891: EdUp Mini Series, "The Currency of Change", Part 2 - with ⁠Dr. Jamie Brownlee-Turgeon⁠ & ⁠Andy Benis⁠⁠

Andy Benis: And welcome back everybody to episode two, part two of our six-part mini-series called "The Currency of Change" in this special edition of The EdUp Experience podcast. Once again, my name is Andy Benis and I am currently the Associate VP of Marketing and Communication at Los Angeles Pacific University, as well as interim VP of Enrollment. I guess I should tag that on for the time being. And my co-pilot for this mini-series is...

Dr. Jamie Brownlee-Turgeon: I am the Vice Provost of Operations for Graduate and Professional Studies at Point Loma Nazarene University. And for those of you that were here for part one, you know that's why I let Jamie make her own introduction because I'm guaranteed to... I've even had time since the last time and I didn't practice. So I'm never going to do it. Can we just establish that? It's fine. I got it. I know my name, title, school. I got it. I'm going to let you run with that every time.

Andy Benis: So last week we talked about the higher ed landscape, how quickly things are changing, how adaptable we need to be or become if we're not there yet. That was the "But we've always done it this way" episode and just kind of introduced the concept of how rapidly things are changing. The word "crisis" was used, the word "relationship" was used as far as how we start to lead change from the inside out. And since this is geared towards leaders in the higher ed space, you and I obviously have a bent towards marketing, enrollment and operations, but I think it speaks to anybody, whether again, I hate to draw those lines, but our industry always does the academic side of the house versus the operational side of the house, the administrative side of the house.

Andy Benis: But in this episode, we're talking about something that's universal to everyone, or at least should be. I know there are some who are allergic to it. So we've aptly named this episode "Numbers, Shmumbers." Again, we joke about so many in our industry being allergic to business phrases, right? Like revenue and market share and perspective, anything students or otherwise, we can't call them customers for crying out loud. We're a university. And I think numbers is another one of those areas that comes with some sensitivity, maybe some built-in bias, meaning you either love it or you hate it.

If you're a more logic, numbers, data-driven person just in life - and I don't mean you have to be an accountant professionally to be that, I just mean you see the world and you like things that add up and make sense and whatnot. And you're occasionally looking at your own checking account and liking that things add up. But as it pertains to higher education, and again, like it or not, the overwhelming majority of institutions are revenue-driven, tuition-driven, revenue-based institutions. There's money coming in, there's money going out. We're gonna go ahead and assume that none of your faculty work for free and that everybody's getting paid and that that money's coming from somewhere.

And again, unless your endowment is in the billions and you're living off the interest, God bless you. Feel free to fast forward. But for the rest of us mere mortals, we have to worry about numbers. Some of those are financial, but they all come from somewhere. New student enrollments, retention, persistence, you know, operational overhead, all that stuff. And so we're not going to dive into, because I can see, I can hear ears glossing over, what's the version of eyes glossing over? Ears closing shut? Not a pretty picture, no matter how you butcher that analogy, but there has to be some level of data-driven decision-making to borrow, again, a business phrase, but I don't, I can't think of an organization for-profit, non-profit, higher ed or otherwise that runs full speed ahead running their business without thinking through the data that affects that business, right?

I mean, maybe donut shops are an exception, but even there you gotta figure out how many donuts to make based on how many you sell on an average day so you're not wasting a ton of materials. So this issue of making data-driven decisions I think starts with that gut check. Is the culture of your institution one that does that? Overtly calls it that, calls it data-driven decision making, or is it just a taboo? We don't talk about data, let alone financial data. What's, Jamie, what's your experience in?

Jamie Brownlee-Turgeon: Kind of an interesting one. You know, I hear about my reputation, right? You always hear people say, you're known for...

Andy Benis: Are you allowed to complete that sentence here?

Jamie Brownlee-Turgeon: My name is attached to numbers, imagine that. I always tell people that's kind of my job, but I want to say it isn't that I just care about numbers. I didn't get into higher education because I cared about doing math. I got in because I care about students and seeing them progress and get their education and get their career of choice. How do I do that? I look at the numbers. It tells me if we're doing a good job. It tells me if I need to make a shift because I don't have enough students passing courses.

So the data gives me a picture of how I can better support students. It isn't that I just want to look at data and numbers and forget why I'm doing it. The why is I need to be doing something to help these students move forward, whether it's get accepted into the university or graduate. These numbers tell us how we are doing on that job.

Andy Benis: Yeah. And I think, and I love that you brought in the "why" piece of it, the stories behind why we do what we do. There's stories in every, at every level of a university, whatever piece of the puzzle you're fitting, the stories behind the scenes drive the heart of what we do, right? Always. The data just simply tells us how that's going. I had somebody once tell me, it was very much a creative type, literary, loves the written word, spoken word, music, all about the storytelling. And to them, and I'm, as much as I am a creative person, I also lean very heavily into the logic and math and numerical side of things. 'Cause I love things that add up and make sense and whatnot.

And we were having fun conversations on and off. And at some point, me sort of defending this issue of looking at data deeply, I was able to put it in their terms and say, hey, I look at data as telling a story because we're here for the same reasons. We're trying to accomplish the same things. And those are all life-driven, people-driven story, heart, goals, ambition, all that stuff. The data is simply a tool that tells us how that is or isn't working in any specific area.

Jamie Brownlee-Turgeon: Absolutely.

Andy Benis: So we've got billboards around town, running TV ads, radio ads, digital marketing. If somebody just says, hey, is it working? What's my answer? Well, my neighbor knows who we are, so it must be working, right? Do I just pull random anecdotes out of left field and say that's my answer? A lot of people do.

Jamie Brownlee-Turgeon: Right.

Andy Benis: And look, I'm not going to lie, there are things that are hard to attribute, right? I don't know if a billboard is driving people to the website more than anything else that has lapu.edu on it.

Jamie Brownlee-Turgeon: But I know, right, there you go, see I have to plug the URL. We'll get to yours later.

Andy Benis: It's, okay, so you can start with macro, you can go micro. I have my Google Analytics that tell me how many unique visitors I have for the website in a month. If I try something new, did that number go up or down? I can find out how many minutes on average people are spending on the website, which pages they're on, and if they bounce off the website, bounce rates are when people leave a website page, why? Which pages are they leaving the most frequently? Where are they spending the least amount of time?

Okay, so granted that the data behind the scenes is not sexy. I mean, you're just staring at dashboards and spreadsheets, but what the story tells me as a marketing guy, what I'm putting out to the public is or isn't resonating. What I'm doing to try to reach my potential audience, my prospective student audience, is resonating or it's not, or this resonated more than that. We're not going to get into A/B testing. We're not going to be that marketing specific, but it's those concepts.

Those numbers are telling me a story every bit as if as much I had a live person with an AI level intelligence who just said, hey, let me tell you a little story about what's working and not what's not working. And they just had a nice 15-minute conversation with me over a cup of coffee. It's the exact same thing, but in spreadsheet form. And it's on me to read it, think about it and decide what does this say that might impact my behavior. So take that out of the marketing role, install it into any area of operating an institution. Data-driven decisions are, you have to say mandatory. There's no way around not looking at what the data is telling you. What is your data saying?

I think even the question before that, and I'll let you segue another story on that, do you have the data you need? Right? You and I have both met folks in this industry, when asked a technical question or a really specific question, don't have that off the top of their heads. They don't know the numbers. I think we need to look into that. I don't know what those numbers are, right? Where do you go to get them? Which numbers should you have? Let's just dive into the whole data thing. First of all, where you go to get that data if you're at a particular institution, whether it's for your department or the university as a whole, where are you getting it and do you have the right data?

Jamie Brownlee-Turgeon: Yeah, I am really fortunate at pointloma.edu. PLNU.edu, let's just be overt. So I work really closely with our director of institutional research. We meet regularly and he's wonderful and really is, he is so engaged in getting us the data that we need. So if it's not the right angle, he's saying, what is it that you need for whatever you're doing? So we partner so closely together. I also have a director of data on my team that's able to pull it out of our CRM. And then I have a manager over academic operations that does more of the, some of the specialty projects in terms of looking at data.

But I'm really fortunate that I get to work with some great people to get the right data. I think one of my pet peeves around data is data for data's sake. And so people will ask me for data from someone on our team and I will say, well, why do you need it? What are you doing with it? Because I think there's also this, I need to have data, I need to be data-driven. So just give me everything. My reports need to look better. A table would look really good right here. A chart. A table would look good here, here. And I always say, well, like, why do you need this data? What are you gonna do with it? Because if it's just for curiosity, we don't have time. Because there's enough data out there to look at. So curiosity, that's great on your own time.

Or just because you want to say you have data, but you're not even sure what to do with it or what it means. So it's like, who needs to have what data? So is it telling you a story you need to hear right now?

Andy Benis: Yeah, absolutely.

Jamie Brownlee-Turgeon: So I think because there's so much data in higher education, don't waste time on the wrong data. Be specific about what it is that you are looking at. And for us, we launched last year the 4DX model for disciplines of execution, looking at leading indicators and lagging indicators. So every team, every department now has leading and lagging data points that they track regularly. And that is, you know, leading indicators. You got to look to say, I have a goal. Am I going to hit my goal? And at what point am I flagging, I might not hit my goal. And I need to tell someone, right?

So we meet regularly and every director reports out on their leading and lagging data points because what impacts success might impact our financial team. What impacts clearly with financial aid and packaging right now, what they're doing is going to impact XYZ. And so we share our leading indicators with each other and how we're progressing. And sometimes, I remember our financial aid said there was some hiccup and our success team goes, actually, I think we can help with that because we're having these conversations over here and we can just tack that on. And it was like perfect collaboration, but if we weren't sharing the data with each other, we would have never gotten to where we're resolving some issues on to ensure that we can reach our goal.

Andy Benis: Yeah. And if you're not all acknowledging the same starting point, then all you have is a room full of people offering well-intentioned advice, but it's not aimed at anything specific. To your point, why do you need this data? Are you aiming for something? Although, use the word goal, which might as well be the word target, which might as well be a business phrase, and you just violated all the higher ed academic laws again with this goal target stuff.

But I think you touched on something critical there and it's the difference. I don't wanna say data for data's sake. A lot of this is valuable. There's just so much of it. There's a difference and I think we can explore this a little bit between being data-driven or data-informed and data-inspired. There's so much stuff we do just to be compliant. The Department of Ed needs a bazillion things, the National Center for Education Statistics, right? We are all pumping out required, reported data to any number of agencies because they need it. And because for those of us that rely on, you know, Title IV funding, there's a phone book. Can I reference a phone book in 2024?

Jamie Brownlee-Turgeon: Okay, there's a very thick pile of paper.

Andy Benis: Yeah, you might need to explain that to a few. There are massive requirements in the area of reporting. And so most universities, or a lot of universities struggle just to keep up with the compliance reporting requirements, let alone the good to have, nice to have, this would help me make a decision about our future type of data. And so I think there are probably some folks out there who struggle with, I don't have a place to go to have somebody run a report for me that would tell me really interesting things that I need to know about my division or my program or my department to help us make some decisions. Or I can get it, but I need to put in a request like three months before I actually want the thing delivered to me. Your heart goes out to those folks.

But there's a big difference between the stuff we got to publish because it's required and the stuff we need to have to make data-informed strategic decisions.

Jamie Brownlee-Turgeon: So let me build on that. I always talk about there's three stages. There's compliance, efficiency and optimization. Compliance is what you just said. Like we have to report out that we have to do that. That is the must do. Efficiency gets it to where, hey, I'm able to tell a story about my department and how we're doing. And I can tell you that we're successful or not in whatever that may be. But optimization comes when it's my data is impacting your team and now we are collaborating together to say, now how do we fix it? That's using data for optimization. And I have an example of that because our -

Andy Benis: I thought you might.

Jamie Brownlee-Turgeon: I might, I know. I came in with some examples. No, our marketing and enrollment teams have been working closely the past few years together and marketing and enrollment don't always get along, but we do. And this last year, we changed our meeting structure and we changed one to called, it's a monthly meeting, it's called Data Analysis and Action. I specifically wanted that title so that it's not just analysis and we all leave the room, but it's data analysis and then action. What are we doing with it?

And so every month we come together and we probably have 30 KPIs between the two groups that we are looking at. That's a lot of data, but we have a dashboard and we flag it. So before the meeting, everyone flags. Hey, these are all of our leading indicators. What is green? We're gonna hit this goal. What's yellow? We need to pay attention and maybe do something. Red, we are not gonna hit our goal. Major action needs to take place. And we spend our time then between marketing enrollment saying, how can we help with that?

So if it's my team, can marketing do another email campaign to help nurture people along the pipeline? Is it at this point it's... we're gonna do outreach is gonna do phone calls, referral campaign, I don't know, whatever it may be, but we put together plans to tackle any of those that are tagged red flag. And that we've finally got to a point of optimization with our data because we're talking about it and collaborating and working together to say, what can each team do to contribute to turn this around so we can hit our goal? These meetings are phenomenal. And now you think 30 data points, how are you getting through all of it? We can get it done actually in less than an hour now with how we look at it and how it's laid out.

Andy Benis: Well, that's the habituation piece. You make it a routine, you make it a habit. I think that what we don't want to get lost in that discussion is everybody's looking at the same thing. Yes. And so if you update your portion, and before the meeting starts, everybody comes to the meeting prepared, we all know what the numbers are. They're right there on the sheet. Now we're going to talk about them and what they mean. And you remember from your previous life, you know, our version of that is that run rate accountability meeting. And for those who are not aware, LA Pacific runs six, eight-week sessions per year. So we start and it's rolling admissions. So we start new students every eight weeks. That's fast and it's a lot. And so keeping track of what's actually happening, but a meeting like that, and by the way, you can scale that up or down depending on the cadence of your institution, but who's in the room? Well, marketing's there, the director of enrollment's there, the director of student success is there, director of student accounts is there, and the registrar is there primarily just to listen so they know what's going on. But everything that everybody does affects other folks. And so the spreadsheet that we look at, to your point about the 30 KPIs or whatever your number is, and ours is probably similar, everybody's looking at the same numbers so that they know how many applications enrollment took in last week.

And that means X number of more people in the pipeline who are going to have to submit FAFSAs. Some of them will be required for verification. So the pipeline, it affects everybody. And if the student accounts team isn't prepared for that extra burst, let's say the enrollment had a banner week, for some reason, people just, a ton of applications came in in one week. That on the process of getting them accepted, a bunch of people touched that file. And for them to be aware, oof. We just got more in that pipeline. Are we staffed to handle follow up with those students to make sure we're getting FAFSA submitted, official transcripts submitted and all that stuff that's got to happen before somebody can register for classes. And so half the battle is won when you have that diverse a group looking at the same numbers on a regular basis. That's half the battle. Your last piece of action, massively critical because out of that discussion comes, well, here's what we're going to do. So-and-so is going on vacation next week. So we'll be down a person, but you know what? I'm going to have somebody slide over and pitch in it. All the little things that come out of a meeting like that, whether it's weekly or how often you do it, it's critical. And I would call that, you know, there's data-driven decision making, there's data-informed action where here's the data, we've looked at it and now we're ready to make some decisions on how we allocate resources, people, time, money, whatever you want to allocate, because we actually know what's going on.

I think one piece that I want to dive into a little bit is this issue of, are we looking at the same numbers? Because sometimes every department has their own data, but they're not apples to apples. It's not even fruit to fruit in some cases, right? So forget apples and oranges. It's like monkey wrench and banana. The data definitions, we went through that exercise with most institutions will have some kind of a data glossary or a term glossary. What does it mean? What's the difference between persistence and retention? When we say inquiry or prospective student or lead or prospect or are these synonymous, right? So you'll have a lot of folks will put together and if you don't have one, great suggestion, a data dictionary. What for our institution, when we use this word, what are we talking about? You can't have the conversation unless you have the language in common. And people often have, that's where the silos butt heads is. I'm saying this is what my data shows and you have data that shows something totally different because we're looking at the same thing from totally different angles.

Jamie Brownlee-Turgeon: Well, I would say for us right now marketing and enrollment, because we've had to, they're much further along in having the same definitions, the same collaboration, the same cadence in meetings. They're comfortable talking about the numbers and knowing what to do with it. It's now getting the other side, the student services, financial services, records, student success, getting their data. They have a different system. So the CRM works great for enrollment and marketing. Now we have some issues on the other side, but we're working through them. And if we never came to the table to talk, we wouldn't even know we were talking about different things. So I think, like you said, the first is get the right people in the room. That is key. Once you start having that, then you can start defining, then you can start saying, wait, your report says 200 and my report says 250. Why? Why is it different? And does it matter?

Andy Benis: And does it matter?

Jamie Brownlee-Turgeon: So what are we looking at? Because that might be, hey, I have a different definition I'm pulling from, but we can start to refine it. And we're in that stage right now of refinement. And it is fun. I mean, I say it's fun. Some of my directors might not say it's so fun. They're listening right now.

Andy Benis: But that's just you being disturbed. That's why that's fun. You don't have to be in the same psychological space as Jamie to get a benefit from it, but it doesn't hurt.

Jamie Brownlee-Turgeon: But you have to start where you're at. And I think that's a key. Sometimes with data, people are like, it's so overwhelming. It's like, well, what can you start with? Pick one or two and just get good at that. And then bring people into the conversation on that and not have this expectation that in one month you're going to have all of these meetings, everybody comfortable talking about it, the same reports, this aim definitions. You have to start somewhere.

Andy Benis: No, I mean, then you're responsible for heads exploding and you just, you don't want that. But to your point, and the R word came up in our last episode, relationship. Yes. Bringing cross-functional folks to the table to have mutual discussions about what does this say? What does this mean? You know, hey, this number's down X percent over last month, last year, whatever you're looking at. Well, is that a good thing or a bad thing? What's the impact? Does it affect any other numbers? You mentioned leading and lagging. I mean, if we're seeing this now, we're going to see that later. And of course, you know, our director of enrollment, God bless him, is well aware that you cannot retain students you never get. So there's always this issue of new students versus continuing students. Well, persistence and retention that's on student success and academics and everybody else who helps out current and existing students. Enrollment's responsible for new students in the front door, but hey, you can't graduate someone who never started so there's all these conversations about who's responsible for what and for when and we'd be doing a lot better if they did better. The quickest solution to that silo-ism, it are these discussions where we're all aware whether the numbers tell a happy story right now or not so happy story, let's just acknowledge all of the most relevant data and let's make sure everybody, let's not assume that people know why those numbers are important. Because somebody sitting in the, whatever, student finance office may not fully get the value of what's happening down in enrollment or down in student success or over in academics. What role does an assistant dean have over retention or? The list goes on and on.

Jamie Brownlee-Turgeon: It goes on and on. So I have a perfect example of something that just happened yesterday. So this year, this spring, actually, we rolled out some annual program metrics that faculty fill out. So the program directors over the programs have to fill out data. And it's on conversion data. It's on persistence data. So yesterday, I get an email from an assistant dean who says, hey, our persistence data is low and we have to respond to it, I'm just not quite sure what I'm looking at. I said, hey, let's jump on a call. So he called me, we talked for 20 minutes and he realized, I have a legacy program and we're not using the student success counselors and they're the ones that do retention. So it's all on our faculty and they're focused on teaching. So there we have an issue. I said, yeah, all we have to do is transfer from the legacy model to the new model with student success. We manage that piece. He's like, I can do that?

Andy Benis: I'm allowed?

Jamie Brownlee-Turgeon: Yeah, let's have a dialogue. But so what I love about it is - when everybody's talking about the same data points, thank you for that. And we're talking about the same, it opens healthy dialogue. Now it may take another year or two for that to happen, who knows? But we're having the conversation. If no one's looking at the data cross-functionally and having dialogue, we really are siloed. So yesterday was just - opened so many doors for how we can partner together and how my team can support his faculty and his program, it takes a village.

Andy Benis: That's what I'm talking about. There you go. Jerry Seinfeld's dad agrees. I totally agree. And I think, again, I think shout out to all IR departments everywhere who are probably underappreciated and or underutilized. And again, speaking locally for LA Pacific, you know, we are blessed with a chief academic officer who truly understands the value of data, not for data's sake, but because of the story it tells. He is an elite academic as well, but he doesn't let that keep him from the data. And with our director of IR and that whole group putting out relevant information, asking the right questions, what is it you're hoping to find? And I think it's surprising because you could spend so much time in this. And there is a data overkill at some point. But when you're looking at the most critical issues you're facing today, it would be impossible to fully analyze the situation or the moment without a 360, at least data-driven view of what's actually happening.

I mean, recent collaboration on our end, same thing. We're from initial leads or inquiries, if you don't like the business term leads, student inquiries and by program, you know, which programs are generating the most interest in the most inquiries to application rates, to start rates and then persistence and ultimately graduation rates. And they don't align the way you would expect. So sometimes your intuition leads you down the wrong path and you need actual data to change your thinking. So I'll give you an example. One of our, our, you know, highest in terms of inquiries per program, criminal justice, the bachelor of science in criminal justice, popular program, lots of inquiries, and actually decent application rates as well. But then when you rank or you list programs by start rate, so lead for initial inquiry to start, it's actually not ranked nearly as high. So it's very popular, but doesn't convert in the same way. Why? So we start asking those questions that we wouldn't have thought to bring up like, well, if it brings in the most inquiries, of course, and enrollment converts at consistent rates, it's going to be one of our more popular programs. Then you look at your student population, you go, it's not one of the larger, the program, the student population in the program isn't as proportionate large as the leads coming in. Why is that? Is it something that the public is misunderstanding about the focus of the program? Are there competitors that are just simply more attractive than us for that particular program? And so they inquire, but then they say, thank you, no thank you.

My point was you walk into a situation with a certain set of assumptions. Sometimes it's based on historical data or experience or just your gut. And the data right in front of you is saying something very different. And that's a little mini crisis to go to your sweet spot. There's a little mini crisis internally where you say, do I go with my gut, my comfortable old shoe, what I know, or do I let the data tell me that things are have changed or are changing or things are not what they appear and I now got to go make decisions based on that, not my gut.

Jamie Brownlee-Turgeon: Right. That's hard. Well, we like to tell our, we like to tell the story we want to tell, right? That it's more exciting if I get to tell the story that I want to tell, but the data can slap us in the face and say, that's not your story. Let's deal with statistics joke, right? There's lies, damn lies and statistics. Like I can twist numbers to say anything I want. But that's where you get really good thinking about that optimization is it's not just one data point. You're looking at all those data points along that student journey for a specific program. Now you have a deeper understanding of where you potentially are losing students. You don't want them to fall through the cracks. If you only look at one data point, they might. So now you're able to see a very clear picture and at least get you to say, where do I need to hone in and dig deeper?

Andy Benis: Right? Right. You caught it to where this is where I need to dig deeper on what's happening. Because you're never quite sure what might be revealed. Right? So in my mom always said life was like a box of chocolates. You never know what you're going to get. OK. So I think we now get to morph that to data is like a box of chocolates. You never know what you're going to get. Running the reports is one thing. Sitting down to analyze them is another thing, letting the story leap off the page is a whole different thing. And then finally, cannot be left out. What are we going to do about it? And that becomes kind of the next step. But it's almost, it's going back to the, again, pick the metaphor you want. How are you going to start your journey if you don't know where you are? Do you have a compass or a GPS that tells you where you are right now? You've used the word vision a few times. Where are we headed? What are we trying to accomplish? And more students, more enrollment, just newsflash, not enough. That's not a flag on the hill. That's not a vision. That's a need. How about a new building?

Jamie Brownlee-Turgeon: Yeah. I mean, how many capital campaigns have evolved around building a new building? Because people love the architect's rendering, right? That drawing and that model. It's inspiring. It's real. It draws. It's hype. You're like, that is so cool. Could you imagine it? And now you're vision building. And that's a whole other topic for another mini series.

Andy Benis: What is the flag on the hill that you're having your people march towards? And if that's not well defined, you may not know what data you need to answer, which questions you're trying to answer.

Jamie Brownlee-Turgeon: Well, OK, so let me combine vision with data. You can have a big vision, but the data might tell you you're never going to get there. So it helps create a vision that is practical and realistic. Because there's nothing like saying, hey, we're going to have 10 new buildings in three years.

Andy Benis: No, you're not.

Jamie Brownlee-Turgeon: You're not going to have 10 new buildings in three years. So it's like, how do you use data even to build a vision and make it stronger, realistic? That gains that buy-in. Or, hey, in the next two years, we're going to do CBE better than Western Governors.

Andy Benis: Yes. OK, well, everybody needs a goal. But that's a pretty tall mountain to climb, right?

Jamie Brownlee-Turgeon: Yes.

Andy Benis: I'm just amplifying your point about if you're going to take on the gorilla in the cage, have a realistic set of interim steps you need to achieve, but the data is the one that's got to tell you how to get there. Otherwise, it's all vision without any practicality. And of course, the data is the practicality. So here's to hoping we didn't lose the audience by talking about numbers and data and practical stuff. But there is a little method to the madness here in the mini series, because in our next episode, we're going to be talking about turning things around. So if you're a place where you're finally aware hey, we're stuck, hey, we need to adjust course, hey, we need to change some stuff or a lot of stuff. We also know we need a clear snapshot based on data to know where to go, right? Where to start the process. Just a little teaser here for you. In episode three, we're going to get to turning things around. What to fix, how to fix it, when to fix it, because you can't do all things at once. Stay tuned for that and we'll be joined by a special guest on that episode. So we'll leave that as the teaser there until next time. So until next time, good luck to you all and we'll see you next time on the Ed Up Experience.