e.g., “checkout process confusing”), dig deeper by asking:
Tell me more about checkout process confusing
Prompt for looking up a topic: Want to know if customers talk about a specific feature? Use this:
Did anyone talk about account registration? Include quotes.
Prompt for pain points and challenges: To quickly uncover what customers struggle with:
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: Use this to find out what keeps customers coming back—or what made them leave:
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: Get a quick feel for the mood (positive, negative, neutral):
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: Gather all the improvement ideas and feature requests in one go:
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: Want to uncover where your website still falls short? Try:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more on survey creation or question design, see how to create your customer survey about website usability and best questions for a customer website usability survey.
How analysis works by question type in Specific
Specific is built from the ground up for deep-diving into qualitative data from your website usability surveys. Here’s how it tackles different question types:
Open-ended questions with or without follow-ups: You get a concise summary of all responses, plus grouped themes or highlights from any follow-up questions tied to that topic. No need for spreadsheets or manual clustering—AI does the work for you.
Choices with follow-ups: Each choice gets its own summary page of follow-up responses. This shows why people picked a certain answer in their own words—super helpful for understanding motivations.
NPS (Net Promoter Score): Every NPS category (promoters, passives, detractors) gets a dedicated summary of follow-up feedback, so you can act on what drives loyalty or churn, not just the score itself.
You can do the same thing in ChatGPT, but you’ll find it’s a more manual, labor-intensive process—especially at scale.
How to tackle AI context limits with large survey data
If you’ve collected dozens or hundreds of customer responses, you’ll quickly hit the context limits of any AI tool (including GPT-4 or ChatGPT). Packing all your website usability feedback into a single “chat” doesn’t work when the data gets too big.
There are two smart ways to solve this (both available out of the box in Specific):
Filtering: Filter conversations by user replies. For example, only send conversations where users answered a specific question (“checkout UX feedback”) or picked a relevant answer. This shrinks the data size, making it manageable for AI.
Cropping: Crop questions for AI analysis. Instead of sending entire survey conversations, just select the most relevant questions (e.g., all replies to “biggest frustration using our site”). This lets you control the analysis focus and keeps you under the AI’s context limit.
These approaches keep AI results sharp and actionable, even with massive data sets. For more on this workflow, check out how to analyze survey answers with AI.
Collaborative features for analyzing customer survey responses
Collaboration on survey analysis is often messy. You might have team members combing through spreadsheets, or people dropping findings in scattered docs and chats. For customer website usability surveys, alignment is even more crucial since both product and design teams depend on clear summaries to make real improvements.
Specific lets you analyze survey data just by chatting with AI. Each member of your team can start their own “analysis chat” focused on a specific topic—say, checkout pain points or homepage navigation feedback. Each chat can have its own filters (e.g., only promoters, only mobile users), and it shows clearly who’s running the analysis, making teamwork much simpler.
Every analysis chat shows exactly who said what, including sender avatars. When you and your colleagues work together, you always know where insights came from, and you never lose track of important discoveries. No more version control headaches. For teams who need to dig into website usability survey results collaboratively, this is a genuinely useful time-saver.
Create your customer survey about website usability now
Start turning customer feedback into website improvements in minutes. With conversational surveys and instant AI-powered response analysis, you can uncover the big usability blockers before your competitors do.