This article will give you tips on how to analyze responses from an inactive users survey about reasons for inactivity, focusing on extracting insights with AI-powered survey response analysis.
Choosing the right tools for analysis
How you approach survey analysis depends on the form and structure of your data. For inactive user research, the right tools will make all the difference—especially when you’re working with large or complex response sets.
Quantitative data: When you’re dealing with straightforward stats—like how many inactive users picked a specific reason—it’s easy to calculate trends using tools like Excel or Google Sheets. These platforms are perfect for counts, percentages, and basic visuals of your data.
Qualitative data: Open-ended answers, story-driven feedback, or responses to AI-generated followup questions quickly become overwhelming to analyze by hand. Reading through hundreds of reasons for inactivity isn’t just time-consuming; you’ll miss patterns and hidden insights. This is where AI tools step in and shine. They process large volumes of textual data and pull out the underlying themes that might otherwise go unnoticed. According to recent market analysis, leveraging AI for survey analysis can reduce analysis times by up to 70% while improving insight depth [1].
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Manual export and chat: You can copy your open-ended responses into ChatGPT or a similar AI tool, then prompt it to find patterns or summarize key themes. This works for smaller sets, but gets chaotic with lots of data or when you want to dig deeper.
Practical pitfalls: Each time you collect new responses or want to rerun a filter, you have to export, copy-paste, and structure prompts. It’s doable, but not seamless—context is limited, and the AI can lose track of key threads from multiple conversations.
All-in-one tool like Specific
AI survey + instant analysis: An AI tool built for this job does both parts: collecting responses (with followups) and summarizing the results in one flow. That’s what Specific does—it’s designed to help you actually understand your inactive users, not just gather data.
More context, richer data: When a user gives a short or ambiguous answer, Specific automatically asks AI-powered followups to clarify the reason for inactivity. This means your qualitative data ends up much richer and easier to interpret. If you want a hands-on look at this type of survey, try building one with our AI survey generator—it includes conversation-ready questions by default.
AI summaries on demand: Once your survey runs, Specific’s AI instantly pulls out summaries, key themes, and action points from free-text feedback. You don’t touch a spreadsheet or wade through raw transcript logs. You can also chat directly with the AI (just like ChatGPT) to ask about particular themes, user segments, or trends found in the “reasons for inactivity” data set—and even control exactly what’s sent to the AI for clarity and privacy. For team-based work or iterative analysis, these time savings add up fast.
Integrated features: See how the analysis works in context with real inactive user conversations in our deep-dive on AI survey response analysis.
Useful prompts that you can use for analyzing inactive users survey about reasons for inactivity
Strong prompts are your superpower when diving into survey response analysis for inactive users. The right wording can help AI surface the real reasons for inactivity, spot patterns, and separate signal from noise.
Prompt for core ideas: I like to start high level and quickly narrow in—this is the default prompt Specific uses, but it works well with ChatGPT too. Paste your survey conversations and use:
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
Tip: AI always performs better if you give it context about your survey, your goals, and what you’re trying to learn. For example, add a preamble like:
We surveyed inactive users to find out their reasons for inactivity on our platform. Responses include both open-ended answers and followups. Please analyze these to uncover the top reasons and actionable insights.
Once you’ve pulled out initial ideas, follow up with narrowly focused prompts. For instance, use:
Tell me more about "lack of product updates" (core idea)
This asks the AI to dive deeper into a specific theme and bring out nuances or subgroups.
Prompt for specific topic: If you’re validating hunches—say, whether privacy concerns drive inactivity—ask:
Did anyone talk about privacy concerns? Include quotes.
Prompt for personas: Understanding who your inactive users are is gold. Use:
Based on 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: To get a quick summary of what frustrates users:
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: Don’t forget to explore what might bring people back—sometimes, the “reasons for inactivity” are also clues for re-engagement:
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.
Want more prompt ideas? Browse our guide to building great survey questions for inactive users.
How Specific analyzes qualitative survey data by question type
It’s one thing to get answers; it’s another to structure them for trustworthy analysis. One reason I lean on tools like Specific is the way it automatically adapts its analysis to the format of each survey question:
Open-ended questions with or without followups: For these, Specific’s AI gives you a single summary capturing all direct answers plus any clarifying or probing followups that were triggered. You end up with a focused overview—no matter how deep the followup chain goes.
Choices with followups: Each multiple choice answer (“I’m not using the product because...”) has its own summary aggregating any followup responses attached to that particular choice. This surface-level view plus drilldown makes it easy to move from big-picture trends to granular causes.
NPS scoring: If you’re running a Net Promoter Score (“How likely are you to recommend...?”) for inactive users, the analysis breaks things out for detractors, passives, and promoters—summarizing the “why” behind each group in easy-to-read batches. For more, check out how to set this up at our NPS survey builder for inactive users.
You can replicate this in ChatGPT; it’s just much more manual and requires multiple iterations and planful organization on your part.
Tackling challenges with AI’s context limit
AI models—even the most advanced—have context (memory) limits. If you’ve received lots of free-text responses from inactive users, you might find your data set is too large for the AI to handle at once.
Filtering: One neat solution is to filter conversations based on user replies—so the AI analyzes only those who responded to core questions like “why did you stop using our app?” or gave a certain answer. It keeps the focus tight and maximizes what fits into the AI’s context window.
Cropping: Alternatively, crop your survey down—send only selected questions into the AI for a pass. This reduces clutter and ensures the analysis focuses on the most relevant aspects of inactivity.
Specific makes both of these steps easy out of the box, letting you keep AI analysis crisp and useful as your data grows. If you want to customize filter combinations or crop workflow, explore how Specific handles advanced survey analysis.
Collaborative features for analyzing inactive users survey responses
Analyzing survey data with colleagues can be a minefield. When you’re trying to move fast and surface the most meaningful reasons for inactivity, it’s easy to end up with scattered notes, missed feedback, or overlapping analysis threads.
AI-powered chat analysis in Specific lets you analyze data by chatting directly with the AI. Anyone on your team can spin up a new chat to focus on a particular topic—say, reactivation ideas, pricing friction, or technical blockers. Each chat can have its own filters (“just look at users who churned after 3 months”), keeping your insights organized and targeted to your strategy.
Multi-chat support means it’s never a black box: you always know who is digging into what. Each chat shows who created it, and when other teammates join in, everyone’s avatar appears by their messages—so you can keep contributions clear, collaborate asynchronously, and follow up on analysis threads easily. This approach saves time, reduces confusion, and keeps a living record of your joint analysis.
If you want to see how Specific supports teamwork on survey data—or want to start from a solid survey foundation—read our how-to for building an inactive users survey. Or generate one from scratch with our AI survey maker and test collaboration flows live.
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