AI with NLP and NLU to Improve Customer Outcomes

In this article, we will look at the benefits of automating customer service with after-sales chatbots to boost CSAT and NPS while reducing customer support costs. The article doesn’t get into the details of what consumer satisfaction and net promoter scores to analyze consumer satisfaction, how likely they are to provide referral business or actively promote or discourage others. The increased use of natural language technology in a range of customer interactions is evolving faster than the technology itself.

While NLP is most commonly associated with language in IVRs, consumers are increasingly using SMS platforms, and NLU is also applied to these platforms. Chatbot automation with NLP provides the ability to scale your customer service. Employing more people to handle incoming customer queries and chats can be automated using NLP and NLU (natural language comprehension is a subset of NLP) to help. In this way, automating your customer services with chatbots and NLP can help you reduce your costs.

Let’s start by understanding that conversational AI solutions are a programmatic and intelligent way to offer a conversation experience that mimics conversations between real people through digital telecommunications technologies. Natural Language Processing (NLP) is simply the science of the gain of meaning from text and data to facilitate the interaction between man and machine most simply.

The first attempts at computer-aided understanding of natural language (NLU) date back to before the introduction of personal computers and personal computers, but NLU technology has evolved for decades. As the data available for computer-powered training systems in recent years has grown, NLU has been combined with NLP to understand and respond to what a person says and types in milliseconds. This makes interacted voice response systems ( IVR ) and chatbots handle an increasing number of customer interactions.

Artificial intelligence (AI) is evolving at a phenomenal rate and there are many opportunities for companies to use it to learn more about their customers and to provide them with the support they need. From data collection and voice recognition to message response times, AI can improve the customer experience in any way it can. Companies that know their customers well and respond accordingly to their needs and lifestyles prevail. Customer service chatbots can deliver key business metrics. High-quality chatbots can predict what customers need and take advantage of online interactions that have taken place in the past and what they have said and use advanced tools in natural language processing and analysis. This makes chatbots useful for providing real-time support for customer FAQs, virtual services, and issues that do not require escalation, such as rebooking delivery. Chatbots also work as a first-line customer service option that allows you to store human agents for more complicated queries. You can provide customers with information at any time of day, time zone, and in any language. In the case of chatbots that use artificial intelligence (AI) and machine learning technologies, they can learn from customer interactions and improve over time.

Given that human agents have to process more than 1,000 records of feedback every day, it is essential for companies with a significant customer base who want to understand their data to get help from machines. Since machines are constantly improving and can understand and predict relationships themselves, the ability to read and understand portions of customer feedback will increase inaccuracy. This requires that if you want to automate customer service, it is rules-based and optimized for machine learning. Significant advances in NLU and machine learning have shown that they can do just that.

Product managers of AI offerings have the task of uncovering market gaps and opportunities for customer benefit. Every day, petabytes of text data are available, and companies are trying to figure out how to structure, clean up, and gain deeper insights. The ultimate goal is to create a unique and effective solution, and then developers can bring it to market. Each week, customers ask questions about how they can integrate AI into their services, what potential NLP has in their business, and how NLP can be harnessed for them. As mentioned earlier, customer service chatbots can be deployed across multiple channels, giving brands the ability to create omnichannel channels.

NLP has enormous potential for companies to optimize their customer experience. When customer support requests are complex or beyond the scope of a chatbot, a live agent can give them a seamless process based on their skills and current workload. Businesses can use AI by using Natural Language Understanding (NLP) and NLU to identify customers’ needs and provide the right information to the account manager to deliver world-class customer service.

The combination of techniques uses emotion analysis as part of language extraction and tokenization to analyze the intention behind words. It aims to help enterprise and small businesses find insights into emails, customer reviews, social media, support tickets, and other texts. Through real-time analysis of customer service calls, chats and emails, organizations can understand conversations between customer service representatives and customers.