Anomaly Detection provides a statistical method to determine how a given metric has changed in relation to previous data.
Anomaly Detection allows you to separate “true signals” from “noise” and then identify potential factors that contributed to those signals or anomalies. In other words, it lets you identify which statistical fluctuations matter and which don’t. You can then identify the root cause of a true anomaly. Furthermore, you can get reliable metric (KPI) forecasts.
Examples of anomalies you might investigate include:
Drastic drops in average order value
Spikes in orders with low revenue
Spikes or drops in trial registrations
Drops in landing page views
Spikes in video buffer events
Spikes in low video bit-rates
Analysis Workspace’s anomaly detection algorithm includes
Support for hourly, weekly, and monthly granularity, in addition to the existing daily granularity.
Awareness of seasonality (such as “Black Friday”) and holidays.
