Marketers’ Comprehensive AI Glossary

How Well Do You Really Know Your AI?

Including AI in marketers’ toolkit is quickly becoming a given. Company laptop? Check. Logged into Slack or Teams? Check. AI solution? Check.

But as AI grows in adoption, marketers know well that not every solution is made equal. Some deliver much-needed value, while others could be best compared to AI-washing. But to ensure you choose the best solution, you’ll need a firm grasp of AI language. (Not to worry—we’re not referring to binary code!)

This blog will cover 15 key terms commonly used when discussing AI, including basic definitions, functions, and models. When you understand these concepts inside and out, you’ll be able to quickly discern which AI tools truly deliver innovation and value, and which ones you can shelve.

AI Fundamentals

What is Machine Learning?

The term “machine learning” means the AI is constantly self-optimizing. Every time the AI predicts or automates a task, it grows more accurate and relevant as response data accumulates.

Due to this automated, continual learning, an AI that uses machine learning tends to be far more valuable than AI tools that rely on static models requiring manual updates.

What is Deep Learning?

Deep learning refers to an AI solution that can recognize complex patterns in images, text, and sounds. This incoming data is then used to classify and detect objects, producing insights that are truly impossible to achieve with human effort alone.

What is Data Mining?

When you’re mining for gold, you find a whole lot of rubble before you get to the precious nuggets within. The same can be said for data mining, which is the process of extracting patterns and pertinent information from a vast amount of data. Because data mining is automated, it makes sifting through large data sets a quick and easy process, rather than a herculean manual task.

What is an Ensemble Approach to AI?

Instead of relying on one static model for AI outputs, such as what’s produced in most generative AIs, an “ensemble approach to AI” leverages multiple machine learning models, working together in perfect harmony. For example, when AI is used for the prediction process—determining and generating what customers want to see—they can take several factors into account simultaneously, such as deep insights, unique customer profiles, and content decisioning. In short, marketers can do far more than quickly generate images or copy; they can create content that they know will resonate.

What is Predictive Analytics?

Predictive analytics allows marketers to identify customers’ next move. While that sounds like a crystal ball, this type of AI uses data, statistical algorithms, and machine learning to analyze customers’ historical data and determine the content best suited for them in the next marketing message. The more predictive analytics is used when sending content to customers, the more accurate the AI becomes, allowing marketers to truly surprise-and-delight their customers with fresh content that’s predicted to be a hit.

Natural Language Processing (NLP) and Language Understanding

What is a Large Language Model (LLM)?

Using specific rules and data training, AI language models can understand and generate human language. In basic terms, these language models are the back-end powerhouses that allow your AI to generate copy on the fly (“Write me a catchy ad about our seasonal coffee flavors!”). Language models also play a critical role in various natural language processing (NLP) tasks, which is crucial in analyzing customer behaviors.

What is Natural Language Processing (NLP)?

Natural language processing (NLP) is a type of AI that helps machines understand, interpret, and generate human language—think of it as the translator between machines and humans.

Not only that, NLP systems can read every element within a digital image as text to determine meaning and context. Over time, the NLP can detect patterns within that generated text to help marketers gear their content towards customers.

What is GPT (Generative Pre-Trained Transformer)?

ChatGPT is an extremely popular AI that combines both generative AI and LLM. But what does the “GPT” of ChatGPT stand for? Generative Pre-Trained Transformer. This is a specific type of language model that uses transformer architecture—an encoder/decoder structure that efficiently generates logical outputs—and is pre-trained on vast amounts of data, making it capable of generating coherent, contextually relevant text.

What is Optical Character Recognition (OCR)?

Optical Character Recognition (OCR) is technology that converts pretty much any document—PDFs, digital images, or even scanned paper—into editable and searchable data. This makes it possible to search through large documents quickly and efficiently.

What are Chatbots?

Chatbots are built from automated programs that use AI to simulate conversations with users, making them a great tool for answering common, predictable questions. That’s why every time you try to make an online return, you run into a chatbot.

Computer Vision and Image Processing

What is Augmentation?

Augmentation for AI refers to the process of enhancing machine learning, which can include data enhancement—creating new training examples or improving existing data—or refining content.

What is Computer Vision?

Similar to NLP, computer vision allows users to analyze visual elements and data. From there, the computer can perceive and understand the “environment” of the content. For example, Computer Vision AI for marketing programs will understand which creatives are promotional and which are branded through ingesting each element’s metadata.

What is Generative AI?

Generative AI is the type that generates logical text or imagery based on the user’s commands. While generative AI has exploded in popularity, it’s not a wholly new tool; however, today’s iterations are far more sophisticated, generating high-quality content at the press of a button.

Personalization and Marketing

What is Marketer-in-the-Loop?

Similar to the traditional Human-in-the-Loop (HITL) model where people are needed to control AI inputs and influence outcomes, the marketer and the machine collaborate strengths in the Marketer-in-the-Loop approach. It’s a powerful partnership that achieves results that neither party could reach on their own.

What is Personalization?

Personalization, where marketers leverage customer data to deliver tailored content, has been the linchpin of digital marketing strategies for years. AI-powered personalization is the same concept, but generates even greater results.

Rather than only relying on past actions to determine tailored content, AI allows marketers to look ahead and generate messaging that they know will resonate with customers before they’ve even interacted with it. That means marketers can send completely new content—promoting fresh products and new categories—that they know their customers will love, strengthening loyalty and brand affinity. It’s traditional automated personalization, and then some.

You're Mastering AI

AI is a whole new world for marketers, but with these terms on lock you’ll be able to navigate it with ease and firm understanding. To learn more about the power of AI and how it can captivate your customers, explore further resources.