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Remember Facebook's automated personal assistant, M, that was released in a bid to compete with Alexa and Siri? After a series of embarrassing mishaps due to poorly trained algorithms, Facebook abruptly pulled the plug. They weren't alone; chatbots are infamous for putting their metaphorical feet in their mouths. While these debacles are tough to watch, the underlying problem is not artificial intelligence (AI) itself. AI succeeds when underpinned with sound strategy and well-trained models.


NLP, AI, and Machine Learning: What's The Difference?

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Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that studies how machines understand human language. Its goal is to build systems that can make sense of text and perform tasks like translation, grammar checking, or topic classification. Companies are increasingly using NLP-equipped tools to gain insights from data and to automate routine tasks. This sentiment analyzer, for instance, can help brands detect emotions in text, such as negative comments on social media. But what exactly is Natural Language Processing?


Artificial Intelligence Use in Customer Service Is Rising

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More and more, companies are shifting towards artificial intelligence (AI) as its first point of contact when it comes to customer service. The movement towards AI is already happening, which can only fuel exchange-traded funds (ETFs) focused on this type of disruptive technology. Per a Forbes article by Unabel cofounder Joao Graca, a "2019 report found that 24% of customer service teams were already using AI and 56% were seeking AI opportunities. And when used wisely, it's working: Among teams employing AI for customer service, 82% reported increased first contact resolution (FCR) rates, while 79% reported increased CSAT or Net Promoter scores." The article noted five ways, in particular, where AI would benefit customer service.


5 examples of effective NLP in customer service

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The study of natural language processing has been around for more than 50 years, but only recently has it reached the level of accuracy needed to provide real value. From interactive chatbots that can automatically respond to human requests to voice assistants used in our daily life, the power of AI-enabled natural language processing (NLP) is improving the interactions between humans and machines. NLP is broadly defined as the automatic manipulation of natural language, either in speech or text form, by software. NLP-enabled systems aim to understand human speech and typed language, interpret it in a form that machines can process, and respond back using human language forms rather than code. AI systems have greatly improved the accuracy and flexibility of NLP systems, enabling machines to communicate in hundreds of languages and across different application domains.


How to Implement a Ticket Triaging System with AI

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Customer queries are the bane of most customer support teams, not because they don't like dealing with them, but because they don't have a proper process in place that lets them handle excessive ticket volumes easily and effectively. When a support ticket drops into a queue, or an agent receives an email with a customer issue, the ticket or email might pass through three different agents before finally landing in the correct hands to deal with the issue – leading to bottlenecks and bad customer experiences. Bugs, forgotten passwords, system errors, integration queries… There are so many different issues that agents have to deal with, so that the customer remains happy and the company retains them. And while customer support endeavors to respond to queries as quickly as possible, it's difficult when faced with huge volumes of tickets. On top of that, more and more customers expect immediate responses – 64% of consumers and 80% of business buyers said they expect companies to respond to and interact with them in real time.