In this video we discuss how to create filters in Customer Journey Analytics that utilize more than one data source, and more than one channel. You will also see how these filters can be combined in visualizations.
Hey everybody this is Doug. In this video I want to talk to you about creating cross channel filters in Customer Journey Analytics and using those cross channel filters, and the different ways that we can use those. And so we’re going to talk about a couple of different things, so let’s dive right in. The first thing I want to talk about is creating a filter that uses multiple data sources of the definition. So, if I scroll down here a little bit on the left, and I create a new filter, then of course I have the ability to drag in any dimensions or metrics that could be from any given data set in this data view. So, for example, let’s say that gender was from some CRM data, I could drag in gender and say gender equals male or female and I could also maybe drag in a check out type, let’s say that’s from the analytics data, and I can say that they have one checkout type or another, anyway I can drag in different dimensions that might have originated in different data sets and use those there. I might also use a dimension or a metric that spans data sets. So one that I know of here is orders. I can drag in orders and say an order exists grab that, and I happen to know that that’s true for three of my data sources which is store data or point of sale data, analytics data from on-site sales as well as our call center data, which takes orders on the phone as well. So this is a cross channel filter, even just by using this one metric, because again it spans data sets. I’m going to save that as Purchasers, which means I need to change this from an Event of an order to a Person and then I’ll save that.
And now I have that over here in my availible filters. Now, in my analysis here I have some different visualizations, tables et cetera. You can see that I’ve put orders and orders per person and that I’ve actually even got a Venn diagram down here that shows that there is quite an overlap between my in-store purchasers, online purchasers, and call center purchasers, so a lot of people that are buying are buying in all the different locations or channels. Now if I wanted to follow that analysis a little bit more, I might use a filter and just put a filter on a panel so that I don’t have to include the purchase in every single visualization, but that it’ll be added automatically because the filter will be active for the entire panel. And that’s what I’ve done down here. I’ve created this panel, and we’re going to add a filter here in a minute but I want you to see what we’ve got. We’ve got just the different data sources and we’ve got how many people and how many sessions from those different data sources, again, ad impression data, analytics, email, point of sale, which is store data, call center and survey data. So I’ve got a number of people in each of those data sources as well has how many sessions and then I’ve also got just another one down here which is gender. So I’ve male and female, and we can see that there are a certain number of people there. And so now I can add a filter and limit that data just based on whatever filter I want. In fact, in this case I’m going to do kind of a cool thing here with this drop zone and I’m going to grab Purchasers, and also the In-store Purchasers, Call Center Purchasers and Online Purchasers and I’m going to multi-select that and as I drag that over, I can hold down shift and create a drop down, drop that in there. And now I have this drop down so I can choose any of those filters from my data. And so now I am doing analysis on the people who purchase, not just on the orders themselves. In fact, if I scroll up, you’ll remember up here that I only have orders coming from those three data sets, analytics data, point of sale data and call center data. But, when I go down here and say Purchasers, I even have data on some of these other data sources for example, ad impression data or email data. So how could that be? Well, the way that can be is because we are doing, once again, analysis on a person, and so this is the same person that has purchased in one of these other areas. Right, because they can’t actually buy, they can’t actually become a purchaser in the email data and so they must have actually been a purchaser at the store data, the analytics data, or the call center data. We can also see how that filter limited the data down here for the gender, where we have 109 female and 101 male who have purchased. And because we did the drop down we can now also change this filter from just overall purchasers, to what about people who actually bought stuff in our call center and so I’ll select that. And you can see now, that of example three of those people who were in the email data were also in the call center and actually purchased in the call center. And as we saw above, where there is quite an overlap of people purchasing in all the different venues or all the different channels here, we can see that call center purchasers are also people from the store data and the analytics data as well et cetera. So this is the other way that we can think of cross channel filters is that we have a filter that is really built off of one data set, in this case, call center data and we are assigning it to other kinds of data, other channels here across channels and the way that this is happening again is because the person ID is the same across these channels and it’s merging this data per person so that we can see this cross channel behavior as well. Anyway, hope that was very helpful and that you’ll be able to create and use filters across the different channels with your different data sets.
Good luck. -
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