NLP Techniques for Customer Service Automation

Amazon Alexa, Google Home, and Apple Siri are all becoming more popular. However, the 66.4 million people who own smart speakers aren't just learning about the weather. More people are turning to machine learning to improve their customer service interactions.

 

As a result, Gartner predicts that 30% of technology interactions will be through "conversations" with smart machines, many of which will be voice-based. Furthermore, studies have shown that chat can handle 80% of customer communications.

 

Overview of NLP

 

Natural language processing (NLP) is a subfield of Artificial Intelligence (AI). This is a widely used technology for personal assistants in a variety of business fields/areas. This technology analyzes the user's speech, breaks it down for proper understanding, and processes it accordingly. Because this is a relatively new and effective approach, it is in high demand in today's market. 

 

Natural Language Processing is a developing field that has already enabled many transitions such as compatibility with smart devices and interactive conversations with humans. AI applications in NLP focused on knowledge representation, logical reasoning, and constraint satisfaction.

 

Natural Language Processing, also known as NLP, is a type of artificial intelligence that allows computer programmes to process and analyze unstructured data, primarily free-form text data. The majority of your customer service requests are unstructured. Consider support tickets, emails, and Tweets—all of which are free-form text!

 

Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with natural language interactions between computers and humans. It entails using computational techniques to process and analyze natural language data, such as text and speech, in order to comprehend the meaning of the language.

 

NLP's applications have made it one of the most sought-after methods for implementing machine learning. Natural Language Processing (NLP) is a field that studies how computers and humans communicate in natural language by combining computer science, linguistics, and machine learning. The goal of NLP is to teach computers to interpret and generate human language. This not only improves the efficiency of human work but also facilitates interaction with the machine. NLP bridges the communication gap between humans and electronic devices.

 

Also Read | 7 Real-World Examples of Using NLP in Business

 

Pros of Customer Service Automation

 

The automation revolution is already altering the business landscape. Few industries have been spared, from retail to manufacturing, healthcare to finance. With the advent of artificial intelligence (AI) and machine learning, the possibilities for the coming years are nearly limitless.

 

Automation is transforming the retail industry in a variety of ways, ranging from automated chatbots to RPA implementation. Customer service automation is having far-reaching consequences without our knowledge; the way we interact with our favorite brands has shifted dramatically in recent years. Listed below are some of the advantages of customer service automation

 

1. Quicker responses

 

When considering customer service issues, it's always best to put yourself in the shoes of the customers. Consider the things that have irritated you in the past. It stands to reason that you should try to eliminate these as much as possible in order to improve the customer experience at your business.

 

Slow response times are a major customer complaint. Consider all the times you've waited interminably on the other end of a phone or at a customer service desk, hoping in vain that someone would come to your aid. If this happened to you again, you'd probably go somewhere else, and your customers will probably do the same.

 

2. Reduces the cost of customer service

 

We can't discuss customer service automation without mentioning cost. Engaging with customers costs a fraction of what a live representative would. According to McKinsey, businesses that use technology to improve their customer experience, such as automation, can save up to 40% on service costs. Companies that scale can reduce the need for new hires. The cost savings go beyond labor as you reduce your office footprint while increasing your customer lifetime value.

 

3. Increases efficiencies and time to value

 

Customers benefit from shorter response times thanks to automation. Diverting customers away from your company allows agents to solve more complex problems.

 

Instead of focusing on call times, human agents can concentrate on outcomes. Consider the possibility of resolving issues the first time rather than bouncing customers around multiple people. This strategy results in happier customers and fewer complaints.

 

4. Improved customer service

 

Customer service automation enables you to offer a more responsive and dependable level of service. While many customers remain skeptical of automated assistants, automation provides a more consistent and dependable level of customer service.

 

This means that assistance is always available, even outside of regular business hours. (Complex queries are always handled as soon as a human assistant is available.)

 

NLP In Customer Service Automation

 

Here are four ways AI, specifically NLP and Machine Learning, can help you improve response times and efficiency in your customer service processes.

 

1. Virtual assistants and chatbots

 

Creating chatbots and virtual assistants that can interact with customers and users via text or voice is one of the most popular applications of NLP. Chatbots and virtual assistants can provide information, answer questions, solve problems, and carry out tasks without the need for human intervention. They can also use natural language generation (NLG) to generate relevant, coherent, and engaging human-like responses. Chatbots and virtual assistants can enhance customer service and user experience by shortening wait times, increasing availability, and increasing satisfaction.

 

2. Suggestions for Historical Threads

 

While some support questions can be easily answered with the best answers, others can be complex and necessitate extensive research. Looking into related historical threads (that have been successfully resolved) and understanding how those issues were resolved is one way for CSRs to solve complex issues. Your service representatives will be able to better resolve the issue at hand or provide a more comprehensive answer to the support question as a result of this.

 

3. Analysis of feelings

 

Another application of NLP is sentiment analysis, which is the process of identifying and extracting customers' and users' emotions, opinions, and attitudes from text or voice feedback. Businesses can use sentiment analysis to learn how their customers and users feel about their products, services, features, or brand. It can also assist them in identifying the strengths and weaknesses of their offerings, as well as areas for improvement. 

 

Sentiment analysis can help businesses improve customer service and user experience by allowing them to monitor and respond to customer and user feedback, as well as tailor their marketing and communication strategies accordingly.

 

4. Text categorization

 

Text classification, the process of assigning a text document to one or more predefined categories based on its content, is a fourth application of NLP. Text classification can assist businesses in organizing, filtering, and analyzing customer and user data such as emails, reviews, comments, or feedback. 

 

Text classification can help businesses improve customer service and user experience by automating and streamlining workflows such as routing customer inquiries to the appropriate department or agent, detecting spam or abusive messages, and generating insights and recommendations.

 

Wrapping Up

 

Customer service is an excellent place to use AI and see its immediate impact. This is largely because some of the tasks are manual and repetitive.

 

Furthermore, the massive amounts of data generated during customer interactions can be a boon in disguise. While it is dispersed and unwieldy, it can be used to train several machine learning models to automate some of the inefficiencies with a good data strategy.

Also Read | 7 Applications of NLP in the Education Sector