Understanding How Customer Journey Analytics Uses Identity

Hey what’s up everybody, this is Doug. In this video I want to talk about how Customer Journey Analytics uses identity. And this video is not going to be a deep technical video about identity, but rather more of a practical look at how Customer Journey Analytics uses identity and how it will affect the data analysis, how it will affect these different visualizations and tables et cetera. And how you can stitch data together, from different data sets, in order to do cross channel analysis. Now, I’ve started here in this project, just to kind of remind you about how everything is mapped together in Customer Journey Analytics. So, we’re looking at the project here and instead of a Reports suite, we have a data view and this is called WWSC Data View here. So, if we go to our data views, and we look for that one, there it is and we can find that, and we click into that. And we can see that this is using the connection WWSC Test Connection, so we go to our connections and find that one, and we can click into that connection. And, we can see the datasets that are used from the platform and are brought into Customer Journey Analytics in order to tie this data together in our project and in our visualizations. Now, again, this video is about how Customer Journey Analytics uses identity, or IDs, to stitch all this data together. So, it’s really about the Person ID, and you can find that down here in the bottom right corner, and you can see the ID from the dataset that is being mapped to the Person ID in Customer Journey Analytics. So, for example, if I click on this Email Demo Data, this data set is using the Customer ID from its schema to use it as the Person ID in Customer Journey Analytics. Now, if I click on this Point of Sale Demo Data, you’ll see that it’s actually using the same thing, Customer ID, so it’s going to be able to stitch that data together, as well as the Survey Demo Data same, Customer ID. And then also the Call Center Data is also using the Customer ID. And, again, this maps back to the schema, so if I click on the Call Center Demo schema, this will open up the Platform, in another tab. And we look at this object and we can see that this Customer ID has been identified as an identifier. So I click on it, and scroll down over here on the right, it has been marked as Identity, in fact the Primary Identity or default identity. So, on the different data sets, when the Customer ID, or in Customer Journey Analytics when the Person ID, matches between data sets, then those data sets can be combined or stitched, per visitor. Now, there’s one more thing here that is very interesting, and that is the Analytics Demo Data, it is using a differently named ID, stitched ID has the Person ID. Now, normally you would think, well that’s a different ID, so we’re not going to be able to stitch this data together, that is the analytics data, with those other data sets, like Call Center Data, because the Call Center Data was using Customer ID, Analytics Demo Data was using the stitched ID. But, it just so happens that, even thought those are named differently in their schemas, they are using the same IDs and those IDs are matching up. So when those IDs match up, that’s really all that matters because Customer Journey Analytics really is just trying to match up the Person ID between data sets. So, the fact that it came from a differently named field in the schema, doesn’t really matter. And we’re going to be able to see how all that data is mapped together in the project and in the different reports. Now, before I leave here, I will point out that there are a couple of data sets that do not use the same Person ID, and they’re different IDs completely. So, the CRM Demo Data, or the CRM Data Demo I guess, is using a User ID, and the Ad Impression Demo Data is using Ad Cookie ID, and those don’t match. And so, even though they can be used in the same reports, they can be used in the same project, and even in the same visualizations, they are not going to be able to stitch together the visitors between those data sets. Okay, enough said about that, let’s jump back to the project.

And you’ll see, in the project here, we have some basic analysis and these are all visualizations that are not stitching the data together per person, but rather using the data from the different data sets separately, but effectively, in the same project. So, you can see here we have a table for the different data sources and the traffic that they’re getting, the events, sessions, and people for those different data sources. And so, it’s not stitching this data, it’s just giving you totals and a great way to compare your data there. Same thing with this donut visualization here, where it’s using audience size by data source et cetera.

I like this one a lot because in the same visualization for revenue, we have both online and in store revenue and they’re being used separately. Now those two, we know, are able to stitch together because, again, the Person IDs are the same between the Analytics Data and the Point of Sale Data but, in this case we don’t really need that, we just want to get the totals and display those in this visualization. Anyway, you get the point. I’m going to jump down here, a little further, to where we actually have some Advanced analysis and Multi Channel analysis because this is where the really great stuff comes in, where you can actually stitch the data together and do this multi channel analysis per visitor. And this flow diagram is a great one to start with because we can see that it starts with call center data then it goes to Ad Created data, and then it goes to Store Location data, so you can see how that’s tying this stuff together, even though they are from different data sets. If I scroll down, we have a great Venn diagram here, where we have Call Center purchasers, online purchasers, and in store purchasers. All three different data sets but, since we stitched that data together, per visitor, we can see how many visitors, for example, are both an online purchaser and an in store purchaser, or the combinations of any of these three. Beside this, over here, we can see this Point of Sale to Call Center attribution. So, again, this is combining two different data sets, point of sale data from a store, so these store locations, Salt lake City, Las Vegas, New York et cetera. And matching that up with call center numbers, so you can see the call duration in minutes. So, the people that went to the Salt Lake City store location, also ended up calling in, and needing to talk for 5800 minutes altogether, during this time period. So, we are stitching this data together, between data sets, and it’s just really great. And, I don’t mean to beat the dead horse here but, this is just amazing stuff that we can do with Customer Journey Analytics. And, again, this is all based on the person ID and matching up those different identifiers so that we can stitch that data together and have these wonderful visualizations, through Analysis Workspace, right at our fingertips to do this cross channel analysis. Good luck.