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Name:
SSP Compensation and Benefits Study Enterprise Subscription Preview
Description:
SSP Compensation and Benefits Study Enterprise Subscription Preview
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Upload Date:
2025-05-23T10:45:50.7994334Z
Transcript:
Language: EN.
Segment:0 .
Hello welcome. We're just letting folks come in out of the waiting room.
We'll get started in just a minute.
Hi welcome. We're just waiting for folks to join, and we'll get started in just a minute. So we're going to go ahead and get started. So welcome, and Thank you for attending this webinar.
The purpose of our webinar today is to give you a look at how the platform, the insights platform works that we have done our compensation and benchmarking survey for. And specifically we're going to focus on study today. As a participant in the study in organizations got an executive summary, which is basically sort of a static PowerPoint of the data. In aggregate, that was reported in the study.
But for the enterprise level subscriptions that we offer, you actually can see dynamic data that's filterable. And you have the opportunity to, look at things in different ways. So filter the data for specific sizes of organizations and different roles and things like that. So we want to give you an opportunity to see what that looks like. Whether you've already purchased the enterprise package, and you want a little better idea of how to find what you're looking for, or if you're considering purchasing the enterprise practice and you want to see a little bit under the hood before you make that investment.
So with us today we have Nicole Succop and she is with dynamic benchmarking. And she is the person who I worked with to develop this study. And I'm really excited to have her here today because she really has a great command of the system and can kind of show you different ways to get at the questions that you're hoping to get answered by looking at the data.
So before we start off, I just want to remind everybody that to access the data, if you are a participant or a non-participant that is going to upgrade to the enterprise version of the study, you just go to Insights asp.net org and you actually just click on participant login, even if you're not a participant, and then you're going to from there put in your SSP credentials so your SSP username and password to the same one that you use to access any of our other services.
Whether you're a member or not. So just keep that in mind as you're after today when you go to log in to the system. So I'm actually going to hand it over to Nicole now. And she's going to take us for a drive-through the system. Awesome Thanks, Melanie. Thanks, everybody, for having me. I am definitely looking forward to giving you a show here. So as was mentioned, I'm going to go ahead and share my screen.
We are going to go through the site itself and hopefully focus less on navigation throughout the site, and more for looking at results and how to review some different comparison type data. Look at your different reports and such, and maybe how to apply some filters and make this data, I guess, kind of work for you. So a couple of comments really quickly just to get us started.
And I will try to reiterate this throughout just in case. But any of the access that we're going to be looking at within the system, I am going to be logged into a test account for sharing and administrative purposes. So anywhere that you happen to see any personalized data, just to be very clear, it is fictitious data. It's not anyone else's data in particular, and it is excluded from actual reporting of the data set.
So just so that that's up front, Additionally, I will touch on some support information as well as some basic information around how the platform works, how the tool works with reporting out aggregate versus disaggregate information. Pretty much everything is aggregate. There are a few scenarios that we look at things in a disaggregated format, but they are kind of few and far between.
Otherwise, when it comes to your actual data, content and such. Aggregate output is what the system is designed to do, and the purpose for that is to not only, keep your data intact and anonymous. Right so you're the only one seeing your account's data individually speaking, but it also is giving you, in a comparison format, a better use of the data points right over averages, for example, a median and an average being two different things.
You've now got some percentiles with your median. So that you can really see, OK on the low end, on the high end, and like where do I fall? And then what are you going to do with that. So again, I will touch on a few of those things as we continue to move through the system. But just to make sure everybody knows I'm in a test account for today's purposes. Excuse me.
So as mentioned in the sign in. So there are two different versions, not versions logins to this platform. The reason between the two different ones is because, as Melanie mentioned, the participant login block is what you are using because it uses your SSP credentials. This admin login block is for me, right? Or for Melanie et cetera so that we can assist you should you have any individual errors or challenges or anything along those lines.
So with that login, what typically would happen for you is you're going to be directed right to your SSP page to use those credentials. This, once you perform a sign in, will take you back into the live platform. This is what identifies for you within the platform at your entry, who you are, what permissions do you have, and to make sure that that information is kept secure in the appropriate accounts, et cetera.
Now at first login you're going to come to a pop up welcome message, if you will, at any point in time. If you click out of that message, you can always come back to it by using that link right up here. The nice thing about this message is it's going to give you it's a dynamic box that's going to give you an update for your account at the time of login.
So it's going to tell you where we are as far as data collection results available in future years, there will be additional years of data collection and results available, et cetera it's going to tell you what kind of permissions you have. Now again, I'm in a test account. We do have everything open and turned on for me in this test account, so we are not currently collecting 2024 data, just to be clear on that.
But ultimately, for our purposes today, I have everything open and on. So this will be an updated message that may look a little bit different at your next login. And then a couple of just additional resources here. There's other places to find these throughout the page, but again, really easy for you to identify what part of the process. We are in at the time that you've logged in.
The other piece, from an organization perspective, that's going to be really important to pay attention to. And you can see that here in the pop up as well as on your home dashboard page. Right up here under the header image is to know which account you are signed into. Now, the reason I mentioned that is because with all of our organization accounts, there are individuals that are associated with those accounts.
If you are an individual that also which is most likely is working in this publishing field, you may have your own personal data that you chose or could choose in the future to participate on the individual side of the study if you wish to. Point being, they are two different accounts. Easiest way to think about that is organization. Data is a lot about policies, compensation standards, things like that.
Where individual is what's actual. What were you actually paid? What was your actual bonus last year? What is your position title specifically or your exact roles? And then also think about what if you changed jobs and you change organizations, right. We can keep your personal information and access intact in your personal individual account, while then separating the organization account and moving access for that organization data to the other appropriate org contacts.
So again today we're going to focus on the organization data. A lot of applying filters and such is going to be comparable on the individual side. But it is a little bit it more complex, I would say on the organization side, which is a lot of the reason that we're going to focus on that for you today. So I'm going to click into, Oh, one step backwards between the two studies.
They technically are all within the same platform of course. So it is a very similar study. The questions are similar between the two sides where they are comparable for organizations. In any case, organizations will not have participated on the individual side of the study. And that again, is by design, right? An organization does not have personal work satisfaction levels about any one position or another.
That that's not the way this does. The study is designed, of course. So just keep in mind that as an organization, when you're looking at this home dashboard, you'll have access to both halves, because when you upgrade to any of the enterprise or above organization. Purchase upgrades.
And when you upgrade to one of those, you also get access to the data that's available on the individual side of the platform. So that's what you've got these two differences on this home dashboard for. Lastly, as an organization authorized representative, if you do, in fact, have your own personal account with or without data in any of these cases, this drop down up here is how you could easily switch between your two accounts.
And I say two because it is assumed that you'd only really be connected with two being your organization or your individual account. And that's how you'll see them named. Of course, I've got a bunch of test accounts here, but you would see for me it would be Nicole Succop, and then it would also be test administrative as my account organization. Sorry organization account.
And you can switch this view at any point in time. When you're in the page, you don't have to be back on this home dashboard. But the point in showing this is that on the individual side, you'll never have access to the organization data. So if you happen to be in your personal account, you've got to switch back to your org account to get to the organization data.
In the organization side, this should look pretty familiar, especially for those that are our participants. Again, I'm in a test account, so we've bypassed any of this information here that says that I don't yet have access, because I do a couple of quick notes about some of the things in here. Participation is based on a couple of things. When we talk about participation and a threshold, there is a progress threshold that we have the system designed to identify for our participants.
And the point for that is really you give some data to get some data right. If we don't receive data in the first place, we then don't have data output that we can share with you and share with our other participants, et cetera. So we've got it designed that way so that again, we get data to be able to share data. But in addition to that, there are certain metrics that maybe are higher on the priority list, if you will, whether it be for SSP themselves as the organization or for other individual users or individual organizations.
As we help to compile this question set back in the beginning of the study, there are certain questions that we know. Or in other words, we're able to indicate with required status to say that these are at a minimum, right. It's a Comp and benefit study. So we wanted some basic demographic details to be able to apply some filters, make it useful, make the data bit more useful for you when it comes to looking at your comparisons and such.
And then also for some of the, you know, bottom line Comp and benefit study. So those are the data points that you're going to see most of the time is where you're requirements fall in naturally because of the difference in threshold and because of the difference in the requirement levels and such throughout the platform. Not every question is answered by every user, right? So keep that in mind when you are looking at your results.
And when we are looking at number of responses, perhaps on individual metrics or individual sections, because again, required doesn't mean it's been answered by 100% of participants. Required simply means in order to get to the progress threshold and gain access to basic level results required is one of those questions that had to be answered doesn't mean that it was always answered. The same is true with anything that's not required.
Not every question is applicable to every individual or every organization, so there will be some metrics that are just naturally less responded to. Think about, for example like a describe other. Well, if you didn't mark other in the previous question, you have no need to use the describe other question as a fill in box. OK, so let me just double check a couple of points on this home page.
From a support perspective, I did also just want to identify that for you individually or for anybody else in your organization. If there's any information that you're looking for most specifically about how the data output is presented and our data integrity, et cetera, that information is all able to be reviewed on the Support link right from your home dashboard. So really from anywhere within the site, you can click on that support OhioLINK and you can review some information.
It does give you a pretty good breakdown about percentiles, et cetera and where the data is stored. All of that if you have questions, we do have support information here that, whether it's with SSP or myself, we'll make sure that we can get you the answers that you should need. All right. So without further ado, I'm going to go ahead in.
I'm going to start on the Reports tab because I think it's a little bit easier to explain, at least initially. So what I want you to see here is you've got this box up top and this box is called our it's a filter box right. So ultimately again I'm in a test account. I'm also in an administrative account. So some of these filters may not truly be accessible for you. These will depend on your level of account permissions.
So organization basic has limited filters, limited reporting, limited comparisons, and with the upgrade you do gain access to more information and more. I would say functional information as far as being able to drill down with additional filters. When you apply any of these filters up in this box, they're going to automatically apply to the reports below. These reports below are pre-designed by SSP identified for you as the participant, as the upgrade purchase, whatever that may be, they're pre-designed to give you.
It's all the same data throughout the platform, but predetermined as far as charts and the way the visual representation of this data. These filters will automatically apply to the reports as you download them. And this is as simple as clicking Edit, making whatever possible selections you could want about a lot of these are about position, which we're going to get to. But if you wanted to even just look at some basic demographics, and maybe you only wanted to pull a report based on that benefits package options based on like size organizations, for example.
You could do so by setting your filters and then downloading a report. These reports are template. So they're going to look at first glance, the same every time you download them. But they are going to be different. And there is a cover page in all cases that tells you the date that you pulled it, as well as the year of collection that you pulled it for in the future will have multiple years as options, and then also any filters that were applied at the time you downloaded the report.
So that's that cover page is going to really indicate for you. If there are differences in the data sets side by side, they may not look drastically different. That cover page is going to tell you what it is that you're actually looking at. Now, I'm going to clear this out and move over to comparisons. This is where we're going to spend most of the time. So in this view should look really similar to data entry.
Right comparison wise. What you're going to get here when you click into and I just went into organization profile to get us started. You've got your filter box again with your filter options. And then below you've got each of the individual questions as it was asked in data entry for the org study. And this is true for every section of comparisons. You go into the different tabs and the questions are listed below.
You can carry through just like we did in data entry, so you can easily go back to comparisons as a dashboard and take a look. But you can click into any of the areas that you want to focus on. I'm going to go back into profile for a bit. Again, you've got your filter options. What I want to highlight here is a couple of things to identify for you.
What you're seeing below. First and foremost, you've got this blurb right at the bottom of your filter box. It's going to give you an idea of how many questions are displayed on the page. So each section has its own number of questions. That number is going to vary. Some questions are not applicable for everyone. So keep that in mind that there may be results for less than the total number of questions on the page.
Again, that may occur. And that's OK. And then right here you're going to see an average number of responses per question. Now this is an average. The reason for that is because, again, not every question is answered by every participant. Not every question is applicable for every participant. So that said you are going to see this number shift from time to time too, especially as you continue to layer in or remove filters.
All of the data below. When you're seeing this input by metric, I'm going to go in order here. So we've got a numeric metric. And then we've got a multiple choice metric. And those are the two main metrics that we have that we're looking at resulting output in your numerics you're going to have on the left 25th percentile. In the middle is the median the 50th percentile.
And on the right is your 75th. So what you're not seeing is your lowest answer, and you're not seeing the highest answer, and you're not seeing any of the specific answers in between. But if you think about this being plotted on a scatter graph, for example, each, you know, each participant is marked in here somewhere, and then there's a line that connects them all. This indicates me data.
Personal data. Again, I'm in a test account. So this isn't anybody else's patent. It's just fictitious. But on your organization page, you're going to see your data provided that you answered data. If you did not answer any specific metric, you still would have access. Again, assuming that you've got access to the appropriate charts and filters, et cetera, you'll have access to be able to review this chart, for example, but you just won't have a my organization data point on it because you didn't respond.
That's also what this text in blue above the line is showing is the me data. So any time that that data, again, is different, it's going to show you what your response was. And then the percentile roughly that you're falling around from a multiple choice perspective. Blue is my answer. But this is of the times. It's the selection rate.
Right so by all of our for all of our participants, we can see that us was selected 76% of the time, in many of these countries weren't selected at all. Not surprising for this year. I'm going to scroll down past these. The same is true when we come into US states. You can see where I marked our response versus others. So you can also quickly see some states that maybe had better participation than others.
The reason I chose to start on this demographics section is because this area can help you. As you move through the results, to determine what filters, you may want to apply and what filters maybe would be useless in its own right. So let's just say and that's for this year, for this round of data collection, maybe I don't have any participation in Alaska, but I could make my own.
I could think through my own excuse me view of what would be comparable. Well Alaska maybe. I don't know, maybe shipping and receiving of products is. You know, that much more expensive from a logistics perspective. OK, well, where else would that be? Common, right. Alaska, Hawaii, et cetera.
You can choose what other filters or regions maybe you want to take a look at. Additionally, you've got some other multiple choice questions that while these above were choose one. I do actually believe type of organization is also choose one. There will come some options where it was a multiple choice. Choose all that apply or potentially choose up to three or something like that. Keep in mind that those percents are not always going to equal 100% And that's why.
So these are your selection rate options. And then again you've got a chart that populates for this. And here you can see just highlighted quickly in color. I chose this blue bar. This is the selection rate of the other options within the metric. OK from here I want to switch gears and go to comparisons because this is where we're really going to see some.
Complexity OK. So again, you've still got your options for your filter box up top. But what I want to indicate or to show you here that's a little bit different is this box right here that's kind of a separator if you will. If you remember when we were doing data entry, we asked for participants to enter a number of positions in their organization that they plan to report compensation data about one section being non-supervisory so they don't oversee staff, a different section being supervisory meaning indicating that they would oversee staff.
And of course, we've got a difference in levels of the hierarchy of an organization between both. Now for today's conversation, these two tabs, compensation per position, they're going to operate really similarly. I just wanted to we're going to focus on one at a time. So that it doesn't get too complex or too confusing. Now for the time being I've got no filters applied. What you're automatically seeing is here's that dropdown list from a non-supervisory positions perspective of which position options I guess, job title, if you will, were initially available.
As I make a selection to each of these additional options you see here, my data has updated. So first and foremost moving from manager to supervisor. Manager had data available for show because we saw it. We saw lines and I'm going to go back to it so you can see it again. Versus supervisor on this side does not. That's why you see this hourglass here. It means that there's not enough data available to be able to show some sort of graphical output.
Now again, keep in mind all that means is of our participants. There were not five or more that said they had a specifically titled supervisor in the non-supervisory positions that were available. It doesn't mean that there isn't this type of level of position available elsewhere. What we're doing when we looked at these positions and the categories, and this is true on the individual side as well.
We recognize that not every position title is going to be a one to one right. You may have a director of marketing. You may have a marketing director. Technically, they are the same, but when you're looking at them from a categorical standpoint and from the statistics of like a data input and output site, they are different. So what we really wanted to hone in on was more about of overall level of a hierarchy compiled with or maybe compounded with roles, job responsibilities, and maybe overall function or specialty, if you will.
So as we work through each of these different position titles, if you will, you're going to then see the breakdown of reported managers with what function areas, what specialty areas and focus, et cetera and you're able to do that for each of these different options. Now, this is what I would refer to as a forced filter, meaning I don't have any of my optional filters selected above.
I have not drilled down into any of this data. I have not said I want to see managers in just my region of the US, or just in the US compared to other countries. I haven't applied any of those filters. The only filter that is applied is by this position level so far. You can layer additional filters, but as this is our inaugural year of data collection we have, it's definitely a ramp up for data collection.
And for the amount of time that we had for data collection being available. And also a lot of times people don't understand what's going to be available, which is what I'm showing you today. And so hopefully that will continue to lead to more participation in the future. Now, as our participants of our first year, you'll have a couple added benefits in the future that others will not have.
And that's just even simply in data entry, not just in reporting output, because you'll now have, as a participant two years you would have trends or potentially starting trends for yourself as well as for overall participants. Right so maybe state of the industry. But from a data entry perspective, there's also going to be an option for you in the future to copy prior year data and then edit from there.
Point being, maybe you don't have significant changes in your staffing levels from year to year. Great copy over last year's data and then tweak it from there. Your benefits packages haven't changed. Maybe the actual compensation in a position hasn't changed, or only what changed was actual bonus payout or something along those lines. You'll have that as an added feature, which will hopefully make data entry for you a little bit more efficient, time efficient for you in the future.
Lastly, what I want to point out is the differences that you're seeing on these pages. So it's really similar to what I mentioned earlier, but you have two different data sets that you're looking at. The reason being as a participant, if I said I have, I don't know, six positions non-supervisory that I was going to enter about and maybe I have two sales managers and then two marketing and communication managers.
Titles could be whatever they actually are, but in general I have two of each. When I was reporting this data, I was identifying how many positions of similar type I was entering data for. So what you're seeing here, granted, in blue, I don't actually have any data entered, but if you do, you're going to see your responses in blue compared to the aggregate of all responses in Black.
And you're going to see that as you change for each position. Same thing. These will filter and update sorry. These will update when you apply filters. And then there's one numeric for all positions that is going to look similar but a little different. And actually I made myself a note here. I want to look at one that does have data. So you can really see the difference between what that looks like.
Sorry give me a second. I want to switch gears. I'm switching over to our supervisory positions and I'm going to switch to Director. So you see, now I've got directors and a couple of different fields, and I've got my different selection rates compared to others. And when I go down here OK.
So again, you've got your percentiles. What you're seeing here is the difference similar to your multiple choice. You're seeing my test account fictitious data up top. And you're seeing the aggregate of all others below. So this box is just a little bit more complex than a standard 25th, 50th and 75th percentile. With my data point referenced, it's got my combination of data points.
So in this case, I had a number of directors. Looks like I had probably one, two, three, maybe four directors that I entered information about. And so I have potentially 4 or 5 or multiple more data points. My percentiles are up top and everyone else is in the bottom. Again, you're looking at this from percentile view. And then in a scatter report you have a little bit of flexibility.
Here you can see a single line, which is just all data points not included not necessarily indicating mine or two. How do mine compare to everyone else? Just different views in how you want to look at it. I think I also want to switch gears. Let me just pause there really quickly and see if there are. We can come back and do a couple of live examples here in a minute, but are there questions for me to dig in here before I look at countries and benefits, which is similar but different?
Nicole, could you show like use one of the optional filters for, for instance, the functional area on director just so they can see how they can isolate that down to a specific role. So what I would recommend is actually kind of without filters, taking a look first so that you have a more pinpointed target as you're looking. OK so for those that have entered data with directors again I've got no filters currently applied.
I'm going to scroll down and I can see, OK, I know how I responded fine. But let's look at everybody else. Well, I don't necessarily want to apply a functional area of analyst because I don't have data to review there, but I do have data to review in it or in publications and publishing, et cetera. And then if I wanted to add an additional filter on that, you would do the same thing and do a comparison down here with specialty area.
And focus. But let's start there. So I'm going to go ahead and apply just the publications, which makes exact sense for your industry. But now we're looking at supervisory positions, so do keep that in mind as well. You could apply a data filter for non-supervisory function. But that is not going to indicate data about a supervisory position.
So just do keep that in mind that sort of looks like there's duplicates but they are different. I'm going to find my publications and publishing. Oh and this brings a good point. You will have green text that pops up in your filter box that says, there's enough data below for you to see some output or to download a report. Perhaps that's filtered in comparison to not filtered. You will have red text when it says you've gone and drilled in too far, applied too many filters, or maybe conflicting filters that would never possibly be marked the same.
This is going to be an automatic shift. We see that our responses per question has changed. And now when I. Oh, right. OK when I scroll down, it may not look like it at first, but when you look at the percents here they have shifted some. Now keep in mind this is a question that was multiple choice. Choose no more than three.
So you're not going to see just 100% of publications in publishing because some positions, some organizations I should say reported director positions that said, well, those that have publications or publishing as one of their function areas also oversee which other function areas. So you can drill down a little bit further and then let's apply one additional let's also apply operations.
And so again, it's shifted even further. You can apply. OK so of directors that oversee for example, operations and publications. OK I can see, you know, they might be split between journals and media community et cetera. What kind of scope? So again, all of that data below is continuing to update for you as you apply different filters.
A little background here in terms of why we set up the job titles that way. As you know, there are quite a variety of job titles within our organization and quite a variety of size of organization. And it's common that the smaller the organization, the more hats a particular individual in a role where. So it's very difficult to compare, for instance, a director of publishing for a small society publisher that has one journal and that person may wear many hats.
Versus the same role in a large commercial publisher, for instance. So this allows us to actually really look at those roles in terms of what they actually do, and not just in what is a maybe arbitrary title in some cases, or the best fit kind of title. So it really helps us see a little bit better about what that role is doing as opposed to, you know, what a job title is.
That may or may not really be a direct comparison to the job that you're looking to compare it to. Yep and to be very clear, again, when you're applying those filters, this information is shifting down here as well. So we know that from a compensation and benefits study. Compensation is half of the name for a reason. Because what you're trying to identify is OK. Well with not just tier or level within the hierarchy, but also with responsibilities.
Are you a manager of simple one area of media or do you have multiple hats? Do you also oversee something within it or something in accounting, etc.? Point being, you're also going to see then not just their responsibilities, but what shift, if any may occur. Yes, within your organization, although you probably already are aware of that, but more so for the rest of the industry and the rest of participants.
What shift occurs in compensation also based on the shift in those responsibilities and roles? So you're going to see all of that information shift automatically. Now the last thing I'll point out, though, about these filters, which is useful, let's say you've honed in on some data, you've messed around with filters, you've changed your tiers.
You've really come into finding some really good targeted information and you want to take it with you. This would be the time that you're going to flip-flop over to a downloaded report, right? So basically what you've done is you've identified whatever data points you're looking for. You could certainly screenshot or take notes of that information on your own. But these filters and such are all going to remain there sticky.
So when you set your filters on one side, you'll see I've got operations and publication here. I'm going to switch over to reports and that is still applied. So if I wanted, let's say I've already identified a report that's really useful. But then I wanted to apply some filters, found some really interesting information with the different filters to look at the live comparisons.
Now I'm going to go back and download the report, take it with me, share it with other team members as you see fit, et cetera. The one thing to keep in mind, especially when it comes to compensation. If, depending on your permission levels. There are scenarios where some of these reports graphically, as well as just analytically in an Excel report, for example, are going to include your organization data.
So just be cautious of that. It it it could be sensitive data that may or may not be something aggregate. As far as industry information you likely would want to share or be more willing to share with more folks in your team. Whereas you don't necessarily want to give away information about a singular individual and what their compensation is within your organization, to the wrong people.
I am going to switch gears once more, and I'm going to clear these filters out really quickly just to show you once more how I did that, because I sometimes do things too quickly. Sorry when you're in your filters and wanting to edit, it's you can. One by one remove singular filters and reapply others. Or you can simply clear and it will go back to scratch.
No filters applied so you can just start right back from the beginning. Now I switched over here to benefits by country, which would also then cover medical plans, dental and vision, et cetera I do want you to know that this setup is very similar to what we just experienced, with optional filters in a box and forced filters below, so I'm going to switch to medical plans.
For example, we have loads of different countries that were optional, and in the future we certainly expect that we're going to see more participants from more countries as we get more data collection. But in this first year, we have limited participation from countries outside of the US. So we did want to make sure that we noted in, and it is accurately commented on in the appropriate locations.
But in an effort to make it so that, you know, our outside of US participants were able to actually review some data where they might not otherwise be able to. So, for example, if I were to click well, really I'll just click anything, any of these, right? All of a sudden I've got no data available to show we didn't want to do that. Even if there were, let's just say four participants, but you're just shy of that fifth one to be able to see aggregate data.
So in other words, in these appropriately indicated places, there are reporting outputs that actually aggregate for you all participants for the countries. So everyone is combined all into one chart rather than the forced split which is live on the page by country. So again, just wanted to make sure that we were able to provide meaningful and useful output for all of our participants. Participants excuse me in this first year.
Again, in the future, we expect that we'll be able to have more live review available as we continue to gain more participation. And the only other point I would have on that, sorry, was about dollars, or I should say monetary wages and compensation. In all cases where you see monetary format, all monetary output has been converted to US dollars as it was our most chosen, most reported type of monetary input.
So everything has been converted to US dollars using the. US IRS 2024 final conversion, et cetera so for truer peer to peer comparison, we put all monetary figures in the same format. So hopefully that that's fairly easy to understand as well.
I think that covers everything I wanted to talk about. Melanie, do you have anything that I have missed in particular? I don't think so. I mean, I think that would be maybe a good time. If anybody has questions, just pop them in the chat or if you want to raise your hand, we can take you off of mute if it's easier to ask your question verbally.
And in the case that you don't have the question at this exact moment, but you come across it as you start getting into the nitty gritty of the details and applying some filters and downloading some reports. And don't forget we've got support contact information in that welcome message. You can find the SSP email also on your support tab.
You can always reach out with questions. We can especially I can review all data and I can actually review it from your perspective as a participant. So that, you know, I can help identify what are you seeing, what should you be seeing, what does this mean, et cetera or maybe how to get to that data. If you're not sure. And we're doing this, you know, this study for our community.
So if there's feedback that you have, we'd also love to hear that if you there's a data point that you would really like to see us include in the next iteration, we're going to do a data collection cycle in the second half of this year for 2025 data. We are committed to doing this for three years, so we want to make sure that becomes a meaningful tool for the folks that are in our community that want access to this type of data.
The other thing I think I put this in the chat, if there are multiple people in your organization that you want to have access to the platform, we can definitely do that. As Nicole also mentioned, just keep in mind they are seeing that any data that you input for your own organization, they're going to be able to see that as well. So that's your choice whether you want to add folks to the list of authorized users for your account.
But that is an option. And you can just simply email at asp.net org if you have additional folks that you want to have access to that. And then I think the other thing we didn't really talk about much, Nicole, was the fact that as an enterprise subscription description folder. You actually get access to all of that individual data as well. I think maybe you did mention it, but it's very but I was just thinking about it myself as well, wanting to just switch screens really quickly.
Again, I'm going to remain in my organization level account, just clicking on this header image. To get to this dashboard to see the two options, switch to your individual side. Now again, you won't necessarily have been a participant on the individual side, certainly not from organization data entry perspective. But when you think about positions and compensation, whoops.
I don't want to be in data entry. I want be in comparisons. Similar setup. You've got filters available. And then keep in mind this is exactly the same criteria that we just described on the org side for how individuals indicated their role and their responsibilities. So again applying filters, looking at data you can easily again I've got no filters applied on this individual side, and typically you won't have me data here either when you're on the org account, but you can see just right off the cuff what positions or what roles tend to play a higher, just higher choice across the board for those that reported.
So what's interesting here is what you're comparing between individual and organization. Data is a little bit of a tweak in how the question was asked, right. Organization standards, organization policies, including compensation policies, whether it be bonus potential, maybe relocation packages, anything along those lines. But then you're seeing on the individual side actual.
Right so keep in mind any kind of negotiation that a singular individual had with their organization. Maybe they negotiated outside of the company policy, but that in itself is also can be really interesting to see what is standard and maybe what is standardly reported. What are the differences there? What are the differences in expectation of responsibilities versus actual responsibilities, et cetera.
So when you're looking at that, it could be really interesting to compare. And the reason that Melanie chose SSP as a whole, the team chose to also offer for individual data is because naturally we have more overall participants on the individual side, because for every one organization you may have 50 or 100 employees, and that's 50 or 100 possible singular participants who enter data into the platform for us.
So the difference there is exactly as described organization policies and standards compared to individual actual reporting. So if you're not seeing enough data points in a particular compensation role on the organizational side, you can always pop over to the individual side, put in the same exact filters and see you might get some additional data points there that will help you round out your data set and your comparison.
Yep, Yep, just a couple different views. Same layout. As far as the comparisons versus reporting, different permissions are available again in different package options. So I know individual basic for example, is much more limited than what organization enterprise is able to get to on the individual side. So just from those perspectives, there are some really significant differences in those upgrade options for just how much data you can gain access to and just how much filtering you have available.
OK I think maybe last chance for questions here. Anybody have a question? Pop it in the chat. Pop it into Q&A. Raise your hand. Yeah and again from a feedback perspective about metrics. Also about the platform itself. If there's a difference in the way that you think, you know, you like one report chart, but you'd like to see it in a different format, or there might be more that we have to negotiate or have to look at to be able to make that work.
But it it's I certainly am not a professional on in your industry. So I know data and basics, and I can certainly tell you what this aggregate data represents. But the way that you want to use the data, the way you want to see the data presented back to you so that you can make better business decisions in the future, et cetera for yourself, personally or for your organization.
That feedback is all super welcomed, so that we can only improve as we continue to move into the next round of data collection in the future. All right. Well, Thank you so much, Nicole, for walking us through this. It's been such a pleasure working with you and dynamic benchmarking to put this together.
And we hope that our community really finds that this data is super valuable and that we can continue to make improvements in the reporting and the data collection process. So again, if you have any questions at all, you can reach out to me obviously, or the email address and we'll try to get you set up. If you haven't, if you want more information about the enterprise subscription, let us know.
We'll get you the information that you need. You can also find it on our website. And I can paste a link in the chat there as well where you can find that information. Yep there's no discounts for that as well. So please we want to make sure that we, you know, this is something that lives on and that continues to be a useful resource. So as much participation as we can get, that will definitely be a signal that it's a needed resource in our industry.
So Yeah. And then just be on the lookout. We will be opening the next round of data collection later on this year, and looking forward to having return participants as well as new participants. So tell your friends, get everybody in here. More data, the better, because that's how we'll really be able to use this tool. Well for you.
Absolutely Thanks so much, everyone, for joining us. Thank you Nicole, and hopefully we'll hear a few questions from you offline. Thank you so much. Thanks so much. Hi
Segment:0 .
Hello welcome. We're just letting folks come in out of the waiting room.
We'll get started in just a minute.
Hi welcome. We're just waiting for folks to join, and we'll get started in just a minute. So we're going to go ahead and get started. So welcome, and Thank you for attending this webinar.
The purpose of our webinar today is to give you a look at how the platform, the insights platform works that we have done our compensation and benchmarking survey for. And specifically we're going to focus on study today. As a participant in the study in organizations got an executive summary, which is basically sort of a static PowerPoint of the data. In aggregate, that was reported in the study.
But for the enterprise level subscriptions that we offer, you actually can see dynamic data that's filterable. And you have the opportunity to, look at things in different ways. So filter the data for specific sizes of organizations and different roles and things like that. So we want to give you an opportunity to see what that looks like. Whether you've already purchased the enterprise package, and you want a little better idea of how to find what you're looking for, or if you're considering purchasing the enterprise practice and you want to see a little bit under the hood before you make that investment.
So with us today we have Nicole Succop and she is with dynamic benchmarking. And she is the person who I worked with to develop this study. And I'm really excited to have her here today because she really has a great command of the system and can kind of show you different ways to get at the questions that you're hoping to get answered by looking at the data.
So before we start off, I just want to remind everybody that to access the data, if you are a participant or a non-participant that is going to upgrade to the enterprise version of the study, you just go to Insights asp.net org and you actually just click on participant login, even if you're not a participant, and then you're going to from there put in your SSP credentials so your SSP username and password to the same one that you use to access any of our other services.
Whether you're a member or not. So just keep that in mind as you're after today when you go to log in to the system. So I'm actually going to hand it over to Nicole now. And she's going to take us for a drive-through the system. Awesome Thanks, Melanie. Thanks, everybody, for having me. I am definitely looking forward to giving you a show here. So as was mentioned, I'm going to go ahead and share my screen.
We are going to go through the site itself and hopefully focus less on navigation throughout the site, and more for looking at results and how to review some different comparison type data. Look at your different reports and such, and maybe how to apply some filters and make this data, I guess, kind of work for you. So a couple of comments really quickly just to get us started.
And I will try to reiterate this throughout just in case. But any of the access that we're going to be looking at within the system, I am going to be logged into a test account for sharing and administrative purposes. So anywhere that you happen to see any personalized data, just to be very clear, it is fictitious data. It's not anyone else's data in particular, and it is excluded from actual reporting of the data set.
So just so that that's up front, Additionally, I will touch on some support information as well as some basic information around how the platform works, how the tool works with reporting out aggregate versus disaggregate information. Pretty much everything is aggregate. There are a few scenarios that we look at things in a disaggregated format, but they are kind of few and far between.
Otherwise, when it comes to your actual data, content and such. Aggregate output is what the system is designed to do, and the purpose for that is to not only, keep your data intact and anonymous. Right so you're the only one seeing your account's data individually speaking, but it also is giving you, in a comparison format, a better use of the data points right over averages, for example, a median and an average being two different things.
You've now got some percentiles with your median. So that you can really see, OK on the low end, on the high end, and like where do I fall? And then what are you going to do with that. So again, I will touch on a few of those things as we continue to move through the system. But just to make sure everybody knows I'm in a test account for today's purposes. Excuse me.
So as mentioned in the sign in. So there are two different versions, not versions logins to this platform. The reason between the two different ones is because, as Melanie mentioned, the participant login block is what you are using because it uses your SSP credentials. This admin login block is for me, right? Or for Melanie et cetera so that we can assist you should you have any individual errors or challenges or anything along those lines.
So with that login, what typically would happen for you is you're going to be directed right to your SSP page to use those credentials. This, once you perform a sign in, will take you back into the live platform. This is what identifies for you within the platform at your entry, who you are, what permissions do you have, and to make sure that that information is kept secure in the appropriate accounts, et cetera.
Now at first login you're going to come to a pop up welcome message, if you will, at any point in time. If you click out of that message, you can always come back to it by using that link right up here. The nice thing about this message is it's going to give you it's a dynamic box that's going to give you an update for your account at the time of login.
So it's going to tell you where we are as far as data collection results available in future years, there will be additional years of data collection and results available, et cetera it's going to tell you what kind of permissions you have. Now again, I'm in a test account. We do have everything open and turned on for me in this test account, so we are not currently collecting 2024 data, just to be clear on that.
But ultimately, for our purposes today, I have everything open and on. So this will be an updated message that may look a little bit different at your next login. And then a couple of just additional resources here. There's other places to find these throughout the page, but again, really easy for you to identify what part of the process. We are in at the time that you've logged in.
The other piece, from an organization perspective, that's going to be really important to pay attention to. And you can see that here in the pop up as well as on your home dashboard page. Right up here under the header image is to know which account you are signed into. Now, the reason I mentioned that is because with all of our organization accounts, there are individuals that are associated with those accounts.
If you are an individual that also which is most likely is working in this publishing field, you may have your own personal data that you chose or could choose in the future to participate on the individual side of the study if you wish to. Point being, they are two different accounts. Easiest way to think about that is organization. Data is a lot about policies, compensation standards, things like that.
Where individual is what's actual. What were you actually paid? What was your actual bonus last year? What is your position title specifically or your exact roles? And then also think about what if you changed jobs and you change organizations, right. We can keep your personal information and access intact in your personal individual account, while then separating the organization account and moving access for that organization data to the other appropriate org contacts.
So again today we're going to focus on the organization data. A lot of applying filters and such is going to be comparable on the individual side. But it is a little bit it more complex, I would say on the organization side, which is a lot of the reason that we're going to focus on that for you today. So I'm going to click into, Oh, one step backwards between the two studies.
They technically are all within the same platform of course. So it is a very similar study. The questions are similar between the two sides where they are comparable for organizations. In any case, organizations will not have participated on the individual side of the study. And that again, is by design, right? An organization does not have personal work satisfaction levels about any one position or another.
That that's not the way this does. The study is designed, of course. So just keep in mind that as an organization, when you're looking at this home dashboard, you'll have access to both halves, because when you upgrade to any of the enterprise or above organization. Purchase upgrades.
And when you upgrade to one of those, you also get access to the data that's available on the individual side of the platform. So that's what you've got these two differences on this home dashboard for. Lastly, as an organization authorized representative, if you do, in fact, have your own personal account with or without data in any of these cases, this drop down up here is how you could easily switch between your two accounts.
And I say two because it is assumed that you'd only really be connected with two being your organization or your individual account. And that's how you'll see them named. Of course, I've got a bunch of test accounts here, but you would see for me it would be Nicole Succop, and then it would also be test administrative as my account organization. Sorry organization account.
And you can switch this view at any point in time. When you're in the page, you don't have to be back on this home dashboard. But the point in showing this is that on the individual side, you'll never have access to the organization data. So if you happen to be in your personal account, you've got to switch back to your org account to get to the organization data.
In the organization side, this should look pretty familiar, especially for those that are our participants. Again, I'm in a test account, so we've bypassed any of this information here that says that I don't yet have access, because I do a couple of quick notes about some of the things in here. Participation is based on a couple of things. When we talk about participation and a threshold, there is a progress threshold that we have the system designed to identify for our participants.
And the point for that is really you give some data to get some data right. If we don't receive data in the first place, we then don't have data output that we can share with you and share with our other participants, et cetera. So we've got it designed that way so that again, we get data to be able to share data. But in addition to that, there are certain metrics that maybe are higher on the priority list, if you will, whether it be for SSP themselves as the organization or for other individual users or individual organizations.
As we help to compile this question set back in the beginning of the study, there are certain questions that we know. Or in other words, we're able to indicate with required status to say that these are at a minimum, right. It's a Comp and benefit study. So we wanted some basic demographic details to be able to apply some filters, make it useful, make the data bit more useful for you when it comes to looking at your comparisons and such.
And then also for some of the, you know, bottom line Comp and benefit study. So those are the data points that you're going to see most of the time is where you're requirements fall in naturally because of the difference in threshold and because of the difference in the requirement levels and such throughout the platform. Not every question is answered by every user, right? So keep that in mind when you are looking at your results.
And when we are looking at number of responses, perhaps on individual metrics or individual sections, because again, required doesn't mean it's been answered by 100% of participants. Required simply means in order to get to the progress threshold and gain access to basic level results required is one of those questions that had to be answered doesn't mean that it was always answered. The same is true with anything that's not required.
Not every question is applicable to every individual or every organization, so there will be some metrics that are just naturally less responded to. Think about, for example like a describe other. Well, if you didn't mark other in the previous question, you have no need to use the describe other question as a fill in box. OK, so let me just double check a couple of points on this home page.
From a support perspective, I did also just want to identify that for you individually or for anybody else in your organization. If there's any information that you're looking for most specifically about how the data output is presented and our data integrity, et cetera, that information is all able to be reviewed on the Support link right from your home dashboard. So really from anywhere within the site, you can click on that support OhioLINK and you can review some information.
It does give you a pretty good breakdown about percentiles, et cetera and where the data is stored. All of that if you have questions, we do have support information here that, whether it's with SSP or myself, we'll make sure that we can get you the answers that you should need. All right. So without further ado, I'm going to go ahead in.
I'm going to start on the Reports tab because I think it's a little bit easier to explain, at least initially. So what I want you to see here is you've got this box up top and this box is called our it's a filter box right. So ultimately again I'm in a test account. I'm also in an administrative account. So some of these filters may not truly be accessible for you. These will depend on your level of account permissions.
So organization basic has limited filters, limited reporting, limited comparisons, and with the upgrade you do gain access to more information and more. I would say functional information as far as being able to drill down with additional filters. When you apply any of these filters up in this box, they're going to automatically apply to the reports below. These reports below are pre-designed by SSP identified for you as the participant, as the upgrade purchase, whatever that may be, they're pre-designed to give you.
It's all the same data throughout the platform, but predetermined as far as charts and the way the visual representation of this data. These filters will automatically apply to the reports as you download them. And this is as simple as clicking Edit, making whatever possible selections you could want about a lot of these are about position, which we're going to get to. But if you wanted to even just look at some basic demographics, and maybe you only wanted to pull a report based on that benefits package options based on like size organizations, for example.
You could do so by setting your filters and then downloading a report. These reports are template. So they're going to look at first glance, the same every time you download them. But they are going to be different. And there is a cover page in all cases that tells you the date that you pulled it, as well as the year of collection that you pulled it for in the future will have multiple years as options, and then also any filters that were applied at the time you downloaded the report.
So that's that cover page is going to really indicate for you. If there are differences in the data sets side by side, they may not look drastically different. That cover page is going to tell you what it is that you're actually looking at. Now, I'm going to clear this out and move over to comparisons. This is where we're going to spend most of the time. So in this view should look really similar to data entry.
Right comparison wise. What you're going to get here when you click into and I just went into organization profile to get us started. You've got your filter box again with your filter options. And then below you've got each of the individual questions as it was asked in data entry for the org study. And this is true for every section of comparisons. You go into the different tabs and the questions are listed below.
You can carry through just like we did in data entry, so you can easily go back to comparisons as a dashboard and take a look. But you can click into any of the areas that you want to focus on. I'm going to go back into profile for a bit. Again, you've got your filter options. What I want to highlight here is a couple of things to identify for you.
What you're seeing below. First and foremost, you've got this blurb right at the bottom of your filter box. It's going to give you an idea of how many questions are displayed on the page. So each section has its own number of questions. That number is going to vary. Some questions are not applicable for everyone. So keep that in mind that there may be results for less than the total number of questions on the page.
Again, that may occur. And that's OK. And then right here you're going to see an average number of responses per question. Now this is an average. The reason for that is because, again, not every question is answered by every participant. Not every question is applicable for every participant. So that said you are going to see this number shift from time to time too, especially as you continue to layer in or remove filters.
All of the data below. When you're seeing this input by metric, I'm going to go in order here. So we've got a numeric metric. And then we've got a multiple choice metric. And those are the two main metrics that we have that we're looking at resulting output in your numerics you're going to have on the left 25th percentile. In the middle is the median the 50th percentile.
And on the right is your 75th. So what you're not seeing is your lowest answer, and you're not seeing the highest answer, and you're not seeing any of the specific answers in between. But if you think about this being plotted on a scatter graph, for example, each, you know, each participant is marked in here somewhere, and then there's a line that connects them all. This indicates me data.
Personal data. Again, I'm in a test account. So this isn't anybody else's patent. It's just fictitious. But on your organization page, you're going to see your data provided that you answered data. If you did not answer any specific metric, you still would have access. Again, assuming that you've got access to the appropriate charts and filters, et cetera, you'll have access to be able to review this chart, for example, but you just won't have a my organization data point on it because you didn't respond.
That's also what this text in blue above the line is showing is the me data. So any time that that data, again, is different, it's going to show you what your response was. And then the percentile roughly that you're falling around from a multiple choice perspective. Blue is my answer. But this is of the times. It's the selection rate.
Right so by all of our for all of our participants, we can see that us was selected 76% of the time, in many of these countries weren't selected at all. Not surprising for this year. I'm going to scroll down past these. The same is true when we come into US states. You can see where I marked our response versus others. So you can also quickly see some states that maybe had better participation than others.
The reason I chose to start on this demographics section is because this area can help you. As you move through the results, to determine what filters, you may want to apply and what filters maybe would be useless in its own right. So let's just say and that's for this year, for this round of data collection, maybe I don't have any participation in Alaska, but I could make my own.
I could think through my own excuse me view of what would be comparable. Well Alaska maybe. I don't know, maybe shipping and receiving of products is. You know, that much more expensive from a logistics perspective. OK, well, where else would that be? Common, right. Alaska, Hawaii, et cetera.
You can choose what other filters or regions maybe you want to take a look at. Additionally, you've got some other multiple choice questions that while these above were choose one. I do actually believe type of organization is also choose one. There will come some options where it was a multiple choice. Choose all that apply or potentially choose up to three or something like that. Keep in mind that those percents are not always going to equal 100% And that's why.
So these are your selection rate options. And then again you've got a chart that populates for this. And here you can see just highlighted quickly in color. I chose this blue bar. This is the selection rate of the other options within the metric. OK from here I want to switch gears and go to comparisons because this is where we're really going to see some.
Complexity OK. So again, you've still got your options for your filter box up top. But what I want to indicate or to show you here that's a little bit different is this box right here that's kind of a separator if you will. If you remember when we were doing data entry, we asked for participants to enter a number of positions in their organization that they plan to report compensation data about one section being non-supervisory so they don't oversee staff, a different section being supervisory meaning indicating that they would oversee staff.
And of course, we've got a difference in levels of the hierarchy of an organization between both. Now for today's conversation, these two tabs, compensation per position, they're going to operate really similarly. I just wanted to we're going to focus on one at a time. So that it doesn't get too complex or too confusing. Now for the time being I've got no filters applied. What you're automatically seeing is here's that dropdown list from a non-supervisory positions perspective of which position options I guess, job title, if you will, were initially available.
As I make a selection to each of these additional options you see here, my data has updated. So first and foremost moving from manager to supervisor. Manager had data available for show because we saw it. We saw lines and I'm going to go back to it so you can see it again. Versus supervisor on this side does not. That's why you see this hourglass here. It means that there's not enough data available to be able to show some sort of graphical output.
Now again, keep in mind all that means is of our participants. There were not five or more that said they had a specifically titled supervisor in the non-supervisory positions that were available. It doesn't mean that there isn't this type of level of position available elsewhere. What we're doing when we looked at these positions and the categories, and this is true on the individual side as well.
We recognize that not every position title is going to be a one to one right. You may have a director of marketing. You may have a marketing director. Technically, they are the same, but when you're looking at them from a categorical standpoint and from the statistics of like a data input and output site, they are different. So what we really wanted to hone in on was more about of overall level of a hierarchy compiled with or maybe compounded with roles, job responsibilities, and maybe overall function or specialty, if you will.
So as we work through each of these different position titles, if you will, you're going to then see the breakdown of reported managers with what function areas, what specialty areas and focus, et cetera and you're able to do that for each of these different options. Now, this is what I would refer to as a forced filter, meaning I don't have any of my optional filters selected above.
I have not drilled down into any of this data. I have not said I want to see managers in just my region of the US, or just in the US compared to other countries. I haven't applied any of those filters. The only filter that is applied is by this position level so far. You can layer additional filters, but as this is our inaugural year of data collection we have, it's definitely a ramp up for data collection.
And for the amount of time that we had for data collection being available. And also a lot of times people don't understand what's going to be available, which is what I'm showing you today. And so hopefully that will continue to lead to more participation in the future. Now, as our participants of our first year, you'll have a couple added benefits in the future that others will not have.
And that's just even simply in data entry, not just in reporting output, because you'll now have, as a participant two years you would have trends or potentially starting trends for yourself as well as for overall participants. Right so maybe state of the industry. But from a data entry perspective, there's also going to be an option for you in the future to copy prior year data and then edit from there.
Point being, maybe you don't have significant changes in your staffing levels from year to year. Great copy over last year's data and then tweak it from there. Your benefits packages haven't changed. Maybe the actual compensation in a position hasn't changed, or only what changed was actual bonus payout or something along those lines. You'll have that as an added feature, which will hopefully make data entry for you a little bit more efficient, time efficient for you in the future.
Lastly, what I want to point out is the differences that you're seeing on these pages. So it's really similar to what I mentioned earlier, but you have two different data sets that you're looking at. The reason being as a participant, if I said I have, I don't know, six positions non-supervisory that I was going to enter about and maybe I have two sales managers and then two marketing and communication managers.
Titles could be whatever they actually are, but in general I have two of each. When I was reporting this data, I was identifying how many positions of similar type I was entering data for. So what you're seeing here, granted, in blue, I don't actually have any data entered, but if you do, you're going to see your responses in blue compared to the aggregate of all responses in Black.
And you're going to see that as you change for each position. Same thing. These will filter and update sorry. These will update when you apply filters. And then there's one numeric for all positions that is going to look similar but a little different. And actually I made myself a note here. I want to look at one that does have data. So you can really see the difference between what that looks like.
Sorry give me a second. I want to switch gears. I'm switching over to our supervisory positions and I'm going to switch to Director. So you see, now I've got directors and a couple of different fields, and I've got my different selection rates compared to others. And when I go down here OK.
So again, you've got your percentiles. What you're seeing here is the difference similar to your multiple choice. You're seeing my test account fictitious data up top. And you're seeing the aggregate of all others below. So this box is just a little bit more complex than a standard 25th, 50th and 75th percentile. With my data point referenced, it's got my combination of data points.
So in this case, I had a number of directors. Looks like I had probably one, two, three, maybe four directors that I entered information about. And so I have potentially 4 or 5 or multiple more data points. My percentiles are up top and everyone else is in the bottom. Again, you're looking at this from percentile view. And then in a scatter report you have a little bit of flexibility.
Here you can see a single line, which is just all data points not included not necessarily indicating mine or two. How do mine compare to everyone else? Just different views in how you want to look at it. I think I also want to switch gears. Let me just pause there really quickly and see if there are. We can come back and do a couple of live examples here in a minute, but are there questions for me to dig in here before I look at countries and benefits, which is similar but different?
Nicole, could you show like use one of the optional filters for, for instance, the functional area on director just so they can see how they can isolate that down to a specific role. So what I would recommend is actually kind of without filters, taking a look first so that you have a more pinpointed target as you're looking. OK so for those that have entered data with directors again I've got no filters currently applied.
I'm going to scroll down and I can see, OK, I know how I responded fine. But let's look at everybody else. Well, I don't necessarily want to apply a functional area of analyst because I don't have data to review there, but I do have data to review in it or in publications and publishing, et cetera. And then if I wanted to add an additional filter on that, you would do the same thing and do a comparison down here with specialty area.
And focus. But let's start there. So I'm going to go ahead and apply just the publications, which makes exact sense for your industry. But now we're looking at supervisory positions, so do keep that in mind as well. You could apply a data filter for non-supervisory function. But that is not going to indicate data about a supervisory position.
So just do keep that in mind that sort of looks like there's duplicates but they are different. I'm going to find my publications and publishing. Oh and this brings a good point. You will have green text that pops up in your filter box that says, there's enough data below for you to see some output or to download a report. Perhaps that's filtered in comparison to not filtered. You will have red text when it says you've gone and drilled in too far, applied too many filters, or maybe conflicting filters that would never possibly be marked the same.
This is going to be an automatic shift. We see that our responses per question has changed. And now when I. Oh, right. OK when I scroll down, it may not look like it at first, but when you look at the percents here they have shifted some. Now keep in mind this is a question that was multiple choice. Choose no more than three.
So you're not going to see just 100% of publications in publishing because some positions, some organizations I should say reported director positions that said, well, those that have publications or publishing as one of their function areas also oversee which other function areas. So you can drill down a little bit further and then let's apply one additional let's also apply operations.
And so again, it's shifted even further. You can apply. OK so of directors that oversee for example, operations and publications. OK I can see, you know, they might be split between journals and media community et cetera. What kind of scope? So again, all of that data below is continuing to update for you as you apply different filters.
A little background here in terms of why we set up the job titles that way. As you know, there are quite a variety of job titles within our organization and quite a variety of size of organization. And it's common that the smaller the organization, the more hats a particular individual in a role where. So it's very difficult to compare, for instance, a director of publishing for a small society publisher that has one journal and that person may wear many hats.
Versus the same role in a large commercial publisher, for instance. So this allows us to actually really look at those roles in terms of what they actually do, and not just in what is a maybe arbitrary title in some cases, or the best fit kind of title. So it really helps us see a little bit better about what that role is doing as opposed to, you know, what a job title is.
That may or may not really be a direct comparison to the job that you're looking to compare it to. Yep and to be very clear, again, when you're applying those filters, this information is shifting down here as well. So we know that from a compensation and benefits study. Compensation is half of the name for a reason. Because what you're trying to identify is OK. Well with not just tier or level within the hierarchy, but also with responsibilities.
Are you a manager of simple one area of media or do you have multiple hats? Do you also oversee something within it or something in accounting, etc.? Point being, you're also going to see then not just their responsibilities, but what shift, if any may occur. Yes, within your organization, although you probably already are aware of that, but more so for the rest of the industry and the rest of participants.
What shift occurs in compensation also based on the shift in those responsibilities and roles? So you're going to see all of that information shift automatically. Now the last thing I'll point out, though, about these filters, which is useful, let's say you've honed in on some data, you've messed around with filters, you've changed your tiers.
You've really come into finding some really good targeted information and you want to take it with you. This would be the time that you're going to flip-flop over to a downloaded report, right? So basically what you've done is you've identified whatever data points you're looking for. You could certainly screenshot or take notes of that information on your own. But these filters and such are all going to remain there sticky.
So when you set your filters on one side, you'll see I've got operations and publication here. I'm going to switch over to reports and that is still applied. So if I wanted, let's say I've already identified a report that's really useful. But then I wanted to apply some filters, found some really interesting information with the different filters to look at the live comparisons.
Now I'm going to go back and download the report, take it with me, share it with other team members as you see fit, et cetera. The one thing to keep in mind, especially when it comes to compensation. If, depending on your permission levels. There are scenarios where some of these reports graphically, as well as just analytically in an Excel report, for example, are going to include your organization data.
So just be cautious of that. It it it could be sensitive data that may or may not be something aggregate. As far as industry information you likely would want to share or be more willing to share with more folks in your team. Whereas you don't necessarily want to give away information about a singular individual and what their compensation is within your organization, to the wrong people.
I am going to switch gears once more, and I'm going to clear these filters out really quickly just to show you once more how I did that, because I sometimes do things too quickly. Sorry when you're in your filters and wanting to edit, it's you can. One by one remove singular filters and reapply others. Or you can simply clear and it will go back to scratch.
No filters applied so you can just start right back from the beginning. Now I switched over here to benefits by country, which would also then cover medical plans, dental and vision, et cetera I do want you to know that this setup is very similar to what we just experienced, with optional filters in a box and forced filters below, so I'm going to switch to medical plans.
For example, we have loads of different countries that were optional, and in the future we certainly expect that we're going to see more participants from more countries as we get more data collection. But in this first year, we have limited participation from countries outside of the US. So we did want to make sure that we noted in, and it is accurately commented on in the appropriate locations.
But in an effort to make it so that, you know, our outside of US participants were able to actually review some data where they might not otherwise be able to. So, for example, if I were to click well, really I'll just click anything, any of these, right? All of a sudden I've got no data available to show we didn't want to do that. Even if there were, let's just say four participants, but you're just shy of that fifth one to be able to see aggregate data.
So in other words, in these appropriately indicated places, there are reporting outputs that actually aggregate for you all participants for the countries. So everyone is combined all into one chart rather than the forced split which is live on the page by country. So again, just wanted to make sure that we were able to provide meaningful and useful output for all of our participants. Participants excuse me in this first year.
Again, in the future, we expect that we'll be able to have more live review available as we continue to gain more participation. And the only other point I would have on that, sorry, was about dollars, or I should say monetary wages and compensation. In all cases where you see monetary format, all monetary output has been converted to US dollars as it was our most chosen, most reported type of monetary input.
So everything has been converted to US dollars using the. US IRS 2024 final conversion, et cetera so for truer peer to peer comparison, we put all monetary figures in the same format. So hopefully that that's fairly easy to understand as well.
I think that covers everything I wanted to talk about. Melanie, do you have anything that I have missed in particular? I don't think so. I mean, I think that would be maybe a good time. If anybody has questions, just pop them in the chat or if you want to raise your hand, we can take you off of mute if it's easier to ask your question verbally.
And in the case that you don't have the question at this exact moment, but you come across it as you start getting into the nitty gritty of the details and applying some filters and downloading some reports. And don't forget we've got support contact information in that welcome message. You can find the SSP email also on your support tab.
You can always reach out with questions. We can especially I can review all data and I can actually review it from your perspective as a participant. So that, you know, I can help identify what are you seeing, what should you be seeing, what does this mean, et cetera or maybe how to get to that data. If you're not sure. And we're doing this, you know, this study for our community.
So if there's feedback that you have, we'd also love to hear that if you there's a data point that you would really like to see us include in the next iteration, we're going to do a data collection cycle in the second half of this year for 2025 data. We are committed to doing this for three years, so we want to make sure that becomes a meaningful tool for the folks that are in our community that want access to this type of data.
The other thing I think I put this in the chat, if there are multiple people in your organization that you want to have access to the platform, we can definitely do that. As Nicole also mentioned, just keep in mind they are seeing that any data that you input for your own organization, they're going to be able to see that as well. So that's your choice whether you want to add folks to the list of authorized users for your account.
But that is an option. And you can just simply email at asp.net org if you have additional folks that you want to have access to that. And then I think the other thing we didn't really talk about much, Nicole, was the fact that as an enterprise subscription description folder. You actually get access to all of that individual data as well. I think maybe you did mention it, but it's very but I was just thinking about it myself as well, wanting to just switch screens really quickly.
Again, I'm going to remain in my organization level account, just clicking on this header image. To get to this dashboard to see the two options, switch to your individual side. Now again, you won't necessarily have been a participant on the individual side, certainly not from organization data entry perspective. But when you think about positions and compensation, whoops.
I don't want to be in data entry. I want be in comparisons. Similar setup. You've got filters available. And then keep in mind this is exactly the same criteria that we just described on the org side for how individuals indicated their role and their responsibilities. So again applying filters, looking at data you can easily again I've got no filters applied on this individual side, and typically you won't have me data here either when you're on the org account, but you can see just right off the cuff what positions or what roles tend to play a higher, just higher choice across the board for those that reported.
So what's interesting here is what you're comparing between individual and organization. Data is a little bit of a tweak in how the question was asked, right. Organization standards, organization policies, including compensation policies, whether it be bonus potential, maybe relocation packages, anything along those lines. But then you're seeing on the individual side actual.
Right so keep in mind any kind of negotiation that a singular individual had with their organization. Maybe they negotiated outside of the company policy, but that in itself is also can be really interesting to see what is standard and maybe what is standardly reported. What are the differences there? What are the differences in expectation of responsibilities versus actual responsibilities, et cetera.
So when you're looking at that, it could be really interesting to compare. And the reason that Melanie chose SSP as a whole, the team chose to also offer for individual data is because naturally we have more overall participants on the individual side, because for every one organization you may have 50 or 100 employees, and that's 50 or 100 possible singular participants who enter data into the platform for us.
So the difference there is exactly as described organization policies and standards compared to individual actual reporting. So if you're not seeing enough data points in a particular compensation role on the organizational side, you can always pop over to the individual side, put in the same exact filters and see you might get some additional data points there that will help you round out your data set and your comparison.
Yep, Yep, just a couple different views. Same layout. As far as the comparisons versus reporting, different permissions are available again in different package options. So I know individual basic for example, is much more limited than what organization enterprise is able to get to on the individual side. So just from those perspectives, there are some really significant differences in those upgrade options for just how much data you can gain access to and just how much filtering you have available.
OK I think maybe last chance for questions here. Anybody have a question? Pop it in the chat. Pop it into Q&A. Raise your hand. Yeah and again from a feedback perspective about metrics. Also about the platform itself. If there's a difference in the way that you think, you know, you like one report chart, but you'd like to see it in a different format, or there might be more that we have to negotiate or have to look at to be able to make that work.
But it it's I certainly am not a professional on in your industry. So I know data and basics, and I can certainly tell you what this aggregate data represents. But the way that you want to use the data, the way you want to see the data presented back to you so that you can make better business decisions in the future, et cetera for yourself, personally or for your organization.
That feedback is all super welcomed, so that we can only improve as we continue to move into the next round of data collection in the future. All right. Well, Thank you so much, Nicole, for walking us through this. It's been such a pleasure working with you and dynamic benchmarking to put this together.
And we hope that our community really finds that this data is super valuable and that we can continue to make improvements in the reporting and the data collection process. So again, if you have any questions at all, you can reach out to me obviously, or the email address and we'll try to get you set up. If you haven't, if you want more information about the enterprise subscription, let us know.
We'll get you the information that you need. You can also find it on our website. And I can paste a link in the chat there as well where you can find that information. Yep there's no discounts for that as well. So please we want to make sure that we, you know, this is something that lives on and that continues to be a useful resource. So as much participation as we can get, that will definitely be a signal that it's a needed resource in our industry.
So Yeah. And then just be on the lookout. We will be opening the next round of data collection later on this year, and looking forward to having return participants as well as new participants. So tell your friends, get everybody in here. More data, the better, because that's how we'll really be able to use this tool. Well for you.
Absolutely Thanks so much, everyone, for joining us. Thank you Nicole, and hopefully we'll hear a few questions from you offline. Thank you so much. Thanks so much. Hi
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