Key takeaways

In this module, you learned how to:

• A4T is any Target activity that uses Adobe Analytics as its reporting source. When A4T is enabled, test results are pushed into the Analytics interface — making them available in Analysis Workspace, Data Warehouse, Report Builder, and Customer Journey Analytics. This gives marketers the best of both worlds: Target's experimentation engine combined with Analytics' far more powerful reporting, segmentation, anomaly detection, and ad-hoc analysis capabilities.
• A4T's analytics segments are retroactive — a key advantage over Target's native audiences. Because the Analytics implementation is already capturing data independently of Target, you don't need to pre-define which segments you want to analyze before the activity launches. Any Analytics segment can be applied to the results after the fact, reaching back through the full test history. Target-native report filter audiences, by contrast, only apply from the moment they're added forward.
• Automated Personalization is the one activity type A4T does not currently support. A/B Test, Multivariate Test, Experience Targeting, and Recommendations can all use A4T. Automated Personalization is the exception — it must use Target's native reporting.
• Selecting Analytics as the reporting source removes the multiple goal metrics option from the Target interface. When you'll be analyzing results in Analytics, there's no need to pre-select metrics in Target — you can add as many metrics as you want in Analytics after the fact. Target removes this option from the Goals and Settings step when A4T is chosen, along with the advanced counting methodology settings.
• Save the A4T activity before making it live to reduce classification latency. A4T adds approximately 5–7 minutes of latency on top of Target's standard 4-minute delay. In the worst case, it can take up to 72 hours for experience-level data breakdowns to appear after a brand new activity is first saved. Saving the activity early — before activating it — starts the classification process ahead of time, so once the test goes live, data appears within the normal report suite latency window instead of the worst-case scenario.