The best NPS survey questions after customer support interactions reveal whether your team truly resolved issues and strengthened customer relationships. By timing NPS survey questions directly after a ticket closes, you get to the heart of how well your support experience meets—and often exceeds—expectations.
These targeted post-support surveys let customers share feedback while the experience is still fresh. The smartest questions capture both immediate satisfaction with support and signal long-term loyalty. It’s why so many brands deploy conversational post-support surveys to dig into the real drivers of customer delight or frustration.
Core NPS questions for post-support feedback
When you tailor NPS surveys for post-support scenarios, you move beyond generic loyalty metrics into actionable feedback. Here are the essential NPS questions to ask after a support ticket closes:
“Based on your recent support experience, how likely are you to recommend us to a friend or colleague?”
This standard NPS question, reframed with reference to support, zeroes in on immediate satisfaction and overall loyalty. You’ll understand if your support turned a problem into a positive impression.“Did our support team fully resolve your issue today?”
This direct question clarifies whether resolution was achieved—a critical driver of NPS in a support context.“What could we have done to make your support experience even better?”
An open-ended prompt that unlocks detailed customer stories, revealing how processes or agent actions can improve in the future.“Was there anything about your support experience that surprised you—good or bad?”
This encourages the customer to share moments of delight or frustration, going beyond the root cause of their score.
NPS is a powerful loyalty metric. Backing this up, NPS surveys are a widely-used metric that measures customer loyalty, helping businesses assess overall customer sentiment and predict future growth [1]. But for support teams, it’s these focused questions—especially when embedded in conversational surveys—that probe deeper and draw out the actionable insights.
Generic NPS Question | Post-Support NPS Question |
|---|---|
How likely are you to recommend our company? | Based on your most recent support interaction, how likely are you to recommend us? |
What is the primary reason for your score? | What is the primary reason for your score regarding our support? |
What could we do to improve? | How could we improve your next support experience? |
Traditional surveys can miss nuance, but AI-driven conversational survey tools dig beneath the surface. By asking pointed post-support NPS questions, you gain not just a score—but the context and stories that reveal what’s working, and what needs to evolve.
Smart follow-ups based on NPS scores
The magic of NPS isn’t just the score—it’s how you follow up. I customize dynamic follow-ups for each segment:
Promoters (9-10): Celebrate their satisfaction and mine for advocacy:
What did you love most about your support experience?
Would you be open to sharing a testimonial or review?
Can you name the support agent who made the difference?
Passives (7-8): Pinpoint the gaps between “good” and “great”:
What held you back from giving us a 10 today?
Was there anything confusing or frustrating in the process?
How could we improve next time?
Detractors (0-6): Quickly surface pain points and turnarounds:
What was most disappointing about your support experience?
What could we have done to resolve your issue better or faster?
Is there anything we can do now to make things right?
Segmented follow-ups make customer feedback much more actionable—by addressing promoters, passives, and detractors differently, you increase relevance and unlock specifics that drive better support [4]. With AI-powered follow-up questions, these next steps adapt instantly to how each customer responds, making every survey feel personal, not robotic [5].
Prompt for promoters:
“After a customer gives us a 9 or 10 in an NPS survey about support, follow up with: ‘What did you love most, and would you mind sharing a shout-out for your agent?’”
Prompt for passives:
“If a customer gives a 7 or 8 in our post-support NPS, ask: ‘What could we have done to earn a 10 from you today?’”
Prompt for detractors:
“When a detractor scores 0-6 after support, follow up: ‘I’m sorry to hear that—what was the main source of your frustration, and how can we make it right?’”
Automated follow-ups like these don’t just capture more feedback—they create a real, conversational dialogue about the support experience.
When to trigger your post-support NPS survey
The timing of NPS surveys is critical for response quality. The sweet spot? I recommend sending your post-support survey 24 to 48 hours after a ticket or chat session closes. Customers’ memories are fresh, and you’ll see higher open rates and more thoughtful answers [2].
Let event triggers handle the heavy lifting—when a support request is closed in your helpdesk or ticket system, automatically deliver your NPS survey. With frequency controls, ensure customers don’t get hit with surveys for every ticket—once every month or quarter creates a balance between value and fatigue.
Timing | Pros | Cons |
|---|---|---|
Too Early | Quick feedback, issue still top of mind | Customer may not have fully tested solution yet |
Just Right (24-48h) | Accurate, detailed feedback; high response rate | None—this is the ideal window |
Too Late | Allows time to evaluate solution’s effectiveness | Experience may be forgotten; lower response |
Specific’s advanced targeting means you can deploy post-interaction surveys exactly when it matters. Don’t just rely on manual triggers—automate based on ticket status, user identity, or even issue type.
Behavioral targeting ensures you’re not sending surveys at random. Triggering surveys in-product, right after meaningful customer milestones, leads to richer feedback. You reach the right user, at the right moment, every time.
Turn support feedback into actionable improvements
Collecting NPS survey responses after support is only step one. I analyze these results to pinpoint exactly where support shines—and where it’s holding customer satisfaction back.
Here’s how I approach this:
Filter results by score to see what detractors complain about. Is it speed, empathy, technical knowledge, or problem resolution?
Look for patterns or recurring words in open-ended comments. Common themes usually point toward the most impactful fixes.
Use AI to surface pain points at scale, no matter how much qualitative data you have. With AI-powered response analysis, you get summaries and key takeaways instantly.
Prompt for AI analysis:
“Summarize the top 3 reasons detractors gave low NPS scores after support interactions last month. Suggest specific improvements for each.”
Prompt for team strengths:
“Identify which support agents received the most positive feedback in NPS surveys and what customers highlighted about their approach.”
AI-powered analysis turns mountains of feedback into meaningful actions. Chat-based reporting highlights which processes create customer love and which drive them away. Every insight—whether praise for a specific agent or a trend in unresolved issues—becomes an opportunity to level up both your support team and your overall customer journey.
Findings like, “Customers who got follow-ups within two hours consistently gave higher scores,” or “Personalized responses drove satisfaction” will shape your next move faster than any spreadsheet could.
Ready to measure your support team's impact?
Post-support NPS surveys show how effective—and impactful—your customer service truly is. With conversational surveys, you collect richer feedback that goes beyond a simple score. Discover how AI-driven conversations make every customer feel heard, and create your own survey that’s tailored, dynamic, and naturally insightful.