New Terms and Concepts in Customer Journey Analytics

In this video we will discuss key terms and concepts in Customer Journey Analytics, how they map to terms and concepts in Adobe Analytics, and also where they can be seen in the Customer Journey Analytics interface.

Transcript
Hello and welcome to this training on New Terms and Concepts in Customer Journey Analytics.
In this training, we’ll learn how to define common Customer Journey Analytics terms and concepts. Compare CJA terms and concepts in the context of standard Adobe Analytics and recognize where these terms are used in the CJA interface.
So what we’re going to do here, is we’ll talk about a term, I’ll give you the definition and then we’ll actually go into the interface and I’ll show you where it is. So first, data schema, a database schema is a set of formulas called integrity constraints that are imposed by a database. So basically, it’s a blueprint on how a database is to be constructed. A data schema really doesn’t have any equivalent within the interface of Adobe Analytics. But let’s jump over to the interface of CJA and take a look at this. Now I am already in a connection, and we’re looking at a specific data set, and within this data set, as we look over to the right here, we can see the name of the schema and we can actually click on that which I’ve already done. So, here we see the list of our schemas, and this first one is the schema that the data set is built on. I can click on this and we can get into the details of the schema, but the schema has to be created before we can actually have a data set.
Data sets can be anything from email to CRM to point of sales data or comes straight out of Adobe Analytics on your website. Each data set consists of the schema and the batches of the data. There are three types of data that are compatible with Customer Journey Analytics. This is event, profile and lookup data.
Just a brief definition of each of these, the event data is basically representing the data that comes in through your website, any kind of interactions that have a timestamp, a customer ID and it’s just the sequence of events that are going on. The equivalent of that is basically clickstream data, profile data is applied to your visitors or your users or customers in the event data. This is basically uploading CRM data about your customers, this would be customer attributes in Adobe Analytics. And lastly the lookup data is used to look up values or keys found in either your event or your profile data. For example, you might upload the product name, that maps to the product ID in your event data set, and this is like the classifications in Adobe Analytics. And again, if we jump over here to the interface, we can look at this type of data. For example, let me grab a couple more of these data sets and bring it in here. And now as I click on one of these over on the right, we can see what kind of data this is. Here it says, the data set type is event and it has a timestamp. Notice also that it has a person ID and we’ll talk more about that in a bit. But if I click on these other here’s another event data type and then this one is a profile data type, and notice it does not have any timestamp.
A connection establishes a link between AEP data sets and CJA that lets you integrate multiple data sets for your analysis and reporting, so this is basically how we pull the data out of AEP.
And again, there’s no equivalent in Adobe Analytics, because Adobe Analytics has the data right there with it or here we’re pulling it in. And here on the interface, we’re actually in the area where we create these data connections, where we can have multiple data sets in a single connection and because of this person ID, we can see how this information links together. All three of these data sets, have the same ID that link the information together. And if I just hit cancel there, you can see that connections, we going to have as many connections as we want.
Now, our next element is a Data View. A Data View, is a user defined set of data based on the data set configurations with AEP. A Data View usually consists of multiple types of data sets for a holistic view of our customers. Now, different data views can also be created from the same connection with different settings. And this is basically like having a Virtual Report Suite within reports and analytics, you just curate the information you want and then you can view it. But in CJA, every data view is custom built by the user.
Now, in our interface in CJA, we can actually build as many data views as we want if we want to add a data view, we just come in here we choose which data connection we’re going to tap into. And then we have a few other settings here. We custom build this according to what data we want to analyze. In CJA components, are the basic building blocks of reports, we have dimensions, metrics, filters and date ranges. And this is very much the same as what we have in Adobe Analytics. These components are in workspace as we build reports and it’s like the curation process in a Virtual Report Suite. In our interface, the easiest place to show this, is in a project.
When we build a project, we’ve got over on the left hand side, the dimensions, metrics, filters, date ranges and we just drag those over to build the different reports that we want to show.
Now filters are just a subset of individuals based on the characteristics or dimensions that we use to define the subset of users. Now, in Adobe Analytics, we’ve called this a segment and so a filter is actually just a segment but we’ve used a more accurate name for what we’re doing in CJA. Now inside filters we have different containers and in CJA, we call these containers person, session and event were in Adobe Analytics, we call them visitor, visit and hit. So, if we come in here and pull up a filter, let’s create a new filter.
And again, we have got our components that we use to build the filter and here we can include in the container, the person, session or event containers. Thanks for taking this training. -

For more information about Customer Journey Analytics, visit the documentation.

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