The Artificial Intelligence Glossary for App Marketers: Key Terms To Know

AI isn’t just hype anymore — it’s now doing the heavy lifting for app marketers, from automating ad spend decisions to personalizing user experiences for millions of customers at once.

Teams without AI capabilities are falling behind. What was once a competitive edge is now table stakes for efficient growth, deeper user engagement, and better ROI. But there’s a problem: AI terminology has exploded faster than most marketers can learn it, creating a language barrier that’s genuinely frustrating for teams trying to adopt these tools.

That’s why we built this glossary — to close that knowledge gap. You might be looking for AI tools, working with data scientists, or just trying to understand what’s actually happening in your campaigns. Either way, speaking the language matters.

Here, you will find straightforward definitions for the core concepts, tools, and platforms — from predictive AI and generative AI to overfitting and XAI — that are shaping the future of app marketing. Keep this guide handy. It’ll help you sound smarter in meetings, ask better questions about AI tools, and finally put these powerful technologies to work in your campaigns.

List of AI terms that app marketers need to know

A

• AI Agents are sophisticated software programs designed to operate autonomously, performing complex tasks proactively without direct human command. They perceive their digital environment, make decisions, and use various tools—like web browsers or data analysis software—to achieve specific, predefined goals. Unlike simple automation scripts, these agents can reason, plan, and adapt their strategies based on real-time feedback and new information. For app marketers, AI agents represent a significant leap forward in campaign automation and strategic optimization.

• AI System is a complete setup designed to deliver intelligent behavior by integrating models, data, and the necessary infrastructure. It is the entire operational framework, not just the AI model, that allows for the smart functionality to be delivered. The impact of an AI system on app marketing is that it provides marketers with the tools to drive user growth and engagement. Marketers can interact with these complex systems through user-friendly dashboards, APIs, or no-code tools to apply the system’s intelligence to their campaigns.

• Algorithm is a defined sequence of steps that a computer follows to solve a problem or make a decision. It operates by taking an input, such as information from a data set, performing a test or calculation on it, and then generating an output. This method can be used to identify patterns in data and generate predictions based on those findings. The impact of an algorithm on app marketing is seen in tools for A/B testing, where it is used to evaluate and determine which campaign performs best.

• Application programming interface (API) is a set of routines, protocols, and tools designed for building software applications, defining how different software components should interact. In simple terms, an API is a tool that enables separate applications or companies to communicate and exchange data with one another. The impact of an API on marketing is that it allows marketers to connect different software tools and data sources.

• Artificial General Intelligence (AGI) is a type of artificial intelligence that possesses the ability to perform any intellectual task a human can, including generating new scientific knowledge. The concept envisions a super intelligent computer that learns and develops autonomously, understands its environment without needing supervision, and ultimately becomes capable of transforming the world around it. The impact of Artificial General Intelligence on marketing is currently visionary rather than practical.

• Artificial Intelligence (AI) is the science of enabling machines to perform tasks that would typically require human brainpower, such as reasoning, decision-making, differentiating words and images, learning from mistakes, predicting outcomes, and solving problems. It involves using computers to undertake these actions, often with greater speed and accuracy than is humanly possible. In app marketing, the impact of AI is evident in its ability to power personalized recommendations for users and to automate content generation.

• Artificial Super Intelligence (ASI) is commonly defined as a hypothetical form of AI that would possess an intelligence far exceeding that of humans. The concept, while subject to debate, describes a speculative future level of machine capability. The primary impact of the Artificial Super Intelligence concept on marketing is not in current application, but in how it shapes future development. It highlights the critical need for responsible AI governance, which will be essential for creating any future tools, including those used in marketing.

• Adjust Growth Copilot is an AI-powered assistant integrated directly into Adjust’s analytics platform. It is engineered to assist app marketers in making quicker and more intelligent decisions. This tool allows marketers to use plain language to query their performance data, receiving instantaneous and actionable insights to guide their strategies. This AI assistant streamlines complex data analysis for app marketers. Key features include automated anomaly detection to spot unusual performance shifts.

C

• Chain of Thought is a reasoning process utilized by AI models to articulate each step taken to reach a final answer. Rather than jumping directly to a conclusion, the model explains its reasoning process, much like showing the work in a step-by-step math problem. The impact of Chain of Thought on marketing is its usefulness in providing transparency and auditability. This process is valuable for marketers who need to debug or audit AI-driven recommendations, as it allows them to understand the logic behind why a particular suggestion or decision was made.

• Chatbot is a software application created to simulate human conversation through text or voice interactions. It commonly utilizes natural language processing techniques to understand user input and deliver relevant responses, making the interaction feel more natural. For marketers, the impact of a chatbot is its ability to automate customer-facing tasks. They can be deployed to instantly answer frequently asked questions (FAQs), guide new users through an onboarding process, or engage with potential customers to qualify them as leads.

• ChatGPT is a natural language processing chatbot developed by OpenAI, which generates human-like text from prompts and questions. Based on a language model that is fine-tuned with user feedback, it can compose articles and essays, write emails, tell creative stories, and generate programming code. The impact of ChatGPT in app marketing is its use as a tool for generating content and brainstorming. Marketers use it for writing advertising copy or for coming up with creative ideas for new campaigns, streamlining the initial phases of content creation.

• Cognitive computing is an interdisciplinary field that brings together AI and cognitive science to create computerized models that simulate human thought processes. It aims to replicate complex functions like reasoning, perception, and decision-making in a machine. The impact of cognitive computing on marketing is seen in its application through specialized systems. Marketers use these cognitive systems to enable functionalities such as visual search, to monitor and ensure brand safety, or to conduct in-depth customer sentiment analysis.

• Conversational AI is a type of artificial intelligence designed to simulate human-like dialogue through text or voice interactions. It uses core technologies like natural language processing (NLP) and machine learning (ML) to understand user intent, process language, and generate relevant, context-aware responses. This enables software, such as chatbots and virtual assistants, to engage in natural, two-way conversations. The impact of Conversational AI on marketing is that it automates and personalizes customer engagement at scale. Marketers use it to deploy 24/7 chatbots for lead generation and qualification, provide instant customer support, and deliver personalized product recommendations based on user data.

D

• DALL-E is a deep-learning model created by OpenAI that can generate digital images from natural language descriptions. Users provide these text-based descriptions, which are called prompts, as input to direct the AI on what kind of image to create. The impact of DALL-E on marketing is its ability to accelerate the creation of visual assets. Marketers use it to quickly produce necessary images for their campaigns, such as app icons, custom advertisements, or graphics for social media.

• Data mining is the process of closely examining large data sets to identify patterns and glean insights. It involves a thorough analysis of information to discover underlying trends that may not be immediately obvious. The impact of data mining on app marketing is its application in discovering strategic insights from user information. App marketers use it specifically to find distinct customer segments for targeted campaigns or to identify important trends in campaign performance data.

• Data science is a research field focused on processing large amounts of data to identify patterns, spot trends and outliers, and provide insights into real-world problems. It is the practice of analyzing vast information sets to extract meaningful conclusions. The impact of data science on marketing is its ability to inform strategic decisions through deep analysis. It is useful for optimizing user funnels by pinpointing areas of user drop-off, and for creating lifetime value models that predict the future profitability of customers.

• Deep learning is a specific form of machine learning that utilizes neural networks, modeled after the human brain, to process unstructured data like images or voice. Unlike other machine learning methods that require human input to learn, deep learning can take raw, unstructured data such as text, music, or video and independently distinguish between different categories within it.

• Deepfake is synthetic audio, video, or imagery generated by machine learning algorithms to convincingly represent a real person or create a realistic depiction of a person who has never existed. This technology can alter reality by making it appear that real people are saying and doing things chosen by the creator.

F

• F-Score is a metric that measures the accuracy of an AI model’s predictions by combining two other measures: precision and recall. It is especially useful for evaluating the performance of models on classification tasks, where data is sorted into different categories. The impact of the F-Score on app marketing is that it helps marketers assess the quality of their predictive models. For example, it can be used to validate the accuracy of models designed for churn prediction or for classifying different types of users, ensuring more reliable, data-driven decisions.

G

• Generative AI is a subset of machine learning models that can create new content, such as text, video, code, and images, by learning from existing data. These models are trained on vast amounts of raw data, like the text from millions of web pages, to understand patterns and generate the most probable response when given a text-based prompt. The impact of Generative AI on marketing is its ability to automate and scale content creation. Marketers use it to draft ad copy, generate blog posts, or create video content, which significantly speeds up the production of marketing materials.

• Generative Pre-trained Transformer (GPT) is a large-scale AI language model developed by OpenAI that is designed to generate human-like text. It functions by processing input and predicting the most likely sequence of words to follow, based on patterns learned from vast amounts of training data. For marketers, the impact of GPT is its ability to automatically generate various forms of written content. It is useful for tasks such as drafting user onboarding flows, writing support articles, or creating in-app messages, which helps to scale content production and communication efforts.

• Google Performance Max is a goal-based campaign type within Google Ads that utilizes AI to manage and optimize advertising across Google’s entire inventory. It is designed to work with minimal manual input, running ads on Search, YouTube, Display, and other Google channels from a single campaign to achieve specific advertiser goals. The impact of Google Performance Max on marketing is that it automates and simplifies the process of reaching customers across all of Google’s platforms.

• Graphics Processing Unit (GPU) is a specialized microprocessor originally designed to quickly render images for display. Beyond graphics, GPUs are also highly efficient at performing the extensive calculations required to train and run neural networks. While not a direct marketing tool, the impact of a GPU on app marketing comes from its relevance to developers. By enabling developers to optimize in-app AI features, GPUs support the creation of more sophisticated and powerful app experiences, which are then promoted by marketers.

• Guardrails are mechanisms designed to keep the behavior of an AI system ethical, safe, and within specific bounds. They function as safety checks to prevent undesirable outcomes, such as the generation of offensive content or the spread of misinformation. The impact of guardrails on app marketing is that they protect brand reputation and the user experience. App marketers rely on them to prevent user-facing AI tools from producing harmful or biased responses, ensuring that interactions with the brand remain positive and safe.

H

• Hallucination, in the context of AI, is a phenomenon where a model generates content that is not based on actual data or is significantly different from reality. This can manifest as the creation of fake book citations or providing factually incorrect answers to questions, such as naming an elephant as a mammal that lays eggs. The impact of hallucinations on marketing is that they make human review a critical step in content creation.

• Human In The Loop (HITL) describes a system that includes both a human and an AI component working together. In this structure, the human can intervene to train, tune, or test the system’s algorithm, with the objective of making the AI produce more useful and accurate results. The impact of a Human In The Loop system in marketing is its application in refining automated tasks. It is useful in content moderation workflows, where human judgment is needed, and in campaign optimization, where a marketer can provide input to improve an AI’s decision-making process.

I

• Image recognition is the process of identifying and categorizing an object, person, place, or text contained within a digital image or a video. The provided text does not include information on the impact or use of image recognition in marketing.

L

• Large Language Model (LLM) is an AI model that has been trained on large volumes of text, which enables it to understand natural language and generate human-like text. Its core function is to process and produce language in a way that mimics human communication. The impact of a Large Language Model on marketing is its application in creating automated and personalized communication tools. Marketers use LLMs to power the creation of personalized content, to serve as the engine for chatbots, or to build summarization tools for data analysis

M

• Machine learning is a subset of AI in which algorithms mimic the process of human learning while processing data. It focuses on developing models that help machines learn from data to predict trends and behaviors without human assistance, with the algorithms improving and becoming more accurate at predictions and classifications over time. The impact of machine learning on marketing is its use in predictive modeling and customer analysis. It can be applied to forecast customer churn, to perform advanced customer segmentation based on behavior, and to enable dynamic pricing strategies.

• Meta AI is the comprehensive artificial intelligence system that underpins the advertising and user experience features across Meta’s platforms, including Facebook, Instagram, and WhatsApp. It functions as the core engine that processes vast amounts of data to power everything from automated ad delivery and creative optimization to audience targeting. This system is the foundation for tools like the Advantage+ suite, which are designed to automate and improve campaign performance with minimal direct management. For app marketers, it drives automated improvements by using its engine to find the most relevant audiences for app installs and to optimize ad creatives for better engagement.

• Model is a mathematical system that is trained on data to recognize patterns, which it then uses to make predictions or generate new content. For instance, a language model demonstrates this by predicting the next word in a sentence based on the data it was trained on. The impact of a model on marketing is that it serves as the core engine driving marketing automation. These mathematical systems provide the predictive power and content generation capabilities that enable automated marketing tools to function effectively.

• Model Context Protocol (MCP) is a system that allows developers and users to dynamically provide context to AI models to improve the relevance and accuracy of their responses. It enables structured information, like user data or session history, to be fed into a model without requiring fine-tuning, giving AI systems the ability to adapt to real-time context like an intelligent agent while remaining secure. The impact of a Model Context Protocol on app marketing is that it can be used to create more tailored and responsive user experiences. App marketers can leverage it to build personalized in-app journeys, deploy smarter chatbots that understand a user’s current context, and create user flows that adapt based on real-time engagement data.

• Multi-Agent System is a network of interacting AI agents that often actively help and work with humans to complete a task. The most common everyday examples of these systems are virtual assistants on smartphones and personal computers, such as Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana.

N

• Natural language processing (NLP) is a type of AI that enables computers to understand spoken and written human language, powering features like text and speech recognition on devices. The ultimate goal of NLP is to allow machines to read, decipher, understand, and make sense of human language in a valuable way. The impact of natural language processing on marketing is its use in analyzing customer communication and automating interactions. Marketers can use NLP to perform sentiment analysis on customer feedback, to build conversational chatbots, and to automatically tag and categorize user reviews.

• Noise refers to irrelevant or misleading data that can distort an AI model’s training process. A primary source of noise is human input, such as providing poorly labeled data or information that isn’t representative of the real world. For example, if a dataset meant to teach a computer to recognize cats contains images of dogs or is poorly lit, this noise will cause the system to make mistakes on new, unseen pictures.

O

• Open Source refers to software and data that are freely available to be edited and shared. This model is designed to help researchers collaborate, to allow others to check and replicate findings, and to share new developments with the wider community of developers.

• Overfitting is a problem that occurs when an AI model learns its training data too well, including its noise, anomalies, and random fluctuations. This results in poor performance when the model encounters new, unseen data because it has effectively memorized the training set instead of learning the underlying concepts, much like a student memorizing answers to a practice test. The impact of overfitting on marketing is that an AI model may appear to perform well in internal tests but fails when applied to real users.

P

• Parameters are the internal values that a model learns from the data during its training phase. They are the variables that the model itself adjusts to map inputs to outputs. The impact of parameters on marketing is that their adjustment is key to improving the accuracy of predictive models. The process of tuning these parameters is crucial for marketers seeking to get more reliable and precise marketing predictions from their AI systems.

• Pattern recognition is the method of using computer algorithms to analyze, detect, and label regularities within data. This process of identifying patterns is what informs how the data is ultimately classified into different categories. The impact of pattern recognition on marketing is that it powers key personalization and segmentation features. Marketers rely on this method to drive recommendation systems and to perform audience clustering, which involves grouping similar users for more effective targeting.

• Personalization, in the context of AI, refers to tailoring content, features, or user experiences based on individual user data and behavior. AI systems accomplish this by analyzing a user’s past actions, such as clicks, purchases, or usage patterns, to make future predictions or suggestions. The impact of personalization on marketing is its ability to increase user engagement and retention. Marketers use AI-driven personalization to deliver targeted content or offers that are more relevant to individual users, encouraging continued interaction and loyalty.

• Pre-training is the initial phase of training a machine learning model where it learns general features, patterns, and representations from a large dataset without knowledge of the specific task it will eventually perform. This unsupervised or semi-supervised learning process enables the model to gain a foundational understanding of the data’s structure and extract meaningful features for later use.

• Precision in AI is a metric that measures the proportion of correct positive predictions made by a model. This proportion is calculated based on the dataset that was used to train the model, reflecting how many of the model’s positive identifications were actually correct.

• Predictive AI is a branch of artificial intelligence that uses machine learning, data mining, and statistical modeling to analyze current and historical data to forecast future outcomes, trends, and behaviors. By identifying patterns and relationships within large datasets, these AI systems can generate predictions about what is likely to happen next. The impact of Predictive AI on marketing is that it enables marketers to move from reactive to proactive strategies. It is used to predict the lifetime value (LTV) of customer segments, and recommend products or content that a user is most likely to engage with next.

• Prescriptive AI is an advanced form of artificial intelligence that goes beyond predicting future outcomes to recommend specific actions that should be taken to achieve a desired goal. It analyzes data and potential scenarios to determine the best possible course of action, essentially providing data-driven advice on what to do next to optimize results. The impact of Prescriptive AI on marketing is its ability to provide actionable, automated recommendations that guide strategy.

• Prompt is an input that a user feeds to an AI system with the goal of getting a desired result or output. It is essentially the instruction or question given to the AI to guide its response.

R

• Recommendation engine is an AI system that analyzes user data to suggest relevant content, products, or actions. It utilizes techniques such as collaborative filtering, content-based filtering, or hybrid models to identify and predict what a user is likely to want next. The impact of a recommendation engine on app marketing is that it powers features designed to boost engagement and sales through automation. Examples include “you might also like” product suggestions, automatic playlist generation, and targeted push notifications, all of which help increase conversion rates and user satisfaction.

• Reinforcement learning is a type of machine learning in which a model learns by interacting with its environment. It receives positive reinforcement for correct predictions and negative reinforcement for incorrect ones, adapting its behavior over time to maximize rewards.

• Reinforcement Learning from Human Feedback (RLHF) is an AI training process that uses a combination of both direct human feedback and automated reinforcement signals. This dual approach allows a model to learn from both explicit human guidance and its own trial-and-error interactions. The impact of RLHF on marketing is seen in its use for refining customer-facing AI. It is particularly useful for fine-tuning AI chatbots, allowing marketers to improve their conversational tone and overall usefulness to better align with brand identity and customer service goals.

S

• SplitMetrics Acquire is an AI-powered mobile growth platform specifically designed to optimize Apple Ads campaigns. It operates using predictive and prescriptive AI and is powered by the SplitMetrics AI model family called Samba. The impact of SplitMetrics Acquire on app marketing is its ability to improve campaign efficiency and financial returns. App marketers use it to predict and improve ad performance, manage keyword bidding, and automate user acquisition, with the goals of reducing cost-per-install (CPI) and maximizing return on ad spend (ROAS).

• SplitMetrics AI is the mobile industry’s first dedicated AI research and development center focused on solving complex marketing problems. Founded in 2022, its core purpose is to build and refine sophisticated AI technologies to power a suite of mobile marketing solutions. The impact of SplitMetrics AI on marketing is that it provides app marketers with advanced tools to automate and optimize their campaigns, particularly on Apple Ads. By leveraging its AI models, marketers can achieve better campaign outcomes, such as improved cost-per-acquisition (CPA) and return on ad spend (ROAS) through multi-campaign optimization, ultimately driving more efficient and profitable user acquisition.

• SplitMetrics Samba is the AI model family that uses predictive and prescriptive AI to power features specifically designed for optimizing mobile advertising campaigns, with a primary focus on Apple Ads. By analyzing millions of data points, Samba enables the automation of complex tasks like ROAS optimization, multi-campaign bid optimization and bid simulation. The impact of Samba on app marketing is its ability to drive campaign efficiency and maximize profitability.

• Symbolic Artificial Intelligence is a type of AI that utilizes symbolic reasoning as its method to solve problems and represent knowledge.

• Synthetic data is AI-generated data that is used to train models or simulate user behavior in situations where real user data is limited or unavailable.

T

• Token is a basic unit of text that a Large Language Model (LLM) uses to understand and generate language. A single token can be an entire word or, in some cases, just a part of a word.

• Training data is the information or set of examples that is given to an AI system to enable it to learn. This data is the foundation that allows the model to find patterns and develop the capability to create new content.

U

• Underfitting occurs when a model is too simple to capture the underlying patterns in data, causing it to perform poorly on both the data it was trained on and new, unseen data. This failure happens because the model has not learned enough, similar to using a single basic template for all users regardless of their differences. The impact of underfitting on app marketers is that it results in generic and ineffective outreach. This can show up as broad, impersonalized recommendations or poorly targeted messaging that does not reflect specific user preferences or behaviors, leading to lower engagement.

• Unsupervised learning is a form of machine learning in which algorithms analyze and cluster unlabeled data sets. This is achieved by looking for hidden patterns in the data, and it works without the need for human intervention to train or correct the algorithms.

V

• Vibe coding is a design-focused term in AI-driven creative development that describes how coders and creators fine-tune the “tone” or emotional quality of AI outputs. It is often used in media, branding, or conversational UX to shape the feel of the generated content. The impact of vibe coding in a marketing context is its use in aligning AI-generated content with a brand’s specific identity or emotional goals, such as being fun, formal, or edgy. It is frequently applied through subtle prompt engineering to develop AI-generated copy, dialogue for virtual assistants, or interactive content that must resonate emotionally with users.

X

• XAI (Explainable AI) refers to artificial intelligence systems that are designed with the ability to explain their decisions or the reasoning behind their outputs. The impact of XAI on marketing is that it provides crucial transparency for automated decisions. It is vital for marketers who need to understand and justify why an AI system made a particular targeting choice or content recommendation.