This article will give you tips on how to analyze responses from an inactive users survey about usability issues. If you want clear, actionable insights, here’s exactly how you can use AI to make sense of your data and move fast on what matters most.
Choosing the right tools for analyzing survey responses
The approach and tools you use really depend on the type of data you collect. Let’s break down what works best for different response formats:
Quantitative data: When your survey gives you hard numbers—like how many users selected "the site is too slow" or rated an aspect 1 to 10—tools such as Excel or Google Sheets do the job. You can quickly filter, count, and visualize the data without much friction.
Qualitative data: This is where open-ended responses come in. When you ask users what frustrated them or get freeform feedback, the result is mountains of text. Reading every answer isn’t realistic, especially as your sample size grows. Here, AI tools can help summarize, group, and highlight what truly matters for usability improvements.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
The copy-paste method: Export your survey data to CSV and copy the text into ChatGPT, then ask it for themes, pain points, or direct summaries. It’s flexible—you can prompt it any way you want, but it quickly becomes cumbersome with higher volumes of feedback. Most GPT tools also have context (length) limits and don’t handle survey structures like NPS or follow-ups out of the box. You may find yourself slicing and dicing the data or re-running prompts repeatedly.
All-in-one tool like Specific
Purpose-built AI workflow: Tools like Specific go beyond generic chat models. You can both collect your survey responses and instantly analyze them, in one place, without leaving the tool.
What makes this powerful is the ability to automatically ask follow-up questions, tailored in real time, to dig beneath users’ first responses. That yields richer input—backed by data showing that AI-powered surveys have completion rates of 70-80% compared to 45-50% with boring old-school forms [1].
AI analysis in Specific means your qualitative responses get summarized, themes are uncovered, and action items pop out instantly. No spreadsheets. No manual coding. Plus, just like ChatGPT, you can chat with the AI about your results—but with more context and features such as filters, follow-up highlights, and chat history tailored for survey data. Learn more about AI-powered survey analysis in Specific.
Useful prompts that you can use to analyze inactive users’ survey responses on usability issues
When you’re tackling survey analysis, prompts are your secret weapon, especially for those mountains of text feedback. Here are a few that really work for usability-focused surveys with inactive user responses:
Prompt for core ideas: Great for extracting key themes or repeated topics from all survey responses, whether you use ChatGPT or Specific. It’s also the default in Specific’s analysis chat:
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 dramatically better if you provide it with background about your survey’s context and goals. For example, paste this before the main prompt:
This data comes from a survey of inactive users about usability issues with our app. My goal is to pinpoint what stopped them from using it regularly, and spot improvement opportunities that could win them back. Please keep this in mind when extracting themes.
Go deeper on a topic: After you find a strong theme, just ask: “Tell me more about [core idea]” and the AI will expand on it, showing you supporting quotes and context.
Prompt for specific topics: Want to check if users complained about something specific, like “slow loading”?
Did anyone talk about slow loading? Include quotes.
Prompt for personas: If you want to segment user types based on responses:
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: Spotting where people struggle is critical in usability research. Use:
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 unmet needs and opportunities:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
You can find more detailed prompt ideas and survey question strategies in the best questions for inactive users survey guide.
How Specific analyzes survey responses for different question types
The way Specific handles qualitative analysis is tailored to your question types:
Open-ended questions (with or without follow-ups): You get a summary of all responses to a given question and any associated follow-ups, making it easy to see recurring usability themes such as “navigation confusion” or “feature not found.”
Choice questions with follow-ups: Each choice option—say, “site was slow” or “layout was confusing”—gets its own bucketed summary for related follow-up responses. You immediately see what those users actually meant by their choices.
NPS questions: Detractors, passives, and promoters are all analyzed separately. For each group, Specific provides a summary of follow-up responses, so you can see what specifically frustrated—or delighted—each segment of users.
You can mimic most of this in ChatGPT by filtering and re-prompting for each subgroup, but it’s a lot more manual work. If you want to see the details of how automation helps here, read how Specific handles follow-ups automatically.
How to work around AI context limits for large response sets
Dealing with a high volume of inactive user feedback on usability can quickly hit the limit of what a GPT-based AI can analyze at once. If you copy lots of survey answers into AI chat, you’ll likely get cut off mid-conversation, or the model will “forget” earlier entries.
There are two solid strategies—both built into Specific—to handle this:
Filtering: Only include conversations where respondents answered a specific question or selected a certain option. This way, you analyze just the relevant conversations without overloading the AI. It’s an efficient way to focus on, say, users who mentioned “checkout issues” or “password reset problems.”
Cropping: Analyze responses from just the most important questions, skipping background or off-topic answers. This ensures you feed only the meat of the dataset to the AI, letting it spot key issues without running out of space.
Both of these approaches let you explore more conversations, faster, and always stay within context boundaries. You can read more on AI survey analysis workflow in Specific.
Collaborative features for analyzing inactive users survey responses
Analyzing usability surveys from inactive users can get messy fast, especially when your team wants to pull insights from the same dataset. Keeping track of each person’s questions, discoveries, or analysis filters can become a real headache.
Analyze together—just by chatting: In Specific, you and your colleagues can chat directly with the AI about the survey data. There’s no need to explain context each time, because everyone shares the same up-to-date workspace with access to all chats.
Multiple chat threads, focused views: You’re not limited to just one analysis session. You can spin up separate chats for different angles—like one digging into abandonment reasons, another zooming in on mobile usability pain points. Each chat clearly shows its creator, making handoffs and follow-ups easy.
See who’s saying what: When multiple team members join in, each message in the AI chat shows the sender’s avatar. This makes collaboration more seamless—no confusion, no duplicated work, just shared progress.
These team features not only save time, they surface more useful ideas collectively. For ideas on building your own survey, check out the AI survey generator preset for inactive users and usability issues.
Create your inactive users survey about usability issues now
Unlock richer insights, save hours on manual analysis, and reveal urgent usability problems with an AI-powered approach—get started and create your own survey for inactive users on usability issues today.