4 min
Artificial Intelligence (AI) consists of very real and tangible technologies such as Natural Language Processing (NLP), Natural Language Understanding (NLU), and Machine Learning. Contact centers use these technologies to improve the customer experience (CX) and increase their efficiency.
What Are the NLP and the NLU?
Natural Language Processing (NLP) is a technological process that enhances the ability to convert text or audio into encoded structured information. Machines using NPL can understand human speech and respond appropriately. This allows humans to interact with computers using conversational speech.
Natural Language Understanding (NLU) is a subcategory of NLP that enables machines to understand incoming text or audio. Its counterpart is Natural Language Generation (NLG), which allows the computer to “respond.” When the two come together, conversations with humans are possible.
Uses of Natural Language Processing (NLP)
- Convert voice to text when sending an SMS.
- Ask to speak to an “operator” when interacting with an IVR.
- Use grammar or autocomplete checkers when writing.
- Use online translation tools on a website.
The NLP and NLU use machine learning to get smarter over time.
What is Machine Learning?
It is a form of Artificial Intelligence that enables computers and applications to learn from the additional data they consume rather than relying on programmed rules. Systems using machine learning have the ability to automatically learn and improve from experience by predicting outcomes without being explicitly programmed to do so.
Artificial Intelligence (AI) of the Call Center
AI is appearing in cloud contact centers by incorporating Artificial Intelligence into core applications, as well as creating new solutions that effectively leverage the enormous amount of data that call centers produce.
What Are the AI-powered Capabilities that Are Improving Call Center Bottom Line?
- Recommend forecasting algorithms. Artificial Intelligence workforce management software can analyze large amounts of historical data and recommend the best forecasting algorithm to use, providing more accurate results.
- Routing interactions according to the personality and preferences of the customer. When Automatic Call Distributors (ACDs) use AI, organizations can personalize interactions by matching customers with agents who can resolve their query.
- Enable more natural self-service experiences. Self-service tools that use AI, such as conversational IVRs and virtual agents, allow users to solve their problems in their own words.
- Real-time agent coaching. Real-time interaction targeting solutions can analyze customer sentiment and agent behaviors and provide agents with real-time advice to improve interactions as they occur.
These are just a few examples of how Artificial Intelligence is transforming contact center processes. And most of these new capabilities wouldn’t be possible without Natural Language Processing (NPL) and Natural Language Understanding (NLU).
Conversational IVR
Traditional Interactive Voice Response (IVR) systems greet customers at the beginning of incoming calls, allow callers to interact with menus, and facilitate self-service.
Fortunately, the NLP can be used to greatly improve the IVR user experience. The first iteration of using NLP with IVR eliminated the need for callers using their phone keypad to interact with IVR menus. Instead of “press 1 for sales,” callers can say “1” or “sales.” This is more convenient, but is very rule-based and still leaves customers to deal with often overly complex menus.
The introduction of conversational IVRs completely changed the user experience (UX). When customers are greeted with, “How can we help you today?” They can state their problem and the NLP / NLU will understand them and allow them to skip menus entirely. This translated entry tells the IVR what to do next. By For example, if the caller says, “I need to activate my new debit card,” the IVR will know to route the caller to a qualified agent, or perhaps route the customer to the self-service where the NLP will once again it would allow interacting with the system conversationally.
Conversational IVRs create a more natural customer service experience, as callers can say what they need help with and complete more effective and satisfying self-service transactions. Additionally, conversational IVRs allow for faster and smarter routing, which can lead to faster and more accurate resolutions, lower handling times, and fewer call transfers between agents or departments. It may take a while, but the NPL is intended to improve user perceptions of IVRs.
Chatbots
Chatbots are everywhere these days and they have a wide spectrum of capabilities. At the simple end of the spectrum are chatbots that don’t use AI, which can be useful for handling strictly defined tasks, such as answering common questions, but cannot understand human speech beyond the terms for which they have been programmed to recognize, and they will never get better without human intervention.
At the other end of the spectrum are advanced chatbots, also known as virtual agents that use NLP, NLU, and machine learning to provide conversational customer support. These bots are not limited by rules and programmed keywords. The NLP enables them to understand what users are saying and to respond accurately or take the correct action.
Chatbots that take advantage of Artificial Intelligence offer better and more effective CX than rule-based bots. Because they can understand human speech and user intent, they are capable of executing a much broader set of tasks, including facilitating full end-to-end self-service.
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