While Natural Language Processing (NLP) has been around for some time, it has only recently achieved the level of accuracy required to provide real value in customer engagement platforms. Artificial intelligence-enhanced natural language processing (NLP) is improving human-machine interactions through interactive chatbots.

There is an overload of requests at customer service support centres and help desks. Using NLP in customer service, much of this work can be offloaded for routine and simple questions, allowing employees to focus on more complex and detailed tasks that require human interaction.

Here are 9 powerful ways to utilize NLP in customer service

Customer service chatbots

The use of chatbots by organizations in their customer service departments is becoming increasingly popular. AI-powered chatbots are helping companies improve customer loyalty and experience.. Chatbots can be trained to learn industry language and answer industry-specific questions with the help of NLP and can be integrated into any third-party APIs to fetch data required to assist in resolving the customer query.

Customer sentiment analysis

NLP tools can also help customer service departments understand customer sentiment – analyze customer feedback and discover common topics of interest, identify complaints and track critical trends over time. NLP is now being implemented in customer service through sentiment analysis tools that automatically monitor written text, such as reviews and social media posts, to collect sentiment in real-time. NLP and customer service solutions can then be used to proactively respond to complaints and negative remarks.

Customer satisfaction and trend spotting

With the aid of NLP, conversational agents track and analyze customer behavior and determine whether they are satisfied with a product or not. In order to handle large volumes of customer feedback, companies are increasingly turning to machine learning and NLP software.

Multi-lingual bots for businesses spread worldwide

Most customers first preference of communication is their native language over any other. Chatbots and NLP tools are also being used by companies to improve customer engagement. An NLP tool can interact with the customer in the language initiated by them.  A single bot that can interact in multiple languages is the need of the hour.

Auto-route Questions

Using machine learning and NLP techniques, support questions can be automatically routed to the appropriate service representative. The incoming questions can be classified into predefined categories. These categories can be used to route questions to the appropriate representatives or teams. You’ll ensure a swift, timely response by intelligently routing questions to relevant experts.

Understanding customer feedback

The use of NLP helps identify common words and phrases. Words such as “modern,” “intuitive,” and “expensive,” for example, might indicate to your customers that you are a luxury brand. Topics mentioned in feedback forms can also be found using NLP. The words that might be used are “easy onboarding” or “affordable plans.” NLP combined with sentiment analysis provides a comprehensive overview of customer opinions, making it a time-effective way to analyze customer feedback.

Agent support

According to Salesforce, 69% of high-performing service agents actively look for opportunities to use artificial intelligence. Conversational AI can handle queries that don’t require much attention. Agents can then devote more time to handling complex queries and emotional questions.

Business data analysis

Businesses can analyze qualitative data from customer feedback using NLP. It can also retrieve information from elsewhere and outline common trends for your team to follow. Customer complaints are well served by this method. The NLP module can identify trends in the data, whether they’re coming directly from emails or through other question boxes on the cancellation form, and notify your team before they become a problem.

Search bars in knowledge bases

The results for your users’ query on a search bar must display relevant information. As a result, key metrics such as bounce rate, conversions, and time on site are affected. However, your search bar won’t show relevant results for those queries without NLP. The machine learning software interprets those queries. The algorithm understands what the user typed, even if it wasn’t in plain English, contains grammatical errors, or is misspelt.

The use of NLP tools is helping companies better engage with customers, better understand customer sentiment, and improve overall customer satisfaction. Therefore, AI-powered bots with NLP tools will continue to show ROI and positive results for all organizations.