In this video, learn how to create and configure new metrics that are subsets of other metrics, as well as the use cases that can make this a very powerful analysis tool in Data Views.
Hey, this is Eric Matisoff and my role at Adobe is as the principal evangelist for analytics and data science. Today, I’m going to show you some ways to customize your data views when it comes to including or excluding filters applied to specific metrics within your customer journey analytics data view.
Now a good example of doing so could be right here in your freeform table where you have a whole slew of products and perhaps you want to be able to break them down by the different amount of revenue associated with each of those products. The challenge however, is you’ve got all sorts of different products listed here, and you don’t know unless perhaps we pull in the number of orders or something like that, how much each of these different products costs when they were purchased? So good example for a new metric to create within your data view would be to say, you know what, I want to focus only on the products that were purchased for more than $50. So, let’s go ahead and try to create that. This is possible here in our data view by searching for the metric that we’re looking for and then we could go ahead and scroll down and edit the include and exclude values. But you know what we’re going to do, is first we’re going to click this duplicate button so that we can see them actually side by side. So, we’ll say revenue we’ll call them a High Value Revenue and it’s a revenue of greater than 50 bucks.
When we scroll down, you’ll see our currency remains in our duplicated metric here, but we can also set some include and exclude values associated with them. Now the criteria are quite simple. We can choose all or any for that criteria if we want to add multiple rules but our rule right now is pretty simple. We want to say, if the revenue is greater than or equal to 50, then we will include this high value revenue metric in our data view. So, I’ll go ahead and click Save and move on over into our data view here.
And you’ll notice that we don’t actually see it initially. All we need to do is go to Components and then refresh components.
And within just the second you can see now we have High Value Revenue listed here. And when we pull that into our dimension, look at that, look how fast that loaded. We’ve got all of these different specific products that are available and are broken out all because they are $50 or more at the purchase price. Now we have some fun things that we can do from here. We can also perhaps break this down by the unique identifier. That is to say the hit identifier, the event identifier, where these items were captured. So, we can see each specific time every row of data that we’re capturing in to customer journey analytics, we’re able to see the associated revenue for this product at this specific time. You’ll notice in fact, if I were to also pull an event at the event level think of that as instances and traditional Adobe analytics, then we’re going to see oh, it’s one-to-one. So, every identifier is basically the unique ID for each row of data that is being imported into customer journey analytics. Now that’s all well and cool but what’s really cool is our ability to slice and dice this data which is at the sub event or sub hit level because that is actually how we’re applying that filter that we created just a second ago within our data view. So if, for example, there is a purchase of $75 and that $75 purchase includes a $55 product and a $20 product, we want to exclude the $20 product but include the $55 product. And we’re able to do that all thanks to sub-event filtering which is made possible in this data view. And so take a look at this exact example right here where we’re able to see normally the revenue associated with this row of data, this hit, this event, this purchase was in total $310 but when we apply our data view filter, which is at the sub event level, we filter out the products that were less than $50. In fact, we can even check into them to see what exactly they work. So, we can break this down and see that the products that were $50 or more, those are these three here. Those are included as having a value for high value revenue, but these cheaper products the field messenger bag, the overnight duffle bag, et cetera those were $45 each less than 50, therefore they are not a member of this high value revenue metric which is in my opinion, pretty cool. So, I hope you enjoyed this walkthrough of how we can set include and exclude functionality within our data views and our metrics. And you’ll notice if you’re curious, you can, of course filter based on a whole slew of criteria equals doesn’t equal is greater than is less than et cetera. And that’s all there is to it. So, enjoy your customized data views. Thank you. - -