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What are the different types of virtual agents?
There are four different types of virtual agents these days. Not long ago, the sound of a single computer-generated voice answering the phone may have filled the caller with a sense of dread. Early generation automated agents were often so limited in the answers they could provide that they were often seen as simply a roadblock to a live person.
Today, artificial intelligence (AI) and machine learning (ML) technologies have matured to a level where many virtual agents can successfully answer questions and fulfill customer service requests with no human intervention at all, providing faster service to callers and greater value to businesses. In fact, according to an article in VentureBeat, virtual support agents are expected to drive $1.2 trillion in business value by 2030.
The four types of virtual agents are:
- Voice agents,
- Artificial intelligence (AI) agents,
- Chatbots, and
- Visual agents.
Voice agents use scripted rules while AI agents speak naturally with customers to recognize needs. Virtual chatbots do the same but online or through text. Visual agents provide animation or virtual reality-based services.
What is a learning agent?
Types of learning agents use a combination of AI and machine learning to increase its ability to recognize and respond to customer requests. If customers continually use new terms to express the same thing, the learning agent will add responses to its own library to minimize the customers’ need to repeat themselves.
What’s the difference between virtual agents and IVR?
Growing in popularity at the turn of the millennium, interactive voice response (IVR) is defined by IBM as “an automated telephone system that combines pre-recorded messages or text-to-speech technology with a dual-tone multi-frequency (DTMF) interface to engage callers.” IVR is based on programmed menu options, routing calls that cannot be answered to live representatives.
Virtual agents go beyond IVR technology as they don’t rely on keyword matching to be effective. Instead, they allow customers to speak naturally and automatically detect multiple intents if a customer has several questions or switches topics quickly. Virtual support agents do not aim to deflect or re-route calls, but to resolve customer issues fully.
What are the most important factors in selecting virtual agents?
Organizations that are considering employing virtual agents should keep the following factors in mind before making a final selection:
- Effectiveness. The best virtual agents should be able to serve customers as well as or better than live agents. Both AI and ML capabilities should be mature enough to handle natural language processing as well as “learn” from previous customer interactions to improve future performance. If needed in rare cases, they should be able to intelligently transfer calls that require human intervention.
- Data. Based on customer interactions, virtual support agents should be able to provide the business with data insights about the types of calls received, the typical call flow, the answers provided, and customer or problem categories. This data can then be analyzed to make systemic changes or improvements in products and services.
- Integration. A best-in-class virtual agent solution should seamlessly integrate into a company’s contact center, CRM, and telephony systems, making it easy to share and access needed information.
- Features. Virtual agent systems will vary in terms of features such as speed-to-deployment, outbound capabilities, data visualizations, security and maintenance.
Replicant’s Intelligent Solution
Replicant’s AI-driven solutions for contact centers welcome natural language conversations with customers. They can be trained to detect multiple intents, help to resolve customer issues quickly, and provide insights and analytics. Learn more about our intelligent virtual agent now. Let us help you improve the efficiency and effectiveness of your call center.
