When it comes to customer satisfaction analysis, the questions you ask after support ticket closure can make or break your CSAT insights.
This article shares the best questions for CSAT support surveys, including smart follow-up probes that uncover what really drives satisfaction.
We’ll also cover how to set up automated CSAT collection using Specific’s API/JS SDK and manage recontact timing for optimal results.
Essential questions for post-support CSAT surveys
The foundation of any support CSAT survey is the simple, proven question:
On a scale of 1 to 5, how satisfied are you with the resolution of your support request?
This is your core CSAT metric. It’s direct, easy to understand, and delivers a clear pulse on your team’s customer impact.
To enrich your customer satisfaction analysis, I always include these additional questions:
How easy was it to resolve your issue with our support team?
This lets us measure customer effort, which is a strong predictor of future loyalty. According to Gartner research, reducing effort can have a bigger impact on loyalty than delighting customers with bells and whistles. [1]How likely are you to recommend our support services to others?
This goes beyond the individual case by tapping into the Net Promoter Score (NPS) concept. If someone would recommend your team, things probably went very well.Did our support team address all your concerns?
This ensures we understand completeness—sometimes tickets get closed with unresolved threads that a standard CSAT won’t detect.
Most surveys stop at quantitative questions. But if you want actionable insights, you need to go deeper. That’s why I recommend conversational surveys that adapt follow-ups in real time. With AI-powered probing, you automatically reveal the “why” behind each score. If you’re considering a smarter approach, check out automatic AI follow-up questions on Specific for a deeper dive.
Smart follow-up probes for resolution quality
AI-powered follow-ups take CSAT support from surface-level stats to real understanding. They adapt based on how the customer scored their experience, making each conversation as unique as the case itself.
For low CSAT scores (0–6): I want to know what went wrong, where we missed expectations, and exactly what pain points someone encountered. That’s where AI shines, digging into specifics that traditional surveys miss.
What was the main reason you were dissatisfied with your support experience?
Were there any expectations that weren’t met during your support interaction?
For neutral scores (7–8): The follow-up focuses on “what could have made your experience better?” Here, we uncover low-hanging improvements or small friction points that prevent a great rating.
What’s one thing we could have done to improve your recent support experience?
Was there anything about the resolution process that could be made easier?
For high scores (9–10): Let’s double down on strengths. These follow-ups ask what worked well, which is vital for finding patterns we can amplify.
What stood out to you about your support experience with us?
Which part of the process made resolving your issue especially smooth?
By combining adaptive follow-ups with AI, you gather not just reaction, but the deeper “why” behind the numbers. When it’s time to analyze responses, conversational AI makes it effortless. For example, I often reflect on survey results with a prompt like:
Show me recurring themes in negative feedback and suggest the top three improvements we should prioritize.
If you want to see how AI can surface patterns in your support data, explore AI survey response analysis in Specific.
Measuring customer effort alongside satisfaction
Let’s be honest—nobody wants to jump through hoops to get support. That’s why measuring customer effort (alongside satisfaction) is so powerful. Studies show that 96% of customers with a high-effort experience become more disloyal, versus just 9% with low effort. [2] If your support feels like work, no amount of kindness can compensate.
The classic Customer Effort Score (CES) question is:
On a scale of 1 to 5, how easy was it to resolve your issue with our support team?
To dig deeper, follow-up questions might include:
What steps took the most time or effort during your support experience?
Did you need to contact us more than once to fully resolve your issue?
Let’s compare high vs. low effort signals—here’s how I frame it during customer satisfaction analysis:
High Effort Indicators | Low Effort Indicators |
|---|---|
Multiple contacts, long waits, repeating information | First-touch resolution, clear guidance, no repetition |
Combining CSAT with effort metrics delivers a full, honest snapshot of support quality. Conversational AI surveys make all these measurements feel natural instead of overwhelming—respondents interact like they would in chat, not filling out a burdensome form. (If you want to see an example, here’s how a Conversational In-product Survey can collect layered feedback without friction.)
Setting up automated CSAT collection with API and JS SDK
Great feedback needs solid systems. Here’s how I automate post-support CSAT collection with Specific:
Install the JS SDK in your app or helpdesk. You’ll find step-by-step installation is as simple as adding a script and a few lines of configuration.
Event-based triggering: Set up the system to automatically fire the CSAT survey as soon as a support ticket closes or changes status. You can customize this with code or by using built-in triggers—no heavy engineering required.
API integration: If you want more control, use Specific’s API to send survey invites at the perfect moment, passing user info and context directly for seamless user experience.
No-code options are available, so even if you aren’t a developer, you can get started quickly. For in-depth guidance, view the in-product conversational survey documentation.
Managing survey frequency with recontact controls
Ask too often, and people tune out—or worse, get annoyed. But ask too rarely, and you miss vital moments. That’s where recontact controls come in for customer satisfaction analysis.
Specific lets you set global recontact periods—a minimum time between survey invitations to each customer, no matter how many tickets they open.
Per-survey limits: You can further cap how often an individual survey appears, ensuring nobody gets bombarded with repetitive requests.
Smart timing: Adding the right delay after ticket closure (I recommend waiting 1–6 hours, so it’s still fresh but never intrusive) balances relevance with respect for your customers' time.
Good practice | Bad practice |
|---|---|
One survey per customer per month | Survey every ticket instantly |
With these controls, you prevent fatigue, improve response rates, and keep quality high. Specific’s conversational approach also means the feedback process feels smooth on both sides. I always want survey creators and respondents to have a frictionless, even enjoyable, experience—because that’s how you get honest, thoughtful answers.
Transform your support feedback into actionable insights
Conversational CSAT surveys take your customer satisfaction analysis from checkbox to breakthrough. You’ll spot problems before they grow, discover surprising wins, and let AI follow-ups and analysis turn raw scores into clear stories your team can act on. If you’re not running these surveys, you’re missing out on both red flags and high-fives—insights that could make or break your support reputation.
Don’t wait to make your customers’ voices heard. Create your own survey and start transforming feedback into action.