Otherwise known as an IVA (Intelligent Virtual Agent), virtual agents have grown increasingly popular in recent years.
Indeed, as companies continue to search for new ways to streamline customer service, reduce call waiting times and improve efficiencies, virtual agents have emerged as a valuable complement to the standard human employee.
These software programs use scripted rules, AI, and machine learning to provide automated service and guidance to customers and sometimes employees.
For instance, a virtual agent may help a customer complete a simple action, such as making a purchase or perhaps route a conversation to the right agent.
Virtual agents can support employees. They can offer help-desk-type services, assisting team members with resetting passwords or making requests.
What Are Virtual Agents?
Virtual agents are AI-enabled assistants that can autonomously address customer queries, doubts, and grievances entirely through a conversational experience that uses a natural language, like English.
They act and appear just like live agents but do not require any human intervention. The conversational flow, script, and decision-making triggers are pre-programmed into the algorithm, and the AI incrementally improves with every interaction.
Virtual agents differ from bots in that they have far more extensive cognitive capabilities. They mimic human personality and tone of voice, understand complex queries, and have names or even animated avatars to recreate a personal touch.
Finally, it is a technology growing fast. Indeed, Gartner expects virtual agents to automate six times more customer conversations by 2026.
How Do Virtual Agents Work? Key Technologies
While the exact functionality of a virtual agent varies depending on the technology in question, most modern tools use the following functionalities to process queries.
- A Conversational Interface – The front end of a virtual agent appears as a conversational interface, which can be either voice or text-based. The customer types in or says their query in their natural language, the agent responds, the customer reacts, and the interaction continues until the issue is resolved or redirected to a live agent.
- Natural Language Processing – The virtual agent is capable of natural language understanding (NLU) and natural language generation (NLG) to convert customer queries into a machine-readable format and the resulting response back into a human-readable format
- Text and Sentiment Analytics – The agent can recognize essential words and phrases to understand your exact query and intent. It can even detect mood undercurrents like anger or frustration, using the appropriate script accordingly. The analytics data is fed into a back-end system to power 360-degree customer intelligence for future interactions.
- Speech Analytics – Voice-based virtual agents use speech analytics instead of or in addition to NLP to make sense of human-uttered speech.
- AI and ML – Keeping in mind that natural language processing is an AI technique, virtual agents also use ML to become incrementally more accurate. If the customer corrects the agent the first time, it learns from the error and avoids making the exact inference the next time. Another AI technique virtual agents use is optical character recognition (OCR), which is helpful when extracting the text from a customer screenshot, almost as if a human was reading it.
These systems can also connect with various back-end systems, such as service desks, knowledgebases, and CRMs, to provide more personalized responses to users. In almost all cases, virtual agents automate standard processes and streamline workflows for agents and consumers. They can understand specific requests given to them in real-time and use extensive AI technology to scour through vast amounts of information in moments.
Today’s CCaaS systems often come with tools that allow companies to design and customize their virtual agents with unique scripts, knowledgebases, and data loads to deliver a more impressive service to customers and employees.
