Hi this is Jen Lasser with Adobe Analytics Product Management. In this video I’m going to show you some new features we’ve added for comparing date ranges within tables. If you’ve been a fan of the summary change visualization in the past and wanted that capability for tables you’re now in luck. So day comparisons are very helpful for putting current data in perspective with historical data. It’s always recommended that you support current data with historical data whether that be through a trend graph, a summary change visualization or within tables via our date comparisons. Each option will give the reader of the report a better idea of what is good versus bad performance. So let’s take this table, for example. Here we’re showing our marketing channels report for last week and we’ve pulled in visits and orders by each of our channels. While this data is meaningful for last week, you may also want to answer the readers next question of well is this data good or bad. Is it up or down compared to the prior week or this time last year? So we can add day comparisons in two ways and this will help address that readers next question by right clicking on any metric column. You have two options of add time period column and compare time periods. So let’s start with add time period column. The options when you make that selection will be prior week. So this will give you kind of a week over week view this week last year which will give you year over year and custom date range where you can choose your time period compared to. Now these options will be different depending on what you’ve selected in your panel. So we’ve chosen last week in the panel. That’s where we’re getting weak related time period comparisons. If we change this to last month, we’re gonna get some different options under our time period comparisons. So we’ll have month over month, year over year so same month last year and again a custom date range.

I’m going to go ahead and add in prior month to this date range. Now add time period column we’ll just add the prior month data. So just those raw numbers. So now we’ll have November orders automatically added in compared to December’s orders or last month’s orders. So this is great. We can already kind of eyeball here and see that our direct channel is down a little bit this month same thing with referring domains and moving on down the list.

The next question that your reader is probably gonna have though is you know instead of eyeballing all this are the numbers just up or down. Give me the percentages. So rather than having them do the math. We can go and add in the the percentages for them via compare time periods. Compare time periods will give you the same options as before. So I’ll choose prior months to this date range again but instead of just adding the the raw number column it’s also going to add and calculate for you the percent change. So we see the same November column from before We have our last month December column here as well And then we’ve calculated the percent change for you. In addition and calculating the percent change we’ve layered in conditional formatting so you can very quickly see if performance is is down or up. So red indicates a decline across all the channels and in terms of orders.

The percent change is a complex calculated metric that we’ve created on your behalf so you can click the little eye here see how it’s been defined. It is a internal workspace metric meaning you won’t see it in your calculated metric component list outside of this Workspace. If you do want to leverage this outside of this particular Workspace, you can make the metric public. And then subsequently you can share that public metric with others.

Additionally if you don’t like the percent change name, you can always change that with a little pencil. This is again just a calculated metric that we’ve created for you so you can add in you know month over month percent change if you want to make it a bit more descriptive. Clicking save, that name will be adopted back in the table. -