This article will give you tips on how to analyze responses from an inactive users survey about feature awareness. If you want actionable insights, don’t get stuck in spreadsheets or surface-level stats—let’s go deeper.
Choosing the right tools for analysis
Your approach to analyzing responses depends entirely on the kind of data you get. Here’s how I break down the tooling options:
Quantitative data: When questions are closed (yes/no, ratings, multiple choices), Excel or Google Sheets do the trick fast. These tools quickly count how many inactive users selected each option or rated a feature.
Qualitative data: But when open-ended responses and follow-ups start piling up, reading each by hand is overwhelming. And let’s face it: it’s impossible past a few dozen replies. That’s where AI-powered tools come in. They digest massive text blocks, spot hidden patterns, and find voices worth listening to.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can export inactive user survey responses to a spreadsheet, copy them, and paste them into ChatGPT (or another large language model). Then, just chat about the data and ask for summaries or topic extraction.
This gets you started quickly—but it’s not very convenient. You’ll juggle spreadsheets, try to fit all your raw data into the AI’s context window, and repeat manual steps for every analysis round. Also, you don’t get structured summaries, respondent filters, or an easy way to keep different survey sessions organized. And, with really large sets of responses, you’ll hit context size limits fast.
All-in-one tool like Specific
Specific is built for capturing and analyzing qualitative survey data from the ground up. It collects responses using AI-powered, conversational surveys that automatically ask smart follow-up questions—so feature awareness insights from inactive users are far richer than what old-school forms can provide.
AI-Powered Analysis: Once you’ve collected the data, Specific instantly summarizes all the responses. It identifies key themes, ranks what matters most to users, and turns conversations into actionable insight without any spreadsheet work. You can chat directly with the AI to ask questions, just like with ChatGPT, but everything is already filtered and organized for you.
Extra Features: You control which questions to analyze, filter by respondent type, and manage what data goes into the AI context. These workflows let you dig deep without manual copy-paste pain. Learn more about AI survey response analysis with Specific, and if you’re ready to create a survey, the AI survey generator for inactive users is just a chat away.
Useful prompts that you can use for analyzing feature awareness feedback from inactive users
Prompts make or break your feature awareness survey analysis—especially when you’re chatting with AI. Here are some of the most productive ones I reach for:
Prompt for core ideas (theme extraction)
Use this to spot the main topics your inactive users talk about most, especially which features confuse them or stay undiscovered. This is Specific’s default analysis prompt, and it also works well in ChatGPT:
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
Give AI more context for deeper analysis: Always tell AI what your survey is about and what your goal is (e.g., “I want to know why inactive users don’t use our analytics dashboard”). This helps it frame results properly. Example:
Survey context: We’re asking inactive users about their awareness and usage of core product features in our platform. Main goal: Understand which features are not being used or noticed, and why.
Dive into specifics with follow-up prompts: After you have your core ideas, ask:
Tell me more about “XYZ (core idea)”
Validate if specific topics come up:
Did anyone talk about XYZ? Include quotes.
Prompt for personas: If you want to zoom in on clusters of similar users, try:
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:
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: Understand the mood of your inactive user base:
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 and ideas:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
For even more prompt inspiration, check out this guide to the best questions for an inactive users feature awareness survey.
How Specific analyzes by question type
Specific adapts its analysis workflow depending on the type of survey question, making it painless to work with both structured and unstructured responses from inactive users:
Open-ended questions with or without follow-ups: You get a summary that highlights the main themes across everyone’s answers, including insights from AI-driven follow-ups about feature awareness or confusion.
Choices with follow-ups: Each choice gets its own summary, so you instantly see what respondents who picked that option said about the feature—perfect for zeroing in on why users ignore or misunderstand certain features.
NPS: Detailed AI summaries for promoters, passives, and detractors let you dig into feedback based on users’ sentiment towards your product. If you need a ready-to-use NPS survey format, the NPS survey creator for inactive users can spin one up in seconds.
You can do the same sort of analyses by prompting ChatGPT manually, but it’s more work—lots of copy-paste, and you’ll need to split data by question type or response filter before analyzing.
How to tackle AI context size limits in survey analysis
Modern AIs like GPT have input “context size” limits, so if you have a huge pile of responses, not all can fit at once. This is especially real when surveying lots of inactive users or running deep feature awareness studies. In Specific, these problems are solved with two strategies:
Filtering: Only include conversations where users replied to a selected question or picked an important answer. For inactive users, that could mean filtering for only those who answered a feature awareness open-ended question or who rated a certain feature as never used.
Cropping: Send only selected questions into the AI analysis. Maybe you want to focus just on “Why didn’t you use Feature X?” and exclude others to maximize your context budget.
Both approaches let AI focus on what matters—no wasted context on side chatter or less relevant answers.
Collaborative features for analyzing inactive users survey responses
Getting to insights from a feature awareness survey on inactive users is a team sport. Different teammates might want to ask AI their own follow-up questions or slice the data in unique ways. Collaboration pain points can emerge—who did what, where’s that summary, which filters are active?
Multiple AI chats make teamwork a breeze. In Specific, you can spin up several chats on your survey data at the same time, each with custom filters or focus—maybe one chat is digging into missing feature usage, another is about upgrade blockers, and a third is on UI discoverability. You always know which team member started a chat and what filters are applied.
Transparency through avatars. Every message in a chat shows who sent it, making it much easier to coordinate, follow conversations, and document your findings for stakeholders.
No more clunky exports or fragmented analysis docs. The entire analysis history, summaries, and ongoing questions all live in one place—so you’re set up for faster, deeper, and more collaborative insight generation. For an easy starting point, you can use the guided survey generator for inactive users feature awareness or build your own in Survey Generator.
Create your inactive users survey about feature awareness now
Turn hidden feedback into clear insights—start your AI-powered survey, collect richer user stories, and get summaries with zero busy work. AI follow-ups, flexible analysis, and collaborative workspaces make it effortless to learn what really matters.