AI Glossary for Marketers

A method of comparing two versions of a webpage, email, or advertisement to determine which one performs better in terms of a desired outcome (e.g., click-through rate, conversion rate). AI algorithms can analyze A/B test results and optimize campaigns in real time based on performance metrics.

Refers to technologies, systems, or processes where artificial intelligence (AI) is used to enhance or improve existing processes, capabilities, or workflows. In AI-augmented systems, AI tools or algorithms are integrated into human-operated systems to provide additional insights, automation, or assistance.

Describes technologies, platforms, or solutions that are built from the ground up with artificial intelligence (AI) capabilities integrated into their core architecture. Unlike systems where AI is added as an enhancement or feature, AI-native solutions are designed to leverage AI algorithms and techniques as fundamental components, enabling seamless integration, scalability, and optimization of AI-driven functionalities.

Refers to technologies, platforms, or solutions that leverage artificial intelligence (AI) algorithms or techniques to enhance their capabilities or functionalities. These systems use AI to automate tasks, make predictions, analyze data, and adapt to changing conditions, performing tasks autonomously or semi-autonomously, ultimately improving efficiency, accuracy, and performance.

Algorithms are sets of rules or instructions used by computers to perform tasks, solve problems, or make decisions. In the context of AI and machine learning, algorithms are crucial for processing data, learning from patterns, and making predictions based on statistical analysis.

A branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions.

Computational model inspired by the human brain's structure and functioning. ANNs are a type of machine learning algorithm used to analyze large amounts of data and recognize patterns within it. They consist of interconnected nodes, or "neurons," organized into layers. In marketing, ANNs are utilized for various tasks such as customer segmentation, predictive analytics, and recommendation systems.

The process of assigning credit to various marketing touchpoints along the customer journey to understand their impact on conversions or sales. AI-powered attribution models use advanced algorithms to analyze multi-channel data and allocate credit accurately to each touchpoint.

Churn models are analytical tools used to predict and understand customer churn, or the rate at which customers stop doing business with a company. These models analyze customer data to identify patterns and factors that contribute to churn, allowing businesses to take proactive measures to retain customers.

Customer intelligence refers to the insights and information derived from analyzing customer data. It involves collecting and analyzing data from various sources to gain a deeper understanding of customer behavior, preferences, and needs. Customer intelligence helps businesses make informed decisions and develop strategies to better serve their customers.

The customer lifecycle represents the stages that a customer goes through during their relationship with a company, from initial awareness to post-purchase loyalty. These stages typically include awareness, consideration, purchase, retention, and advocacy. Understanding the customer lifecycle allows businesses to tailor their marketing efforts and provide personalized experiences at each stage of the customer journey.

The predicted net profit attributed to the entire future relationship with a customer. AI algorithms can analyze historical data to calculate CLV and segment customers based on their potential long-term value to the business.

Customer modeling involves the creation and analysis of mathematical or statistical representations of customer behavior, preferences, and characteristics. These models leverage historical data to gain insights into customer segments, predict future actions or outcomes, and optimize marketing strategies, enabling organizations to tailor their offerings and communications to meet the unique needs of their target audience.

The process of dividing customers into distinct groups based on shared characteristics or behaviors. AI algorithms can analyze large datasets to identify meaningful segments and tailor marketing strategies to target each group effectively.

The process of discovering patterns and relationships in large datasets, used to extract valuable insights from customer data, such as purchasing behavior and demographics.

A specialized form of machine learning (ML) that involves artificial neural networks with many layers (hence the term "deep") designed to mimic the human brain's structure. Deep learning algorithms are particularly effective for tasks such as image and speech recognition.

Descriptive analytics is the process of analyzing historical data to understand what has happened in the past. It focuses on summarizing and visualizing data to provide insights into trends, patterns, and relationships. Descriptive analytics helps businesses gain a better understanding of past performance and make informed decisions based on historical data.

Diagnostics analytics involves analyzing data to understand the underlying reasons behind various marketing outcomes, such as changes in sales, customer behavior, or campaign performance. It delves into the metrics and patterns to uncover the factors driving success or failure in marketing efforts.

Refer to algorithms or statistical techniques used to detect and analyze changes in data patterns over time. Drift models are particularly valuable for understanding shifts in consumer behavior, market trends, or other relevant variables. These models help marketers anticipate changes and adjust their strategies accordingly, ensuring that marketing efforts remain effective and aligned with evolving market dynamics.

First-party data consists of information collected directly from individuals or customers by a company through its own channels or interactions. This data is obtained firsthand and includes customer demographics, purchase history, website interactions, and other behavioral data, providing valuable insights into customer preferences and behaviors.

A subset of artificial intelligence (AI) that focuses on creating content, such as text, images, or music. GenAI tools are employed to automate content creation processes, allowing marketers to generate large volumes of diverse, high-quality content efficiently.

A marketing strategy that involves tailoring products, services, and messaging to individual customers based on their preferences, behavior, and demographics. AI enables hyper-personalization by analyzing large amounts of data to deliver highly targeted and relevant experiences.

Incrementality refers to the measurable difference in outcomes between a group exposed to a marketing campaign and a comparable group that was not exposed. In marketing, incrementality is used to assess the true impact of a campaign by determining whether the observed changes in behavior, such as increased sales or conversions, can be attributed to the campaign itself rather than other factors.

The process of monitoring and analyzing the various stages that a customer goes through from initial contact with a company to becoming a loyal advocate. It involves tracking customer interactions and behaviors at each stage of their journey to understand their preferences, needs, and patterns of engagement.

Lift model refers to a statistical or predictive modeling technique used in marketing to measure the incremental impact of a marketing campaign or intervention compared to a baseline scenario. It helps marketers understand how much additional benefit (or "lift") a particular marketing action provides.

A subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed. ML algorithms identify patterns and make predictions based on input data.

The use of software platforms and AI technologies to automate repetitive marketing tasks, streamline workflows, and improve efficiency. Marketing automation tools can automate email marketing, lead nurturing, social media management, campaign optimization, and more.

The use of AI and ML techniques to analyze data, identify opportunities for improvement, and optimize marketing strategies, campaigns, and budgets for maximum effectiveness and ROI.

The ability of computers to understand, interpret, and generate human language. NLP is used for tasks such as sentiment analysis, chatbots, and content generation.

Outcome-based marketing focuses on achieving specific, measurable results through clear goals and AI-driven analytics. AI enables marketers to optimize campaigns in real time based on performance metrics and customer behavior.

The practice of delivering customized experiences, content, or recommendations to individual users based on their preferences, behaviors, and demographics. AI-powered personalization enables marketers to create more relevant and engaging interactions with customers across various channels.

The use of data, statistical algorithms, and ML techniques to identify patterns and predict future outcomes or trends. Predictive analytics can help marketers anticipate customer behavior, optimize campaigns, and make data-driven decisions.

Predictive insights refer to actionable intelligence or forecasts derived from analyzing historical data patterns and applying predictive analytics techniques. These insights enable organizations to anticipate future trends, behaviors, or outcomes, empowering them to make informed decisions, optimize strategies, and seize opportunities ahead of time.

A form of advanced analytics that uses data mining, machine learning, and AI techniques to provide recommendations for actions to optimize marketing strategies. It goes beyond descriptive and diagnostic analytics by suggesting specific actions to achieve desired outcomes.

Actionable recommendations derived from prescriptive analytics, guiding marketers on the best course of action to achieve their marketing goals. These insights provide clear directions on what steps to take to improve performance and drive better results.

A propensity model is a statistical algorithm or predictive model designed to forecast the likelihood or probability of a specific event or behavior occurring in the future. These models analyze historical data and various predictor variables to generate insights into customer behavior, such as purchasing a product, clicking on an ad, or subscribing to a service.

The analysis of data as it is generated or received, allowing marketers to monitor performance metrics, detect trends, and make data-driven decisions in real time. AI-powered real-time analytics enable proactive responses to changing market conditions and customer behaviors.

An AI-powered system that analyzes customer data to provide personalized recommendations for products, services, or content. Recommendation engines are commonly used in e-commerce, streaming services, and content platforms to enhance user experience and drive engagement.

Information collected by one company and shared directly with another, typically through partnerships or direct agreements. It differs from first-party data, which a company collects directly from its own audience, and third-party data, which is collected by external sources. Second-party data is often considered more reliable and valuable because it comes directly from trusted sources.

A type of machine learning where the model is trained on labeled data, meaning that the input data is paired with the correct output. Supervised learning algorithms are used for tasks such as classification, regression, and spam detection.

Information collected by external sources, typically not directly controlled by the company using it. It includes demographic, behavioral, and firmographic data obtained from various sources such as data providers, publishers, and other businesses. Third-party data is often used to enrich first-party data and improve audience targeting and segmentation in marketing campaigns.

A type of machine learning where the model is trained on unlabeled data, meaning that the input data is not paired with the correct output. Unsupervised learning algorithms are used for tasks such as clustering, anomaly detection, and market segmentation.

Zero-party data refers to intentionally provided information or preferences shared voluntarily by individuals with a company or organization. Unlike first-party data, which is observed or inferred from customer interactions, zero-party data is actively and explicitly provided by individuals in exchange for personalized experiences, content, or offers, such as survey responses, preference settings, and feedback.