Using StudentTracker & IPEDs to Understand Admissions Funnels Webinar Recording (43 minutes)

Furman University is a pathway school to post-graduate programs. Join David Eubanks, Assistant Vice President for Assessment and Institutional Effectiveness, who has used StudentTracker for more than 8 years, to hear how he combines the StudentTracker data in his research at Furman University to understand how Furman stacks up competitively in institutional value, learner pathways, and researches potential new programs to offer.

Resources

• R for the IR professional created by Furman University
• r/ThIRsdays created by David Eubanks, Furman University
• StudentTracker for Colleges & Universities (including systems of institutions) Knowledge Base

Video Transcript

[Jill Indugula]: I just wanted to say a hello to everyone and thank you all for being with us today. My name is Jill Indugula and I am the product manager for what we refer to as STCU, which is StudentTracker for Colleges and Universities. On the Clearinghouse side, I'm joined by two colleagues. Rita Mersdorf, who is our guru extraordinaire when it comes to these presentations and taking care of the logistics. So, we appreciate her. As well as Casey Wallen who is one of our regional relationship managers. We are thrilled that you were able to join us today and we are delighted to have as our guest uh David Eubanks which whom we refer to now affectionately as Dave as we've gotten to be friends and colleagues through this process. Dave is the assistant vice president for assessment and institutional research at Furman University and he's going to be uh talking to us about uh StudentTracker and IPEDs to understand admission funnels. So without further ado, we will turn it over to David. We will let you know that there is a chat feature and if Rita has any other tidbits that she needs to share, we'll invite her to chime in. But if you do have any questions, we do encourage you to pose those um in the appropriate window. Some of them we may answer directly and some of them we may take at the end of the presentation. So feel free to um let us know what you're thinking and what kind of questions you have and uh in the interim we'll go ahead and turn it over to Dave with our thanks again for being here.

[David Eubanks]: Great. Thank you all. Uh good afternoon. It's uh great to be here and fun to talk about stuff that we're all interested in. I'd like to thank the Clearinghouse and Jill and Casey and Rita for making this possible, especially Rita for all the technical stuff and um delighted to be here and looking forward to this. Um this conversation is about, you know, connecting things. I like to uh joke when people ask me what I do. I tell them sometimes I tell them that my job is to be the director of exotic plumbing, you know, uh implying that there's all these crazy pipes at the university and somebody's got to make sure that the hot water stays wired together and that it's meaningful somehow. That's what data work sometimes feels like. And this talk is about that. It's about connecting u a couple of things that on their own are interesting, but when you put them together, it's kind of magic. So, oh, I should mention before I get going too far that I created a uh discussion board on social media uh a free place on Reddit and I'm going to post with permission the uh link to it in the chat before I forget. You can find the slides there if you want them later on or if you want to give get them to somebody else conveniently. And I'll I'll watch that uh board in case you all have follow-up questions or you want to try something out and contribute to the conversation yourself. So,

so you know the Clearinghouse is really a unique source of data for an institution. There's nowhere else to get certain kinds of information. Not only that, it's very high quality in terms of the reliability of it and the completeness of it. So it's really an extraordinary kind of data source that you know I think most most industries that are using data to make decisions would be very envious of our ability to track what happens to students to see you know plausibly what was our effect in those trajectories is quite amazing and sometimes it could be overwhelming because there's so much choice and so much data. So I'm going to narrow the scope to talk about one particular type of data that we use out of query and it's a declined admissions query. Now I want to say up front that I am by no means an expert on all this stuff. So there are experts uh on on this call that can jump in if I say something that's wrong. Um but declined admissions can um can mean a couple of things. It could mean that we declined the students admissions material or it could mean that we admitted them and they declined us. They didn't... they chose not to attend. And I will talk about the latter case. And I'm going to call those for clarity. I'm going to call those lost admits. So we we had students apply. We like the qualifications. We made we said yes, you're admitted, but then ultimately they didn't enroll. And typically, you know, the percentage of admits who choose not to attend is going to be greater than 50% at most institutions. So, it's a sizable data set. So, um we we know who those students are and what the Clearinghouse helps us understand is where did they go? They if they didn't come to our institution, um many of them, most of them probably will go somewhere else to some other institution. What are the characteristics of that institution that they went to and how how does that help us understand our admissions process? A frequent way to do that would be to just look at the top schools in that list. You know, here's our top 10 cross admit schools or something like that and then try to infer from that some generalized uh thing, you know, conclusion generalized conclusion about loss of admits. But in fact, it's it's like a power distribution or something. You got few schools where a lot of students will go, but then you got this long tail, lots and lots of schools where only a few students go. We don't want to ignore all the data. So that's one of the plumbing pieces, the National Student Clearinghouse. Another one is we know things about the uh students because they applied. And you can see on the right there we use slate and you can see some of the data fields that we collect from Slate admissions materials, common app for us as a primary source. And all of this gets sucked into our data warehouse and institutional research that we can pull it out by student ID. So all the things you normally think of their academic qualifications, demographics, some financial aid type information, where they're from, and so on. And for us in our context, we're primarily interested in first-time full-time freshmen. That may not be the case for you, but just so you could contextualize uh where I'm coming from from this talk, it's mostly going to be concerned with first-time full-time freshman or cohort students.

So, that's two sources of data, Clearinghouse and our internal admissions data. The third source is the IPEDS' gigantic database of nearly incomprehensible data. At least that's how I tend to think of it when I start looking at these massive tables and then try to sort through the dictionary definitions. Um I'm sure there are people out there who are much more proficient using the IPEDS data than I am and I'd love to meet you all and buy you a coffee. But um there are some kind of simple things that we can get out of IPEDS without too much work like graduation rates. Um and you know if there's a little bit more work we can get like tuition and um net institute or institutional aid averages so we can find a net net revenue or net cost um sort of calculation. So those are the three data sources and here's how we connect them. Here's the plumbing. Our admit data has a student ID. It's unique. We send that to the Clearinghouse when we do that query for lost admits, we would send it, you know, usually about this time of year because it takes a while. And um, you know, Jill or Casey or Rita, maybe you can tell me. Um, I don't know exactly when institutions get everything updated. you know, they're getting a new class come in. They've reported that to the Clearinghouse, but I know that doesn't take place instantly. So, I like to wait a little while to try to let institutions catch up. But we send the uh student ID along with request and what we get back is the most interesting piece of information. What we get back is where did that student go, which is an OPEID. Um there's a crosswalk in IPEDS, so you can either use that ID or turn it into the IPEDS' unit ID. Um and then once we've made that connection and connect all these pipes together, then we've got access to everything we know from IPEDS about those schools. Of course, it's a couple years out of date typically, but that many of these indicators like graduation rate, cost, so forth don't change instantly. So, uh, you know, we can accept that. And if you have questions, please go ahead and post them in the chat as we go along. I think we've got time to discuss these ideas. Something I say doesn't make sense or you've got to follow them. So, please. All right. So, here's an illustration of what we might get. We can put together these pieces and draw pictures.

Uh what this particular picture is is our assessment and I want to say upfront that these slides that I'm showing you with data on them are partly real data and partly made up data because I don't want to give away all of Furman's secrets obviously. Um but there's you know this is real data but it's got some hypothetical stuff thrown in for the sake of discussion. So, um, for us, the admissions department assigns a rating based on high school transcripts, standardized tests, admissions ratings, interviews, all that. I'm not sure what goes all into that secret sauce, but it's a 2 to 7 scale. Uh, these charts were created in R using GG plot. Um, you might have a different scale for admissions. You might use standardized tests. You might use high school GPA that's normalized somehow or something else. There are multiple ways that you can create the admissions scale. Surprisingly to me, the admissions rating that admissions creates is actually pretty good at things like predicting a first year college GPA and so forth. So, so I'm just going to use that. Two is low and seven is high. So that means that generally on average students with higher ratings are going to be more academically prepared for college and therefore will have more opportunities may have opportunity to get um merit aid and so they are generally the ones that are the most desirable and the most elite type. The two lines here are kind of crude interpolence of the blue dots and the red dots. Those represent individual institutions that uh our lost admits attended. So every dot on there is some college that we lost students to at the end of the admission cycle. The blue ones are public, the pink ones are private. Now imagine well let me say what the y- axis is. The y- axis is four-year graduation rate which is a quick proxy for institutional success rates. It correlates with um within the privates it correlates with US news rank and endowment. It correlates at the socioeconomic level with outcomes of students like career earnings and things like that. So, it's a quick although obviously approximate proxy for institutional quality in that in those characteristics I just described. Um wouldn't work for a community college that doesn't care about graduation rates and transfer but for our our sort of you know institution it does make sense. So the dash line is a hypothetical institution that represents us in this scenario. So that's our school that we're talking about. Let's imagine that we're a public university and this is our admissions picture. That dash line kind of goes through the middle of the publics. Most of the publics are a little bit below, but there are some that are above. And then we've got a bunch of private competition we're losing students to at on that pink line up above. So there's a couple of really interesting questions here. First of all, why should there be two lines at all? Why wouldn't they overlap? That would be a different line for publics than privates. Four-year grad rate is pulled from the admissions rating is assigned on our end during the admissions process. You might you probably have something equivalent maybe based on high school GPA or something.

I use four-year graduate, by the way, because it's the one with the least lag. I want to use six year because then it's two years older. Anyway, in my uh in my thinking about this, probably there's a couple of things going on that make these two lines separate. One is that the private colleges might have different or more stringent admissions requirements. So some students couldn't get in perhaps or I think probably more significant is the cost difference on average between private supplies. If that's the case, if that's significant, it suggests that students are opting for uh cost savings and what they're giving up is some graduation rate. And if so, that's a really interesting insight into our admissions process. got students who are on that bubble of deciding is the value of the private institution worth it given what I have to cost what I have to pay and some of the follow-up questions might be how are we making the case but for the moment imagine we're a public institution so we look okay compared to the other publics in here but we're losing a lot of students to the privates. That might be a good thing because those schools schools are on average a lot better than we are graduation rate. So how could we attract some small share of those students perhaps? Maybe we could argue to them, hey, yeah, our graduation rates this, but look, we've got this honors program. If you look at just the honors program, it's small and it the students are very successful and you're getting essentially a private education in this honors program. It's just as good as what you get at one of those redline schools except you're paying $20,000 less a year. Have I convinced you yet? You know, this is all sort of made up at this point, but I I'm just illustrating the kinds of conversations that we could have between institutional research and enrollment about this kind of data that goes way beyond what's our top three schools and how do we stack up against them. Got another one of these later that I'll present as a quiz so you can decipher it for yourself. So I talked quite a bit about cost but cost isn't actually evident on this graph, we're just inferring a cost difference being positive here. We can actually look at cost also willing to dig into the IPEDS data a little bit more. Um, there are some design choices and I'm not sure I've got the right one here. But what I did was I looked at the total cost sticker price plus room and board fees, added all that stuff up out of IPEDS, and then subtracted average institutional aid. That's like the discount, funny money that we give back, so to speak. And so that represents kind of a real cost, but it's a real cost with an asterisk because it doesn't include like PELL grants, state grants, um, stuff like that that's paid but not institutional.

Oh, how do I distinguish public private? That's a field in the um, IPEDS directory table.

I uh the Clearinghouse I believe actually does return that but you can um this is a great question actually. In addition to breaking up the data by public and private in the IPEDS characteristic or directory table there are all several breakdowns by Carnegie classification by size, by rural city, you know rural urban all sorts of things. So if you wanted to break this, you know, your your your individual situation may be such that you would want to see what's, you know, we're a small rural school. How do we compete against urban schools or how are we competing? Those kinds of questions are perhaps addressable by using some of those other characteristic variables. Great question. And again, it's linked because once you've got the school's unit ID, that unique identifier, then you've got access to all of that. Um, so here the four-year grad rate is still the y- axis. Each dot is still one school and each dot size is how many we lost to that particular school. But now the x-axis is cost. So it's kind of like cost versus benefit in a kind of a average approximate way. Um, and I've drawn in some hypothetical lines to show where our hypothetical institution is with respect. So, our pretend institution has a net cost of 30,000 a year. It's a private imagine it's a private institution. Now, we've got a graduation rate of 60 something. This um quadrants. The quadrants formed by that division are interesting because the schools to the lower right quadrant speckles in the lower right box are the ones that are more expensive than us and lower quality using graduation metric. So that's like the sweet spot. We'd like to see a whole lot of those because that's our opportunity to grow. The uh the threat is in the upper left box because those schools are cheaper than us and higher quality. And in this picture, there are a whole lot of those. So that's what should focus our attention in this particular graph. Why is it that those schools can offer a better education uh more cheaply? And again, I'm using broad strokes. Graduation rate isn't really quality, but just to get the conversation started, this is perhaps a place to begin. Um I also annotated this graph with aspirant schools. The gray boxes you see are the US News top 100 I think for liberal arts co national liberal arts colleges. So if you've got a set of you know like a peer group of schools that you'd like to be like you can annotate the graph this way too and this would say that well we might like to be like those schools and we're priced right but our graduation rates too low to compete with them.

Questions?

And again, we could make lots of different varieties of this graph. And uh I'm sure you have better ideas than I do. If you dig into this, I would love to see what you come up with. So, here's uh here's the quiz. Um,

let me let me pitch the quiz and then I'm going to read the chat. This is the same graph we saw earlier, but I I said imagine we're a public institution and the graduation rate was kind of smack in the middle of public institutions. Now, imagine that we're a private institution and the dotted line represents our graduation rate. If you looked at this and tried to make sense of it, what are some possible discussion points uh that we might have with the enrollment team? Give you a minute. There's no right or wrong answers here. We're just brainstorming. Um just as an exercise. If you haven't thought about that, please put it in the chat.

[Jill]: Dave, this is Jill. While uh folks are ruminating on that question, uh there was a a question posed uh asking whether you had any ideas for analyzing quote unquote um Oh, I see it disappeared. One second. Uh admission quality going forward with so many schools going test optional. Wondered if you had any thoughts on that. [Dave]: Oh, I didn't see that one. Um, right. The test optional thing. I don't know if that shows up in IPEDS. Maybe somebody else on the call knows if there's a um a data point that's being collected now that tells us about that.

Um, I'm trying to think maybe the percent of scores submitted. Don't know. Um, it's a great question because it is going to change how how schools assess the admissions attractiveness of candidates. We're we are um test optional school and around I'd say 15% of our admissions students who apply are applying as test optional. So it's defin that's a great question. I don't have a thought about it. I don't have a great answer for you.

Looking at the chat.

I have a method to normalize the data to account for the difference in student pools. Um

yeah so so definitely socioeconomic background is important to student success. No doubt that's a great uh a great idea. How could we take that into account? I think probably at that point we've got to make it more complicated than we can graph easily. So you know very often in life we got an outcome variable we care about that's caused by more than one thing. So, we would probably move to a regression model at that point and we could try to include in the regression model um the average family income for the county that the school's in or something like that. I've had some success with that method in modeling faculty salaries for example. Um haven't tried it with this. Um there probably is some IPEDS data like on percent power recipients that would get at socioeconomics. In fact, I think there are financial aid statistics in in income bands. Um that's not universally reported, but if you've got enough data on how many students are in each income band, then those could be explanatory variables in there. Yeah, that'd be great. I'd love to see a paper on that. [Jill]: Yeah, Dave, this is Jill again. There's been a request in the chat uh to ask if you don't mind when we're answering a question to please pose the question and then give the answer because the questions are coming in just to the panelists and the attendees are not able to see them. So, if we could give that would be helpful. [Dave]: Oh I'm sorry, yes. [Jill]: I wasn't thinking about that either, but somebody made that suggestion and it's a really good one. So, I just wanted to ask if you could do that going forward. Thank you so much.

[Dave]: Thank you. I see now on the two field it says all panelists. It doesn't say everybody. I apologize. Um the last question that I answered about socioeconomics was how could we take socioeconomics into account because it definitely affects graduation rates. Um I mean a stellar example would be Berea College. They intentionally recruit students from Appalachia as their mission and that definitely affects the graduation rate. Um, so I think there are probably some good inputs from IPEDS to take that into account. But as I said, we've got to at that point we probably want to start start making regression models and then figure out what to graph after that so we can communicate. Somebody says in the use of our predicted models, we found no significant no significant variables for family income. Yeah, it's going to depend probably on your particular pool. For us, uh, the most important student success indicator is academic preparation, but socioeconomics definitely figures.

Question. If we're using four-year grad rate in our quiz, would our our grad rate have a bar that matched

the differing levels from admissions two through five? Sort code.

I'm not sure what that question is exactly. If you can elaborate. Um, since I just made this up, it's possible that this scenario doesn't make any sense. But what I was thinking when I drew that dash line

um is that if most of our competition like in this case is uh you know if most of the students we lose are going to lower grad rate schools and we're kind of losing on quality and undoubtedly we're losing on price. We're just losing. It's not 100% that way, but we would like our competition, you know, to include significant number of schools that are better than us, so we have some market to aim for. Here, um, it looks like we've got some systemic problems maybe with financial aid, how we're, you know, how we're making offers to students or how we're selling that value proposition. Anyway, this is not probably not a great picture for a school to have that that diagram of the grad rate so far above students are actually going.

Reading.

Yeah. Okay. Does anybody else have um question? Okay, here's one. Do you get the class level StudentTracker data?

These are all from a particular entering class. Um, let me rephrase that. This is aggregated over several years of entering classes, but each dot is one admissions or is the accumulation of one admissions cycle. In other words, we're only looking at full-time, first-time freshmen, if that's the question.

Somebody mentioned another combination I should call out here.

Common say... somebody mentions that common data set has data elements like high school GPA that could be useful. And my dream is that somebody would create a database with an API of all common data sets so that I could just query it automatically pull that stuff down because it's not worth my time to go find all these CDS's and download them and figure out whether it's PDF or Excel and try to extract stuff. I have some code that can get the things or most of them, but extracting it is still beyond me. So if anybody wants to collaborate on that, give me a holler.

Someone says that IPEDS has a field for percent of new students submitting a score. So that gets back to the um test optional question. Great observation. I did not all that.

Oh, here's a really interesting observation. Does our graduation rate change for the admissions rating? Uh, instead of the flat dotted line, would your admissions rate have some slope? Wow, I didn't think of that. I mean, this this pays for the session right here. Of course, it would. So, our lower admissions ratings would have a lower graduation rate. So, our dotted line should actually be slope, too. When I get off this call, I'm going to go back and fix my report. So, that works. That's great. Thank you.

So that would that would definitely change our interpretation of some of these graphs and improve the visibility of uh how we could put all this paint all this data on one page by allowing our own admissions rating to influence graduation rate instead of presenting a gross average across there.

Oops.

[Jill]: Hi Dave, this is Jill again. So we have another question that says how have you handled small subsets of class data for example if you parse the data by major. You have any thoughts?

[Dave]: I haven't done yeah, so I have not tried to parse this by major. Honestly within the last year has been my first attempt to really dig into this data um it took some plumbing because I needed to get a data warehouse in shape to have all this stuff in it. Um I don't like to just dump a bunch of CSV files in a in a folder and then start to work anymore just don't have time for that. So I had to do a lot of prep work to get the like all the IPEDS data we now ingest into our data warehouse and then I built um uh functions that make it easy to get things like graduation rates and all of that plumbing took some time. So that's a great question. Could we disagregate by well not just major we could look at gender we could look at instate out of state um all of the things we know about students all the things we know about institutions lots and lots of interesting questions there.

So I'm scanning through the chat.

[Jill]: okay while you're reading. Oh, I was just going to say while you're reading, I'm really thrilled to see the the level of engagement and the questions and suggestions and ideas people are having. Like you said, this has been a very generative use of 35 minutes. So, we appreciate everybody's uh comments. Keep them coming.

[Dave]: I'm glad you snuck that in there, Jill, because I don't have a clock in front of me. So, I'm just going to move on. Um, this is sort of how we do the plumbing. There are lots of ways to do it. I have concluded that it's not worth my time to spend time in front of Excel much anymore. So I don't use Excel much anymore except to just pull up something and look at it because I want it to be reproducible and automated as much as possible. My metaphor is that a lot of IR work is like being in a leaky rowboat. We spend most of our time just bailing it, right? We don't get anywhere. And every once in a while between bales we grab the the ores and row a little bit. So I'm trying to move toward more rowing and less bailing. That means automating as much as possible where it's reasonable to do so. Um so we use R and R studio and in particular the tidyiverse set of functions makes R very understandable for IR people. Um there are lots of ways to get information about that. I do free workshops once in a while. Drop me a line if you're interested. Also, there is a National Student Clearinghouse library for R that preps the files, the upload files. Um, so you don't have to spend a lot of time trying to get that format just right. Last time I did this last week, ran one of these, my total time invested is probably five minutes. I just went in, changed a couple parameters, ran it, went to the FTP site, uploaded it. Um, and we use SQL Server for our database in the back end. The, um, one of the advantages of using R Studio is you can write reports right in the interface. So, you can see what's in front of you there's actually part of a report where the text is in there. And when you need a graph, you just specify how the graph is generated rather than generating it and sticking an image of it in there because inevitably it needs to be changed at some point and it's just quicker to specify how the graph gets generated or the table rather than going out and running some other process to generate it. Last thing I'll say is you can take all these ideas and um turn them into an interactive report engine. I I did this because I kept getting so many questions about US News and these strategic indicators. So, this is an app um that runs off of IPEDS data and I include the National Student Clearinghouse Lost Admit stuff. That's the connection to this talk. It's that dotted line on these graphs. So I can I can plot our trajectory over time with respect to per student cost of instruction for example in the top left graph there. And that because the board cares about US News a lot, I put US News rankings on average. And then I can also add where our lost admits are sort of where our local competition is at at the point where we are now. So all of that context is easy to put in one graph. And once we've got that infrastructure, we can grab basically anything that's in IPEDS, US News, the athletics database or or whatever else we want to connect it up to. So that's a bonus. So that's my contact. Please uh do reach out. I love to make new friends and learn new ideas. I've already got got some new ideas from your questions. So love to stay in touch. So, thanks again Jill and everyone at the clearing house for making this possible.

[Jill]: Absolutely. Um, and again, the lion share was done by Rita, so we really appreciate her work on this. Uh, we will stick around for a few minutes. It's it's me and Casey Wallen, one of our relationship managers, as well as Rita, and then we'll ask Dave to stay as long as he can. So, if you have any other questions, feel free to pose them in the chat box and we'll do our best to get you some help. And we want to thank Dave immensely for his time and and efforts and not only sharing this information but prepping and delivering today and being so attentive to uh to those questions. So, to him and and all of our participants, many thanks for your time today.

[Dave]: Thank you for all the nice comments. I I need to particularly thank Rita for making the slides pretty because my original slides were not very pretty.

[Rita]: We really appreciated it. And we do have a couple questions. The first one we might have already hit and I just missed it and didn't mark it off.

So, do you use Slate to pull IPEDS data into your warehouse? [Dave]: No. The slate data has an interface where I can pull in the admissions data. And then for IPEDS, I go to the IPEDS site and I grab one of the options is the access database which has all the tables in it. Um, this is not a very efficient way to do it. I've got to find a better way to do it. But so then we pull over all the IPEDS tables one at a time. I've got to automate that. Um there are other ways you could do it but they they are separate operations.

Um do I have any workshops coming up about using R to automate? Please drop me an email if you're interested in a workshop. Um these are very lightweight, no obligation. And I do 30 minutes at a time once a week. And um you can drop in if you like, drop out, whatever, whatever suits your schedule. And I record most of them so you can come back and work better.

[Jill]: I think you're our new star, David, because I see lots and lots of fabulous comments in the chat about thank you. This is great. Excellent work. So we are definitely indebted to you for your time today. Go ahead, Rita. Sorry.

[Rita]: And then we've already answered this. Um, the final report was constructed in R.

[Dave]: Yes, this is my folder with tickets. This is all the jobs we use. Jira, J I R I. I pay $11 a month for a ticket system so I can keep my sanity because I have a brain like a goldfish. Um, and so every every job like that lost in met report gets a folder. This is an R project and the tools we have and they're all free. These tools are amazing. Um, the problem is to stop bailing long enough to learn how to row more efficiently. That's our problem. That's why I hope as a community we can share ideas like the person there's an IR director who created that Clearinghouse R package which has saved me an enormous amount of time. I wrote to him and thanked him for it. But it's an example of how we can you know how we can collaborate to save each other's time.

Hey David, I see David and on there. Okay. So you can you can contact David Andre too because he is a wizard with this kind of stuff.

[Rita]: We're getting lots of positive comments thanking you. And then the other question we have is asking about is there an API for StudentTracker to automate the upload download of files?

[Jill]: Oh, I'm so glad somebody asked this question. Um, for those of you that haven't heard and don't know, we are working on what I like to call student tracker next generation. It is going to be released sometime next year, starting with the high school market and then the college market will come later. We're doing it in that order because we recently put some time and effort into a rewrite of the colleges. But one of the things we're doing in this product is evaluating our market's needs. What have we heard from you over the years about your pain points? Where are you having struggles? What kind of needs do you have that aren't being met? And we are very much trying to address them in this new product. It's going to be much more interactive, intuitive, uh easier to access. And one of the things that we have heard from folks is the upload and download process is very clunky. It's very rigid. It's very tedious. So, we are certainly looking into the file intake and upload process and API is at the top of that list. So, I can't make any commitments today about the when or or any of the details, but I will let you know that that is definitely something we've heard from the market in droves and it's high on the consideration list. So, stay tuned for more information on that. But, fantastic question.

[Rita]: All right, and those are all the questions I see. So, thank you all for joining us. Thank you so much, Dave, for presenting on this. And one of the things that Dave and all of us were talking about before we kicked off the webinar is how can we really form a community around this and offer more meaningful sessions to you. So, if you have any suggestions on that, please let us know and thank you all. Have a great rest of your day.

[Jill]: Absolutely. Thanks everyone.

[Casey]: Thanks Rita. Thanks Dave. Bye.