Dos

AI In Business: Employing Transformer NLP Models In Customer Service

Present-day companies receive customer support requests and inquiries from different customer support channels. This may include phone calls, emails, ticketing systems, tweets, live chat conversations with customer support agents, and more. That’s a lot of data that companies deal with each day, data that is primarily unstructured and scattered in nature, making it extremely hard to analyze and manage.

The thing is, all this word-based data can actually be leveraged to increase the speed in responding to customer service requests and reduce the overall volume of incoming support tickets. Do you know that the average response time to customer service inquiries is more than twelve hours? Needless to say, that’s too long! So, how can you reduce customer service response times while also being practical and on top of your game?      

This can partly be done through automation thanks to natural language processing and machine learning, both subfields within artificial intelligence. As conversational AI has the power to humanize customer language and solves customer queries without the need for human input, the future of customer service lies well within the walls of AI technologies and their latest advances. This is particularly true for natural language processing, in which the latest developments can help your customers obtain prompt answers to their queries without actually speaking to the person on the other end. 

Photo from InfoWorld

In this informative blog post, we’ll take a look at the five most significant areas of customer support where AI and NLP can help streamline your customer service workflow so that you can increase your overall efficiency and reduce response times. But first, let’s take a closer look at natural language processing and how transformer NLP models are modifying the customer service industry for good.

What Is Natural Language Processing? 

Natural language processing, or NLP, refers to the division of computer science, or more specifically the branch of artificial intelligence, concerned with giving computers and machines the ability to comprehend text words in the same manner human beings can. NLP combines computational linguistics (rule-based modeling of human language) with machine learning, statistical, and deep learning models. Collectively, these technologies permit computers to successfully process human language in the form of voice or text data to understand its whole meaning, together with the writer or speaker’s intent and sentiment. 

Photo from SAP Blogs

NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize vast volumes of text in real-time. Chances are, you’ve already interacted with an NLP-powered software or system in the form of digital assistants, voice-operated GPS systems, speech-to-text dictation software, customer service solutions, and other consumer conveniences. In addition, NLP plays a massive role in modern-day enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. 

As a matter of fact, as NLP can interpret, analyze customers’ verbal statements, and provide them with an answer all without human intervention, NLP solutions like the Y Meadows software are high on the list of tools that drive the customer service industry and the successful resolution of customer service tickets to enhance customer satisfaction and other important KPI metrics

Transformer NLP Models: Models That Understand Text And Perform Sentiment Analysis 

As you may know, a transformer is a new type of neural network architecture. This type of neural network architecture has improved accuracy and efficiency across different technologies, and natural language processing makes no exemption. 

Photo from YouTube

Recent advancements have helped computer scientists develop transformer NLP models that stand the potential to find patterns within the existing data, understand text, perform sentiment analysis, answer questions, summarize reports, and generate new text. The integration of the Y Meadows model, which is predominantly built on transformer NLP models, within your existing systems can successfully help your organization and customer service department to: 

  • Understand the relationship between sequential elements of each written or spoken sentence that are far from each other. 
  • Accurately understand what the customer issue is, and provide a prompt solution. 
  • Account for faster processing of more data in lesser time. 
  • Work with any kind of sequential data. 
  • Perform customer sentiment analysis to offer your company invaluable insights into what your customers think about your product or service.
  • Predict what the customer on the other end wants and what could happen next. 
  • Prove to be very helpful in anomaly detection for internal processes and provide viable solutions. 

In other words, with transformers in place, the process of modeling the relationship between words became more effortless than ever. Below, we’ll take a look at four genuine ways to use transformer NLP models in customer service.  

4 Ways To Employ Transformer NLP Models In Customer Service 

Recommending Answers 

Customer service agents spend most of their time researching answers, or solutions, to customer requests, inquiries, and questions. So, when a service agent tries to respond to a customer request, they can easily get overwhelmed with determining the best possible answer for the customer from a pool of possible solutions. In reality, customer service representatives need one or maybe two answers to address the request rightfully.

Photo from Rework Blog 

Most companies maintain an exhaustive list of problems and adequate answers that the customer service agent has to search through, sometimes even manually. This routine often proves to be painfully slow and energy-draining if the agent needs to search their system for each customer request or question. Enter natural language processing. 

Transformer NLP models can be advantageous here in suggesting the best answers given a support request. Even more, an “answer score” can be easily generated to indicate the likelihood of a solution resolving the customer issue. With this approach and the help of natural language processing technology, instead of explicitly searching for each customer issue, customer service agents can now have the needed information pushed to them automatically, preventing breaks in their workflow and efficiency.

By not spending time searching for answers to frequent questions, response times can be significantly improved, which means that the service agents will have more time to address a larger volume of customer support tickets.

Suggesting Historical Threads 

While some customer support inquiries can be easily answered with the recommended best answers, others can be pretty complicated, demanding comprehensive research and extensive human input.

One smart way for customer service representatives to solve a complex issue is by looking for related historical threads that have been successfully resolved as well as understanding how they were resolved. With this, customer service agents can better resolve incoming matters at hand or develop a more complete answer to new support questions. 

Transformer NLP models help you easily automate this process by recommending any related historical threads for any given support request. This can save the agents from performing extensive searches and contacting peers and managers for help on a given issue. 

Again, this will undoubtedly improve response times and first contact resolution as the customer service agents are better equipped to handle issues. On top of that, your company will reduce a significant part of the follow-up support requests.

Grouping Similar Questions And Requests 

Context switching can be tricky. For example, going from resolving issues related to billing to signups and then back to billing can be a productivity killer for each agent out there. Ultimately, disturbing your energy over a great variety of tasks can reduce your effectiveness the same way interruptions can. 

By grouping similar support questions and requests, customer service agents can address similar problems in bulk, where they’ll have to tap into the knowledge source, and the pool of prospective answers and solutions are related. With NLP, you can automatically group similar questions and requests to maintain the same train of thought while resolving issues. In some instances, the solutions may be identical or very similar, while in others, the agents will know which steps to take to resolve a request while everything is fresh in memory.  

Auto-Routing Requests 

Customer support requests and questions can be incredibly messy. One question may be affiliated with billing, another related to a hacked account, and the next may be related to login issues. Routing customer support tickets to a service desk that manually assigns them to a qualified support team or agent can be inefficient, slow, and an error-prone strategy. Regrettably, delays in ticket assignment to the right agent can delay resolution and leave the customers feeling woeful with the company’s service. 

With the help of machine learning and transformer NLP models, support requests can be automatically routed to suitable service agents. This can be done by classifying each incoming request into a predefined set of categories. Then, the categories can be automatically routed to the agents or team of agents that are best at handling such topics. Thus, by auto-routing requests and questions to people who possess the relevant expertise, you ensure a timely, fast response and better resolution rates. 

Photo from Business 2 Community

In Conclusion 

Customer service is a fantastic place for leveraging artificial intelligence and its subcategories and an area where you can reap the benefits of these remarkable technological advancements. This is primarily due to the repetitive, manual nature of many tasks that are ever-present in the customer service efforts of each organization, tasks that could be easily automated thanks to AI, machine learning, and NLP transformer models. 

So, in case you’re interested to find out more about how AI and NLP can vitalize your customer service efforts, get in touch with our sales department and see what Y Meadows can do for your organization!

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