Create Cross-Channel Visualizations in Customer Journey Analytics

In this video, discover how Customer Journey Analytics allows you to create visualizations that include data from multiple datasets across multiple channels, including merging the data per visitor.

Hey everybody, this is Doug. In this video, we’re going to talk about creating cross channel visualizations in Customer Journey Analytics. So we’re here in a project for Customer Journey Analytics, you can see that I don’t really have anything in it yet. So we’re going to add some visualizations. Now if you are familiar with Analysis Workspace, it’s going to be very similar but we’re just going to have some added coolness here. So first of all, we are going to select a freeform table.
And it’s going to be really important for you to understand which dimensions you have available, which metrics, et cetera because of your schema for the data that you are bringing over into Customer Journey Analytics from the platform. And in this connection and data view, we have several different data sets and so we have a data sources dimension that we have created just so we can get an idea about the different data types that we’re bringing in here. So if I go up here to search components and type in data sources and we can grab it and we can drop it in this freeform table and we have the different data sources, right? We’ve got these different data, we have ad impression data and analytics, email, point of sale, et cetera but we have this events metric over here which is basically, what are the everythings that happened for these data sources. So we can get a little more specific and we can even just put in people which is like visitors if you’re familiar with Adobe Analytics and we will bring that over and we will replace that.
And so now we have some basic data on these data sources, how many people have come in from these different data sets. And we can create additional visualizations. Maybe I’ll just grab all of that and I’ll go over to my visualizations and we will drag in say a donut and that will show me that data visualized here in this donut visualization or again, we can look at it in the table and in here, we can say, make sure that we lock that to that data. Okay, great and now wherever we click, it will remain constant there with the data that is in this table. Okay, we’re good. Now I said we were going to work on creating cross channel visualizations and well, this is really more like multi-channel visualizations, I guess because this data is not stitching the same person across this data, right? We just have kind of totals for this different data source. So now we want to get really cool and add some stuff that will actually cross these data sources, these channels and be able to more or less stitch the data together per individual. Now that is really only going to be possible when these different data sets use the same person ID, right? The same identifier so that we can stitch the data together per individual but assuming that they do which we can assume with this demo data, then we will go to our visualizations, I’m going to scroll this down a little bit and we are going to go down to a Venn diagram and we’ll bring that over.
And you’ll be able to see down here that we can add up to three filters and a metric. So I’ve already created a few filters, if I scroll down here, you can see that these top three are ones that I’ve just created here, not created by Adobe but created by me. And if you want to see what they’re about, for example, you can click on this little info bubble and this one is the data source is point of sale data or in-store data and orders is greater than or equal to one. So it’s basically the same thing for call center and online, just different data sources so that we know a person has purchased and it was from that data set. So we can go over here and we can drag over In-store Purchasers and Call Center Purchasers and Online Purchasers and since we already have the orders in those filters, we can just drag over how many people and we drop that in there and build it.
And now we can see the overlap of the different purchasers coming from these different data sets, right? So we can see that we have in-store, call center and online and as you mouse over those, you can see those data sets, very specific data sets or if you mouse over the areas, you’ll be able to see the data for all of them put together in this case. So you can see that we have 786 people who are in all three of these segments. And that’s really the power of these cross-channel visualizations in Customer Journey Analytics. You can treat it basically like one data set if the IDs match across the data sets, so really just puts it all together and you can basically treat it as one data set. So you could even create a flow diagram and see how they flow between information in one data set to another data set, did they go from these stores or from this store to this call reason to going back to the online stuff and looking at this page or whatever. So you can see how they all merge together. And again, that’s one of the great benefits of using Customer Journey Analytics so that you can bring all these data sets in and then merge them based on the person ID. Good luck. -

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