What NLP, NLU, and NLG Mean, and How They Help With Running Your Contact Center

Nearly 9 out of 10 contact center leaders say they’re using AI and automation in their contact center operation, according to our new study.

Why?

AI plays an important role in automating and improving contact center sales performance and customer service while allowing companies to extract valuable insights.

In fact, the global call center artificial intelligence (AI) market is projected to reach $7.5 billion by 2030.

So how does this work within the contact center environment?

In order to understand the impact of AI we have to first understand NLP, NLU & NLG and the differences between them.

‍What is NLP?

Natural Language Processing (NLP) refers to the branch of artificial intelligence or AI concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. It is a component of artificial intelligence that enables computers to understand human language in both written and verbal forms. One of the common use cases of NLP in contact centers is to enable Interactive voice response (IVR) systems for customer interaction. Other use cases could be question answering, text classification such as intent identification and information retrieval with features like automatic suggestions. Using NLP, customers can interact with menus using natural speech.

What is NLU?

Natural Language Understanding (NLU) is a subset of natural language processing. It is a field of AI which analyzes what human language means, beyond what is said. Using NLU, AI systems can precisely define the intent of a given user, no matter how they say it. Within the contact center environment, NLU is used to power the quality management process and real-time use cases like live guidance. By connecting NLU to all the types of customer interactions like calls, chats, messaging or emails, businesses can perform customer sentiment analysis which can help how their agents can respond to ensure high customer satisfaction and retention.

What is NLG?

Natural Language Generation (NLG) is another subset of natural language processing. NLG enables AI systems to produce human language text responses based on some data input. NLG also includes text summarization capabilities from data input. One of the common use cases for NLG in contact centers is call summarization. Using NLG, contact centers can quickly generate a summary from the customer call. NLG, powered by Generative AI, can create different forms of summaries.

NLP vs NLU vs NLG for Contact Centers

In summary, NLP is the overarching practice of understanding text and spoken words, with NLU and NLG as subsets of NLP. Each performs a separate function for contact centers, but when combined they can be used to perform syntactic and semantic analysis of text and speech to extract the meaning of the sentence and summarization. Using NLU, AI systems can precisely define the intent of a given user, no matter how they say it. NLG is used for text generation in English or other languages, by a machine based on a given data input.

Three broad ways NLP, NLU and NLG can be used in contact centers to derive insights from conversations

Contact center operators and CX leaders want to improve customer experience, increase revenue generation and reduce compliance risk. Here are three broad ways in which NLU can be used in contact centers.

Post-Call Interactions Analysis - Once the call is completed, contact center operators run quality assurance processes to evaluate the agent's performance to coach and train them. Quality management is used to improve the CX by tracking agent calls and interactions against a checklist of scoring criteria in a spreadsheet. But doing this the manual way is time consuming, inefficient and inconsistent and prevents scaling the QA operations. Using natural language understanding (NLU) to automatically evaluate the post call interactions, contact centers can quickly identify issues across large volumes of data while eliminating human bias and error. Additionally use calibration as a continuous improvement process for Improving automatic quality assurance accuracy and performance

Real Time Interactions - Contact center operators want to grow the revenue and improve the conversation outcomes by providing the sellers and agents real-time guidance and coaching. Whether it is to drive more sales, reduce compliance risks or improve customer experience real-time agent assist and supervisor assist applications provide live assistance improving the agent performance at every touchpoint. Natural language Understanding (NLU) is used to assist the agent during a live call by analyzing the context of the conversation as it happens and suggest knowledge, reminders and suggested responses to improve the overall customer experience. Natural Language Generation (NLG) is used to provide call notes to agents by identifying critical information from the conversation.

Sentiment, Empathy and Tonality - Using natural language understanding (NLU) and machine learning, the software can detect emotions, sentiment and more in customer communications. For example, conversation intelligence can detect negative sentiment and emotions to provide the right responses, on whatever channel customers are using.

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What NLP/NLU/NLG capabilities to consider when looking for a contact center AI Platform

Over the past decade, how businesses sell or perform customer service has evolved dramatically due to changes in how customers interact with the business. This is forcing contact centers to explore new ways to use technology to ensure better customer experience, customer satisfaction, and retention.

With conversation Intelligence powered by NLP, NLU and NLG, businesses can build a solid contact center platform that can service the needs of customers today and tomorrow. Here are some of the capabilities that businesses should look for in the AI stack for NLP, NLU & NLG capabilities for their contact center operations:

Ease of Use: This is an important aspect to consider when selecting the software. The graphical interface and the UX should be able to provide an easy way to set up intents, entities as well as performing advanced operations.
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Accuracy: Transcription accuracy is key to providing the best customer experience. For example, the difference of a few basis points from 85% to 93% can mean the technology can't consistently recognize your brand name or key terms of your business. From quality assurance to real-time assistance, contact centers need highly accurate transcription as a foundation.
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Improved Quality Assurance: Contact centers perform a variety of quality management operations and often these are manual and time consuming. The AI software should be able to automate these operations to help increase the scale of coverage while not compromising on the efficiency and accuracy. Additionally there should be a way of calibrating and improving the automated quality assurance process.
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Intelligent Customer Insights: Customer interactions are a treasure trove of information that can be used to get valuable insights that are key to improving customer satisfaction, retention and sales. For example, ML models can quickly identify the sentiment of the customer. By analyzing and accurately labeling speech segments from transcribed text, contact centers can help agents track customers' feelings and feedback about products and services.
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Real-time AI - Using real-time AI, contact centers can significantly improve the agent’s performance. Agent assist capability developed using real time AI empowers agents to sell better, handle objections, stay compliant and resolve issues faster. When combined with post-interaction AI like coaching, quality assurance and analytics, contact center supervisors can deliver personalized coaching to the agent during or after the conversation.
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Omnichannel - The world has changed and the way customers interact with the business has changed. Businesses today need to have the ability to reach and help customers no matter which channel they use. Using AI and NLU contact centers can understand the intent and meaning from conversations whether the customer is using voice, text, chat, email or other channels.

How Observe.AI uses Conversation Intelligence for contact centers

From the time we started, we have been using AI technologies like NLP, NLU & NLG to boost the contact center performance with live conversation intelligence. Our AI engine is able to uncover insights from 100% of customer interactions that maximizes frontline team performance through coaching and end-to-end workflow automation. With our AI technology, companies can act faster with real-time insights and guidance to improve performance, from more sales to higher retention.

At Observe.AI, we are combining the power of post-call interaction AI and live call guidance through real-time AI to provide an end-to-end conversation Intelligence platform for improving agent performance.

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Research papers from Observe.AI

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Conversation Intelligence using NLU, NLP and NLG is the future of customer service and sales

Using conversation intelligence powered by NLP, NLU, and NLG, businesses can automate various repetitive tasks or work flows and access highly accurate transcripts across channels to explore trends across the contact center.

Real-time agent assist applications dramatically improve the agent’s performance by keeping them on script to deliver a consistent experience. Similarly, supervisor assist applications help supervisors to give their agents live assistance when they need the most, thereby impacting the outcome positively.

NLU is a crucial part of ensuring these applications are accurate while extracting important business intelligence from customer interactions. In the near future, conversation intelligence powered by NLU will help shift the legacy contact centers to intelligence centers that deliver great customer experience.

With advances in AI technology we have recently seen the arrival of large language models (LLMs) like GPT. LLM models can recognize, summarize, translate, predict and generate languages using very large text based dataset, with little or no training supervision. When used with contact centers, these models can process large amounts of data in real-time thereby enabling better understanding of customers needs. We will cover this and innovation with GPT in our next blog.