We are excited to launch a causal contribution analysis capability in Amazon Lookout for Metrics that helps you to understand the potential root causes for the business-critical anomalies in the data. Previously, you were only given the root causes for a single anomaly per measure. You had to analyze to determine if causal relationships existed between the detected anomalies in different measures. When focusing on a single anomaly, you can easily miss the anomaly’s downstream (or upstream) impact. For example, you may see a spike in your checkout cart abandonment and know that your revenue will decrease. However, you may not know what caused the checkout carts to be abandoned at a higher rate. The causal contribution analysis feature can tell you that the spike in checkout cart abandonment may be due to spikes in transaction failures or sudden changes in prices due to promotion expiration.
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