If you have been following this blog for a while (thank you!), it shouldn’t surprise you if I claim: Adobe Analytics is the best web analytics solution available today. But if we’re honest, it has been around for a long time, which has been leading to a situation very familiar to anyone working in the tech industry: The things that we build today might limit us in the future when new technology becomes available. This is also true for Adobe Analytics.
When Adobe Analytics was created, it was necessary to build features like the Visitor Profile or Props in a certain way with what was available at that time. Back then, it was necessary to store Visitor Profile information in a database and add it to the data as it was processed (something I also used in a previous series of posts). The database engine on top of that data then was built to work with those prerequisites. At some point, even a company like Adobe might be dragged down in their options to innovate on existing products because of dependencies like that.
Knowing this it’s very logical that Adobe’s new and shiny Customer Journey Analytics brings us a lot of the nice things we wanted out of Adobe Analytics for years. With CJA, things like cardinality limitations or a limited number of dimensions and metrics are finally gone, while market-leading features like Calculated Metrics and Segmentation in Analysis Workspace are still there. On top of that we get all of the goodness that Experience Platform brings us, like being able to crunch our data ourselves with Query Service. And the recent Adobe
Experience Platform Summit included some truly mind blowing practical use cases, like bringing Google Analytics data into AEP and Customer Journey Analytics. It’s a very exciting time.
Today I want to tell you about something that, even given all the excitement around the Dimension Builder for CJA Sneak (think of Calculated Metrics, but for dimensions), went live largely unnoticed and addresses one of the most requested features for Adobe Analytics. For years we wanted to be able to use Calculated Metrics within Segments in Adobe Analytics, which, for the reasons outlined above, has not made it into the product yet. But with the recent release of Customer Journey Analytics, we got something that in my opinion is very, very similar to what most people would want out of that feature. So, let’s take a look!
Even better than Data Views: Data Views V2!
As you might already know, Customer Journey Analytics uses something similar to Report Suites and Virtual Report Suites from Adobe Analytics to organize data. While Report Suites are now called “Connections” (and hold the actual data), the users only ever have access to Data Views, which is like a Virtual Report Suite on steroids.
In Adobe Analytics, a Virtual Report Suite can only contain dimensions and metrics that are also included in the “physical” Report Suite that it is built on. This was also true for Data Views in CJA, at least until the recent update. With the new and enhanced Data Views, we can now go even beyond what the underlying Connection holds in terms of data. This is a fundamental change compared to what Adobe Analytics can do, where the number of components in a VRS will always be lower or equal to what you would find in the underlying RS. But now, you can just drag and drop a dimension or metric into the same Data View multiple times with different settings, like I did here:
In the first two rows, you can see that I just casually added a dimension twice to a Data View and changed the Persistence to two completely different value. This turns the Attribution IQ feature as we know it from Adobe Analytics up to eleven: In Analysis Workspace I could now break down those two dimensions by each other to compare the first value for a user to the last value in a session. That is so much more than what we can do in Adobe Analytics, where Attribution IQ only works for Metrics! Also, we don’t have to think about all those different dimension settings as we collect data but can just add the same source dimension multiple times after the fact. How cool is that!
In addition to the Persistence settings, we can now also change a lot more options for dimensions:
The first option should also make you very happy: We can now define which value should show up for empty values. An obvious example for this would be the Mobile Device Type dimension, which is empty by default for everything that is not a mobile device. Now we can change those empty fields to be treated as an actual value and name it “Desktop”. No more explaining what “Other” means! And, if we want to pay respect to Trevor, we could even name our missing values “poop”. Add this feature to the list of what might get me fired one day.
Now let’s talk about the second option there: We can now include and exclude values! Finally, we can filter out all the unwanted data for the whole Data View. Do you have more poop than you would like? Just filter it out! But there are even more cool use cases: For example, you could add your Page Name dimension twice, but filter the second one to only include values that contain the word “error”. Now you have an error page dimension, created out of thin air! Or do you want to have a dedicated dimension only containing your campaign pages? Just filter for that term!
The possibilities are basically endless, and we can create it all long after the data has been captured. Again, I love how Customer Journey Analytics changes how I think about all the data in my analytics tool and how I need to track it. And I haven’t even gotten to the what I promised you in the title!
Is it a dimension? Is it a metric? Why not both!
Before I try to explain how the best part of this release works, let me quickly show you something before I start. In addition to adding the dimension mentioned above twice as dimensions, I actually added it once more in the metrics section:
That headline is not a glitch! I actually added a dimension as a metric to the Data View. Now we have a new metric available in Analysis Workspace that will increment whenever we have a value in that dimension. And we can even change that metric to be filtered to certain values as described above, so we can create an error page or poop counter on the fly!
The best thing about that: This new metric now behaves like a native event, so we can use it in all visualizations (like the histogram) and even in Segments! This is what I was hinting at in the title. This use case for the feature is very similar to how a lot of users use Calculated Metrics, where they would filter a counter metric by a given dimension value. With an error pages metric we would now be able to segment for users how were bothered by two or more error pages. Again, all of this is defined after the data has been captured, on the fly, in a non-destructive way. I love this a lot!
There is one more setting that is unique to metrics:
We can filter metrics for numerical values (for example, exclude all revenue larger than 1 Billion to clean up data) as we already know it from dimensions. But above that, we see a new option in this screenshot: We can change how this metric should behave even for numerical values. For example, we could choose to include a revenue metric two times: If we select “count values”, we would get the actual revenue in our metric (so “100” if there was an order value of 100), but if we use “count instances”, we would get a value of “1” every time there is a revenue value in the data, effectively giving us an order metric.
There is yet one more great feature, because this workflow also works the other way around. We can just as easily add a numeric field as a dimension, that would then contain all the different values of that metric. Think about that like the Visit Number dimension in Adobe Analytics, where you can see all the different values in a table. Once more: All this conversion happen on the fly, after the fact, as often as we would like.
So, do I like this update? You guessed it: I love it a lot. With all those new options, we can really see how the new engine behind Customer Journey Analytics leaves Adobe Analytics’ engine far behind. Together with Query Service, we have so many options to how we can crunch our data even after it has been captured. Gone are the days where we would need to decide on dimension and metric settings before we even start collecting data.
As we can see Customer Journey Analytics and its version of Analysis Workspace catch up with some existing Adobe Analytics features like scheduled projects and Report Builder, we get very close to feature parity on the frontend as well. I’m more than optimistic that Customer Journey Analytics will take the lead even in terms of frontend features in the near future. I’m excited to see what will come next.