Ready to start building your own projects? Learn how to build an Analytics Workspace project from scratch, including how to construct a table, attach a visualization and more.
Hello, this is Travis Sabin with Adobe Analytics Product Management and in this video I’m going to be walking you through how to build a project and workspace from scratch. So, if you’re new to workspace and you log in, you’ll typically see a panel with a bunch of different options right here. The most common place to start is with a freeform table. Freeform table is kind of like our version of a pivot table, so in here you can drag and drop different components, which are dimensions, metrics, segments, onto the table here, and it will start building and populating data for you. Your dimensions typically go right here, your metrics go up here. So to start, I’m going to click and drag the page dimension onto my table and it automatically generates a metric or occurrences for me by default and I can see all my different page values here, going down the table itself. So if I don’t want occurrences by default and I want to use a different metric, I can choose from my list here, now this shows only the top five, I can click here or I can click show all and it will give me my full list of metrics. In this case I’m actually wanting to use the visits metric, which is up here at the top, so I click and drag it over here, I can put it next to it to add it and add another column, I can put it over it to replace it and replace this value which is what I want to do for this. Now another option for you with metrics, here you see there’s a little plus sign, if you click that, that’s an option to create a calculated metric which is basically combining one or more metrics together to create a new metric, so that is another option available to you. So once I have this table here with my page dimension and the right metric with visits, I can make some manipulations to the table, I can change how many rows of data are displayed, if I only care about the top 10, then I can reduce it, if I want to filter it to specific values, I can search for those here and only bring specific values into the table, there’s some things I can do really quickly to make some changes. Now I’m interested in the home page and this is my first level dimension but if I want to dig any deeper, I can pick another dimension and drop it on top of home and it will give me the values associated with that homepage for that new dimension that I’m picking. So, in this case I want to break down my values by day and see how many visits I’m getting to the home page on a daily basis. So this right now, my date range is for this month, so I have May 1st up until today and I can see the different visit values over here on the table so I’ve broken down my home metric into the specific days for this given month. Now, another thing I can do, if I’m interested in a specific segment of users, I can come and choose from my list of segments here. Now, again, I have these top five here, I can show all, I can create a new segment by clicking on the plus, but I am going to drag on, let’s see, where is my mobile user segment? I’m going to drag this on and if I put it above the metric, it’s going to filter my visits by mobile users. So, once I put that on, my data now it’s updated to reflect my mobile users who visited the home page during the month of May. Now, once I’ve got the table how I want it, I probably want to visualize it in some way, besides just looking at the raw numbers, so our visualizations are located here if you go to the far left rail, we have these three options, right now the components, which are these, the dimensions, metrics, and segments are under this third icon. The second one is where our visualizations are stored. Now most commonly with a freeform table you’re either going to use a line or a bar chart, you could maybe use stacked as well, but you could have area and a few others, scatter plot, but I’m going to stick with the tried and true line. You drag it on and it will automatically connect to the dataset below here and it will visualize the top dimension value by day, so now I can see it trended over the month of May, how it’s performing. Now some of the cool things that the line chart does, out of the box on here half is it builds these, what we call confidence bands. So it uses intelligence to predict what it expects your value to be, that’s what this dotted line is on any given day and then the upper and lower bounds of what those values could potentially be, so that’s what this kind of subtle blue area is behind our main line here and then along the way, if we have any values that are outside of that confidence band, you’ll notice I’ve got these dots here and here, which are above and below our confidence bands so those are what we call anomalies. If you click on an anomaly, it gives you an option to tell you that an anomaly has been detected and then you can click on analyze and perform what we call contribution analysis to help try and uncover what might’ve led to that specific anomaly so some intelligent services that we built right into the visualizations and make it really easy for you to kind of dig in deeper and explore further. Another thing I can do right now, again, this is just pulling from my top dimension here for this data, if I want to only show, let’s say the first week, I can highlight the first week in the table and my visualization up here will automatically update to reflect those days, if I click it again, it’ll go away and resume what it was previously, so if I want to just change the data that’s displayed, I can easily highlight what’s in the table and do that. So also here on our panel, we have a couple of other options up here, so again, right now I’m using the month of May, if you click on this, it opens up our date range picker. You can choose any date range, any start and end date range that you’re interested in or we have a bunch of presets in the drop down here for last 30, last month, last full weeks, last year, and so on, so you can choose presets if you want to change that and then you can apply it to the panel. If you want to use a different data set from a different report suite, this is where it shows you the reports that you’re using in this panel itself, you simply click on that, it’ll show you other report suites that you can choose from to change your data set, so those are some options as well to change what you’re looking at on your table and your visualization. And then if you want to add another, again, this entire project here is built on this panel, if you wanted to add another panel, the top item here on our far left menu is the panel menu. A blank panel is what we started with today but there are some other ones that offer some more sophisticated choices for you if you weren’t doing some advanced analysis or in the case of quick insights, that’s kind of a beginner builder tool to help you start from scratch with a little bit more guidance but this thing allows you to add multiple panels to your project so if you wanted to collapse this one and add another one, you could drag on a new one and you could start with a brand new blank freeform table and do some analysis here, so that is another option to you. Other things worth noting and exploring is most tables or visualizations have a little gear icon here, which will have some settings in it if you want to make some adjustments changes, additionally on the table especially, there are a lot of right click actions, so if you right click on any specific value, you’ll get a list of a bunch of different things that you can do to analyze your data even further beyond what you’re already looking at here, so just some things to look for if you’re trying to figure out something else that you might want to do or adjust, look for a gear icon or a right-click action. So that is getting started in Adobe Analytics and building a project from scratch. Hopefully these tips have given you some guidance and ideas on how to get started with your own analysis, thanks. - -
For more information, please visit the documentation.