Real-time Campaign Optimization
Definition AI in Real-time Campaign Optimization refers to the automated process of adjusting and enhancing marketing campaigns in real-time using artificial intelligence. This involves using machine learning algorithms to analyze campaign metrics and improve performance based on real-time data. It allows marketers to make immediate alterations to their campaigns for maximum effectiveness and efficiency. Key […]
Random Search
Definition Random Search in AI marketing refers to an optimization algorithm that selects random combinations of variables to determine the most effective marketing strategy. Unlike systematic or grid searches, it doesn’t follow a set pattern, making it potentially more effective in high-dimensional spaces. Its goal is to find the best possible results within a specified […]
Robust Principal Component Analysis (RPCA)
Definition Robust Principal Component Analysis (RPCA) is a mathematical technique in AI often used in marketing for reducing multi-dimensional data into fewer dimensions, simplifying analysis. It separates an observed data matrix into the sum of a low-rank matrix and a sparse matrix, which helps in detecting outliers, anomalies, and patterns within large datasets. In marketing, […]
Reversible-Jump MCMC
Definition Reversible-Jump Markov Chain Monte Carlo (RJMCMC) in the context of AI and marketing refers to a sophisticated algorithmic approach used to estimate probability distributions and detect patterns within complex, multi-parameter space that can’t be easily analyzed with traditional techniques. It allows a model’s structure and parameters to be updated simultaneously, making it capable of […]
Restricted Boltzmann Machines (RBMs)
Definition Restricted Boltzmann Machines (RBMs) are a type of artificial neural network used in machine learning. They are a specialized form of a Boltzmann Machine, with the restriction that their neurons must form a bipartite graph. This makes them effective for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modelling in the field […]
Reward Function
Definition In AI marketing, the Reward Function is a crucial component that guides machine learning algorithms. It’s the method employed to measure and rank the outcomes of decisions made by an AI system, thereby aiding in decision-making optimization. This function gives ‘rewards’ or ‘penalties’ to the AI system to adjust its behavior, encouraging it to […]
Recurrent Neural Networks (RNNs)
Definition Recurrent Neural Networks (RNNs) are a type of artificial intelligence used in marketing that can remember or learn patterns in sequential data, making them ideal for text and speech analysis. They’re called ‘recurrent’ because they perform the same task for every element of a sequence, such as a sentence, with the output being dependent […]
Reinforcement Learning
Definition Reinforcement Learning in AI marketing is a machine learning approach where an AI system learns how to make decisions by taking actions that maximize a reward in a certain environment. The ‘reward’ is feedback that the AI gets from its actions – positive if it does well and negative if it doesn’t. It uses […]
Robotic Process Automation (RPA)
Definition Robotic Process Automation (RPA) in marketing refers to the use of software algorithms or ‘robots’ to automate repetitive tasks and processes, thus reducing manual labor. The AI comes into play by enabling these robots to learn from their experiences, improve over time, and make data-driven decisions. Overall, it enhances efficiency, accuracy and allows teams […]
Rejection Sampling
Definition Rejection Sampling in AI and marketing is a statistical method used to generate observations from a complex probability distribution. It works by randomly drawing samples from a larger pool and only retaining those fitting a specific criterion, while rejecting the inappropriate matches. Essentially, it’s an algorithmic way of optimizing an ad or marketing campaign […]
