Use the experimentation panel

Learn how to configure and use the experimentation panel, which automatically builds some visualizations based on your experiment data.

Hey, everybody, it’s Doug. In this video, I want to show you how to use the experimentation panel in Customer Journey Analytics. Now, the cool thing about being able to use this panel is that you are going to be able to bring in any experimentation data, even if it didn’t come from an Adobe solution. So other test data that you can bring in, and I’m going to talk about the format that it needs to come in as. But whatever solution you have doing your experimentation data, you can bring that data in again if it’s in the right format. Now, one Adobe solution that it can come in from, of course, is Adobe Journey Optimizer. And so if you have that data, you can bring that in as well. Again, it has to be in the right format. And what is that format? I’m going to go up to this other tab and we’ll click into some documentation here for a second. And you can see down in step one, you’re going to create a connection to the experiment dataset in the platform. This data does need to be in an object array. That’s the data type. And then it needs to contain the experiment data and the variant data in two separate dimensions. Now, if they do happen to be in one dimension with a delimiter, then you can use the feature in data views to split that out. Now, one thing to note right now is that at the time of this recording, A4T or Adobe Analytics for Target data brought into the platform via the Analytics Source Connector cannot be analyzed in the experimentation panel. And you can see here, we expect a resolution to this in 2023. So let’s jump back to the project and let’s do this. Now, before I show you how to set up the data views and things like that, I’m going to give away the ending. Let’s go ahead and just jump right in and put this panel in there. So I’m going to go to the panels. I’m going to drag in an experimentation panel. Now, you will have a data view selected up here. If this data view does not have the right kind of data in it for this experimentation panel, you’re going to get an error. And so let me show you that if I just bring up a random one here. How about like this one? Yeah, perfect. This does not have it set up. So you can see it’s saying, please configure the experiment and variant dimensions in the data view. And you know, this will take you in there. So we don’t need to do that because this is not the data view that we have the data in. So when you choose the right data view that has that kind of data in it, I’m going to select this one. Then you don’t get the error and the experiment data will load. So you can see I have one here and I’m going to go ahead and select that. That will then retrieve the experiment information and it’s going to actually bring in two things. First of all, it’s going to bring in your variants. I’m going to choose my control variant, which is my default content, and it will also bring in the dates where it’s going back and looking at this experiment and bringing in the right dates and just trying to, you know, do this for you. But you can select any dates that you want. You may want a subset of these dates. So you can go ahead and change that by clicking on that and change that if you want. The next thing I’m going to do is select my success metrics. So what do I want the success based on? In this case, I’m going to choose revenue. Everybody likes revenue, right? And I’m going to do a second one, which is orders. And you know, we could do a few more as well. I’m going to leave those two. And then I’m going to leave this normalizing metric as people, and I’m going to build.
Okay, when this builds, you’re going to see some things here. You’re going to see a summary you’re going to see the lift and confidence summary numbers you’re going to see some other information, a table here, and also line charts, these kinds of things. So you can see here, first of all, in this summary text, the experiment is conclusive. And with this date range, the best performing variant is called custom max creative. It’s got a conversion rate of 0.05. We can also see that down here, and it equates to a 208.5% lift at 100% confidence. So it’ll kind of give you that information here. If you want to look at it, of course, you’ll see it here, and you can also see it down here in the table. If there were more variants, those would be here in this table as well. We only had one variant besides the default content in this test. Now you can see that this table and this line chart here are both based on revenue, revenue, revenue. That was the first one that we selected. In fact, this summary information here and the summary numbers, this is based on revenue, which you can see again as these match what is in that chart that is listed by revenue. But for each metric that you select, we will give you again a table and a line graph for each of those that you select so that you can see the lift and confidence for those different success metrics as well. It’s just that this summary stuff up here is only going to be for the first metric that you selected. Now, as far as filters, and you might think of this again as a segment, but the one that is automatically assigned is this exclusion filter to keep out people who have had exposure to multiple variants. So we really only want to base this information on people who have seen one variant. If you wanted to drop in another filter, say you wanted to drop in a geofilter or something like that, you can do that but you have to actually do it before you build the panel. So instead of just dropping it in now, you’d have to go back and click on the little edit button here and then put the filter in and build it again. Let me close that. Now, we kind of mentioned, you know, how did things get populated into this experiment dropdown and also the control variant dropdown? Well, that has to be set up in the data view. So you can click into data views. I already have it over here and I’ve selected that same Cross Industry Demo Data data view. So as that data comes in, you know, first of all, as I mentioned in the documentation, you have to make a connection and bring in the data from the platform into Customer Journey Analytics that has the experimentation data in it, in that object array with both the experiment information and the variant information. So in here we need to say, where do I find the experiments and where do I find the variants? So that’s for you to know, right? You’ll need to know where that data and what the component name is for that data. You can see here I’ve just searched for experiment and I have that in here 'cause I happen to know that this component here, this variant name component is the one that has the variant data in it. So once I’ve selected that, you go over here to this context label dropdown and select it. Now I’ve already selected that, so I’m just going to mouse over this and you can see that this is the experimentation variant data. I’ve labeled this one as such. That way it will show up in the variant dropdown that we saw. Now I’m going to go ahead and search again for like activity because I have this other one called activity name and that is the component that I set up as the experiment. So you can see here that it’s the experimentation experiment. So that is the one that, again, I did this context label, did the dropdown and selected, this is the experiment data. So you have to set those two components up, and then, that is the data that will show up in those two dropdowns. Anyway, that’s pretty much it. You bring in the data, you make the connection with that right data from the datasets with your experimentation data in it. You come in here and set up the data views with these context labels over here, and then you go into your project, add that panel, and do the setup and select the items that you want. And boom, you’re good. Hope that was helpful. Have a great day. -

For more information, please visit the documentation.