Overview of configuring Data Views for Customer Journey Analytics

In Customer Journey Analytics, Data Views are similar to Virtual Report Suites in Adobe Analytics; they allow you to configure the data that comes into CJA so that it can be most useful for your reporting and analysis. This video gives you an overview of configuring Data Views for Customer Journey Analytics.

Hi, this is Trevor Paulsen from Adobe Analytics Product Management. And I’m here to show you some of the really cool new capabilities that we’ve added to data views for customer journey analytics. You can see here, I’ve got a brand new data view based on a connection of data that I’ve created. I’ve given it a name and I’ve selected a timezone. One of the new things that we’ve added on this first page, however, is the ability to rename specific containers to be whatever you want. Traditionally in customer journey analytics, the containers were limited to the names of person, session, and event. Now, if your organization prefers a different name or wants to use something other than person, session or event, you can now change that. And those new names that you specify will be respected throughout the analysis workspace experience. For example, creating segments or using calculated metrics or even the attribution IQ models that you may use. Moving to the next page of the data views builder, you can see we’ve really overhauled the way that you build metrics and dimensions within a data view. First of all, you can see that we’ve taken all of the different data sets that were part of my connection and grouped them based on whether they were a profile, lookup or event data set. And when I click into any of these folders, you can now see that we’ve arranged all of the fields in those data sets based on their schema type. And we’re no longer assuming that certain schema types are automatically dimensions or metrics. In fact, strings and numeric type fields can be used as both metrics and dimensions. By dragging a string field into the metric well, I can now increment an metric every time this string field contains a value. Additionally, by clicking on this metric, you can see we’ve added a whole host of new settings and pieces of information that are useful. I can now see what dataset type it came from, the specific data sets that it applied to, and even edit the component ID, which is how the API or projects refer to this specific component. I can change the formatting of this specifying whether it should be displayed as a decimal, time, percent or currency metric. And we support many different currencies from all over the world. Additionally, I can specify whether this metric should be formatted as green when it’s big or red when it’s big, a nice convenience for using this metric in specific visualizations. But one of the most useful features that we’ve included is the ability to set include and exclude values for this string field. For example, if I only want to count the times that this specific string field contained a customer complaint, all I would have to do is specify that we should increment this metric whenever it contains the phrase complaint.
I can also rename this metric to customer complaints. One of the cool things about data views though is that I can use the same string field to create multiple metrics. In this case, if I wanted to also create a field for calls where a customer was calling because they forgot their password, I can do that as well. By simply specifying that this metric should increment whenever the string field contains the word password I can now create a second metric off of the same string field. Additionally, I can even specify default attribution models that will be applied whenever this metric is used in reporting. Numeric fields can also be used to create dimensions. When selecting a numeric dimension, you’ll notice we’ve also added the ability to do value bucketing. This allows me to bucket specific numeric values into dimension item buckets that will appear in reporting. By clicking on standard components on the left-hand side, you can also access metrics and dimensions that were created by the system automatically, things like the session starts or session ends metric, or the time spent metric. We’ve also included several system based dimensions like time spent per event session or person, or platform dataset ID, or platform batch ID, which can be useful for debugging. Finally, on the last page you can see we have the ability to add filters to the data view, essentially filtering the data down to the data that’s relevant only to the consumers of this data view, as well as configure the session settings here renamed visit because I renamed it on the previous page. This allows me to specify a specific timeout period or even begin a session with a metric. With all of these new metrics and dimensions that I’ve created, I’m now able to use all of them interchangeably in my analysis workspace experience. For example, dragging in call duration minutes allows me to see the number of events that occurred for each of the specific bucketed numeric fields. I can also drag in total call minutes as a metric to see how many minutes in aggregate were spent in each of those buckets. Because metrics are fully supported across all of the different aspects of analysis workspace, I can even create a calculated metric based on the data view metric I previously created allowing me here to multiply that number by 60 and format it as a time period. Notice that when I apply conditional formatting to this column, it automatically knew that the the largest values should be red and that the lowest values should be green because I selected that in my formatting options when I created the metric. I can also bring in metrics based on string fields, like call center calls, customer complaints, or password resets, all of which were originally based on the same string field. Finally, I can break down any dimension by another, in this case, breaking down call duration by call reason. Now, what I’ve shown you here is only a very small part of what’s really possible with the new data views functionality that we’ve added to customer journey analytics. Be sure to watch any of the follow on videos that we do showing you all sorts of tips and tricks and ways that you can use all of these new settings in conjunction to get interesting and exciting new insights from your data that you may not have been able to get before. Thanks for watching and good luck. -

Please see additional Data Views videos to focus on specific functionality that can make your data more useful.

For more information, please visit the documentation.