Understanding the key differences between chatbots and virtual agents


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The differences between a virtual agent and a chatbot are actually bigger than you might think. To help distinguish between the two technologies, it’s helpful to draw a parallel with another popular technology—the smartphone.

When it comes to understanding the difference between chatbots and virtual agents, there are parallels to the evolution of a technology that has evolved significantly and is not referred to differently than it used to be—the smartphone. Fewer and fewer people regularly use the words ‘telephone’ and ‘smartphone’ interchangeably anymore, primarily because they are technically and functionally referring to two very different devices.

Both a phone and a smartphone can be used to make calls, but that’s where the similarities stop. The modern-day smartphone has a wealth of extra functionality, like continuous access to the internet, countless, useful apps and powerful cameras, all of which make it such an essential part of modern life.

How is this example applicable to chatbots and virtual agents? While it’s not a complete one-to-one distinction—virtual agents and chatbots are not physical products, after all—the easiest way to discern between the two is to ask, what can they do, and how much do they really understand?

The first chatbots saw the light of day in the 1960s, which makes them older than the world’s first mobile phone. For instance, the Motorola DynaTAC 800x launched in 1983. Even though the functionality of chatbots has improved over the years, they are still built with simple, rules-based technology, leaving them limited to matching the questions they receive with the most probable, predefined answers.

This limitation does not make chatbots well-suited to represent an organization or a business with high volumes of customer-service chat traffic since human language is far too complex and nuanced to be narrowed down to a predefined selection of questions and answers.

Virtual agents, on the other hand, are not powered by the same rules-based programming. Instead, these advanced customer service tools are built with conversational AI (artificial intelligence) at their core, designed to both accurately mimic human conversations and understand the underlying context, content and intent of a customers’ request.

To achieve this, it’s crucial that any conversational AI based platform, has a robust natural language understanding (NLU) foundation which combines deep learning and machine learning models with foolproof natural language processing.

It’s technical, for sure, but then we should expect no less from something that can understand and respond to any input, in any language, while continually improving itself with every interaction. Research by Accenture indicates that 56 percent of respondents see conversational AI as advancing disruption in their industry, and 43% noted that their competitors are implementing these technologies.

In fact, virtual agents go beyond simply answering questions. They offer guidance around products and services, and can perform a range of complex transactional actions on behalf of users. In addition to providing richer information and handling transactions, a virtual agent can also analyze, advise, sell and up-sell, based on customer data. They are capable of even connecting with other virtual agents other across a network for a truly next-level customer experience.

A recent report by analyst firm Forrester Research illustrates this point, revealing that 90 percent of customer service leaders see personalization as playing a crucial role in automation, it is clear that chatbot technology falls short. In fact, two out of every three customers will not return after a poor customer assistance experience.

Customers are more connected than ever, yet many organizations still struggle to make themselves available in a time- and cost-effective manner, especially at scale. Conversational AI enables them to do just that, allowing customers to talk to machines naturally when it’s relevant to do so.

The most common use in the enterprise is to apply conversational AI to customer interaction through a chat window. Indeed, it is now increasingly recognized that ensuring a good customer experience, as well as a positive return on investment, is predicated on the use of conversational to close this gap between a company and its customers.

Conversational AI offers businesses so much more than just the 24/7 availability of simple chatbots. The emergence of cloud services and heavy machine power through graphic processing units (GPUs) have made it possible to train the deep learning algorithms on which a true AI-based virtual agent relies.

Virtual agents have evolved to offer a customer experience that is instant, eminently useful and meets consumers where they spend their time—on their smartphones—in a way that is as natural as a human conversation. Chatbots, like mobile phones, just can’t be expected to keep up with this kind of technological advancement and customer demands.


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