VITAS Healthcare is an innovative Invoca customer committed to serving patients with personalized, efficient, and accurate care. Gregg Johnson, Invoca’s CEO, sat down with VITAS Healthcare’s CMO Drew Landmeier for a conversation about how 2020, conversational intelligence, and more.

Are you really starting a conversation when you ask someone “How are you?” and keep walking? How do you create a safe space to start a conversation?

LiveEngage is the entire asynchronous conversational ecosystem powered by bots and humans allowing brands to manage their entire contact center and onsite communications operations (customer care, sales, service and marketing) from a single place.

1. Connect to every consumer-facing channel using LivePerson’s connectors – the most available in the industry.
2. Manage all of those incoming conversations (messaging, social, email and voice) at scale using the powerful combination of human agents and bots.
3. Build, manage and scale automations easily through Conversation Builder – allowing a brand to move bot design from an IT function to a contact center agent function, where good conversations are already happening every day between your brand and customers.
4. Deeply integrate into your enterprise systems easily through an open architecture and robust set of APIs, allowing you to run functions as a service based on each customer’s individual intent, making each interaction hyper-personalized and meaningful.
5. Underpinning the conversational interactions, is Maven AI, which analyzes each conversation in real-time and ensures the brand can improve each interaction quickly.

Conversation Intelligence: Extracting Insights from Conversations

Sales conversations are a valuable and still underutilized asset for organizations. Recording and analyzing these conversations allow companies to quickly train new representatives, identify optimal behaviour, share and enforce best practices and also propagate customer requests and pain points to other parts of the organization helping product designers prioritize the best features. To create value from recorded conversations one needs a Conversation Intelligence (CI) stack. We outline the different layers of our CI stack, namely Diarization (also known as Speaker Separation), Automatic Speech Recognition (ASR) and various Natural Language Processing (NLP), Deep Learning and data science algorithms for extracting insights. CI is a relatively new field because until recently conversations were not amenable to automated analysis at scale due to the high bar of applying speech recognition. However, recent developments in ASR technology (based on Deep Learning) have given rise to significantly higher accuracy rates and with that the ability to robustly extract information from conversations. We review recent advances as well the cutting-edge approaches we use for Speaker Separation which is critical to understanding the roles various speakers play in a conversation, review latest methods for embedding a segment of speech for speaker clustering and review overall system structure for a multi-modal solution. On top of these we demonstrate how both standard and novel NLP and Data Science approaches can help reveal conversational insights including: weak language, inclusive language or identifying customer propensity to buy. The insights are drawn from over 2 million sales calls we analyzed at Chorus.ai to help our customers’ sales reps have better conversations and close more deals.