What is the Contextual Conversation Clustering
When you receive thousands of calls in your contact center in a day, how do you know (i) What exactly are they talking about? (ii) Why are they calling you repeatedly? (iii) Why isn’t your agent able to upsell the new policy? (iii) Where exactly is the call turning bad?
Well, the traditional way requires you to get your auditor on board and go through the calls one by one. Not anymore, our contextual conversation clustering technology does the same, in a fraction of time, for all your requests.
Answering these tough but essential questions in contact centers is done by auditing/quality teams where they listen to the complete calls and comparing them manually.
Our C3 technology innovatively solves the said problem. It’s able to scan through unlimited calls and make groups of requests based on over 40 parameters. These groups help identify hidden conversational patterns in a user-bot session.
The identified patterns are then used to either generate situational reports of why a particular pattern is happening, provide an actionable suggestion to a human-agent in case of a transfer, and power our conversation monitoring technology.
Even after transferring a call to a human agent, C3 can analyze the interaction happening between the human agent and the user to categorize and learn about that specific interaction. This allows our voice-bot to learn over time and ensure that it is able to attend a similar call next time without involving a human agent.
Going beyond the trainable conversational agents based on supervised machine learning, C3 allows our bots to learn from the interactions and patterns in an unsupervised manner, reducing the overall time it takes to learn about a new concept.
It allows our system to be learning all the time by providing a continual learning environment.