Deepen CSAT Feedback
Your CSAT scores hide the real problems. CSAT surveys collect a rating after ticket close. Your evaluator asks what happened — were they transferred too many times? Did the resolution actually work? Would they reach out again or just churn quietly? Now your team can fix the process, not just the ticket.
What's inside this template
Start from this conversation and adapt it to your team — change any question, add your own logic, and connect the tools you already use.
Questions it always asks
The core fields every response captures.
Always ask if the issue was fully resolved to their satisfaction
Ask how easy it was to get help on a scale they can explain
How it adapts
Follow-ups that change based on what people say.
If they mention being transferred, ask how many times and if they had to re-explain
If the issue recurred, ask how many times they've contacted support about it
Where it routes people
Different paths for different answers.
If they're satisfied, offer a link to leave a review
If they're frustrated, create a follow-up ticket for their CSM
Automations it can trigger
Actions that fire the moment a response comes in.
Post conversation summary to #support-quality in Slack
Create a Zendesk follow-up ticket for unresolved issues
Update Gainsight health score based on support experience
How this AI template works
After a support ticket is resolved, your evaluator reaches out to understand the full experience. It asks about response time, agent knowledge, resolution quality, and overall effort. Frustrated customers get flagged for follow-up. Process issues get surfaced to leadership. Happy customers get asked to leave a review.
Getting started
- 1
Share your current post-ticket CSAT flow — we'll enhance it
- 2
Define what matters most: resolution quality, speed, agent knowledge
- 3
Connect to Zendesk for ticket context and Slack for alerts
- 4
Deploy post-resolution and start hearing the real story
Template Details
- Agent Type
- Evaluator
- Industries
- SaaS / Tech
- Roles
- Customer SuccessSupport
- Integrations
- Slack, Zendesk, Gainsight
- Times Used
- 2,200+
Forms collect fields. Conversations capture context.
Static forms force complex situations into rigid dropdowns. Perspective captures structured data and the reasoning behind it — so your team makes better decisions, faster.
The static form
No context. No follow-up. No next step.
- Static CSAT forms give you a number but never explain what went wrong - a 2-star rating doesn't tell your support team what to fix or your product team what feature failed.
- Fixed rating scales miss the emotional context behind dissatisfaction - you can't tell if a low score means minor annoyance or business-critical failure that will cause churn.
- Pre-written follow-up questions ask the same things regardless of the satisfaction level, wasting opportunities to understand what delighted happy customers or what specific steps would retain unhappy ones.
The AI conversation
"Tell me more about the timeline — when did this start, and is there a deadline your team is working against?"
Extracted & structured automatically
Category
High-priority
Urgency
Deadline: 2 weeks
Sentiment
Frustrated but hopeful
Next step
Route to senior team
Right team. Full context. Instant action.
- Adaptive follow-ups explore what specifically exceeded expectations for satisfied customers and what exact improvements would retain dissatisfied ones, turning ratings into retention strategies.
- Conversational depth captures the business impact and emotional weight behind each score, helping you prioritize which satisfaction issues actually drive churn versus minor friction.
- Dynamic probing identifies patterns in your best experiences that teams can replicate and specific failure points that need immediate attention, not generic improvement suggestions.
What is a CSAT conversation and how does it work?
A CSAT conversation starts with the standard satisfaction rating question, then uses adaptive follow-up questions to understand the reasons behind each score. Instead of fixed questions that every customer answers the same way, the AI asks different follow-ups based on satisfaction level and previous responses. Satisfied customers get asked about specific highlights and referral likelihood. Dissatisfied customers get probed about what went wrong and what would improve their experience. The result is structured satisfaction data plus contextual explanations your teams can act on.
FAQ
Frequently Asked Questions
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Replace drop-off, poor qualification, and missing context with AI conversations that capture structured data and real understanding. Set up in minutes.
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