A good virtual agent or chatbot can understand and interpret what a customer is saying, regardless of the use of any informal language. The inability to do so might frustrate a consumer and drive them away.
Many virtual agents use deep learning and natural language processing (NLP) to build their understanding capabilities. But these techs have their limits as they cover just the basics, such as training data, predicting right intent, and cleaning up customer requests. However, having adding proprietary language understanding algorithm can give a virtual agent the boost they need. But how?
The answer is Natural Language Understanding (NLU). NLU it the brain of a conversational AI-powered virtual agent having three key components:
- Deep learning
- Natural language processing (NLP)
- Additional proprietary algorithms
Better language understanding results in higher automation rates and better customer experience.
How can NLU improve business operations?
NLU is not limited to assist the customers in a specific domain, but it allows a better understanding of all human interactions.
- Sharing knowledge: a virtual agent can be used as a ‘front-end’ to tie data scattered across various silos of legacy systems through a familiar chat inference.A virtual agent can be used to handle employee requests, provide the answers, streamline employee onboarding, greet employees as they log in, distribute daily info updates, or any changes in company policy.
- Offering support: NLU enables a virtual agent to answer thousands of questions during customer-facing support and service with incredible accuracy. When combined with APIs and third-party integration, a virtual agent cab performs personalized core business functions, on behalf of logged-in customers, in chat.
- Sales: Driving sales can be a little complex for virtual agents. Therefore NLU is significant in this arena. NLU enables a virtual agent to understand the needs of the customer precisely and provide them with accurate results.
NLU helps identify any mistakes made by deep learning models and corrects them, which reduces false positives to a minimum.
In short, NLU can handle and decode complex nuances to human language with ease.
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