The Difference Between AI, NLP, NLG, and NLU – What It Means for Marketers?
These acronyms refer to key elements involved in language technologies. Collectively they offer marketers a way to “listen” to what their customers are saying and provide solutions for their pain points, wishes and wants. Most companies have so much data generated from text but do not have the working hours to successfully extract the actionable insights housed in the information gathered. Everything people are saying on social media, typing into search queries, sharing with brand chatbots, communicating in free-form messages, etc.. reveal their intent. Being able to monitor their intentions helps companies target their products or services that speak directly to what they need.
Companies have invested major resources into computational linguistics in being able to build systems that allow for direct unsupervised, socially acceptable interactions with customers. The outcome is to have these systems appear more human for the user. Think of Amazon’s “Alexa” or Apple’s “Siri.” This is no small feat. Helping machines to understand human’s natural language requires distilling speech into a structured ontology. This is done to understand a user’s “intent.” The technology is attempting to allow for a conversation to take place between a human and a computer/robot.
Let’s unpack four terms that are instrumental in making sense of these conversations…
Artificial Intelligence (AI)
The Tech Target definition of AI states that it is the simulation of human intelligence processes by machines, especially computer systems. These AI processes include learning, reasoning, and self-correction.
The AI is currently in use in:
- Face recognition software
- Self-driving cars
- Computer games AI players
- Reasoning and Advisory systems like approving or declining a mortgage application.
For AI to operate optimally, it uses machine learning (ML) to perform tasks. AI has to learn explicit rules; whereas, machine learning analyzes examples to find meaning. ML keeps evolving and learning from every interaction, offering more opportunities for understanding large data sets to predict future customer behavior (predictive analytics). Artificial intelligence gives marketers information to be able to personalize content for targets at each touch point in the sales funnel journey.
By understanding an individual’s behavior, characteristics, location, and personal preferences, marketers can tailor make ad campaigns to target with relevance. This allows for accurate segmenting and the ability to create lookalike audiences for their offering.
When customers are exposed to a company’s brand at the right time, in the right context, they can build deeper relationships through sustained, relevant engagement. The future of AI marketing is moving towards a prescriptive model, where the AI can potentially suggest how to spend a company’s marketing budget and even execute the option chosen.
Natural Language Processing (NLP)
According to Webopaedia NLP “deals with analyzing, understanding and generating the languages that humans use naturally to interface with computers in both written and spoken contexts, using natural human languages instead of computer languages.”
This means that humans and computers can have a “natural” conversation in the preferred language of the user. NLP encompasses the systems working behind the scenes to facilitate these end-to-end interactions.
What the NLP AI can do is:
- Take in what is being said by the human
- Deconstruct it into sections for analyzing (Query Parsing)
- Comprehend the meaning of the speech
- Decide on the most appropriate action to take
- Reply to the user in their preferred language
Chatbots and speech-to-speech applications like Siri, Google Home, and Alexa all use NLP applications to have “conversations.” The insights gleaned from all of these conversations are an invaluable resource for marketers to give customers exactly what they are looking to buy.
NLP is also referred to as “text analytics” because it can comprehend what individuals say or write in conversation. These computations occur at high speeds and scale that would be impossible for any human to complete in the same time frame. There is an enormous amount of Big Text in the form of unstructured data that has been mined from social media posts, emails, call center logs, product reviews, etc., from natural language. The ability of NLP AI to make sense of it and deliver insights from this mountain of info. helps businesses target with accuracy and predict with precision. Those companies not using the insights leveraged from Big Text will start to lose customers to more data focused organizations.
Natural Language Understanding (NLU)
NLU in artificial intelligence is involved with the complexities of comprehension. Its job is to understand unstructured inputs. This is a highly difficult task for an AI system to undertake. Humans can understand the meaning behind colloquialisms, contractions, swapped words, mispronunciations, while machines find it difficult to make sense of these unpredictable inputs. NLU is trying to understand everything that we say and is slowly succeeding in working out what humans mean in their interactions. With so much data on platforms like social media, being able to find meaning in this unstructured dialogue will give brands even more accurate information about their customers and their actual intent.
Natural Language Generation (NLG)
NLG looks at all of the data that has been generated and creates easy-to-read reports using language. The narratives NLG generates can summarize, explain and describe the data findings. NLG is attempting to find the best possible way to communicate its conclusions to a company. It can take everything it has learned about a data set or target customer group and transform the knowledge into the language preference of the organization. These reports are pivotal documents that give marketers valuable insight to use for customer engagement and retention as well as for operational efficiency.
NLP, NLU, and NLG are all vital components of artificial intelligence that allows organizations to create their content strategy in their marketing plan to be able to engage their customers with the best conversations that lead to conversions. AI personalized interactions will exceed customer expectations and ensure meaningful engagement to build long-lasting relationships.