AI Marketing Glossary

AEO (Answer Engine Optimization)

The process of optimizing content to be easily understood and featured in AI-generated answers and overviews (like Google's AI Overviews).

AI Agent

An autonomous software program powered by AI that can perceive its environment, make decisions, and take actions to achieve specific marketing or business goals without constant human intervention.

Algorithm

A set of rules or instructions given to an AI system to help it learn from data or solve a specific problem.

API (Application Programming Interface)

A set of protocols that allows different software applications to communicate with each other, often used to integrate AI models into existing marketing tools.

Artificial Intelligence (AI)

The simulation of human intelligence processes by machines, especially computer systems, including learning, reasoning, and self-correction.

Automation

The use of technology to perform tasks with minimal human intervention. In marketing, this often involves scheduling, email sequencing, and data entry.

Chatbot

A computer program designed to simulate conversation with human users, especially over the internet. Modern chatbots use LLMs for natural, context-aware interactions.

Computer Vision

A field of AI that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs.

Conversational AI

Technologies, like chatbots or voice assistants, which users can talk to. They use large volumes of data, machine learning, and natural language processing to help imitate human interactions.

CRM (Customer Relationship Management)

Technology for managing all your company's relationships and interactions with customers and potential customers. AI-enhanced CRMs can predict customer behavior and automate outreach.

Data Mining

The practice of analyzing large databases in order to generate new information, often used in AI marketing to discover patterns in consumer behavior.

Deep Learning

A subset of machine learning based on artificial neural networks with multiple layers, used for complex tasks like image and speech recognition.

Generative AI

A type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

GEO (Generative Engine Optimization)

Strategies designed to optimize a brand's visibility and presence within AI chat interfaces like ChatGPT, Claude, and Perplexity.

Hallucination (AI)

When an AI model generates false, misleading, or nonsensical information but presents it as a fact.

Hyper-personalization

The use of AI and real-time data to deliver highly customized content, products, and services to individual customers.

Knowledge Graph

A knowledge base that uses a graph-structured data model to integrate data, often used by search engines to enhance search results with semantic-search information.

Large Language Model (LLM)

A type of AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate, and predict new content.

Machine Learning (ML)

A branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Multimodal AI

An AI system capable of processing and generating multiple types of data simultaneously, such as text, images, audio, and video.

Natural Language Processing (NLP)

A branch of AI that helps computers understand, interpret, and manipulate human language.

Neural Network

A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Omnichannel Marketing

A multichannel sales approach that provides the customer with an integrated customer experience. AI helps synchronize messaging across all platforms.

Predictive Analytics

The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Prompt Engineering

The practice of designing and refining inputs (prompts) to guide generative AI models to produce optimal and highly accurate outputs.

RAG (Retrieval-Augmented Generation)

An AI framework that improves the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM's internal representation of information.

Reinforcement Learning

A machine learning training method based on rewarding desired behaviors and/or punishing undesired ones.

Semantic Search

A data searching technique in which a search query aims to not only find keywords, but to determine the intent and contextual meaning of the words a person is using for search.

Sentiment Analysis

The use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information.

Supervised Learning

A machine learning approach that's defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately.

Unsupervised Learning

A type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision.

Zero-Party Data

Data that a customer intentionally and proactively shares with a brand, which AI can use to deliver highly personalized experiences.

Zero-Shot Learning

A machine learning setup where a model is trained to recognize objects or concepts it has never seen before, without specific training examples for those categories.