Conversational AI: What It Is and How It Works

Celia Cerdeira at Talkdesk explains how conversational AI powers contact center tools like virtual agents and chatbots. It uses machine learning (ML) and natural language processing (NLP) to help customers and give them a great experience with a brand.

It’s an important tool for contact centres to use, as it powers channels such as an AI chatbot or virtual agents.

Artificial intelligence enables these tools to comprehend human language and conduct human-like interactions with customers. It saves time and cost for contact centres because it can take care of routine inquiries from customers, leaving more complex cases that are better for human agents to handle.

What is Conversational AI?

Conversational AI is a general term for the engine that makes tools like AI-powered chatbots, voicebots, and virtual assistants usable as functional tools to help customers.

The technology components of Conversational AI include natural language processing (NLP) and machine learning (ML). Conversational AI enables companies to deliver better customer service and as a supportive tool to human agents.

Because of this, conversational AI applications help shorten wait times and create an overall better customer experience.

What is an Example of Conversational AI?

An example of a tool that leverages conversational AI is a virtual agent. Virtual agents are used by contact centres to provide 24/7 service to answer customer questions, especially frequently asked questions.

A virtual agent can process and understand human language in a way that it can identify customer needs and either answer the question or route the customer to the appropriate human agent.

By using conversational AI, virtual agents can provide two-way, natural dialogue for solving customer inquiries. This technology allows virtual agents to go beyond basic, scripted answers and interact with customers in a human-like manner.

How Does Conversational AI Work?

It works by using technology such as natural language generation, machine learning, and natural language understanding.

They can provide either text-based or voice assistance. As an example of how it works, we can look at the steps that an AI chatbot takes to leverage conversational AI and respond to a customer accordingly:

1. Input generation—the customer either says or types their request.
2. Input analysis—natural language processing (NLP) cleans up the request so the artificial intelligence engine can understand it better. In addition, natural language understanding (NLU) can be applied to enable the artificial intelligence engine to comprehend even more nuances behind the request.
3. Natural language generation—the AI engine formulates a human-like response and sends it back to the customer.

To better explain these steps, let’s define some key terms as well:

Machine Learning

Machine learning is a key component of artificial intelligence. It means that the system can learn and improve itself over time, without a human needing to input additional information. This process is based on pattern recognition.

The AI engine uses neural networks to spot patterns in data and then provide outputs. Over time, programmers will correct these outputs if they are off course, and then the AI engine will gradually produce more and more accurate outcomes.

Natural Language Processing (NLP)

Natural language processing enables AI engines to take words from text or voice-based conversations, and derive meaning out of them.

It uses both literal meanings of each word, along with context-driven insights. This way, it can understand the category of the request.

For instance, the word “free” could refer to something that is received at no cost (i.e. “is the product demo free?”), or it could mean that someone is available during a certain time (i.e. “I am free on Tuesday at 10 am”). NLP empowers conversational AI to tell the difference between these two situations.

Natural Language Understanding (NLU)

Natural language understanding is a subset of natural language processing. While NLP can categorize what the customer is talking about in a general sense, NLU can identify even more details.

It can understand the sentiment, deep context, semantics, and intent of the request. NLU is even built to overcome obstacles such as mispronunciation, sub-optimal word order, slang, and other natural parts of human speech.

What Are the Benefits of Conversational AI?

This technology provides lots of benefits for agents and customers alike. AI chatbots give customers quicker ways to resolve their issues, clearing up agents’ schedules and leading to greater customer satisfaction.

Plus, tools like virtual assistants can help agents perform their jobs better, as it leverages conversational AI to give relevant suggestions to the agents during real-time interactions with customers.

Save Time

Conversational AI can help both agents and customers save time. With tools like AI chatbots, customers can receive personalized advice at any time of the day or night. Because this process can happen without human intervention, it saves time for call center agents as well.

In addition, internal-facing tools such as virtual assistants can help agents on the back end of call centre operations.

This saves time for agents by pulling up relevant shortcuts or next steps as the agent is on a real-time phone call with a customer.

Better Customer Satisfaction

Conversational AI solutions lead to a better customer experience because they provide readily available support for customers.

This also means shorter call queues for customers who have more complex requests and need to speak with a live agent.

Virtual assistants can lead to better customer satisfaction as well, by providing quick follow-up conversation points or resources for human agents to share during real-time conversations.

These hints throughout live conversations lead to better-equipped and proficient agents, who can then give better responses to customers.

Plus, agents who use a virtual assistant usually require less supervision from management because they are well-supported as they make phone calls.

Improve Customer Engagement

Conversational artificial intelligence tools enable customers to locate relevant information faster, which can improve their views of the brand in general.

When a company provides these types of helpful, efficient tools to customers, they are more likely to enjoy the brand and increase their engagement. This leads to a lower customer churn rate as well.

Final Thoughts on Conversational AI

This technology is transformational for contact centres. Many contact centres have been relying on basic artificial intelligence functions like interactive voice response for several years.

While they are important, tools like IVR lack a good flow of conversation, if used on their own. Instead, conversational AI tools—like AI chatbots and virtual assistants—facilitate helpful, human-like conversations and responses that can help both customers and agents.

This blog post has been re-published by kind permission of Talkdesk – View the Original Article

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Talkdesk is a global customer experience leader for customer-obsessed companies. Our contact center solution provides a better way for businesses and customers to engage with one another.

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: Talkdesk