Learning how to analyze survey data becomes much simpler when you ask the right questions from the start, especially when measuring onboarding friction. Getting to the heart of user struggles during onboarding means zeroing in on the moments where people get stuck, confused, or turn back.
Great questions for onboarding friction help uncover where users stumble, what confuses them, and why they might abandon the process. These insights transform onboarding optimization from guesswork into a targeted, actionable project.
Let’s walk through the best types of questions to target friction, and dig into how AI-powered follow-ups can clarify and amplify what your users are really telling you.
Understanding onboarding friction through strategic questions
Onboarding friction happens whenever users hit obstacles, confusion, or lack vital information the very first time they experience your product or service. Traditional surveys—checkboxes and long forms—often miss these nuanced “pain point” moments because there’s no chance for real back-and-forth. If an answer seems vague or incomplete, you’re stuck guessing why.
That’s where conversational surveys with AI-powered follow-ups shine. Each response kicks off real-time probing, gently nudging users to explain the “why” behind what blocked them, in their own words. This uncovers details no static form can reach.
First-run obstacles. These are the things that flat out stop users before they even get going: an app that won’t load, unclear hardware or permissions requirements, login issues, or confusing setup steps.
Unclear steps. These are the classic hotbeds of confusion. Maybe users finish one step and aren’t told what’s next, or they get lost in a maze of navigation, unsure of how to move forward. Clarity gaps here often lead to serious drop off.
Missing information. These moments leave users guessing—either about what a key feature actually does, or how to complete a process. It might be unclear documentation, zero context around why something matters, or a lack of relatable examples.
Conversational surveys built around these friction types capture sharper, actionable insight than traditional forms—and generate far higher engagement. In fact, AI-driven surveys see completion rates of 70-80%, compared to only 45-50% for traditional forms, while cutting abandonment rates in half. [1]
Questions to uncover first-run obstacles
First-run obstacles prevent users from taking any real first step. By asking the right open-ended questions, you reveal all sorts of blockers that may never be mentioned in a standard survey.
Initial setup difficulties: This question gets to the root of the earliest blockers, whether technical or simply overwhelming.
What, if anything, made the initial setup or installation harder than you expected?
Technical requirements or prerequisites: If certain specs or permissions are needed, users often run into silent roadblocks here.
Were there any technical requirements (software, permissions, hardware) that surprised you or caused issues?
Account creation friction: The signup process is often full of hidden hurdles that lead to abandonment.
Did you have any trouble setting up your account or logging in for the first time? Tell us what happened.
AI follow-ups dig deeper, asking clarifying prompts like “What specifically made that difficult?” or “How long did it take you to resolve this?”—allowing you to separate systemic flaws from one-off incidents. Learn more about this approach on our AI follow-up questions page.
Identifying unclear steps and confusing moments
Even highly motivated users will quit in frustration if the next step isn’t clear enough. These are moments brimming with hesitation, second-guessing, and silent abandonment. To catch them, focus your survey both on navigation and on cases where instructions just didn’t land.
Confusing terminology or jargon: Sometimes, internal language slips into onboarding—and users are lost from the get-go.
Were there any words, phrases, or instructions during onboarding that you didn’t understand?
Unclear navigation or workflow: Step-by-step confusion can be subtle, so it pays to invite detail here.
At what point did you find yourself unsure about what to do next? What was happening at that moment?
Unexpected behavior or results: When the product doesn’t do what someone thought, friction is guaranteed.
Did anything behave differently from what you expected as you moved through onboarding? Describe what you thought would happen.
With an AI survey builder, follow-ups can ask, “Which specific terms were confusing?” or “What did you expect to happen instead?” This approach makes the experience feel like a real conversation. For a deeper read on this, check out resources on designing AI-powered conversational surveys.
It’s also worth noting that users stick around for these kinds of dynamic, interactive surveys: AI surveys manage abandonment rates of just 15-25%, compared to up to 55% for older, static methods. [1]
Discovering missing information and context gaps
Often, onboarding fails not because of what’s on the page, but what isn’t. Missing guidance, lack of “why,” or no hands-on examples leave users stranded, unsure about why they should care or how to get started.
Missing guidance or documentation: Ask about the moments where nothing was explained, or a detail felt skipped.
Was there any part of the process where you wished you had more guidance, hints, or a help article?
Unclear value propositions: If users don’t “get” the benefit, they won’t push past friction.
During onboarding, was it always clear why each step or feature mattered? When wasn’t it?
Missing examples or templates: Many users want to see a template or demo they can copy, instead of starting from scratch.
Did you wish you had a real example, template, or walkthrough at any point? Describe where and why.
If you’re creating a comprehensive onboarding survey, leverage an AI survey generator to inject dynamic, probing follow-up questions, such as “Can you share a specific place where examples would have helped?” AI allows you to drill into user anecdotes, surfacing guidance or sample content you might not realize is missing. And, thanks to AI-powered surveys, organizations have reported a 70% reduction in time spent analyzing open-ended feedback. [2]
Measuring onboarding success and emotional journey
Understanding user definitions of “success” is just as important as fixing friction points. But sometimes, the most valuable feedback is about feelings: confusion, frustration, relief, or confidence that data points alone might mask.
Defining successful onboarding completion: Invite users to describe what “done” looks like for them—sometimes, it’s different from your internal checklist.
How did you know when you had “successfully” completed onboarding?
Emotional state at different stages: Ask for check-ins on their emotional journey. Peaks of anxiety or confusion point directly to prime friction spots.
At which points during onboarding did you feel the most confused, frustrated, or relieved? What made you feel that way?
Confidence after onboarding: Ultimately, a confident user is a retained user.
How confident did you feel using the product right after onboarding was over? Why?
Here’s how Conversational AI surveys stack up against old survey forms in surfacing these insights:
Traditional surveys | Conversational surveys with AI |
|---|---|
Set “one and done” questions; limited context for follow-up | Prompt real-time clarification & deeper exploration |
Higher abandonment, lower completion | Engaging, higher completion and data quality |
Manual data coding & slower insights | Automated theme extraction, chat-based analysis |
Often, your most actionable improvement opportunities are hiding in users’ emotional responses, not their ratings or multiple-choice clicks. Don’t miss those signals—emotions point straight to what needs fixing next.
Turning onboarding feedback into actionable insights
It’s not enough to collect raw responses—you have to turn those stories into a clear action plan. The problem? If you’re handling dozens or hundreds of qualitative answers, manual analysis gets overwhelming, fast.
This is where AI-powered survey analysis shines. You can surface recurring blockers, spot key themes by user segment, and instantly highlight the highest-impact friction points from across your onboarding journey. Plus, you can chat directly with your survey data: Ask, “What are the top three blockers for new users?” or “Which cohorts have the hardest time with setup?” to hunt for patterns.
Pilot your fixes by prioritizing what users mention most often and what seems to cause the most frustration or delays. If you’re not running these kinds of onboarding friction surveys, you’re missing out on the “why” behind abandoned onboarding—and losing countless users who might have stayed if you’d only listened deeper.
Implementing your onboarding friction survey
The right questions don’t just help you spot issues—they unlock a whole new level of onboarding optimization. The timing of your survey matters: run it right after onboarding, while every moment is still fresh in users’ minds.
For the highest-quality insights, deliver surveys right inside your product using conversational in-product surveys at the perfect moment, or follow up via email with a shareable conversational survey page. Conversational surveys keep users talking, reduce drop-off, and surface details invisible to forms.
Don’t settle for old-school static feedback forms. Start identifying and fixing onboarding friction with a dynamic, AI-powered conversational survey—create your own survey and put these insights into action today.