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Analytics and Reports for Internal Requests


Hey everyone. Today we’ll be going through the analytics tab as part of Jetdocs. Now, the analytics tab is meant for admin role permissions, and so, members and editors will not have permissions or viewership of this tab. So, analytics is broken up into four main sections. The first is this general Control Center or Dashboard that we’re currently on. There’s aggregate responses which I’ll show you in just a minute, followed by users and all tickets, and I’ll go through those quickly as well.

Now, as part of this dashboard, I mention that word “Control Center”. And the idea is that on a week over week basis, you’re able to check in and understand how your organization is dealing with different requests and tickets that go through your catalog.

And so, whether that’s for one specific category and one specific template as part of that, or your overall organization as a whole. The idea is that you can quickly drill down to see how things are moving or different resources being used. So, there’s the last week’s summary, which is pretty self-explanatory. And then as we jump down, things get a little bit more detailed. So, under historical data.

Five main events that are tracked within Jetdocs:

The first is created, the second is completed, we then have rejected below as well as canceled. So, those four are pretty self-explanatory. And then the other one is First Approval. First Approval is tracking how long from when a ticket is submitted to the first action on that ticket as part of that workflow or approval steps.

So, if I jump into this next tab right here, you’ll see that there’s this Approve and Reject Call to Action buttons, and the first step has already been complete. Now the time from when this ticket was submitted to when this first approval took place is what is really being. Tracked in that graph.

And so, the time series piece of that, I’ll show you how that works in a different graph. But for the graph that we’re currently referencing, it’s just tracking the absolute number of first approvals within the time frame that you’ve referenced right here.

So, that would be this particular graph here. As we scroll down, we’ll get into the ticket event transitions. And so, this is using the same period of time listed above. And this is really from timestamp to timestamp. And these five graphs are broken out into the first two, which are your organizational or organization as a whole. So, the time it takes from when that ticket was submitted to when it’s fully complete, and to get a ticket complete, for example, there’s four approval steps here. If I click this Approve button three more times, that would get me to the fourth step being complete, and that would count as a complete status.

Or under this hamburger menu, there’s this quick complete button. For various reasons you might need to do that, but that will also, push that status to complete without going through the rest of the approval steps.

Next, we have Create the first approval. And so, I just mentioned the different approval steps. You know, what constitutes as that first approval. So, that would be this particular workflow step here. And in this chart or graph, this is showing you from a time perspective how long does it take to actually complete that first approval from when that ticket was submitted.

Now the three remaining graphs are all relevant as part of the general queue. And so, from Create to Assign, what does that mean? So, if I jump into our ticketing center and you can see that there’s currently two tickets in our general queue, if I click this Self assign button or I jump into an actual ticket and click Self Assign here, that would be or constitute as the Assign section or event as part of this graph. So, it would track how long does it take from when a ticket is submitted to how long so,meone assigns that to someone else or self-assigns it to themselves.

Very similarly, the next chart would track time from when it was assigned. So, the second step here we went from Create to Assign. Now that we’re in assign, it would then track Assign to Complete. So, for example, if I click Self assign on this vendor management ticket, that’s when the clock would start ticking until it reached that complete status. Then lastly, we have just generally Create to Complete. And so, this is only for general queue tickets. And this can be helpful for when you’re managing a team of people where you have multiple people who are picking it up, who are self-assigning or assigning to so,meone else. And so, that’s just a different way to look at Create to Complete versus Create to Complete in the first chart, which is everything.

Now as I scroll to the top, we’re going to jump back into these aggregate responses. And so, before we do that, I’m going to go into this vendor management template that I have created, and we have this title or short summary listed here as the first input field. And then as the second input field we have a multi select data object. And so, if I click Aged Enterprise for example, or this interchello group, those would be the selections when I go to submit this.

So, this is relevant because when I jump into the next tab, which is the aggregate responses here, you have the ability to select an individual category and the relevant template to understand the data that’s actually flowing through that particular data object.

So, in this particular one, this is the answer to which vendor and country is this request for? And that would be say for example, I just selected AGV Enterprise and when I go over here you’re able to understand. So, for example, this AGV Enterprise, there’s five selections out of the total sum of 41. And if you’d like, you can actually export this to XLS, Excel workbook or a CSV file.

Now, one particular piece that I had shown you here is this ability to select and that is available in the historical data on the first tab as well.

So, you can understand on an organizational level, on a category level, or an individual template level, and you can mix and match how you’d like to understand that different data.

Now, the next step would be jumping into the users tab. And so, this, again, you can understand from a different kind of segmentation on your levels to see who’s part of different templates and things like that. But this is really how you can understand the different resources in your organization being used. So, this is a demo account, so, we have limited users in here, but this is showing you that Andrew is clearly submitting the majority of tickets, followed by admin. And who’s currently responsible for responding to all of these. Again, I’m sending a lot of these to myself, so, I’m responsible for these.

However, this is a great way to understand what’s assigned to different user groups in your organization, to different individuals. And this is really a great way to quickly highlight who’s currently handling the brunt of the work versus who else has excess capacity.

And the same thing goes for initial ticket responders. Maybe this is helpful because say, for example, one person during a given week, they have too large or too many people have been sending them requests and they’re inundated with going through that first step of the process, which usually results in a lot of additional work. So, this is kind of an additional way to understand that. And then lastly, if we jump into the All Tickets tab, this is an easy way for admins to check out tickets that are going through your organization to get a kind of quick bird’s eye view.

But additionally, I think the most useful graph here is just understanding the count by status. So, over a given time range or in a given category or template, how many have been completed, how many are currently outstanding, and you have different ways to filter that through.

So, if you have any other further questions, please let us know at Jetdocs and you can jump into the Blue chat below and someone will help you out. Otherwise, we hope you enjoy your analytic experience.