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Nivision
Use case

Hear how customers really feel.

Surveys reach a fraction of customers and arrive too late. Nivision reads sentiment on every conversation as it happens - so the experience problem surfaces while you can still fix it.

Customer experience & sentiment

You learn about churn after it happens.

By the time a customer fills out a low survey score - or simply leaves - the conversation that lost them is weeks old. Frustration, repeated objections and a quietly souring relationship all show up in the calls long before they show up in the numbers.

AI Call Analytics Platform | Conversation Analytics Software

Track the relationship, call by call.

Nivision scores sentiment and emotion on every conversation, detects objections and frustration as they surface, and builds each customer a profile with their full call history and an aggregate read on relationship health. Alerts flag a souring relationship early, and AI chat lets you ask why customers churned last week - grounded in the real calls.

01

Sentiment on every call

Emotion and sentiment scored on each conversation - and tracked as a trend per customer and across the floor.

02

Customer profiles

Every customer's calls in one place, with an aggregate read on sentiment, open alerts and relationship health.

03

Early-warning alerts

A souring relationship or rising escalation risk raises an alert before the customer is gone.

04

AI chat over conversations

Ask why customers churned or what is driving complaints - answers grounded in your real call data.

Customer experience & sentiment

The outcome

  • Experience problems caught while they are still fixable
  • Churn signals surfaced from calls, not lagging surveys
  • A clear, aggregate read on every customer relationship
Customer experience & sentiment

FAQ

How do you measure customer experience from calls?

The system tracks sentiment across each call - how the customer arrives, where the tone flips, how the call ends - and aggregates it into trends by agent, team and topic.

Can it identify a customer at risk of churning?

Yes. A competitor mention, a threat to leave or repeated frustration are conditions you can define real-time alerts on - while there is still time to save the customer.

What is the advantage over satisfaction surveys?

A survey captures a small share of customers, late, in words they choose to write. Call analysis captures every customer, in the moment, in the words they actually said.

How does this connect to metrics like CSAT and NPS?

Call insights explain the 'why' behind the metrics - which topics drag satisfaction down and which agents lift it - so you can act instead of just measuring.

What is customer service call analytics?

Analysis of service conversations for the things a manager cannot hear at scale: recurring complaints, sentiment and escalation, handling time drivers, and which issues come back. It is the same machinery as sales call analysis pointed at retention rather than revenue.

Can AI detect customer churn risk from calls?

It can surface the signals — frustration, repeated contact about the same issue, cancellation language, unresolved promises — as classifiers you define, and alert on them. Whether that predicts churn for your business depends on your own data; treat it as an early-warning list to act on, not a score to trust blindly.

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Turn your conversations into action.

See Nivision analyze calls like the ones your team handles every day. A 30-minute walkthrough, no slides.

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