A well-crafted user experience survey can reveal exactly where your customer support excels and where it frustrates users.
We'll explore specific questions that uncover support pain points and demonstrate how to extract actionable insights from responses—especially around resolution quality and self-serve gaps.
Questions that reveal support resolution quality
When users reach out to customer support, what matters most isn’t just speed—it’s the actual resolution quality. While fast replies are great, customers won’t care if the core problem is still lingering. In fact, a massive 90% of consumers believe support should offer accurate resolutions and well-informed reps, not just fast answers. [1]
So, what should we ask to measure whether users walk away from support happier (or not)? Let's focus on questions that get to the heart of issue resolution:
First-contact resolution — Example: “Was your issue fully resolved in your first interaction with support?”
This cuts straight to the pain: users expect to avoid repeat contacts. If most users say no, your team may be rushing answers or lacking the authority to solve issues fast.Solution effectiveness — Example: “How well did the provided solution actually solve your problem?”
This probes for surface fixes vs. deep solutions. It’s easy to say “your ticket is closed,” but if users are still blocked, their experience suffers. Use a follow-up to ask why the fix didn’t help.Clarity of explanation — Example: “How clear and understandable was the support response you received?”
A technically correct answer is useless if users walk away even more confused. Clarity = confidence.Follow-up willingness — Example: “If your issue wasn’t resolved, what would have made it easier for you?”
Invite open feedback; sometimes the best insights are in user suggestions.
When negative experiences arise, follow-up questions open the door to understanding what went wrong. Most survey forms miss this step—but with automatic AI follow-up questions, your survey can instantly dig for context, clarifying the real obstacles users face. That’s the kind of probing a human interviewer might do—without the scheduling hassles.
Uncovering self-serve gaps in your support experience
Sometimes, users end up contacting support just because they can’t find answers on their own—and that’s a sign of a self-serve gap. Each unnecessary ticket is not just a cost, but also an opportunity to improve your knowledge base, guide flows, or feature discoverability.
Here are effective questions to uncover those gaps and reduce support load:
Good practice | Bad practice |
|---|---|
“What information were you looking for that you couldn't find on your own?” | “Was the help center helpful?” (yes/no) |
“Before contacting support, where did you look for help?” | “Did you try searching our documentation?” (yes/no) |
Follow up: “How would you improve our self-serve options?” | No follow-up or generic “Any comments?” |
Documentation gaps — Example: “What information were you looking for that you couldn't find on your own?” This gets users to pinpoint where your documentation, onboarding, or in-app tips fail to answer their real-world needs.
Feature discovery — Example: “Before contacting support, where did you look for help?” By studying these answers, you’ll spot when users need more in-context help, or when they're not aware features exist at all.
Well-structured conversational surveys don’t stop at a dead-end “yes/no.” When they sense confusion, they ask real follow-ups. For example, after a user shares why they couldn’t self-serve, your survey can gently probe for specifics—just like a considerate human would.
Transform support feedback into actionable insights
Manual analysis of open-ended support feedback is tedious—and, let’s be honest, most of us miss important patterns. This is where AI analysis shines: it finds recurring themes and actionable signals in a flood of responses, making strategy far more data-driven. In 2022, 76% of executives emphasized that customer feedback should inform every customer decision. [2]
Using Specific’s analysis chat, you can chat directly with your survey data and generate insights instantly. Here are concrete ways to use it:
Identifying recurring support themes
Quantifying the impact of specific issues on satisfaction
Segmenting feedback by type of user, region, or issue
For each use case, here’s a prompt you can copy-paste into Specific’s analysis chat:
Example 1: Identifying recurring support themes
What are the top three recurring issues users mention as outstanding or poorly resolved in support survey responses?
This quickly surfaces systemic pain points—perfect for quarterly reviews.
Example 2: Quantifying the impact of specific issues
How many users who mentioned documentation gaps also reported that their issue wasn’t fully resolved? What percent of all responses is this?
Now you can spot which issues are mere annoyances versus true dealbreakers—and prioritize accordingly.
Example 3: Segmenting feedback
Segment all support survey feedback by subscription tier. Are there resolution quality differences between free and paid users?
This is invaluable for deciding where to focus training, or whether to offer premium support tiers.
Specific’s AI survey response analysis tools let you run these investigations in minutes—no coding, dashboards, or exports required.
Support surveys as relationship builders, not just data collectors
Too many teams see support surveys as a box to tick or a data grab. But when you use conversational surveys, you actually foster better user relationships—showing you care about their experience, not just their stats. It’s no wonder that in 2025, 82% of customers said the agent’s demeanor and approach is critical to the support experience. [3]
Done right, surveys with a conversational tone make users feel heard and respected, not interrogated. You can create sharable survey pages or install in-product surveys where it fits the user journey best.
Timing matters — Send post-support surveys right after the issue is closed—while details are fresh, but emotions have settled. This timing gives you candid, high-quality insights and ensures you’re not asking users at random, frustrating moments.
Tone and empathy — Using a conversational approach ensures users answer more thoughtfully and honestly. They feel like they're talking to a skilled interviewer, not clicking through a rigid form. Add a personal follow-up (“Thanks for your feedback! If you’d like, tell us more…”)—it signals you’re listening and value their voice.
When you use Specific’s conversational surveys, the entire experience is smooth and engaging—for both creators and respondents. Personalized follow-ups and a friendly tone build trust, helping you stand out in a world of forgettable forms.
Start improving your support experience today
Running genuine support UX surveys is one of the best moves you can make. You’ll catch resolution gaps, spot self-serve opportunities, and discover hidden drivers behind churn or delight. If you’re not running these, you’re missing out on direct, actionable insights and a chance to forge stronger bonds with every user who needs help.
Turn your support operation from a cost center into a true competitive advantage—create your own survey with a conversational approach that users will actually want to answer.