You’ve found something odd in your data. Unearth the reasons behind it.
Data anomalies are a real headache. Sometimes they mean things are going better than expected. But too often, it means that someone’s tagged something wrong or a campaign ended prematurely. Or worse. The ability to automatically call out anomalies is one powerful feature of Adobe Analytics, but catching them is only one part of the equation. Just as important is understanding what caused the anomaly. For many analysts, this second step took time and required manually digging through large and varied datasets.
With the Contribution Analysis feature in Adobe Analytics, this time-consuming task can now be done literally with the click of a button. Contribution Analysis works together with Anomaly Detection to help you quickly understand what’s going on in your data. Built into the Analysis Workspace, Contribution Analysis queries tens of millions of dataset to replace long and difficult analysis with useful visualisations that help you to make the best decisions.
Focus only on the data you want.
Hide duplicate or uninteresting dimensions, outliers and junk data in reports to get the most relevant answers.
Uncover statistical relationships between dimensions.
Contribution scores help you to analyse the significance of your data dimensions relevant to the anomaly.
Build new audiences with ease.
Reveal hidden audience segments based on the contributing factors they have in common.
Use AI to drive faster insights.
Contribution analysis uses intensive machine learning to uncover contributors much more quickly than would otherwise be possible.