Context: the contact center employs a large number of operators, you need to understand the quality of their work, since their effectiveness and sales efficiency directly depends on their following the script. Validation of manually - quite a long and expensive process, it is necessary to automate it and to propose a methodology of assessing quality.
Decision: a lot of topics were built based on replicas inside the CC dialogues. A model was built that decomposes the entire new dialog into replicas, and marks the replicas with appropriate themes. Then a "map" of dialogs with the highest quality sales was built - an ideal operator script. For new dialogues, the sequence of replicas was checked against the ideal map and the" quality " of the dialogue was measured.
Selected topics: 41 topics;
The quality of the allocation: 75% accuracy on average;
Integration into the Bank's BI software quality assessment of operators;
Increase the conversion rate and the percentage of successful dialogues.
To build the model, we used:
Dialogs between contact center operators and the client;
Information about the success of the dialog;
A priori expert knowledge of topics;
Set of themes for operators and clients ' dialogs;
Transition graph between topics for successful and unsuccessful dialogues;
The thematic segmentation tool of replicas of the dialogue.
Customer: Finance, Banking
Technology stack: Thematic modeling, Syntactic relations, Neural network models of text segmentation, BigARTM, PyTorch, gensim, nltk, Python.