Using Document Understanding ML Models on Conversations

Conversations in customer interactions offer valuable data for organizations to gather feedback, improve products or services, and boost retention. Organizations need conversation intelligence tools to track and utilize these conversations effectively. Unlike documents, conversations require a different approach to analysis due to their free-flowing nature and contextual elements. Extracting value from human-to-human conversations can be done through real-time and asynchronous modes, which involve AI-powered actions such as sharing documents or offering discounts during calls, and analyzing data after the conversation ends. Advanced conversation intelligence has come close to helping employees better support customers mid-conversation by relating entities and temporally defining them, but it's a complex system that requires contextually understanding conversations. Organizations can leverage tools like Symbl.ai to process and analyze conversations across channels and extract value from customer conversations.

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