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Making the most of Adobe Analytics

Another key technology that we harness, on behalf of a range of clients, is Adobe Analytics. Adobe Analytics is the best-in-class analytics package for enterprise level companies in today’s analytics market. It offers a range of excellent features, with great flexibility and customisation to adapt to a company’s complex business reporting requirements. However, Adobe Analytics has several top features that we believe are not utilised enough to maximise your business requirements.  

The hidden gems you may have missed

Whilst we have worked with a plethora of clients on various analytical implementation builds or surgeries, we have often seen valuable features be ignored for one reason or another, but below should help with explaining why we believe they can add real value to your analytics implementations. 

Adobe Analytics’ workspace reporting is the best and most intuitive reporting tool currently available. There are some great additional features within the tool that can be used to provide more valuable insight for your reports.

  • Calculated Metrics

The Calculated Metrics feature is a very insightful tool to provide more specific reporting abilities to capture your business requirements in one report instead of requiring third-party reporting programs or tools, like Looker studio or Excel. Here’s an example:

This example shows how you can create really bespoke calculated metrics, this one being a measure of your homepage subscription conversion rate. Not only can you use custom events, but you can also create or apply custom segments to really drill down into valuable insight.

  • Functions

To expand upon the value of the calculated metric, there is more than metrics and dimensions that can be used to create a calculated metric. As detailed above, custom segments and time-based segments can also be used, but there is a very powerful extra calculated metric addition with functions. Functions provide the ability to add advanced mathematical functions that can provide deep statistical analysis to your calculated metrics, which then provide more in-depth value than other analytical tools can. If you are interested, please see the links for basic and advancedfunctions.

  • Classifications

Another hidden gem within the Adobe Analytics configuration is Classifications. Classifications allow you to use only one dimension (prop or eVar) with multiple values in one string and you can use regex or other logic to split the one string into multiple values in your reporting. For example:

There are five questions on a site for a survey which are true or false. You can use a prop or eVar to capture the results as shown below:


You could create 5 classified dimensions to allocate question answer values to the newly classified dimensions setup. An example report may look like:

This is a basic classification which can be set up using Classification Rule Builder, but you can also create more bespoke classified eVars or props to get all the tracking requirements in one dimension. You can use Classification Rule Builder to append more value to a dimension based on a key value system. A common use for this is to append more value to a tracking code to get more information, for instance:

Tracking Code = AGGMSMTest_101

Marketing Channel will be determined by the marketing processing rules if set up correctly, so no need to get the value from AGG for Aggregator

The report would look as follows:

This demonstrates how you add lots of internal information to your report and expand on your reports just from one string, which is the power classifications can add to your reporting.

  •  List Variables

List Variables are another valuable Adobe Analytics feature that seems to only be used for plugins or integrations, but this feature allows you to capture multiple values in a list format, which can then be used to provide structured values from one string like classifications, but in a different format.

A common use case for list variable is form field error tracking. Instead of sending loads of network requests every time a user triggers an error, an ideal setup would be to create a list variable that would capture the form field errors in a list format. This example would be better placed in a listVar as the error messages may be more than 100-character limit of a prop. You could implement and report the following with a listVar:

ListVar1=Please input your firstname;Please input your lastname;Please select an address;Please tick for your consent

To expand on this example, you can combine features together which is a brilliant feature that many examples that we have seen haven’t utilised to its full extent.

ListVar1=1|First Name|Please input your firstname;2|Last Name|Please input your lastname;3|Choose Your Address|Please select an address;4|Do you Agree?|Please tick for your consent

So, as detailed, we can not only list this out using the list variable feature, but we can also apply a classification to the list variable to make more value and event more insight from this tracking requirements, so an example report you can pull from this is:

This is a base example to demonstrate the value of list variables combined with classifications, this applied to requirements can really dig down and give you the granular information to get that insight that is critical to turn into valuable actions. 

Adobe Analytics meets AI 

Adobe Analytics is actually a little ahead of the curve in terms of AI, with Adobe Sensei the name for Adobe Analytics’ AI and Machine Learning tool that provides predictive analytics. There are two main current Adobe Sensei features in Adobe Analytics that you may not be even aware of. 

  • Anomaly Detection

Anomaly detection is a statistical method of determining in comparison to historical data if a metric has significantly changed. This is a great feature to see if there is something malicious affecting your data, as well as a quick look method to see if your latest campaign is driving an increase of traffic.

  • Contribution Analysis

This is where the true value can be seen when an anomaly has been detected. Contribution Analysis looks at patterns of a dataset to see how statistical anomalies or correlations are present in your data.

Anomaly Detection and Contribution Analysis are limited based on your Adobe Analytics entitlement, as detailed in the table below:


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