Upper Confidence Bound (UCB)

Definition In AI marketing and reinforcement learning, Upper Confidence Bound (UCB) is an algorithm used to solve the multi-armed bandit problem which involves balancing exploitation and exploration to maximize an overall level of rewards. It provides an exploration factor, based on uncertainty or lack of confidence in an option, to balance with exploitation of what […]


Definition Underfitting in AI marketing refers to a modeling error which occurs when a machine learning model is not complex enough to capture the underlying patterns in the data it is trained on. This results in poor performance and inaccurate predictions, as the model is unable to generalize from its training data to unseen data. […]

Unsupervised Learning

Definition Unsupervised Learning in AI marketing refers to the machine learning approach where systems identify patterns within data sets without any prior instruction or labeled outcomes. It’s often used for clustering or segmenting audiences based on their behaviors or characteristics. It can help marketers uncover hidden patterns or trends that might not be noticeable during […]

User-Generated Content Schedule

Definition User-Generated Content Schedule in AI Marketing refers to a strategic plan that outlines when and how user-created content will be shared or promoted across various marketing channels. It involves leveraging artificial intelligence tools to automate the process of sourcing, curating and distributing user-generated content at optimal times for maximum engagement. This promotes authenticity and […]

Unsupervised Domain Adaptation

Definition Unsupervised Domain Adaptation in AI marketing refers to the process wherein an artificial intelligence algorithm is trained to adapt to a new, unlabeled dataset – the ‘domain’ – using its knowledge from the already labeled source dataset. It’s ‘unsupervised’ as it does not rely on manual labeling of the target data. The goal is […]