AI’s Game Changers in Customer Interaction


The Gist

  • Revolutionizing communication. NLU and NLP are transforming business-audience communication by enabling unprecedented personalization and relevance in marketing messages.
  • Enhancing customer experience. These AI technologies are redefining marketing, improving efficiency in consumer interactions, and offering more engaging and seamless customer experiences.
  • Global outreach and understanding. NLU and NLP break language barriers, allowing brands to connect with a global audience while maintaining cultural sensitivity and relevance.

Natural Language Understanding (NLU) and Natural Language Processing (NLP) are pioneering the use of artificial intelligence (AI) in transforming business-audience communication. These advanced AI technologies are reshaping the rules of engagement, enabling marketers to create messages with unprecedented personalization and relevance. This article will examine the intricacies of NLU and NLP, exploring their role in redefining marketing and enhancing the customer experience.

Introduction to NLU and NLP

NLU and NLP have greatly impacted the way businesses interpret and use human language, enabling a deeper connection between consumers and businesses. By parsing and understanding the nuances of human language, NLU and NLP enable the automation of complex interactions and the extraction of valuable insights from vast amounts of unstructured text data. These technologies have continued to evolve and improve with the advancements in AI, and have become industries in and of themselves. According to projections from Fortune Business Insights, the global NLP market is expected to grow from $24.10 billion in 2023 to $112.28 billion by 2030, while the NLU market, which is also experiencing rapid growth, is expected to grow to $35 billion by 2025.

Nikola Mrkšic, CEO and co-founder at PolyAI, a provider of AI-based voice assistants for call centers, told CMSWire that the Natural Language Understanding models that power conversational AI technologies are sophisticated enough to glean the intent of a customer’s conversation rather than just try to identify keywords in what was said like less-capable instances of automated support. “In addition to understanding what the conversation was actually about, NLU models enable virtual agents across digital channels to engage with a customer in language that is natural enough to be mistaken for a human agent.” 

Mrkšic related that as callers start to experience what’s possible with NLU/NLP, the conditioned and well-recognized norm of saying “live agent” or smashing zero over and over to try and bypass automated support will fade into history, resulting in an exceptional customer experience from a scalable business process.

The promise of NLU and NLP extends beyond mere automation; it opens the door to unprecedented levels of personalization and customer engagement. These technologies empower marketers to tailor content, offers, and experiences to individual preferences and behaviors, cutting through the typical noise of online marketing. By analyzing customer feedback, social media discourse, and other digital communications, NLU and NLP provide the tools needed to draft messages that resonate on a personal level, creating a sense of understanding and intimacy with a brand.

Additionally, NLU and NLP are pivotal in the creation of conversational interfaces that offer intuitive and seamless interactions, whether through chatbots, virtual assistants, or other digital touchpoints. This enhances the customer experience, making every interaction more engaging and efficient. The integration of NLU and NLP in marketing and advertising strategies holds the potential to transform customer relationships, driving loyalty and satisfaction through a deeper understanding and anticipation of consumer needs and desires.

Related Article: Natural Language Processing and Conversational AI in the Call Center

Foundation of NLU and NLP

NLP and NLU are closely related fields within AI that focus on the interaction between computers and human languages. NLP is the broader discipline that encompasses techniques for processing and analyzing large volumes of natural language data, with the objective of enabling machines to understand and interpret human language in a way that is both meaningful and useful. It includes tasks such as speech recognition, language translation, and sentiment analysis. NLP serves as the foundation that enables machines to handle the intricacies of human language, converting text into structured data that can be analyzed and acted upon.

NLU, a subset of NLP, delves deeper into the comprehension aspect, focusing specifically on the machine’s ability to understand the intent and meaning behind the text. While NLP breaks down the language into manageable pieces for analysis, NLU interprets the nuances, ambiguities, and contextual cues of the language to grasp the full meaning of the text. It’s the difference between recognizing the words in a sentence and understanding the sentence’s sentiment, purpose, or request. NLU enables more sophisticated interactions between humans and machines, such as accurately answering questions, participating in conversations, and making informed decisions based on the understood intent.

The history of NLU and NLP goes back to the mid-20th century, with significant milestones marking its evolution. In 1957, Noam Chomsky’s work on “Syntactic Structures” introduced the concept of universal grammar, laying a foundational framework for understanding the structure of language that would later influence NLP development. This was around the same time as the Georgetown experiment in 1954, which showcased the potential for machine translation by automatically translating more than sixty Russian sentences into English, setting an optimistic but ultimately premature expectation for the field’s development.

The 1960s and 1970s saw the development of early NLP systems such as SHRDLU, which operated in restricted environments, and conceptual models for natural language understanding introduced by Roger Schank and others. This period was marked by the use of hand-written rules for language processing.

A significant shift occurred in the late 1980s with the advent of machine learning (ML) algorithms for language processing, moving away from rule-based systems to statistical models. This shift was driven by increased computational power and a move towards corpus linguistics, which relies on analyzing large datasets of language to learn patterns and make predictions. This era saw the development of systems that could take advantage of existing multilingual corpora, significantly advancing the field of machine translation.

Blue lights, computer and neural connectionsrepresenting artificial intelligence (AI), machine learning and modern computer technologies concepts in piece about NLU and NLP.
A significant shift occurred in the late 1980s with the advent of machine learning (ML) algorithms for language processing, moving away from rule-based systems to statistical models.putilov_denis on Adobe Stock Photos

The introduction of neural network models in the 1990s and beyond, especially recurrent neural networks (RNNs) and their variant Long Short-Term Memory (LSTM) networks, marked the latest phase in NLP development. These models have significantly improved the ability of machines to process and generate human language, leading to the creation of advanced language models like GPT-3. Such models have demonstrated impressive capabilities in generating human-like text and solving complex language-based tasks, although they do not truly “understand” language in the human sense but rather simulate understanding through sophisticated programming and statistical inference.

Today, NLP and NLU technologies are the basis for a wide array of applications, from voice-activated GPS systems and customer service chatbots to sophisticated language translation programs and virtual assistants such as Siri, Alexa and Google Assistant. These technologies have transformed how humans interact with machines, making it possible to communicate in natural language and have machines interpret, understand, and respond in ways that are increasingly seamless and intuitive.

Related Article: 4 Ways AI, Analytics and Machine Learning Are Improving Customer Service and Support

NLU and NLP in Consumer Insight Gathering

The application of NLU and NLP in analyzing customer feedback, social media conversations, and other forms of unstructured data has become a game-changer for businesses aiming to stay ahead in an increasingly competitive market. These technologies enable companies to sift through vast volumes of data to extract actionable insights, a task that was once daunting and time-consuming. By applying NLU and NLP, businesses can automatically categorize sentiments, identify trending topics, and understand the underlying emotions and intentions in customer communications. This automated analysis provides a comprehensive view of public perception and customer satisfaction, revealing not just what customers are saying, but how they feel about products, services, brands, and their competitors.

Diana Zheng, head of marketing at the Canadian ecommerce shipping company, Stallion Express, told CMSWire that Natural Language Understanding and Natural Language Processing have transformed marketing, advertising, and CX by helping her brand understand the nuances of consumer language. “NLU and NLP allow marketers to craft personalized, impactful messages that build stronger audience relationships,” said Zheng. “By understanding the nuances of human language, marketers have unprecedented opportunities to create compelling stories that resonate with individual preferences.”


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