This article will give you tips on how to analyze responses/data from Inactive Users survey about Barriers To Returning. Read on to learn the right strategies, tools, and prompts to uncover why inactive users don’t come back.
Choosing the right tools for survey response analysis
The method and tools you choose for analyzing Inactive Users Barriers To Returning survey responses really depend on your data’s structure.
Quantitative data: If you’re looking at simple counts—like how many respondents chose specific barriers or rated a process as difficult—classic tools like Excel or Google Sheets work perfectly for summarizing numbers, charts, and simple stats.
Qualitative data: When your survey includes open-ended questions (“What prevented you from returning?” or conversational follow-ups), manual reading quickly becomes impossible as the response volume grows. This is where AI tools are absolutely essential—they’ll sift through text responses and discover hidden trends you might miss by eye. AI-driven analysis is especially important when you want to understand nuanced motivations, pain points, or sentiment behind users’ words.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
ChatGPT offers a versatile—but not always convenient—way to analyze open-text survey data. Export your responses (usually as CSV), copy-paste them into a ChatGPT session, and start chatting about what comes up. This approach works for smaller data sets or sample analyses, but it quickly gets messy as response counts climb or when you need to pivot between multiple questions or respondent segments. Copying, cleaning, and maintaining response privacy add extra hurdles.
AI can provide great summaries or extract core themes—but you’ll need to handle cleaning, structuring, and sometimes splitting response sets yourself. For anything but the smallest samples, you’ll spend more time wrangling the data than talking about it.
All-in-one tool like Specific
Specific is designed for conversational, AI-powered analysis from end-to-end, purpose-built for working with qualitative survey data—including Inactive Users about Barriers To Returning.
You can create, distribute, and analyze a survey—all in one place, with the real advantage that the same AI both collects and analyzes responses.
The power of automatic follow-up: When respondents answer, Specific’s AI asks clarifying or probing follow-up questions in real time. This leads to much richer, deeper answers than traditional forms. Learn more about automated follow-ups here.
AI-powered analysis is instant: Results are summarized automatically—Specific will pull out key themes, pain points, and even underlying motivations among inactive users, without you lifting a finger. You can chat with the AI about your results, just like you would with ChatGPT, except you’ll have specialized features for managing context and focusing on what matters. See more in the AI survey response analysis feature.
All this means you avoid hours wrangling data and move straight to insight—perfect if you need fast, actionable answers about why users are disengaged.
Keep in mind: industry data shows that complex processes and lack of perceived value are leading reasons for user drop-off. 30% of applicants will abandon a process if it’s complicated—so tools that help surface this feedback can drive measurable change in your retention strategies [1].
Useful prompts that you can use to analyze Inactive Users Barriers To Returning survey
Having the right prompts is as important as the right tool. Here are several prompts that work well with both ChatGPT and Specific for tackling barriers to returning:
Prompt for core ideas: Use this to identify what’s really driving inactive users’ behavior in just a few lines.
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 always performs better with clear context about your survey or business. For example, add background like this:
You’re a product researcher exploring why previously active users have dropped off and not returned. This survey focuses on what stopped them from coming back, including product usability, perceived value, and support issues. My goal is to prioritize which barriers to address to lift reactivation rates.
Dive deeper on specific topics: If you spot a trend, try this prompt:
Tell me more about [core idea, e.g., “complex application process”]
Direct validation of themes: Sometimes you just want to check if users mentioned a topic:
Did anyone talk about [security concerns]? Include quotes.
Prompt for pain points and challenges: Useful for surfacing concrete reasons for users dropping off. This is especially valuable considering that unresolved issues and pain points are cited as key factors driving disengagement [2].
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: Helps you see if any segment of Inactive Users could be won back (“what would make you return?”):
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: Useful when you want to split broader opinions into emotional “buckets”—positive, negative, neutral. Negative experiences (e.g. poor customer support) are especially common among inactive users [2].
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 & opportunities: Users may drop off because products or services aren’t meeting certain needs. Since research has shown inactive participants often have significantly more unmet needs (such as for financial assistance or support) [3], this prompt is crucial:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For even more prompts and ideas tailored to Inactive Users barriers surveys, check our guide to survey questions and prompts.
How Specific analyzes qualitative responses by question type
Specific processes different types of survey questions in a way that helps you get clarity much faster:
Open-ended questions (with or without follow-ups): The AI gives you a summary combining all responses, alongside deep dives for each follow-up question. For example, if a user says “the checkout was confusing,” and then the AI asks why, both the initial reason and follow-up explanation are captured and summarized.
Choices with follow-ups: Each selected choice gets its own summary of follow-up responses. So if someone selects “security concerns” and leaves a comment, you can see a focused analysis just for that segment.
NPS surveys: By grouping users into detractors, passives, and promoters, Specific provides a summary of follow-up responses for each segment—crucial for comparing different types of inactive users.
If you’re using ChatGPT for this, you can achieve the same end results—you just need to segment your data, manage context switches, and copy-paste specific batches of answers for each question type manually.
For a breakdown on how to set up these question types in your own survey, check out our guide to creating Inactive Users surveys about Barriers To Returning.
How to tackle context size limits when analyzing survey data with AI
One technical hurdle with AI analysis: most large language models—including ChatGPT and AI tools like Specific—have a context limit. If you have hundreds of survey responses, you might run into a wall where the tool can’t “see” your whole dataset at once.
There are two main ways to solve this (and Specific offers both out of the box):
Filtering by replies: Filter conversations so that only those who replied to a selected question—or who chose specific answers—get analyzed. This keeps the sample focused and under context limits.
Cropping questions for AI analysis: You can choose which questions are sent to the AI—avoiding overload and making sure only the most relevant parts of your survey are included in each analysis. This makes it possible to scale analysis to large response sets while staying within model constraints.
Pro tip: If segmenting your survey data, keep copies of the raw exports and create subsets by filtering based on key variables (like last active date, type of barrier cited, or user persona).
Collaborative features for analyzing inactive users survey responses
The reality: analyzing Inactive Users barriers surveys is rarely a solo endeavor—it’s often product teams, support, marketing, and execs who want a say.
Chat-driven collaboration: With Specific, you can analyze data simply by chatting with the AI. Each chat session serves as its own “analysis thread,” allowing teammates to explore different questions about the data without interfering with each other’s work.
Multiple chats for parallel analysis: You can create focused chats for different themes—like “payment friction,” “support requests,” or “feature requests.” Each chat can have its own filters applied, and displays who created it—making collaboration and ownership clear at a glance. This also prevents confusion and makes cross-team work far more seamless.
See who said what, in context: Whenever you or a coworker drops a message in a chat, you’ll see their avatar and name. This small touch goes a long way for keeping team context and accountability front and center.
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