This article will give you tips on how to analyze responses from an Inactive Users survey about Onboarding Experience using AI and modern survey analysis strategies.
Choosing the right tools for survey response analysis
The way you analyze survey data depends on the type and structure of your responses. Here’s a quick breakdown of what to consider:
Quantitative data: Numbers (like how many users picked a certain answer) are straightforward to tally and visualize. I usually turn to familiar tools like Excel or Google Sheets—they’re tried-and-true for filtering and quick counts.
Qualitative data: Open-text answers, stories, and those verbose “why did you…” follow-ups are a different beast. Reading every response yourself? Not scalable—especially if you want to go deep and find hidden gems. For this, you need AI-powered analysis tools built for text. These tools help decode meaning at scale and spot common threads that might otherwise go unnoticed.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Quick and flexible: If you export your responses, you can copy big chunks into ChatGPT and start a back-and-forth conversation about your data. This is handy for small batches or when you want to riff on a prompt to see what you get.
Not ideal for scale: Things get clunky when you’re working with big datasets or need to track context across many different questions. You might lose structure, plus it’s easy to misplace context or hit AI context size limits. There’s also manual prep work: organizing, formatting, and pasting everything for every new analysis angle.
All-in-one tool like Specific
Purpose-built and seamless: With Specific, everything happens in one place. You set up your conversational AI survey, collect responses (with the magic of real-time AI follow-up questions that dig for clarity and detail), and instantly analyze your data with AI. No copy-pasting spreadsheets, no manual cleanup.
Instant, actionable insights: Specific’s AI-powered tools find trends, core ideas, sentiment, and themes—turning raw answers into clear summaries. It surfaces what matters most for Inactive Users or onboarding drop-offs, without hours of sifting.
Conversational data exploration: You can chat with AI about your survey results, using natural language. Dive deep, apply filters on-the-fly, and even check how context is managed as you refine your analysis. Tools like AI follow-up questions elevate the quality of what you’re working with right from the start.
Useful prompts that you can use to analyze Inactive Users onboarding survey data
Crafting the right prompts is how you unlock real value from AI survey analysis—especially for onboarding data from Inactive Users. Here are my go-to approaches:
Prompt for core ideas: This is a universal starting point for surfacing big-picture themes from survey responses. Specific uses this same pattern under the hood, but it works anywhere—drop it into ChatGPT, Claude, or your favorite AI:
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
Prompt with rich context performs better: Always give your AI the background. Include your survey’s aim, who answered, and what your main goal is. That way, AI will provide sharper, more relevant output. Here’s a context-boosting example:
Analyze the survey responses from inactive users to identify common themes related to their onboarding experience. Focus on areas where users expressed dissatisfaction or confusion.
Prompt for deeper exploration: Once you’ve got a theme, ask the AI to expand. Try: Tell me more about [core idea]. This drills into specifics, using your data as the source.
Prompt for validation of topics: Want to check for specific issues or hunches (like “friction with step two,” or “no value seen in trial”)? Use this classic prompt:
Did anyone talk about [specific issue]? Include quotes.
Prompt for pain points and challenges: Zero in on barriers and reasons for churn:
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 suggestions and ideas: Learn from what users wish was different:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompts like these bring consistency to your analysis and help you turn raw responses into structured, actionable findings. If you want more inspiration, check out our list of best questions to ask inactive users about onboarding experience.
How Specific analyzes qualitative data by question type
One area where Specific really levels up analysis is by tailoring summaries based on your question types. Here’s how it handles the common survey structures:
Open-ended questions (with or without follow-ups): Every free-text response, plus all the nuanced clarifications and AI-generated follow-ups, are summarized together. This means you get a true sense of context and depth.
Choices with follow-ups: Each answer option—from “Skipped onboarding” to “Too confusing”—gets its own batch of summarized follow-up responses. That lets you compare motivations and pain points segment by segment.
NPS: Detractors, passives, and promoters each get a separately analyzed summary of their follow-up answers, revealing what each group thinks and why.
You can do something similar in ChatGPT, but honestly, the sorting and prepping is far more labor-intensive without a purpose-built tool.
If you want to experiment directly, launch a ready-made survey using our AI survey generator preset for inactive users onboarding experience, or check out a step-by-step guide to creating one.
Dealing with AI context limits in large surveys
Every AI has a fixed context size—the maximum amount of data it can process at once. If your survey racks up 200+ rich dialogues, you’ll run into this limit. Here’s how I tackle it (and how Specific automates it for you):
Filtering: Want to stay focused? Filter so AI only looks at responses from those who answered a specific question or picked a certain option. Smaller, targeted sample = easier analysis and more thoughtful AI output. This is built in inside Specific, but you can simulate it by manually curating your dataset elsewhere.
Cropping: Sometimes it’s about question depth, not breadth. Crop your dataset so only the most relevant questions (or sections) are included for each AI run. This means the AI has enough headroom to go deep, not wide.
With these tricks, you sidestep context constraints and ensure you’re still getting robust AI summaries, even as your survey grows.
Not sure what survey format to start with? Try the in-app AI survey generator, or spin up an NPS survey for inactive users onboarding in a few clicks.
Collaborative features for analyzing inactive users survey responses
Let’s face it: team-based survey analysis usually falls apart with endless spreadsheets, lost emails, and half-baked threads. With inactive users and onboarding feedback, you want everyone—product, CX, research, and support—on the same page.
Effortless team chat analysis: In Specific, you analyze survey responses by simply chatting with AI. There’s no need to copy files or wrangle “latest” versions. Each teammate can open their own thread, try out prompts, or explore the dataset from their angle using dedicated chats.
Multiple focused chats, visible contributors: Each chat can have unique filters, letting you break down analysis by user cohort, product area, or time period. You always see who created each chat, which streamlines both collaboration and audits. Teams can easily trace what’s been discussed and by whom.
Crystal clear collaboration: Inside any collaborative AI chat, every message carries the sender’s avatar. It’s obvious who raised which point, which matters when sharing notes across product managers, UX researchers, or executive reviewers. The chat format makes asynchronous deep dives (even days later) as easy as a group conversation.
Want to tune your survey before sending? The AI survey editor lets you edit and optimize by chatting with AI, so you’re always collaborating at the design stage as well.
Create your inactive users survey about onboarding experience now
Get actionable insights into onboarding drop-off: create a targeted survey and use AI-driven analysis to spot opportunities and pain points in minutes—not days.