Component type settings in Data Views

When configuring components in a Data View in Customer Journey Analytics, you can choose whether a field should be a metric or a dimension. In many cases, strings should be dimensions and numbers should be metrics. However, there are some great use cases to switch it up. Take a look!

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 walk you through a few unique capabilities when it comes to configuring your data views here in customer journey analytics where you can actually set dimensions as metrics or even metrics as dimensions. Now there are some unique reasons that you may want to do this. Perhaps, for example, you recognize that you wanted to have a metric that focuses on anytime your page name equals checkout, for example. Now you could do that by creating a calculated metric and applying a filter to it. But instead you can actually do it right out of the box here in your data view builder. That way everyone has access to it. And it’s nice and neat and powerful. So, let’s give it a shot. We can search for the page name dimension. We’ll go ahead and duplicate it. And what we’re going to do next is we’re going to, give it a nice, friendly name. So, we can call it the checkout pages. And we’re going to switch the component type from dimension over to metric. And I will say this is, increase on checkout pages only. And when we scroll down, we have the usual settings for when it comes to defining a metric here in customer journey analytics data views. What we’ll do is we’ll set an include filter that says if it contains the phrase checkout, then it will increase and count those views as a checkout page view. Actually, we’ll call it checkout page views so it’s nice and simple. Now, of course, if we wanted to, we could add multiple rules and change the match criteria from any to all with one or two, but we’ll just keep it nice and simple for today. If we need to, we can also set the default attribution as well but we’re here to focus on our include and exclude. So, I’ll go ahead and save that new metric checkout page views and slide on over here, where we will search for checkout. And we only see this one checkout component. When we refresh our components, now we have checkout page views as well. And when we drag and drop that into our table, take a look at that. Now we have the count of the number of events where we have each of these different pages associated with a page name and we’ve got our checkout page views metric.
Now, if you’re curious why are the numbers in this column, right here, check out page views, our new metric, different than the metrics over here in our events column? The reason is actually quite simple. We are applying unique attribution persistence to the page name component. So, if I turn that off, click save and then head on back over and give our project a nice quick refresh. Then you’ll see that the metrics actually perfectly aligned. There we go, 19 and 19, 12, 12, because the metrics are using the exact same persistence method.
Now that’s really fun to be able to switch a dimension over to a metric by applying some filtering, but where we can really have some fun is when we do the reverse. We can switch a metric over to become a dimension. Now let’s give that a shot. So once again, we’ll go ahead and duplicate the revenue metric. And this time we’re going to switch the component type of our metric to a dimension. Now check this out. So, we’ve got revenue. I’m actually going to rename revenue to revenue bucket and I’ll show you why in just a second. Because when we switch a metric over to a dimension, we have to define what the different bucket types are going to be. So, we can say the bucket is perhaps less than 10 and we’ll add another one. Maybe 10 to 25 and another bucket 25 to 50, and then we’ll have the last bucket be greater than or equal to 50. So go ahead and save that just for fun.
And then when we refresh our components over here in our data view, watch what happens.
Now we see revenue bucket as a dimension. We can drop that right in here.
Look at this, now we can see the number of events, even the associated checkout page views for each of these different revenue buckets which is quite cool, really, really fun. Now, similarly, if we were to go back to our data view builder and just turn off that bucket value and resave it, and head on back over, refresh our component definitions and then refresh the project, then you can see all of the individual values that revenue is capturing. That’s why I started with revenue bucket. I personally find that to be significantly more valuable. So, we’ll go ahead and turn that right back on, hit save and refresh our component definitions and our project. And voila! Checkout that awesome set of buckets. And what’s really, really incredible is our ability as you would expect to be able to slice and dice that information. Maybe we want to know which products are $50 or more or which products are being purchased for less than $10. And of course, as you would expect, you can pull this anywhere that you need to, perhaps you want to create a, oh, I thought that that would, here we go. We want to pull each of these into a filter dropdown. Then we can apply that right here. So, we can say 50 or more dollars and break that down, perhaps by all sorts of fun things. What are the different product names associated with $50 or more? All sorts of cool stuff. So, I hope you’ve enjoyed this demo focused on switching your components from dimensions to metrics or from metrics to dimensions. Have fun. Thank you. - -