As pandemic forces Contact Centers to operate with work-from-home agents, managers are increasingly looking to real-time speech analytics to drive improvements in agent efficiency (via reduction in AHT) and effectiveness (improvements in FCR, NPS) and achieve 100% compliance.

Before the pandemic, Contact Center managers relied on a combination of in person supervision and speech analytics of recorded calls to drive improvements in agent efficiency and effectiveness.

However the pandemic has upended everything. It has forced contact centers to support work-from-home agents from multiple locations.  Team Leads who "walked the floor" and monitored  and assisted agents in realtime are not available any more. The offline Speech Analytics process - which is still available remotely - is limited and manual. A Call Coach or a QA Analyst coaches an agent manually using a sample 1-2% of the calls that have been transcribed and analyzed.

There is a now an urgent need to monitor and support agents real-time and provide them all tools and support that they had while they worked in their offices.

Real-time Agent Assist is the use of Artificial Intelligence - more specifically Speech Recognition and Natural Language Processing - to help agents real-time during the call in the following ways.

1. Agents can be presented with knowledge-base articles and next-best actions from intents that are extracted from the transcribed text
2. Using NLU algorithms and intents extracted, you can now summarize the call automatically and realize savings on disposition/wrap time
3. Supervisors can monitor sentiment real-time

Real-time Agent Assist can reduce AHT by 30 seconds to 1 minute, improve FCR by 3-5% and improve NPS/CSAT.

What does it take to implement Real-time Agent Assist?

Real-time agent assist involves the realtime transcription of the Agent and Caller Interaction and extracting keywords, insights and intents from the transcribed text and make it available in a user-friendly manner to both the Agents and also the team-leads and supervisors.

There are 4 key steps involved:

1. Audio Capture: The first step is to stream the two channels of audio (i.e agent and caller streams) from the Contact Center Platform that the client is using (whether premise based or cloud based). Voicegain supports a variety of protocols to stream audio. We have described them here and here.   We have integrated with premise-based major contact center platforms like Avaya, Cisco and Genesys. We have also integrated with Media Stream APIs from programmable CCaaS platforms like Twilio and SignalWire.
2. Transcription: The next step in the process is to transcribe the audio streams into text . Voicegain offers Transcription APIs to convert the audio into text realtime. We can stream the text realtime (using web-sockets or gRPC) so that it can be easily integrated into any NLU Engine.
3. NLU/Text Analytics:  In this step, the NLU engine extracts the intents from the transcribed text. These intents are trained in an earlier phase using phrases and sentences. Voicegain integrates with leading NLU Engines like RASA, Google Dialogflow, Amazon Lex and Salesforce Einstein.
4. Integration with Agent Desktop: The last and final step is to integrate the results of the NLU with the Agent Desktop.

At Voicegain, we make it really easy to develop real-time agent assist applications . Sign up to test the accuracy of our real-time model.