Which came first: the chatbot or the messaging app? Well, technically, the chatbot did – if we concede that Joseph Weizenbaum’s ‘ELIZA’ computer programme was the first of its kind back in 1964.
However, some 50 years later the popularity of Facebook Messenger, WhatsApp, WeChat, SMS, et al. has catalysed the use of chatbots commercially. Messaging services have given chatbots a framework that consumers feel comfortable with. In fact, research shows that 47% of consumers would buy items from a chatbot.
So… is there a problem? Well, if we are totally honest it often seems that other than a more sophisticated interface, little has moved on in terms of chatbot functionality in half a century. But behind the scenes, AI machine learning experts are hard at work creating more empathy-driven chatbots; those that can understand context and emotion as opposed to just simple commands.
How are they doing this? Using Natural Language Processing (NLP).
Let us start with the basics. First off, the term ‘NLP ‘is also claimed by ‘Neuro Linguistic Programming’ which (while related) is a much bigger topic to tackle as it relates to behavior change in humans, and by proxy, AI (Artificial Intelligence).
But let us focus on Natural Language Processing. What is it? It is exactly as it sounds: a way of teaching technology to recognize words that people use – as opposed to lines of code. There are three key components: Natural Language Understanding, Natural Language Generation, and Sentiment Analysis.
While NLP’s usage is evident in many areas of technology – from spam filters to summarising information – it really comes into its own where conversations are concerned: when the objective is to make interactions appear as ‘human’ as possible.
And this is exactly why it is so critical to the evolution of communication channels and support, service, and the overall customer experience.
Customers are increasingly using text-based communications and messaging apps to engage with companies. This is a win for everyone, especially when chatbot-based customer support lower cost and is faster than voice-based phone support. They also allow customer service agents to focus on more challenging or focused work than answering routine questions.
But when combined with NLP, businesses like yours have an opportunity to improve the overall customer experience by delivering more personalized messaging services. For example, an NLP-powered bot can detect customer sentiment, sensing when it is time to escalate an inquiry to a human customer service agent. Or by integrating a CRM system with a chatbot, you could improve its access to customer information and offer an immediate level of customer personalization. These features could just as easily be applied to both text and voice-based AI.
As with all forms of AI – voice assistants as well as chatbots – machine learning is a key feature of NLP; so the right kind of information can incrementally improve performance. The AI will learn the most appropriate responses to common questions, while the NLP component is able to deliver a contextually relevant, emotionally intelligent response.
Ultimately, companies that are committed to delivering a more positive and meaningful customer experience consistently, across all digital channels, now have an opportunity to push their conversational tools to the next level.
Here at Mesaic, we see so much potential for these technologies for all kinds of businesses. For us, customer conversations go beyond chatbots. If you are interested in exploring the machine learning, customer service, voice AI, messaging platforms, and the potential of Natural Language Processing, talk to us today.