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What Is Conversational AI?

Stay ahead of customer expectations by learning how conversational AI can help you better serve customers through chatbot and voice channels.

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More and more, conversational AI–powered intelligent virtual agents (IVAs) and chatbots are serving as the first level and in some cases multi-turn customer customer support to provide fast, efficient, automated, always-on service that many customers have come to expect. Whether through voice or chatbot, this form of automation allows customers to directly ask a question or present their problem in a natural interaction with the system and receive an answer. When the AI cannot resolve the query, the matter is escalated to a live agent.

This marks a tremendous improvement over the traditional Intelligent Voice Response (IVR), where customers can find themselves trapped in a press-key or voice-response loop. Customers consistently say that a fast response is a competitive difference maker when making a buying decision. And contrary to popular opinion, many customers across all age groups and geographies prefer to handle basic inquiries without interacting directly with a person.

The challenge is figuring out how to get in the AI game in a way that minimizes the investment risk, yet quickly produces real results. Thanks to the agility of the cloud and the more widespread availability of API libraries that get developers up and running quickly, there are more agile and efficient ways to build or integrate conversational AI into your sales and support processes.

Understanding Conversational AI

Natural language processing (NLP), speech recognition, and machine learning (ML) work together in the contact center to deliver an improved customer experience.

NLP is the technology that allows people to speak or write to a device and enables the device to understand what’s being said. The technology has improved tremendously in recent years to become highly accurate. The speech recognition module — a subset of NLP — processes and rationalizes the spoken word while listening to the human voice.

Turning text into speech enhances the quality of the interaction. Text to speech allows IVAs to speak naturally in various languages and hundreds of voices to deliver lifelike, engaging self-service experiences.

The final part of conversational AI — the ML module — identifies how to respond to the verbal input while working alongside the speech recognition module. These technologies are continually learning and improving as their algorithms process more data.

Automating Conversational AI in the Contact Center

A large percentage of customer interactions involve the exchange of routine information for which you can automate the responses. For example:

  • What are your hours?
  • Where is my order?
  • When is payment due?
  • Is this in stock?

Speed and accuracy are paramount for inquiries like these.

In addition to improving customer satisfaction and lowering abandonment rates, automating routine queries with conversational AI and chatbots can deliver significant cost savings improve. In addition, benefits include:

  • 100% call acceptance, 24x7
  • Ability for customers communicate via their channel of choice
  • More robust metrics and reporting
  • Automatic language detection and conversation in the customer’s language

And for contact centers staffed by live agents, conversational AI can run in the background to complement agents with options or solutions they may have missed. This can also automatically gauge agent performance (including call sentiment) and suggest alternate solutions.

Experiencing Conversational AI

Bringing conversational AI–based IVAs and chatbots to the contact center isn’t simple, but Five9 makes the process as easy as possible. These solutions are custom built for every customer, with the ability to integrate with existing legacy or current cloud apps, and without requiring specialized AI personnel. There’s also a library of apps and functions that can be dragged and dropped to develop Intelligent Virtual Agents for use in a contact center environment.

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