AI Glossary Terms Every Fast-Paced Marketer Needs to Know

The term ‘artificial intelligence (AI)’ can conjure up diverse images. Maybe you picture robots from sci-fi movies, or perhaps you think of chatbots you’ve interacted with online. But what if you’re a seasoned marketing professional who’s comfortable with tried-and-true methods?

While sticking to what works is understandable, AI offers a treasure trove of innovative tools and solutions to boost your marketing strategies, from content creation and research to social media, video marketing, and beyond. This comprehensive AI glossary for marketers will equip you to work smarter and faster by exploring essential AI-related marketing terms.

Here’s a breakdown of key AI terms marketers should be familiar with in 2025. 

Ultimately, knowing the terms related to artificial intelligence and developing your marketing toolkit should help you get more done, faster, with much better results.

A-F AI terms for marketing

A/B Testing with AI: Imagine A/B testing two headlines for a blog post. AI analyzes which one performs better in terms of clicks and engagement, helping you choose the winner and maximize your content’s impact.

Actuators: Actuators are mechanisms or devices used by AI agents to interact with their environment. In physical systems like robotics, they might control movement, rotation, or pressure. In virtual agents, actuators could refer to code-based interactions, such as sending messages or updating a database. Actuators are essential for translating an agent’s decisions into real-world or system-level actions.

Agent Architecture: Agent architecture refers to the design and structure of an AI agent. It includes how components like perception, memory, reasoning, and action modules are organized and how they interact with one another. Different architectures, such as reactive, deliberative, or hybrid, define the complexity and behavior patterns of agents and influence their decision-making processes.

Agent Environment: The agent environment is the external context or world in which an AI agent operates. It includes all the data, stimuli, and interactions the agent can perceive and respond to. Understanding the environment is critical for agent performance, as it informs the agent’s inputs (percepts) and shapes the outcomes of its actions.

Agent-Based Modeling (ABM): Agent-based modeling is a simulation approach that models the behavior and interactions of autonomous agents to explore complex phenomena. Each agent in the system follows simple rules, but their collective behavior can produce sophisticated and emergent patterns. ABM is widely used in economics, social sciences, biology, and artificial intelligence research.

Agentic AI: Agentic AI refers to systems that exhibit agency, meaning they can set goals, make decisions, and take actions autonomously. These systems operate independently, without requiring step-by-step human instructions. Agentic AI is foundational for advanced applications such as digital assistants, autonomous vehicles, and adaptive robotics.

AI (Artificial Intelligence): AI refers to the simulation of human intelligence processes by machines, including learning, reasoning, problem-solving, perception, and language understanding. In marketing, AI technologies automate tasks, personalize customer experiences, analyze data, and optimize campaign performance.

AI Agent: An AI agent is a computer system capable of perceiving its environment, making decisions, and taking actions to achieve specific goals. These agents can be simple (like a thermostat) or complex (like a self-driving car or chatbot). They are typically defined by their autonomy, adaptability, and ability to learn from feedback.

AI Apps: AI apps utilize LLM technologies to perform tasks that typically require human intelligence, including learning, reasoning, problem-solving, understanding natural language, and perception. These apps are used across various industries to enhance efficiency, automate processes, and provide advanced insights. Key examples include virtual assistants like Siri, Google Assistant, and Alexa, as well as customer service chatbots.

Another example is Similarweb’s app intelligence, which is powered by proprietary app data from multiple sources, provides insights into millions of apps across both iOS and Android in over 25 countries. This includes insights on AI apps and any other apps you’re interested in. You can use it to leverage a full suite of mobile app data points to stay ahead of the competition, spanning usage and engagement metrics, retention, app store ranking, and daily performance insights.

AI Chatbots: Chatbots powered by AI can answer customer questions 24/7 on a company website, automate lead generation through chat interactions, and personalize product recommendations, creating a smoother customer experience.

AI Overviews: AI Overviews are a Google Search feature that appears at the top of some search results pages. Powered by generative AI, these summaries provide quick, concise answers to user queries by synthesizing information from across the web. Instead of clicking through multiple links, users can get a high-level understanding directly within the search results. For marketers, AI Overviews represent both an opportunity and a challenge. While they improve user experience by delivering fast answers, they may also reduce traditional organic click-through rates by satisfying search intent without a click.

AI-powered Content Creation: Marketers can use AI to generate content ideas, write drafts, and optimize content for different audiences. This saves significant time and resources, allowing you to focus on strategy and refinement.

Algorithm: Think of an algorithm as a set of instructions a computer follows to solve a problem or perform a task. In marketing, algorithms analyze data to predict trends, personalize recommendations, and optimize campaign performance across various channels.

Anomaly Detection: Anomaly detection involves identifying rare or unusual patterns in data that deviate from expected behavior. It is widely used in applications like fraud detection, system health monitoring, and cybersecurity. Machine learning models are trained to recognize normal data patterns, allowing them to flag anomalies in real time.

AR (Augmented Reality): An interactive experience that combines the physical world with digital elements in real-time. Marketers use AR technology to create immersive brand experiences, showcase products in 3D, and engage customers through interactive storytelling and gamification.

Artificial General Intelligence (AGI): AGI refers to a theoretical form of AI that possesses the ability to understand, learn, and apply knowledge across a broad range of tasks, much like a human. Unlike narrow AI, which is specialized for specific tasks, AGI would be able to reason, plan, solve novel problems, and exhibit common sense across various domains.

Artificial Neural Networks (ANNs): Artificial Neural Networks are computing systems inspired by the structure of the human brain. They consist of layers of nodes (or “neurons”) that process data and detect complex patterns. ANNs are the foundation for many machine learning applications, including image recognition, natural language processing, and speech recognition.

Attribution Modeling with AI: By understanding which marketing channels (social media, email marketing, etc.) are driving the most conversions (customer purchases, sign-ups), marketers can optimize their budget allocation and focus on the most effective strategies. AI-driven attribution modeling helps with this by analyzing customer journeys and pinpointing the touchpoints that lead to conversions.

Automation: The use of technology to perform tasks or processes with minimal human intervention. In marketing, automation tools and platforms help streamline workflows, optimize repetitive tasks, and deliver personalized messages at scale, improving efficiency and driving results.

Autonomous Agent: An autonomous agent is a system that can operate independently, making decisions and adapting to its environment without human intervention. These agents can learn from experience and change their behavior over time to better achieve their goals. Autonomous agents are used in robotics, game AI, and digital assistants.

Belief-Desire-Intention (BDI) Model: The BDI model is a framework for building intelligent agents based on three mental attitudes: beliefs (information about the world), desires (objectives or goals), and intentions (committed plans of action). This model enables more human-like reasoning in agents and is often used in complex decision-making environments.

Big Data: Big data refers to large volumes of data, both structured (website analytics, purchase history) and unstructured (social media conversations, customer reviews). Marketers leverage big data analytics to understand customer behavior, segment audiences, personalize campaigns, and make data-driven decisions to achieve marketing objectives.

Chatbot Analytics: The process of measuring and analyzing data generated by AI chatbots to gain insights into user interactions, engagement metrics, and performance indicators. Chatbot analytics help marketers understand user behavior, optimize chatbot workflows, and improve conversational experiences to drive business outcomes.

Computer Vision: Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and video. Applications include facial recognition, object detection, medical imaging analysis, and autonomous navigation. Deep learning techniques, especially convolutional neural networks, have significantly advanced this field.

Content Personalization: Personalization in content is crucial for advancing customer relationships, boosting sales, and driving long-term growth. It demonstrates a deep understanding of your customers’ needs, encouraging repeat business. By delivering tailored content based on preferences, behavior, and website demographics, AI-powered algorithms analyze user data in real-time to create personalized recommendations, emails, website experiences, and advertisements, thereby increasing engagement and conversions.

Conversational AI: AI-powered technology that enables natural language interactions between humans and machines. In marketing, conversational AI platforms, such as chatbots and virtual assistants, engage with users in real-time conversations, answering questions, providing recommendations, and guiding them through the customer journey.

Customer Lifetime Value (CLTV) Prediction: A predictive analytics technique that forecasts the future value of a customer over their entire relationship with a brand. AI-powered CLTV prediction models analyze historical customer data to identify high-value segments, optimize acquisition and retention strategies, and maximize long-term revenue.

CLV Formula: Wondering how to calculate customer lifetime value on your own? Use this Customer Lifetime Value Calculator (CLTV) to estimate the net profit attributable to a client’s future relationship. CLTV also defines the maximum threshold for client acquisition.

• CLV = (Revenue from a single customer over their lifetime) – (The cost of acquiring them)

If you’re not sure how much a customer has spent over their lifetime with your business, you can use this alternative customer lifetime value calculation:

• CLV = (Average annual revenue from a single customer) × (Number of years) – (Customer acquisition cost for that customer only)

Customer Segmentation: Customer segmentation is the process of dividing a target market into distinct groups based on shared characteristics, preferences, and behaviors. AI-driven customer segmentation algorithms analyze large datasets to identify meaningful segments, enabling marketers to personalize messaging, tailor offers, and optimize marketing campaigns for different audience segments.

Similarweb’s market research tools, for instance, allow marketers to segment any industry and better understand customer behaviors and needs. The Segment Analysis tool lets you analyze specific portions of a website by building a custom segment.

For example, to improve laptop-related sales for Samsung.com, comparing it to HP.com at a site level isn’t enough. Instead, you’ll want to analyze the “laptop” sections of both websites for an accurate, apples-to-apples performance assessment. As part of the capabilities within Segment Analysis, algorithms are used to ensure easier and faster segmentation based on the pages that you want to include and exclude, per website.

Data Analysis: The process of inspecting, cleaning, transforming, and modeling data to uncover insights, patterns, and trends. AI-powered data analysis tools leverage machine learning algorithms to process large datasets, extract actionable insights, and inform marketing strategies and decision-making processes.

For example, with Similarweb’s Data-as-a-Service (DaaS), you are able to harness the power of 30+ billion data points for comprehensive data analysis. That means you can dive deep into competitors’ online activities, analyzing their web traffic and acquisition strategies, or alternatively, monitor market leaders to maintain your business’s top position. DaaS eliminates hours of manual sifting through dashboards, providing efficient and targeted analysis.

Deep Learning: Deep learning is a subfield of machine learning that uses neural networks with multiple layers (“deep” architectures) to model complex data representations. It’s particularly effective for tasks like image classification, speech recognition, and language translation. The depth of the model allows it to learn hierarchical patterns in data.

Deliberative Agent: A deliberative agent uses reasoning and planning to determine its actions. Unlike reactive agents, which respond to immediate stimuli, deliberative agents consider possible future outcomes and develop strategies before acting. This makes them suitable for complex problem-solving and long-term goal management.

Dynamic Pricing with AI: A pricing strategy that adjusts product prices in real-time based on market demand, competitor pricing, and other factors. AI-driven dynamic pricing algorithms analyze data from various sources to optimize pricing strategies, increase revenue, and maximize profitability for marketers.

Edge AI: Edge AI refers to running artificial intelligence algorithms locally on a device, rather than in a centralized cloud. This reduces latency, enhances privacy, and enables real-time decision-making in devices like smartphones, cameras, or IoT sensors. It’s particularly useful in environments with limited connectivity.

Embodied Agent: An embodied agent is an AI system with a physical form, such as a robot, that interacts with the real world through sensors and actuators. These agents can move, manipulate objects, and respond to their physical surroundings, making them valuable in fields like manufacturing, healthcare, and logistics.

Episodic Memory: Episodic memory in AI agents refers to the storage and recall of specific experiences or interactions. This allows agents to reference past events when making decisions, supporting more personalized and context-aware behavior. It’s modeled after the human brain’s ability to remember events over time.

Explainable AI (XAI): Explainable AI encompasses techniques and tools designed to make the decision-making processes of AI models transparent and understandable to humans. As AI systems become more complex, explainability is critical.

Facial Recognition: AI-powered technology that identifies and verifies individuals by analyzing facial features from images or video footage. In marketing, facial recognition technology can be used for personalized advertising, audience segmentation, and experiential marketing activations, enhancing customer engagement and brand interactions.

G-M AI terms for marketing

Generative AI: Generative AI is a type of artificial intelligence that is capable of creating new content forms, such as text, images, music, and videos. It is powered by large foundation models capable of multitasking and performing various tasks like summarization, Q&A, and classification.

GEO (Generative Engine Optimization): GEO, or Generative Engine Optimization, is the practice of tailoring content to perform well in generative AI systems such as ChatGPT, Perplexity, and other AI-powered answer engines. Unlike traditional SEO, which focuses on ranking in search engine results, GEO aims to ensure that content is structured, authoritative, and contextually rich, making it more likely to be cited, summarized, or surfaced by generative models in response to user queries.

Hallucination: In AI, a hallucination also known as confabulation or delusion, is when an AI generates a response containing false or misleading information presented as fact. Unlike human hallucinations, which involve false perceptions, AI hallucinations stem from erroneous responses or beliefs. For example, a chatbot like ChatGPT may embed plausible-sounding falsehoods within its generated content. This is something to always consider when using chatbots.

Hyper-Personalization: A marketing approach that delivers highly customized and relevant content, offers, and experiences to individual customers based on their unique preferences, behaviors, and characteristics. AI-driven hyper-personalization algorithms leverage real-time data and machine learning to deliver personalized messages across multiple channels, driving customer loyalty and engagement.

Influencer Marketing with AI: The use of artificial intelligence to identify, evaluate, and collaborate with influencers for marketing campaigns. AI-powered influencer marketing platforms analyze social media data to identify relevant influencers, predict campaign performance, and measure ROI, helping marketers identify the most effective influencers to reach their target audience.

Large Language Model (LLM): An LLM is an AI program designed to understand and generate text. These models are trained on vast amounts of data from online sources such as blogs and message boards, giving them the ability to recognize and interpret human language. LLMs use a machine learning technique called a transformer model to process this data. In essence, an LLM is a computer program that learns to understand language by analyzing large datasets. The quality of the data used in training can significantly influence the LLM’s ability to grasp and generate natural language, so programmers often use carefully curated datasets.

Machine Learning: A subset of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. In marketing, machine learning algorithms are used for predictive analytics, customer segmentation, content personalization, and campaign optimization, helping marketers make data-driven decisions and automate processes for better results.

Marketing Attribution with AI: The process of assigning credit to marketing touchpoints along the customer journey using AI algorithms to determine the most effective channels and campaigns. AI-driven marketing attribution models analyze multi-channel data to accurately measure the impact of marketing efforts, optimize budget allocation, and improve ROI.

Marketing Frameworks for AI: Marketing frameworks for AI involve structured approaches that help businesses effectively integrate artificial intelligence into their marketing strategies. These frameworks guide the planning, implementation, and optimization of AI technologies to enhance customer engagement, streamline operations, and drive business growth. Here are a few key AI marketing frameworks:

• AIDA: Attention, Interest, Desire, Action
• PAS: Problem, Agitate, Solution
• BAB: Before, After, Bridge

Whenever creating a prompt in your AI tool, make sure to specify the framework you’d like the output to use, along with all the other context we’ve spoken about. Alternatively, ask the AI which framework would be most appropriate and have it use that within the output, too.

You can learn more about this term in the dedicated blog post we wrote, “The SEM Guide to Using ChatGPT.”

Micro Moments Marketing: A marketing strategy that targets consumers during “micro moments” when they turn to their devices to solve an immediate need or answer a question. AI-powered micro moments marketing campaigns leverage real-time data sources and machine learning algorithms to deliver relevant messages and offers to consumers, driving engagement and conversions in real-time.

N-S AI terms for marketing

NLP (Natural Language Processing): Imagine a computer that actually understands your emails or social media posts! NLP allows AI to do just that. In marketing, NLP algorithms power features like sentiment analysis, chatbots, content generation, and voice search optimization. This enables marketers to understand and engage with customers more effectively through natural language interactions.

Predictive Analytics: Ever wish you could predict future customer behavior? Predictive analytics, using historical data and machine learning algorithms, helps you do just that. In marketing, these models analyze customer data to predict purchasing behavior, identify high-value prospects, and personalize marketing campaigns. This allows marketers to anticipate customer needs and optimize marketing strategies for better results.

Programmatic Advertising with AI: Imagine buying and selling online ads with the efficiency of a robot! Programmatic advertising utilizes AI algorithms to automate ad placements, targeting, and bidding in real-time. This translates to personalized ads delivered to individual users across multiple channels, improving ad performance and campaign efficiency for marketers.

Prompt Engineering: Prompt engineering involves crafting instructions that a generative AI model can interpret and execute. A prompt is a natural language description of the task, such as “write a CV focused on field marketing experience,” which the AI then uses to generate the appropriate output.

Real-time Marketing with AI: In today’s fast-paced world, reacting quickly is key. Real-time marketing leverages AI to analyze data and deliver timely, relevant, and personalized messages to consumers based on their current context, behavior, and preferences. This allows marketers to react quickly to market trends, events, and customer interactions, driving higher engagement and conversions.

Recommendation Engines: Have you ever browsed Netflix and felt like they know exactly what you want to watch? Recommendation engines powered by AI analyze user behavior and preferences to generate personalized suggestions for products, content, and experiences. In marketing, recommendation engines can significantly boost sales, engagement, and customer satisfaction by delivering relevant recommendations based on past interactions and preferences.

Sentiment Analysis: Imagine gauging customer opinion from a mountain of social media comments and reviews! Sentiment analysis, a branch of NLP, analyzes text data to determine the emotional tone expressed by users. Marketers can leverage this to understand public opinion, gauge brand sentiment, and identify trends. This enables data-driven decisions and effective brand reputation management.

Understanding sentiment is crucial for AI-led marketing, and Similarweb can help there, too. With Similarweb’s Demand Analysis tool, you gain a comprehensive view of the customer journey from search to clicks, capturing real intent signals. By analyzing search data, you can evaluate genuine intent and reach a broader audience, complementing social listening and traditional survey insights to create a robust picture of consumer trends.

SGE (Search Generative Experience): SGE, or Search Generative Experience, was Google’s first experiment in adding AI-generated answers directly into the search results. Unlike earlier SERP features, SGE introduced generative AI summaries at the top of the page, synthesizing information to quickly answer user queries. It marked Google’s initial step toward integrating generative AI into Search and later evolved into what is now known as AI Overviews.

Social Listening with AI: Keeping your ear to the ground on social media is crucial. Social listening with AI-powered tools allows you to monitor and analyze social media conversations to gain insights into customer opinions, trends, and sentiments. Marketers can use this to track brand mentions, identify relevant conversations, and engage with audiences in real-time, ultimately understanding customer needs, addressing issues, and optimizing marketing strategies.

T-Z AI terms for marketing

Tera Operations Per Second (TOPS): Tera “trillion” Operations Per Second (TOPS) measures the performance of a supercomputer or high-end circuit board, particularly in AI tasks. It indicates the potential peak AI inferencing performance, based on the architecture and frequency of processors like the Neural Processing Unit (NPU).

Transformer: A transformer is a deep learning architecture developed by Google researchers in 2017. It uses a multi-head attention mechanism, enabling faster training by eliminating recurrent units. Originally designed for machine translation, transformers have become foundational in natural language processing, computer vision, and other AI applications, and have led to innovations like GPT and BERT.

Visual Search Optimization: Did you know you can search using images? Visual search optimization utilizes AI algorithms to analyze and interpret visual data, optimizing images and visual content for search engines. This helps marketers improve online visibility, drive organic traffic, and enhance user experience by making visual content more discoverable and accessible to search engines and users alike.

Voice Search Optimization: The way people search is evolving! Voice search optimization optimizes content and websites for voice-based search queries using AI algorithms to understand natural language and intent. This helps marketers improve search engine rankings, increase organic website traffic, and reach consumers who use voice-enabled devices to search for information and make purchases.

VR (Virtual Reality): Imagine transporting customers to a virtual world to experience your product! VR creates a computer-generated simulation of an immersive, three-dimensional environment that users can interact with in real-time. Marketers can leverage VR for interactive storytelling, product demonstrations, and virtual tours, ultimately engaging consumers and driving brand awareness, affinity, and sales.

And that’s a wrap on our AI glossary for marketers!

To explore how AI can improve your own marketing strategy, book a demo with one of our experts here.