Although the study of natural language processing or NLP began somewhere around the 1950s when Alan Turing published an article named “Computing Machinery and Intelligence” which proposed what is now known as the “Turing Test". It’s only recently that NLP reached a high enough level of precision to bring real added value to organizations and more importantly—their customers.

This subfield of artificial intelligence surrounds your daily life probably more than you think:

• It empowers your smartphone’s spell check system;
• It makes it possible for voice assistants like Siri, Cortana, and Alexa to be as practical and valuable as they are;
• It propels all automatic suggestions when you type a search query in Google or in any other search engine.
  

In the domain of customer service and customer care, nowadays, many organizations leverage artificial intelligence and advanced natural language processing solutions to comprehend better and leverage all facets of customer relationship management. These organizations collect vast amounts of unstructured data in the form of text and audio from different channels like feedback surveys, social media, and grievance reports. 

An exhaustive analysis of such data will permit them to fine-tune their business strategies, improve customer retention, establish new contacts, and accelerate growth. This is where NLP is used in customer care to understand customer feedback and translate it into actionable intelligence for organizations to improve themselves and propose more precise human-machine interactions constantly. 

In this post, we’ll explain in simple terms all you need to know about NLP in customer care, starting from the basics of NLP technology and how it works, up to the crossroads of artificial intelligence and linguistics to transcend customer experiences to the next level.

A Basic Definition Of NLP

NLP is the acronym for natural language processing, a technology that is a subfield of artificial intelligence and a branch of data science. NLP is the technology that allows computers to analyze and understand human language and generate interactions by transforming unstructured data into intelligent conversation. 

In other words, natural language processing is the system that allows humans and machines to speak the same language so that companies can automatically analyze the sentences produced by their customers to make the most adapted decisions. 

Within natural language processing, different subparts coexist, with the most notable including: 

• Natural language understanding or NLU has the role of understanding in-depth data and exchanges and identifying the intentions of human writing or speech. 
• Natural language generation, also known as NLG, is the subpart of NLP, which automatically creates and generates exchanges in a particular language, thanks to AI. Data is transformed into text, and organizations can automate specific manual processes.  

Now that we know how NLP allows for the analysis and understanding of human interactions thanks to NLU, and how it can automatically generate original content thanks to NLG, we’re ready to move on from the definition and see how NLP in customer care works.

How Does NLP Work? 

To fully perceive how NLP in customer care works, we first need to take a step back. NLP’s history dates back to the 1950s with the automatic translation of simple sentences. Afterward, in the 1960s and 1970s, the initial chatbot models saw the light of the day, but it wasn’t until the end of the 1980s that research achieved the first machine learning algorithms. 

With the help of those algorithms, language processing and recognition were initially able to perfect themselves. Nowadays, NLP continues its evolution thanks to machine learning, deep learning, and other powerful learning capabilities. This is how NLP’s use breaks down in customer service: 

• Step 1: The human interacts with the machine. In most instances, the customer service request is made via a dynamic FAQ, voice assistant, and other support channels. It doesn't matter if the request is written or verbally said; the user's intention is always expressed in natural human language.
• Step 2: The machine gets the request to analyze it further and better understand it. At this phase, natural language processing algorithms use a couple of different techniques, like semantic analysis to decode the meaning of the text and syntactic analysis to detect potential grammatical issues. Afterward, the request is studied thoroughly and compared in real-time with the data already saved by the company. This is where machine and deep learning come into play, as they are both subsets of AI focused on creating systems that learn or improve performance based on the data they process. The system opts for the best decision before formulating its response for the client. 
• Step 3: The machine responds to the human. Depending on the communication channel, the machine response can be oral or written. Thanks to the work of NLP and how NLP is used in customer care, the generated answer or response is always clear and coherent. The idea with NLP in customer care is to offer the best possible user experience by proposing a relevant response in the customer's language.
  

The Top Advantages of NLP In Customer Care 

Thanks to the technologies that fall under the AI umbrella, NLP can be put in service of the customers and the customer service representatives to better understand and serve them. The advantages of such processes are numerous for users as for organizations and their employees: 

NLP Can Improve The Overall Customer Experience 

The primary benefit offered by NLP in customer care is its ability to improve the overall customer experience. This groundbreaking technology can respond to the new needs of modern-day consumers: immediacy, autonomy, and accessibility. For instance, customer self-service solutions like dynamic FAQs, knowledge bases, and NLP-powered solutions can respond to customer requests in real-time, 24/7.

In the course of the customer journey, NLP will allow you to propose an optimized and highly-personalized conversational experience to your clients. Before, during, or even after a purchase is made, innovative NLP-powered solutions can reinforce customer knowledge, which will capture your clients' natural language to improve your knowledge thanks to their precise feedback.  

NLP Will Support Your Customer Service 

NLP is used in customer care and is a genuine support lever for various entities, but it’s certainly not meant to replace your customer service teams. In fact, NLP in customer care should be viewed and perceived as high-quality internal support because this tool can deliver precious qualitative and quantitative information. 

Moreover, NLP provides considerable saved time to your customer service agents. Assignments of understanding, analysis, and responses are all automated, thanks to AI and deep learning, which complement NLP in the best possible ways. As a result, agents can dedicate themselves to more complicated missions and issues regarding customer support and leave the NLP-powered software to be in charge of your self-service technologies, FAQs, or virtual assistant solutions to treat the more simple customer service exchanges that don’t demand human assistance.

Lastly, NLP is not exclusively an opportunity for customer service departments dedicated to after-sales service, as marketing and sales teams can also take advantage of this tool to improve their strategies, brand image, and competitive positioning. For instance, if a competitor is mentioned by one of your customers during an exchange with a customer service agent, the syntactic and semantic analysis will benefit. This way, you can determine the customer’s feelings towards the competition and act to develop a preference in your way.

NLP Can Help Your Business Gather Valuable Business Data 

Also, one of the most potent ways in which NLP is used in customer care and customer service is on the back end - data analysis. For example, phone call inquiries, customer service tickets, and other ways of contacting customer service departments provide vast amounts of data that could benefit companies if they only find a way to contextualize and sort it. Well, natural language processing provides just that. 

For instance, sentiment analysis can allow companies to match the emotional content of customer communications with actions they take. NLP in customer care can also be used to glean themes and categories from communications so companies can learn more about what their clients are talking about, where the gaps in their offerings or resources are, and how they can address and fix these concerns. 

All things considered, NLP can help your organization extract valuable business insights from different types of customer interaction to provide better customer service support and analyze other business areas by uncovering patterns, trends, and relevant information.

New Developments In Natural Language Processing 

Since NLP is a dynamic and rapidly evolving field, many new techniques, trends, and applications constantly emerge. Or in other words, with NLP, only one thing is certain: it will only continue to grow and expand its features in 2021 and beyond. So here are the top new developments in natural language processing that you could undoubtedly find intriguing and consider incorporating into your business:

NLP Transformer Models 

Ever since their preliminary development, transformer-based architectures have entirely redefined the field of NLP and set the tone for various artificial intelligence-driven benchmarks and tasks. Transformer models are an advanced, highly sophisticated AI model that has benefited from education the likes of which humans might gain in a lifetime. 

These architectures are ordinarily trained in a semi-supervised manner on a massive text corpus. By digesting these huge corpora of text, transformer-based architectures become vigorous language models capable of understanding and performing predictive analytics based on textual analysis. In effect, this level of extensive training permits transformer NLP models to approximate human text cognition at a remarkable level.

Lately, it has been shown that these imposing learning models can also instantly be fine-tuned for upper-level customer service tasks like sentiment analysis, duplicate question detection, and other text-based cognitive tasks. All this results in faster training and better performance of NLP-enriched systems in customer care.   

BERT And ELMO Will Lead The Way 

Perhaps the most significant breakthrough of NLP in 2021 has been the creation and development of machine learning models that can create articles from scratch, with GPT-3 or generative pre-trained transformer 3 leading the way. 

In the upcoming period, the NLP community will be focusing on BERT or bidirectional encoder representations from transformers and ELMO—embeddings from language models. These innovative models are trained on enormous amounts of data in order to drastically improve the performance of a wide range of NLP problems.

Combining Supervised And Unsupervised Machine Learning Methods 

When training a model for NLP purposes, combining supervised and unsupervised methods seems to provide more accurate results. 

Supervised learning, frequently used for tasks like topic classification, demands a vast amount of tagged data and many iterations until a model can make precise predictions. 

On the other hand, in unsupervised learning, there’s no labeled data as the model learns from input data and can detect patterns and make inferences on unseen data on its own. A fine example for unsupervised learning can be made out of clustering, where similar objects are grouped together. 

Combining unsupervised and supervised learning models have been shown to boost the machine learning model’s performance, particularly for text analysis, so expect to see more of it in the time to come. 

Training Models With Reinforcement Learning 

Reinforcement learning is another field of machine learning that is predicted to grow in the years to come. In essence, reinforcement algorithms learn by doing through trial and error using genuine feedback from previous experiences and actions. 

In natural language processing, reinforcement learning is used to speed up tasks like machine translation, question answering, and summarization. Nowadays, most NLP models are first trained with supervised algorithms and only after fine-tuned using reinforcement learning. 

Final Words On How NLP Is Used For An Improved Customer Care 

Like any other form of AI, natural language processing exists to make human life easier and better. And even though its technology is still in constant development, it’s already made great leaps in enhancing overall customer satisfaction in the customer service domain. 

Nowadays, NLP in customer care doesn’t just address the simple task of rerouting calls to agents, but it also provides top-notch data analysis, all while being able to interact with users in the language they prefer.

Even more, companies can reduce operational costs without compromising service quality. And because companies are always looking for ways to deliver more output with less cost, an effective NLP model  can ensure the success of your call centers and customer service department in the most efficient way. 

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