One of the most impactful advances in technology in recent years has been the shift towards automation of customer service. Not everyone sees the advancement as a good thing. Some might feel it’s taking the human element away. Some might feel it takes longer to get issues resolved. The reality, though, is that the progression towards automation of some (not all!) customer service needs means an increase in efficiency, issue resolution and customer preference education.
How? Quite simply, automation enables simple problems to achieve a quick and easy resolution, via tools like chatbots, and frees up live agents to deal with the more complex problems. This means agents are more fulfilled by practicing more meaningful functions. Customers are happy too, satisfied that agents are free to help them when bots cannot, and bots can help them, when agents simply are not needed.
Still not convinced? Ultimately, it’s important to understand conversational AI, what it is, how it works, and how it helps. Once you grasp a greater understanding of conversational AI, you’ll see how it ultimately helps you get the answers you need, easily, on your own, through the power of AI and Natural Language Processing.
Conversational AI is a technology that allows humans and computers to communicate with each other in natural language. This technology is used to create chatbots and AI virtual assistants that can understand and respond to human conversation.
Conversational AI is based on natural language processing (NLP), and on machine learning, which is a technology that allows computers to learn from data. Machine learning is used to create algorithms that can analyze and interpret text. This technology is used to create chatbots and virtual assistants that can learn from human conversation. The more people engage with these chatbots, the more they learn how to respond and support. This reciprocal relationship between man and machine benefits users and businesses to be the tool that can best satisfy businesses and customer alike.
Natural language processing (NLP) is the ability of computers to understand human language as it is spoken. NLP is a subset of artificial intelligence (AI) and machine learning that helps computers to analyze and interpret text by understanding the meaning of words, their context, and their relationships to one another.
On a practical level, that means that that NLP can understand thick accents, muted voices, jargon, and even tone. If a customer is getting frustrated by their chatbot interaction, NLP can pick up that emotional response and indicate the need for a live agent to take over the conversation. That live agent will be given the heads up by the bot that the customer is not happy, empowering them to take the call with proactive support and calm.
Ideally, NLP empowers customers to manage their self-service needs without ever having to engage an agent, by being able to accurately predict, manage and direct those customers to find the right answer and solution.
NLP can also be used to automatically tag text with metadata, extract insights from data, and create chatbots. By being able to access this kind of data, NLP, and agents and managers who review the data, are better able to understand their customers and can make informed adjustments to meet their needs. It does this through Natural Language Understanding.
Natural language understanding (NLU) is a subfield of artificial intelligence and linguistics that deals with the understanding of natural language, as spoken by people.
NLU is a complex process that involves, among other things, recognizing the meaning of words, syntactic analysis, and determining the intent of the speaker. It helps computers to understand human language and respond in a way that is natural for humans. NLU technology is used in many different areas such as customer service, virtual assistants, and automatic translation.
If you’ve interacted with an FAQ chatbot, you’ve had the opportunity to experience the pre-planned journey to your ultimate destination – the answer to your question. By engaging with an FAQ bot, you are engaging with a decision tree that presumes issues and questions and provides pre-populated answers solutions.
Often these FAQs cover very simple, common inquiries:
For simple questions, FAQs offer fast self-serve answers.
That said, FAQs are very limited because they can only answer questions that are in the form of questions, and only for, you guessed it, pre-determined, frequently asked questions.
That’s where NLU is different and adds significant value. NLU can answer any question about your product, not just questions that are in the form of questions. This is beneficial because it allows your customers to get the information they need in a way that is natural to them whereas FAQs require them to think of and ask a question in a specific way. If you don't, you can't find the answer in an FAQ. And if it’s not actually a frequently asked question, the answer might not be available at all.
A company's knowledge base (KB) is a repository of information that helps employees do their jobs. They include training collateral, sales collateral, marketing materials, media and press files, design files, legal documents and other important company information like annual reports. They are vital to the company maintaining a cohesive voice, vision and focus and keep all employees acting from the same playbook. The availability of NLU to help grow a KB is invaluable.
NLU helps create, grow, and improve a company's KB by extracting information from documents and making it easily searchable. This allows employees to find the information they need quickly and easily, which improves their productivity and makes them more efficient.
A company needs a solid KB in order to be successful, and NLU can help them build one. By employing NLU, a company can make their KB as comprehensive and up-to-date as possible making them more competitive and successful. And when they are more competitive and successful, they are better positioned to bring to consumers, the best possible self-serve customer service experience.
It is difficult to predict the future of self-service customer support, but if present day adoption is any indication, and if the technology continues to improve and evolve with enhancements like conversational AI, it’s likely to only continue to grow in popularity.
Companies may increasingly rely on self-service tools to help customers resolve problems and get information without having to speak to a representative. This can allow companies to save money on customer support costs, and it may also make it easier for customers to get the help they need.
What we know for sure is that self-service customer support is here to stay, in some capacity, and it is likely to continue to become more popular in the years to come as people realize the benefits to themselves, their businesses and their time.
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