Traditional customer experience analysis tools often miss the nuances of post-support interactions. I want to share the best questions for post-support surveys—ones that actually capture meaningful insights from your customers.
These questions work for both post-support and post-purchase moments, helping teams uncover resolution quality, customer effort, sentiment, and root causes—the real “why” behind feedback.
I’ll also walk you through making these surveys multilingual and adding branching logic for deeper, more actionable conversations.
Questions that measure resolution quality
Resolution quality matters far more than simple speed. Moving fast means nothing if a customer leaves with issues unresolved or feels misunderstood. Considering 73% of consumers see experience as a key buying factor, nailing quality is what earns trust and drives loyalty. [1]
“Did our team fully resolve your issue today?” (Yes / No / Not Sure)
“What, if anything, could we have done better in this interaction?” (Open-ended, AI follow-up probes for specifics)
“How confident are you that this won’t happen again?” (Scale: Not at all confident – Extremely confident)
“Did you have to repeat yourself or re-explain your problem?” (Never / Once / More than once)
Example prompt for survey analysis:
Analyze which responses indicate unresolved issues or low confidence in the resolution. Summarize the most common reasons.
AI follow-up questions can gently dig for more detail about sticking points or confusion, clarifying how the fix met (or didn’t meet) expectations. Learn more about AI follow-up questions and how they push for richer insights in your post-support conversations.
First Contact Resolution: It matters whether a customer’s problem is solved on the first try. Ask: “Was your issue resolved in a single interaction or did you need to contact us again?” This taps into team effectiveness and identifies gaps that drive repeat contacts.
Problem Complexity Assessment: Some issues are tough—think of complex billing errors or technical bugs. Try: “How complicated did your issue feel to solve?” (Simple / Moderate / Complex). This tells you which fixes need more training or better resources.
Phrase these questions like you’d ask a friend: “Were we able to sort this out for you, or did it drag on?” or “How tricky did your problem feel from your perspective?” Conversational wording removes survey stiffness and encourages honest answers.
Measuring customer effort in support interactions
Effort is a real deal-breaker—people remember how much work it took to reach a solution. The Customer Effort Score (CES) reveals if your customers are fighting friction. Nearly $75 billion is lost every year due to poor customer experiences and unresolved effort issues. [2]
“How easy was it to get your issue resolved with us today?” (Scale: Very Difficult – Very Easy)
“What step took the most time or energy for you?” (Open-ended, AI can prompt for details about steps like waiting or repeating info)
“Did you have to switch channels (email, chat, phone) to get help?” (Yes / No, prompt: “Tell us more” if Yes)
Approach Comparison:
High Effort Indicators | Low Effort Indicators |
|---|---|
Multiple handoffs, repeats, waiting on replies, forced channel shifts | Issue solved in one go, proactive help, clear instructions, no repetition |
Let your AI know to dig for specific friction points: “If a customer mentions switching channels, ask what made them switch and what could have fixed it early.” When effort questions are phrased as part of a chat, people open up—check out what a conversational survey format does to effort scores versus forms.
Time Investment Questions: Always clarify: “Roughly how long did it take to get help from first contact to resolution?” (Minutes / Hours / Days). This quantifies frustration and helps set real targets for improvement.
Channel Switching Detection: Ask: “Did you have to reach out on more than one platform to get this sorted?” and follow up: “What made you switch channels?” The answers highlight gaps in process or cross-team alignment.
Example prompt for analyzing effort:
Summarize effort barriers mentioned by respondents, separating time, communication, and process frictions.
Sentiment questions that reveal true customer feelings
Satisfaction scores alone miss how people actually feel. You want real sentiment—the emotional feedback that drives loyalty or churn. 86% of leaders believe AI will transform the way we deliver customer experience, especially by analyzing open-ended feedback and tone. [3]
“How did this experience leave you feeling?” (Happy / Neutral / Frustrated / Disappointed / Relieved)
“On a scale of 0–10, how likely are you to recommend our support team to a friend?” (NPS for support, not overall)
“What one thing would have improved your mood after this interaction?” (Open-ended)
Use variations of NPS for context: “How likely are you to recommend our help team based on this specific support experience, not the product overall?” These tweaks surface real emotion.
I use the AI survey response analysis tool to dig deep into sentiment: you can chat with AI about keywords, emotions, or subtle trends you’d never spot in a spreadsheet.
Emotional Temperature Check: Phrase questions like “If you had to use one word to describe today’s experience, what would it be?” This gives honest gut reactions—no sugarcoating.
Likelihood to Recommend Support: Be specific: “If a friend had the same issue, would you tell them they’ll be taken care of by our team?” This connects resolution to advocacy.
AI can even shift its tone based on negative emotional cues, responding empathetically or providing a recovery prompt instead of generic thanks.
Example prompt for sentiment analysis:
Highlight the most frequent emotional themes, classifying responses as positive, neutral, or negative. Identify any outlier emotions.
Root cause questions that drive improvement
Standard surveys rarely uncover the true reasons behind problems. Root cause questions expose repeat patterns and process breakdowns, focusing your improvements on what actually matters. Teams that use analytics to find root causes grow 4–8% faster than their peers, showing how powerful this approach can be. [4]
“Was there anything in our process that made your problem harder to fix?” (Open-ended, AI prompts for specific steps, delays)
“Did we meet your expectations for how your support request should be handled?” (Yes / No, prompt: “Where did we miss?” if No)
“If this issue could have been prevented, how?” (Open-ended, nudging for suggestions)
“Did you have to work around our process to get what you needed?” (Yes / No, follow-up probes for details)
Surface Issues | Root Causes |
|---|---|
Slow replies, missing info, vague instructions | Broken handover, unclear ownership, gaps in support training |
Mold your AI logic to follow up where there’s ambiguity, but without feeling pushy. Looking for patterns? In your analytics, spot repeated phrases like “had to follow up twice” or “confusing login”—that’s where action starts.
Process Breakdown Questions: Use: “Was there any step in our process you found unnecessary or confusing?” This goes straight to operational inefficiency.
Expectation Gap Analysis: Try: “How did your actual support experience compare to what you thought it would be?” It’s a goldmine for product-marketing and support teams alike.
Preventability Assessment: Always include: “Do you think this issue could have been avoided? What could we have done differently?” Answers fuel both quick wins and roadmap priorities.
Multilingual surveys and intelligent branching
Serving a global customer base? Multilingual support isn’t just nice—it’s expected. With Specific, surveys are automatically translated and respondents can answer in their preferred language, boosting completion rates and data quality.
Branching logic optimizes your NPS or satisfaction follow-ups: deliver unique flows to promoters, passives, and detractors. This way, every respondent gets a survey path that fits their experience. You can fine-tune everything in the AI survey editor, using chat-style commands for instant tweaks.
Language Auto-Detection: “When you enable this, the survey welcomes each user in their app or browser language—no manual setup needed.”
Promoter Follow-ups: For high scores, ask: “Would you be willing to share your experience, or participate in a testimonial?” Or dig deeper: “What made this interaction stand out?”
Detractor Recovery: Show empathy: “I’m sorry we missed the mark. What would have made this right?” or “If you have a minute, could you share two things to improve?” These aren’t just standard “sorry”—they’re a chance for direct recovery.
Example branching configuration:
If NPS is 9–10: thank, probe for highlights, invite to refer.
If NPS is 7–8: ask what would make the experience excellent.
If NPS is 0–6: apologize, prompt for specifics, offer a recovery action.
Putting it all together: your post-support survey strategy
The most effective post-support surveys combine these questions in a conversational, AI-driven flow—making feedback frictionless. I’ve found that launching directly after the support interaction (within 30 minutes to an hour) gets the highest response quality. The sweet spot is five to seven questions, blending open-ended and structured formats for nuance without fatigue. Conversational formats help, too—users are far more likely to finish a chat-style survey than a stiff form. [1]
Resolution quality (Did we solve your issue?)
Customer effort (How easy, how many steps?)
Sentiment (Emotions, NPS variations)
Root cause (Process, expectation, preventability)
To build your own, use the AI survey generator to create surveys in just a few minutes, customizing for audience, language, and branching needs.
Timing Strategy: Trigger surveys when the experience is still fresh, but after a clear resolution confirmation.
Question Sequencing: Start broad (Did we fix it?), go deeper (How hard was it?), then capture emotion and ideas for improvement, ending with a ‘thank you’ or next steps based on experience.
Example flow:
1. Was your issue completely resolved today?
2. How easy was it to get help?
3. Did you have to re-explain your issue or switch channels?
4. On a scale from 0–10, how likely are you to recommend our support?
5. How did you feel after this interaction?
6. Is there anything that could have made this better?
Create your own survey now and start capturing real, actionable feedback. With conversational surveys, you’ll see higher engagement and deeper insights—giving you the clarity to drive meaningful change with every support