Sentiment model

NLP that scores the sentiment of contact moments and feeds it into health and impact

What it predicts

The sentiment of a piece of customer text, classified into five classes: Very dissatisfied, Dissatisfied, Neutral, Satisfied, Very satisfied. Per customer the recent sentiments are aggregated into a sentiment value and, where surveys exist, into NPS detractor, passive and promoter counts.

Data it needs

Text from contact moments: support tickets and their messages, emails and chat conversations, call notes, survey comments. Delivered through the support and CRM integrations. The model runs per language, so mixed-language bases are supported.

How it works

A natural-language-processing model reads each contact moment and assigns a sentiment class with a confidence. Aggregation weights recent contacts more heavily. Sentiment is an input feature of the health scoring model (typically in the Support or CSM engagement breakdown group) and a metric in its own right.

How often it runs

New contact moments are scored in the daily run.

How to read the output

On the customer page, sentiment explains a health drop that payments and usage do not. On the Impact Dashboard the Sentiments given chart shows the distribution over the selected period, which is the fastest way to see whether a service change landed well.

Where it surfaces

Customer page, Impact Dashboard, health breakdown, audience conditions. The sentiment model is part of the NLP on text add-on.