Proximal Policy Optimization (PPO)

Definition Proximal Policy Optimization (PPO) is a type of reinforcement learning algorithm in AI used to optimize decision-making procedures. In essence, it’s designed to strike a balance between exploring new decision pathways and exploiting current pathways to maximize rewards in complex environments. Its application in marketing can optimize various aspects such as customer targeting strategies, […]

Particle Swarm Optimization (PSO)

Definition Particle Swarm Optimization (PSO) is an AI-based computation technique used in marketing to solve optimization problems. It is inspired by the social behavior of bird flocking or fish schooling, implementing a search strategy to find optimal solutions. Through iterations, each ‘particle’ within the swarm adjusts its position based on its own and its neighbors’ […]

Particle Swarm Optimization

Definition Particle Swarm Optimization (PSO) in marketing refers to a computational method that optimizes a problem by iteratively trying to improve a candidate solution. It is based on the movement and intelligence of swarms, and effectively works by having a population (swarm) of candidate solutions (particles). These particles move in the search-space according to mathematical […]


Definition In the context of AI in marketing, a policy can be defined as a strategy or set of rules that guide the decision-making process of AI systems. It is the guiding principle that helps the AI models to make choices related to marketing, such as personalizing offers or segmenting customers. It assists in achieving […]

Policy Iteration

Definition Policy Iteration in AI marketing refers to a method used in reinforcement learning where the algorithm iteratively improves a chosen policy. This process involves evaluation, where the value function of the current policy is computed, and improvement, where a new policy is generated by acting greedily with respect to the current value function. The […]

Policy Gradient Methods

Definition Policy Gradient Methods are a type of reinforcement learning algorithms in AI, where the objective is to learn a parameterized policy that can maximize a reward over the course of an episode. Unlike value-based algorithms, these methods directly optimize the policy without requiring a value function. They work by taking gradients of the performance […]

Presentation Software

Definition Presentation Software in AI marketing refers to a digital tool that allows businesses to create, manage, and display multimedia content for marketing purposes. Leveraging AI, it can analyze user behaviors or preferences to customize presentations, enhance audience engagement and generate potential leads. It often includes features like graphics, text editing, and multimedia integration to […]

Predictive Lead Scoring

Definition Predictive Lead Scoring in marketing is the use of Artificial Intelligence (AI) to forecast the likelihood of a lead converting into a customer. AI algorithms analyze historical data, patterns, and behaviors, and rank leads according to their conversion potential. This assists businesses in prioritizing their marketing efforts, making them more effective and efficient. Key […]

Progressive Growing of GANs

Definition Progressive Growing of GANs (Generative Adversarial Networks) is a machine learning method that consists of training a GAN by gradually increasing the complexity of the generated output, starting with small, low-resolution images and progressively growing both the generator and discriminator models to output larger, high-resolution images. This technique, used mainly in AI and machine […]

Principal Component Analysis (PCA)

Definition Principal Component Analysis (PCA) in marketing AI refers to a statistical procedure that uses a technique for simplifying a dataset. It transforms a number of correlated variables into a smaller number of uncorrelated variables known as principal components. The first principal component accounts for as much of the variability in the data as possible, […]