Let's make every customer experience better than the last, together.
Hierarchical clustering is a machine learning technique that groups similar items into nested categories based on how much they have in common, without needing to be told in advance how many groups should exist. In a contact center, the items being grouped are usually customer calls, chat transcripts, or specific phrases, and the output is a tree-like structure where closely related conversations sit near each other and broader categories sit further apart.
It runs behind the scenes inside conversation analytics platforms like Zenarate Analyze, automatically grouping thousands of calls or chats by topic, sentiment, or outcome. Instead of a QA or CX leader manually tagging categories, the clustering model surfaces them on its own, for example separating billing disputes from technical troubleshooting from cancellation requests, and then further splitting each of those into more specific sub-topics.
A contact center handling a few hundred calls a week can get away with manually tagging categories. One handling tens of thousands cannot. Hierarchical clustering lets a platform surface emerging trends, a new complaint type spiking this week, a compliance phrase showing up more often, without anyone needing to know to look for it first.
No. Sentiment analysis identifies the emotional tone of a conversation. Hierarchical clustering groups conversations by similarity, which can include topic, language patterns, or outcome. The two are often used together: clustering identifies a group of related calls, and sentiment analysis tells you how customers felt during them.
No. It's typically built into the conversation analytics or QA platform you already use, such as Zenarate Analyze, so it runs automatically in the background rather than requiring your team to build or maintain a model.
The underlying math is often similar, but the application is different. In a contact center, clustering groups conversations by content and outcome to surface QA and CX trends, rather than grouping customers by purchasing behavior.
Related Terms: Conversation Analytics, Sentiment Analysis, Speaker Diarization
Learn more: See how Zenarate Analyze surfaces trends across every conversation automatically. Get a Demo
Machine Learning Director