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Home  /  Lifestyle   /  Technology   /  Artificial Intelligence   /  Four Core Use Cases for Natural Language Understanding
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Four Core Use Cases for Natural Language Understanding

Are you making effective use of your data? Considering that 68% of data that flows into enterprises goes unused, it appears there is room to improve. The majority of this data is often unstructured and text based.

While natural language processing (NLP) can process and organize this language data in seconds, its value is limited by its inability to understand the meaning of text.

Humans can understand language nuances like reasoning and logic, colloquialisms, and speech patterns. However, machines have more of a learning curve. Natural language understanding [NLU] methods provide the requisite knowledge necessary for a machine to achieve human-like comprehension and communication.

To move beyond surface-level capabilities and make the most of your language data, NLU must be a priority. This means finding the appropriate use case for your organization. The following examples are potential options to start with.

1. Conversational Chatbots

Chatbots have become an indispensable tool for customers as they forgo long telephone hold times for instant answers. However, a chatbot that only gives bare-bones replies is not too helpful. Working with NLU means moving beyond NLP to evolve chatbots from basic commands and keyword recognition to conversational capabilities. At their best, chatbots provide instant, 24-7 support throughout the customer journey. They are low-friction channels that allow customers to instantly interact with your organization and solve their problems quickly, yielding benefits like higher customer satisfaction ratings.

From an operational perspective, NLU empowers chatbots to:

  • Answer FAQs
  • Facilitate shipping
  • Provide personalized input and direction

Ultimately, this enables you to address a wide range of user needs at a lower cost. When leveraged correctly, AI can reduce customer service costs by up to 30%.

In areas where humans fall short, NLU helps chatbots pick up the slack. In a call center, it’s challenging to remain continually even keeled with any customer at any time of day or night. A chatbot can maintain a consistently positive tone and keep your brand’s reputation intact.

Secondly, NLU can imbue chatbots, to some degree, with emotional intelligence. It helps chatbots understand a sentiment and form emotionally relevant responses. For example, it can say, “Sorry to hear that you’re having an issue. I’m happy to help you with this.” It’s like you’re having a conversation with a human, without human limitations.

 

2. Automated Ticketing Support

Manual ticketing can mean delays, countless back-and-forth emails, and frustrating interactions. However, this high-volume, manual process can easily be improved with NLU. This, of course, requires your system to understand the text within each ticket to properly filter and route incoming tasks and information to the right department or expert.

NLU raises the level of your support by understanding the actual request and facilitating a speedy response from the right person or team (e.g., sales, legal, help desk, etc.). This provides customers and employees with timely, accurate information they can rely on.

NLU’s ability to drive shorter, more accurate support cycles is particularly valuable for departments such as customer service and IT. By understanding the context and meaning of different requests, your system can recommend solutions or flag urgent priorities to customer service teams.

For IT, NLU can extract relevant data such as known errors and prior and current incidents to make recommendations to support agents. This can help reduce the number of incidents by as much as 47%.

 

3. Sentiment Analysis

Every organization wants to know exactly what people are saying about their brand. Are they having a positive experience or a frustrating one? How do they feel about your customer service? NLU-based sentiment analysis empowers organizations to capture the voice of the customer, extract emotions from text and provide actionable insight.

At a micro level, sentiment analysis can gauge the tone behind a social media post. At a macro level, sentiment analysis can turn copious amounts of unstructured data into structured data that helps you understand your customer.

This enables you to dive deeper into user intent and evaluate customer experiences beyond a one-to-five rating. In turn, customer service teams and marketing departments can be more strategic in how they address issues and execute campaigns, respectively. This can provide an instant boost to your business.

 

4. Automated Document Review

Manually reviewing complex documents is a cumbersome process. You are dealing with lengthy documents full of domain-specific language, which creates a time-consuming review process that is prone to human error. These challenges are only amplified by the number of documents you need to comb through.

However, NLU enables you to review any document from an insurance policy to an application to a contract with speed and accuracy. Rather than narrowly search for key words or phrases, you can perform knowledge-based entity extraction to capture the key information you need to make timely business decisions. By automating the document review process, you can save upwards of four hours in review time.

 

Know What Matters Most to Your Organization

AI can deliver bottom-line results when applied properly to a specific business case. For example, the ability to clean up compliance processes may be more impactful for banking and financial services than other industries. Automating support ticketing could be more affecting for IT help desks at large enterprises than those at smaller organizations.

To maximize its potential, AI needs to go beyond basic input-output generation capabilities. It needs to understand context, emotion, and intent to truly be effective. Natural language understanding will help you bridge those gaps.

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