Abstract: The technology disclosed herein relates to utilizing a performance anomaly detection model to identify performance metrics (e.g., cost-per-click data) that are above an anomaly threshold. For example, communication sessions can be established with one or more servers hosted by one or more third-parties for receiving performance metrics for a first entity. The performance metrics received for the first entity can be used by the performance anomaly detection model, which can be trained using historical performance metrics (e.g., of the first entity, of the first entity during particular time periods, of the first entity for particular geographical locations), for anomaly detection. Based on one or more anomaly detections, one or more notifications or particular displays can be provided to a user device.

Abstract: The technology disclosed herein relates to utilizing a performance anomaly detection model to identify performance metrics (e.g., cost-per-click data) that are above an anomaly threshold. For example, communication sessions can be established with one or more servers hosted by one or more third-parties for receiving performance metrics for a first entity. The performance metrics received for the first entity can be used by the performance anomaly detection model, which can be trained using historical performance metrics (e.g., of the first entity, of the first entity during particular time periods, of the first entity for particular geographical locations), for anomaly detection. Based on one or more anomaly detections, one or more notifications or particular displays can be provided to a user device.