Create summary-level data sources in Customer Journey Analytics

• [Derek] Hi, this is Derek Tangren. I’m a senior product manager at Adobe, and I’m excited to talk about customer journey analytic support for summary level data. This type of data is the kind which isn’t organized by a person or visitor ID and is usually aggregated at a higher level, such as campaign or product, or simply by day or hour. You might have aggregated advertising data that you want to combine with your clickstream data, or maybe you want to bring in metrics from across the different social media platforms you engage in to centralize where people in your organization go for reporting. Summary data can help with a variety of use cases. CJA allows you to bring this type of data into the Adobe ecosystem through the Adobe Experience Platform. That data can then be combined with your other data for analysis and reporting. We’ll look at the process to bring that data in and then how it looks in analysis workspace. To begin bringing summary data into CJA, you’ll start by creating an XDM schema based on the XDM summary metrics schema class. After structuring your schema with the necessary components, you’ll then send the data into AEP using one of Adobe Experience Platform’s methods of ingesting data into a dataset. Once you have data populating into an Adobe Experience platform dataset, you are ready to bring that data into CJA. To do this, you’ll next head into the connection workflow. If you already have a connection created with data, you can simply add your summary data into that connection. Alternatively, you can also set up a new connection. CJA also supports bringing in summary only data to a connection. When we go to add a data set to our connection, we can see the data sets that are of a summary type in a similar way to how we differentiate lookup, profile or event data. Also, like any other dataset you’re bringing into CJA, you’ll select it and be presented with a preview of the data along with some pre-configured settings. We support daily and hourly granularities with summary data, and by default it will be set to daily. If you want to change that along with the time zone, you can use our API and we have examples in our documentation of how to do that.

Now that we have our data added into our CJA connection, we can now configure it for reporting in our data view. Inside of data views, we see on the left, a folder that distinguishes our summary data sets, and as we click into that section, we can see the fields in our summary data, similar to the experience with other types of data sets. As we start to add our summary fields and select one of them, we’ll notice they don’t have all the same configuration options other components from event data sets have. That is due to the fact that summary data doesn’t have the concept of session or person, and so some configuration like attribution or persistence don’t apply and aren’t available. You will also notice a new option in the right rail called summary data groups. This allows you to tell the system you’d like to combine a summary dimension with an event dimension or another summary dimension together for reporting purposes. This is what enables us to combine summary data with our other data for reporting into a single report. In this case, I’m going to select my tracking code dimension, which comes from my event dataset and my campaign ID dimension, which comes from my summary dataset. Once I create a summary data group, CJA will combine the values of those dimensions together. We anticipate you’ll only want users to use a single dimension from a summary group, so you can easily hide the others from the summary group section. Now that I have my summary group created, we save our changes and head into workspace.

Inside of workspace, I can now build my report that will include metrics from both my event and summary data sets. I’ll start with a simple report with the tracking code dimension along with orders and revenue. I can then start to add my summary metrics. We’ll add in cost and impressions. Keep in mind the impressions and cost metrics are coming from my summary data while orders and revenue are coming from my event data. I’ve also created a couple of calculated metrics, cost per impression and return on ad spend. The cost per impression is based completely on summary data. While the return on ad spend is calculated by subtracting cost, a summary metric from revenue, an event metric. I can then sort to see which of my tracking codes are driving the highest amount of return and gain new insights into the performance of my campaigns. This is one example of leveraging the power of summary data inside Customer Journey Analytics. We’re excited to see what other ways you are able to utilize this exciting capability to gain further insights into your business. Until next time, thanks for watching!