Success Story - en

Clusterization of Contact Center dialogs

Description: building a system for automatic processing of incoming calls to the contact center assumes the presence of a specified taxonomy, which will be used to categorize the request and then process it. When working with a large number of contact centers, you need a system for quickly analyzing dialogues and then building a taxonomy of categories for a specific contact center.

Context: it is important for CC analysts to quickly understand what topics are included in the dialog corpus in order to quickly automate their work. Manually dividing such cases into topics is a resource-intensive task.

Decision: a system of hierarchical clustering of contact center dialogs is proposed, which allows creating a taxonomy of intents

Integration into the BI Department for automation of customer contact centers:
Reducing the load on the analyst to allocate automation classes by 80%.

Harmonization of taxonomy:
Reducing the time to identify a new category by 60%;
Allocation of new intents in the flow of requests with a quality of 70%.

The selection of entities and the analysis of interpretability:
Selecting named entities and filling in the client card is 10% more accurate;
Increase in the share of interpreted intents relative to the old model by 40%.

To build the model, we used:
Dialogs for multiple contact centers;
Assessor markup of paraphrases for each dataset.

Simulation result:
Model for soft hierarchical clustering of dialogs;
Final taxonomy of categories with a description;
A server-side custom application with an API interface.

Customer: Contact Center, Telecom

Technology stack: TopicNet, BigARTM, Flask, Python, PyTorch. 
Natural Language Processing Contact Centers