Analytics & Target Best Practices for Analysis

Analytics & Target go together like bacon & eggs. It just makes sense!

If you've heard these comments around the water cooler, your organization could benefit from leveraging Analytics as a data source for your Target activities (A4T).

What's in it for me? Why use Analytics?
  • While the same data is available in both interfaces, the Analytics UIs are best suited for deep analysis, advanced segmentation, and data visualization
  • Analytics metrics & segments are “always on” so there's no advance planning or setup required which saves time & helps gain efficiency
  • Analytics reports are easy to download, email, and share with colleagues and stakeholders
  • Data is also available through Workspace, Ad Hoc Analysis, Report Builder, and Data Warehouse
I know, you're excited! But before you kick off your next Target test using Analytics as the data source, there are a few things you should do to be prepared.
  • Make sure you have the proper permissions to both configure your Target activity & view the data using Analytics. More on that here.
  • Get to know your Analytics variable map.  If the Analytics & Target users at your company are not the same people, schedule a collaborative session to understand what data is (and isn't) being collected and what each variable means.
  • Learn the rules. In Analytics variable allocation & expiration may be different than Target. Get familiar with the nuances so you can correctly interpret your Target activity performance. More on that here and here. For now, only certain Target activity types are supported with A4T.
  • Partner with your Analytics power users. Agree upon how optimization analysis will be performed & shared.

If you're ready to get started and you prefer a video walk-through of the following content, check out a previously recorded webinar entitled Analytics for Target: The smarter way to optimize.

I just launched a Target activity using Analytics as the data source. Where & how should I perform my analysis?

For starters, review aggregate performance to determine which test experience is the overall winner. While either the Analytics interface or the Target interface will show the same data, Adobe recommends using the Analytics interface for test analysis.

If you start in Adobe Target you can simply click "View Report in Analytics" to switch interfaces.

Once in Analytics, click on the name of your activity to drill into the experience level performance. Note that lift & confidence are available just as they are in Target. When you click through from Target the report will detect the start & end dates of your test. If for some reason it doesn't, manually change it using the calendar in the top right.

Tip: The report date range is important because by default the variable collecting the A4T data persists for 90 days, therefore, conversions will continue to be attributed to your activity even after it is disabled. Also, note that the default cookie expiration for Target is 14 days. If your Target cookie has a duration set to something that is different than the tnt variable expiration in Analytics, it's a good idea to align them. For assistance, contact Adobe Client Care to have them set your Analytics tnt variable to match up with your Target cookie duration.

Click to change the metric to see lift & confidence for secondary measures of success. Or, use the ranked view to see multiple metrics side by side, like steps in a funnel.

Tip: Lift & confidence only appear when one metric is selected at a time. To return to that view simply toggle back to the Lift & Confidence report type.

Once you've explored your primary & secondary metrics to establish the overall winning experience, sub-relate & segment the summary report to identify any anomalies in behavior. This exploration will quickly help you discover any experiences that may perform better for certain audiences.

Tip: Common breakdowns include browser, device type, marketing channels, geography, etc.
Tip: Note that when you sub-relate the data the lift & confidence columns disappear. If you uncover something interesting, you can always go back and apply a proper individual segment to the summary report with lift & confidence to see if any differences in behavior are significant enough to act upon.
Tip: In Analytics you have the ability to select multiple segments and apply them at once. The resulting report displays the cross section of visitors that met all of the criteria for whatever segment(s) you have applied. If you think of a new segment after the fact, no problem! You can create it on the fly and apply it to your report.
Which metrics should I use?

If you ask someone who is a tried-and-true Web Analyst, they’ll say visits. If you ask a born-and-bred optimizer, they’ll say visitors. Who’s right?

The best practice is to use visitors as the normalizing metric (denominator). Target is a visitor-based system and activity experiences are assigned and persist at the visitor level, not the visit level. If you use the visits metric, know that it reports on each session that a visitor initiates after they qualify for the experience, regardless of whether they saw the test experience in subsequent visits.

For conversions you can use any of the possible hundreds of conversion events already being collected in Analytics! The Activity Conversion metric (a.k.a. the Target Goal Metric) is also an available option. Note that calculated metrics are available but will not show lift & confidence calculations.

Can I view my Target activity data in Workspace?

Of course! Go deeper with Analysis Workspace to visualize the data or uncover insights hidden beneath the surface. A reminder that lift & confidence isn't yet available in Workspace and there are some minor differences between certain metrics when viewed in the Reports & Analytics vs. Workspace. Also, note that Activity Impression and Activity Conversion metrics can be inflated in Workspace so Adobe advises using unique visitors and your favorite Analytics conversion metrics to perform analysis when in this interface. The Adobe Product team is working to close the lift & confidence and Activity Impression/Conversion counting gaps in Workspace. In the meantime, there are still a number of valuable ways you can perform additional analysis.

A critical step to using Analysis Workspace to understand Target test performance is building an audience for visitors that were exposed to each test experience.

Tip: Adobe recommends using a HIT container for your segment to include behavior only after qualifying for an activity experience. Using a Visit or Visitor container expands the scope of the audience to include actions that happened prior to test qualification.

Apply a test experience segment to a Flow visualization to examine whether your activity treatments fundamentally change how users navigate the site.

Compare each of your activity audiences side-by-side in a Fallout visualization to see how fallout differs downstream.

Leverage the Segment Compare panel to discover differences between visitors who were exposed to the control experiences vs. another experience.

Tip: Segment Compare allows you to compare in depth only 2 segments at a time. Create multiple panels to compare experiences against the control, or even against each other.

For more on Analysis Workspace, check out our tips & tricks guide for Workspace ninjas.

For guidance on implementation of Analytics for Target (A4T), please reference the following materials:

Congratulations! You made it to the end of this tutorial.

Now that you're a pro at performing Target activity analysis in Analytics, if you have improvements you'd like to see incorporated into future product releases, please contribute by submitting a suggestion or voting for existing ideas on our Adobe Experience League.

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Ready for more content?

Visit adobe.ly/analyticsresources for a full list of Adobe Analytics Spark pages & other helpful resources.

Created By
Kaela Cusack


Created with an image by Karolina Szczur - "untitled image"

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