Industry solution
Voice NPS and CSAT surveys for customer support
Support usually measures satisfaction either with a post-call IVR survey — "press a digit" — that people abandon on the very first question, or with a written CSAT over SMS that only a handful answer. Both give a bare score with no reason and a skewed sample: the happiest and the angriest reach the survey, while the silent majority drops out. A voice AI agent instead calls back soon after the inquiry is closed, asks for a score on the CSAT or NPS scale and one open "why" — and the customer answers by voice, without switching to a form, so the answer-through rate is noticeably higher.
The key is that for support the number matters less than its reason and its link to a specific inquiry. The platform transcribes the answer, computes sentiment and pulls topics from the free-text comment, ties the score to the ticket, agent and issue category — and connects a low CSAT not to an abstract "the customer is unhappy" but to the very conversation where something went wrong. A detractor is caught by a rule right after the survey and routed to the owner while the customer hasn't yet left in silence.
Which industry pains this solves
Queue and dropped calls
At peak and overnight customers can't get through: the call piles up in the queue, drops off and becomes a missed contact no one ever learns about.
Quality by gut feel
You only get to review 2–3% of conversations. Whether the script held, whether the agent was polite, whether it was solved on first contact — stays out of view.
How it works
A step-by-step scenario — the same steps as in the full use case, applied to your industry.
- 1
A short survey script
You set up the voice agent's script: a greeting, the score question (NPS 0–10 or CSAT), and one open-ended "why." The score is a required, validated field, so the dialogue always reaches a number.
- 2
The agent calls after a contact
The outbound AI agent calls shortly after a contact, purchase or visit, asks the questions in a natural voice in the right language, and lets the client speak freely (barge-in). The conversation is short — a minute or two.
- 3
Score and open-ended comment
The agent captures the numeric score as a field and records the client's free-form answer. The call recording flows straight into the platform's analysis pipeline.
- 4
Sentiment and topics from the answers
The platform transcribes the conversation and tags it: it computes sentiment, extracts topics and keywords from the open-ended comment, and AI tags ("complaint about delays," "praises the service") are applied by meaning automatically.
- 5
A loyalty-trend dashboard
NPS/CSAT, sentiment and top topics go onto dashboards: trends by week and month, breakdowns by service and operator, period comparison — you see what's rising and what's slipping.
- 6
Alerts and escalating negatives
An automation rule catches low scores and negative sentiment and notifies the owner by email or webhook, and the survey outcome can be written to your CRM — you reach the client before they leave.
Industry scenario
A detractor after a closed ticket: recovery before churn
An agent closes a ticket as resolved, and a couple of hours later the AI agent calls the customer back with a short survey: "on a scale of 0 to 10, how likely are you to recommend us" and "what could have been done better." The customer gives a 4 and explains in the open answer: the issue was sort of fixed, but they had to repeat the problem three times to different agents, and it took a whole day. The score is captured as a field, and the survey recording lands straight into the shared analysis loop.
The platform tags the answer: sentiment negative, AI tag "repeat contacts / bounced between agents," topic "slow resolution." An automation rule catches the below-threshold score together with the negative sentiment and hands the case to the owner — a notify email to the group lead and the survey result written into the CRM/help desk next to the original ticket. The owner sees not just "detractor" but the actual transcript and the link to the inquiry where the customer was run in circles.
On the loyalty dashboard this score joins the CSAT/NPS trend by week, broken down by agent, inquiry category and complaint topic. The manager sees that the "bounced between lines" topic drags down the whole segment, not a single conversation — and fixes the routing, not one agent. The survey stops being a quarterly campaign and works as a continuous thermometer after every closed inquiry.
Key facts
- What it does
- A voice AI agent runs a short NPS/CSAT survey after a contact: a score plus one open-ended question.
- What's extracted
- The numeric score as a field, plus sentiment, topics and keywords from the open-ended answer — tagged automatically.
- Analytics
- A trend dashboard for NPS/CSAT and sentiment by week, service and operator, with period comparison.
- Handling negatives
- An automation rule catches low scores and notifies the owner via email or webhook; the survey outcome can be written to your CRM.
- Format
- A voice conversation in the right language (BCP-47), short (a minute or two); the client can interrupt (barge-in).
Related scenarios for this industry
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