The Media Playback Time Spent Panel enables media users to understand their viewership by the amount of time viewed during the day over a chosen granularity. You can also “zoom in” on a period of the day or create sequential time segments to clearly show the lead in and out of portions through the day.
Hello, this is Jacob Draper with Adobe Product Management. and I’m going to show off a new panel that we’ve created for our media customers, which is the Media Playback Time Spent panel. And we’re introducing a new metric. And also, have some new features that you’ll find sprinkled throughout Analysis workspace. Okay, so this is the Media Playback Time Spent panel. I mentioned the new metric, Playback Time Spent. That gives you the total minutes viewed within your selected granularity. And that includes pause, buffer, and time to start. I mentioned some new granularities earlier. We’re including five, 15 and 30 minute granularities, which we haven’t had before. We’re going to keep it here on minute, for this example. And then what we’re going to do with this demo data, is we’re going to look at some data for a fake football game. What you’ll notice here, in the time picker, is this new Advanced Settings tab, that shows a start and an end time. What this enables you to do is to show less than 24 hours at a time. Which is really great for this example of a football game, because we only want to show the pre-game, the football game and the post game. So that, in our example, is going to go until 4:59 p.m. So I’ve selected only from 12:00 to 4:59, that that’ll show those full five hours there. And we’re going to apply that to our panel. Now we’re going to add all of our summary numbers here. And I mentioned including a pre, post and actual game. We have that now in our new date ranges time picker as well. So what you can do, like I said, is very similar to what you saw before. You can create these smaller chunks of time to represent your pre-game, game and post game. Or whatever you might want to be representing in your graph. And you can create those as little segments. And I’m going to show you some of the ones we’ve pre-baked, much like a cooking show. Here, we have our pre-baked date ranges. Which you’ll notice, start at noon and go through time. We had a prompt start time at 1:00 p.m of our football game, and it ended at 4:21. And then we had our post-game run a little bit later, until five o’clock. So we’re going to pull those over here, onto our series breakdown. What you’ll notice, as I add this second breakdown, is that we have a new date sequence display toggle show up. What that does, is you can show your data as an overlay. Meaning you want them to start basically at zero, and somewhat stack over each other. Or for hours, where we have these periods lining up exactly sequentially. We want to pick our sequential date sequence display, which is really cool. And will show these different time segments that we’ve created in their proper sequence on the graph. Then you can also mess around here with your time format. We’re going to build this now. So the graph is built, and you’ll see that we have our three different segments denoted here by these different colors. Again, in our fake football pre-game, we have building anticipation as more and more viewers tune in. Then the football game, and it seems to have been pretty exciting as it builds all the way to the end. Where maybe your team, or unfortunately the other team, kicked the game-winning field goal. And then you see the viewership kind of drop as we head out to the post-game. You can see how this would be helpful for football. You could use this for any sort of media application that might be useful, where you need to get your playback time spent for content decisions or troubleshooting or any technical thing that you might be using it for. Continuing down here on the graph, you can see our max, min and sums, in hours, minutes, and seconds. And that includes across all of these portions. So that is our new Media Playback Time Spent panel, hopefully that you’ll find is helpful. And please reach out if you have any feedback or any questions about how you might use this to better understand your viewership, thanks. -
For more information, please visit the documentation.