Web Analytics with Adobe’s Customer Journey Analytics, Part 7: Customer Journey Analytics Backend Configuration

This post is the seventh post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we were enriching our basic web analytics data with some advanced fields in Query Service. In this post, we are creating the connection from Customer Journey Analytics to Experience Platform.

At this point in this series, we have a world-class dataset of web analytics data in Experience Platform, ready to be analyzed. I’m personally very proud of the things we were able to achieve in Query Service, especially with the pathing dimensions. With all of that, we have even more than what normal Adobe Analytics would give us!

With all the data enriched, we now have only one step left before we can start analyzing our digital user’s behavior. First, we need to pull data out of Experience Platform and into Customer Journey Analytics by configuring a Connection. Based on that, we can then create the actual set of metrics and dimensions in a Data View in the second step. Those Data Views are what our users actually interact with. Let’s start!

Configuring the Customer Journey Analytics connection

Pulling data from Experience Platform into Customer Journey Analytics is super quick and easy. To get started, go to the Connections section in Customer Journey Analytics and create a new Connection. On the dialog, make sure you select the correct, enriched dataset from the previous post (1). Selecting the raw dataset wouldn’t be that bad, but you won’t have those nice additional fields we created in Query Service:

Creating the connection in Customer Journey Analytics

On important thing to note: In my example, there is only one timestamp-type field in the data, holding the actual timestamp of the event. In your case, you might have added another field with that type, which just means you need to select the correct field when creating the connection.

Same goes for the Person ID field (2). That field is what Customer Journey Analytics uses to identify our users, so it’s essential to make the right selection here. If you followed my examples, you can just select the Identity Map field that we have created in our schema. If you have additional IDs in your data, you might choose to either select them here or use them to stitch your data with Cross Channel Analytics (which would mean you have to select the stitched ID as Person ID). In our case, we might as well select the ECID namespace directly, but leaving the field as preset is just fine.

Once your settings are done, go to the next screen. There are a few important settings here as well:

Connection configuration in Customer Journey Analytics

The obvious thing here is that we need to give our Connection a distinctive name. You can see my creativity in action here too (1). The next two items are far more important: Make sure you enable the automatic import of new data (2), so that Customer Journey Analytics can automatically pull new data from your Experience Platform Data Set in the future. If you don’t enable that, you might end up only importing historical data.

Talking about historical data, you probably want to import all existing data as well. That can be controlled with the second checkbox (3). Since we crunched our data already in the previous post with Query Service, you likely have some data available already that you will want to import, so make sure you select that checkbox.

Last, check your setting for the expected number of daily events. That setting helps Adobe to scale their systems according to your needs, so you don’t have to wait for an eternity for each and every report. Once you are done with all settings, you can hit save and go directly to the new Data View we need to create!

Creating the Data View in Customer Journey Analytics

In your new Data View, we first give it a nice and creative name (1):

Basic Data View configuration in Customer Journey Analytics

The name is also what your users have to select in the frontend, so you might want to do a better job than me actually describing what data it contains. On this screen, make sure you select the correct time zone (2) for your business use case. Sadly, we can’t control the calendar yet, so it will always have weeks starting on Sunday (which is obviously wrong, but that feature is still in development).

If you are moving from Adobe Analytics, you might want to rename the Containers (3) as I did on this screenshot. Those containers are what your users will see when they create segments or use certain visualizations, like the Flow or Fallout viz. I really like this flexibility, because we can really tailor the tool to our needs (with callcenter data, we could for example rename “sessions” to “calls”).

On the next screen, I personally like to start by adding all standard components (since not all are available per default). To do that, go to the standard components tab (1) and just hit add all (2):

Adding standard components in Customer Journey Analytics

One thing that should work (but currently doesn’t) is things like the Device Type dimension, that is available once we use the correct XDM schema fields. Adobe knows about this and I assume we get a fix soon. For now, we can already add those components and wait for them to work.

Again we can rename our Metrics from People to Unique Visitors and Sessions to Visits as to not confuse our users. To do that, click a Metric in the list and change the component name:

Renaming Metrics in Customer Journey Analytics

Now the real fun starts: If we go back to the schema fields tab (1), we can add all the nice components we created in Query Service:

Adding custom components to Customer Journey Analytics Data Views

A nice help Customer Journey Analytics gives us is the contains data filter (2), which will only display fields from our XDM schema that actually hold data. That is super helpful to not add empty fields that could confuse users. With that filter, we can just click add all (3) to include all our data into the Data View.

Before we do anything else, we should now rename the components we have just added. For the metrics, this is how I renamed my fields:

  • hit_depth -> Event Number
  • inverse_hit_depth -> Event Number before end of Visit
  • session_num -> Session Number
  • time_spent -> Total Session Time
  • distinct_pages -> Number of pages in Visit
  • page_views -> Total number of Page Views in Visit
  • page_view_counter -> Page View Number
  • time_spent (2) -> Time spent on Page
  • impressions -> Content Impressions
  • interactions -> Content Interactions
  • impressions (2) -> Interactive Element Impressions
  • swipe_to -> Swipes to Interactive Element
  • swipe_from -> Swipes from Interactive Element
  • column -> Interactive Element Position: Column
  • number -> Interactive Element Position: Element Number
  • row -> Interactive Element Position: Row
  • number (2) -> Content Position: Element Number
  • web.webPageDetails.pageViews.value -> Page Views

Now onto the dimensions. This is how I name them:

  • Identifier (2) -> Event ID
  • entry -> Entry Page
  • exit -> Exit Page
  • following_path -> Event Path after Event
  • full_path -> Full Event Path
  • preceding_path -> Event Path before Event
  • relative_path -> Event Path relative to Event
  • stage -> Launch: Environment Stage
  • type -> Launch: Event Type
  • build_date -> Launch: Library Build Date
  • id -> Launch: Rule ID
  • name -> Launch: Rule Name
  • Name (2) -> Page Name
  • inverse_page_view_counter -> Page View Number before end of Visit
  • next_page -> Next Page Name
  • previous_page -> Previous Page Name
  • name (2) -> Content Name
  • path -> Content Path
  • type (2) -> Content Type
  • name (3) -> Interactive Element Name
  • type (3) -> Interactive Element Type
  • URL (2) -> Referrer URL

As a last step, we should change the type of some metrics to dimensions (yes, we can do that in Customer Journey Analytics!) since we would typically use them as a breakdown or filter, but not do any numerical analysis. To do that, we just have to click on a Metric we want to convert (1):

Changing a Metric to a Dimension

Then, we just have to click on the component type (2) and change it from Metric to Dimension (3). I do that for those Metrics:

  • Event Number before end of Visit
  • Session Number
  • Number of pages in Visit
  • Page View Number
  • Interactive Element Position: Column
  • Interactive Element Position: Element Number
  • Interactive Element Position: Row
  • Content Position: Element Number

There is nothing for us to change on the last screen right now (30 minutes of session timeout is what we have configured in Query Service too), so we just hit Save and Finish and are done for today!


We are very close to building our first project in Customer Journey Analytics now. For me personally, I can’t overstate how much I appreciate the flexibility Customer Journey Analytics offers when it comes to redefining data. Use a different way to identify users? Sure, that’s just a simple setting. Switch a Metric to a Dimension and vice versa? Super easy! And all of that can be done after the fact, without any changes to the data.

Now we are already only one post away from the end of the series. Thank you for reading and see you next time!