Context: the company launches an advertising campaign that aims to create a flow of requests to the contact centre on a specific topic (for example, to sign up for a test drive of a car). The effectiveness of such campaigns is difficult to measure since you need to understand the content of each dialogue. Manual dialogue markup was an inefficient process.
Decision: it was suggested to build a dialogue tagging model based on the created annotation.
The solution is built and adapted for:
- Car dealer companies;
- Real estate sales companies;
- Companies-medical centres.
Integration into the BI cloud platform automation of CRM management for the customer's clients:
- CRM completion is 40% more and 20% more accurate;
- Automation of measuring the level of conversion in the channels to attract;
- Reduce labour costs by 60%;
To build the model, we used:
- Dialogues between contact centre operators and the client divided by replicas;
- Information about the presence of events in the dialogues;
- Expert knowledge of the essence of events.
- Model for predicting the probability of an event;
- Analytics of the dialogue data set;
- Ad campaign conversion Analytics.
Customer: Contact Center, Telecom
Technology stack: text-preprocessing models, classification models, language representation models, Flask, Python, PyTorch.