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Unlocking new business opportunities through advanced NLU

Last updated 26 October 2023

Learn how conversational AI can help improve knowledge, sales and support for your business.

Keeping customers waiting is killing your business. In today’s fast-moving digital landscape, consumers expect simplicity, speed and convenience, and are more than happy to drop a brand and move to a competitor if their customer service needs are not met.

Customer churn is a genuine challenge but it can be avoided, with 67% of consumers admitting that if a brand resolved their issue during the first interaction they would be less likely to leave. The old adage of one unhappy customer telling up to 10 people about their negative experience has never been truer than in today’s world, where information spreads like wildfire on social networks and can potentially do irreparable harm.

Conversational AI presents a unique opportunity to meet the demands of digital-savvy customers. It allows a company to scale its digital customer service strategy to instantly handle thousands of requests simultaneously, automating up to 55% of all customer interactions via chat (with zero human involvement) and hitting resolution rates in excess of 95%.

Achieving these kinds of results requires a number of key factors in place to ensure that a virtual agent can reach its full potential, including a robust implementation strategy, up-skilled human AI Trainers to maintain and manage the project and, importantly, a foundation built on market-leading technology.

What is NLU and why does it matter

NLU, or Natural Language Understanding, can essentially be seen as the brain of a virtual agent. It consists of three key components working together to decode the nuances of human language:

  • Deep learning
  • Natural language processing (NLP)
  • Additional proprietary algorithms

Being able to interpret and understand what a customer is asking about is the basis of any good virtual agent - without this, you’re liable to lead your customers to frustration. This may sound obvious, but it is not actually a trivial matter as customers will often use informal language (including slang and dialect) which can make it difficult to parse what they mean.

Deep learning and NLP are commonly used methods for building the understanding capabilities of virtual agents. They cover the basics such as training data and predicting to the right intent, and also cleaning up customer requests to make them easier for the AI to understand - this includes things like stemming, removing misspellings, etc.

Most solutions, however, tend to stop there and rely solely on known approaches of these technologies which, while competent, can be limited. Where conversational AI really shines, is in adding proprietary language understanding algorithms on top of common ones. These can give a virtual agent the extra push it needs to go from being ‘just ok’ at answering customer queries, to reducing false positives by up to 90%.

Combined with other technologies like self-learning AI, NLU can be used to take out a lot of the manual work required to build and maintain a virtual agent. Conversation data can be analyzed and new topics suggested on-the-fly. Building a virtual agent from scratch also becomes easier - leveraging existing chat logs, a company can reduce the time it takes to have a working prototype that can answer thousands of questions in a matter of days, not months.

The bottom line is that with better language understanding, you get higher automation rates and, importantly, a better overall customer experience.

How NLU can improve business operations

The main advantage of powerful language understanding technology is that it doesn’t confine a virtual agent to only being able to assist across a specific domain. NLU allows for a better understanding of all human-machine interactions and can be applied in a number of different ways.


Getting access to company-wide resources in an enterprise can be difficult. Often, the information that your staff needs is scattered across various legacy systems with no central place to access it. A virtual agent can be used as a ‘front-end’ to tie these disparate systems together through a familiar chat interface.

Whether its questions about HR or IT, the NLU powering a virtual agent can easily parse employee requests pointing them to the answers they need. Similarly, an internal support virtual agent can be used to streamline employee onboarding or even greet employees when they log in each morning, distributes daily info on new products and changes in policy.


The most common virtual agent use case is in customer-facing support and service. NLU makes this kind of virtual agent extremely powerful enabling it to answer thousands of questions with incredible accuracy. It also means that thanks to NLU, you can be assured that your customers are getting the answers they need, opening up the possibility to expand your virtual agent into far more interesting territory.

With a solid language understanding foundation, a virtual agent can be used in a more transactional capacity, combining with APIs and third-party integrations to not just provide useful information, but actually perform personalized, core business functions directly in chat on behalf of logged-in customers. This can include anything from updating a mobile data package to offering certified financial advice. When your virtual agent can truly understand what your customers are asking for, then the sky’s the limit on what it can do for them.


Using a virtual agent to drive sales is a use case that we are beginning to see more often as a direct result of the advances in natural language understanding. This capability can either be standalone or, more commonly, is built on top of a support virtual agent in order to offer a full-service customer experience.

NLU is crucial to driving sales with a virtual agent as these kinds of interactions can be complex, with multiple questions, transactions and procedures taking place over the course of one conversation. NLU makes it possible for a virtual agent to understand precisely what a customer is looking for and this information can be combined with features such as context actions or conversation goals that can help both parties reach an actionable outcome.

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