Indeed’s 2024 US Jobs & Hiring Trends Report With Cory Stahle

How does a company harness job market trends and use data to boost their recruitment process? Simply ask Cory Stahle, an expert from Indeed. In his discussion with William Tincup, Cory shares some fascinating trends and impactful findings from Indeed's 2024 U.S. Jobs and Hiring Trends Report.

As a well-versed economist, Cory starts off by shedding light on how Indeed handles data on an extensive scale. He shares the fact that their data collection process is high-frequency, comprehensive, and grounded in real-time, offering a more precise perspective on job postings than traditional government surveys. He also underscores the fact that Indeed provides its datasets to the public, enabling users to leverage this pool of information and spot patterns in recruitment trends.

Cory's analysis reflects a job market that is undergoing significant shifts as technology and economic trends unfold. His insights underscore the potential for data and AI to revolutionize recruitment - a testament to the power of digital advancement in the hiring space. So, if you're looking to gain a competitive edge in recruitment, perhaps it's time to start exploring the data at your fingertips.

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Transcript

William Tincup: 0:32

This is William Tincup, and you're listening to the Recruiting Daily podcast. Today, we have Corey on from Indeed, and we'll be talking about Indeed's 2024 U. S. Jobs and Hiring Trends Report. So, uh, this is going to be really, really fun. Can't wait. And so let's just jump into some introductions. Uh, Corey, would you do us a favor and introduce yourself and what

Cory Stahle: 0:55

you do at Indeed? Yeah. Thanks, William. It's great to be here. So, just by way of introduction, I'm sure a lot of listeners have maybe heard of Indeed and kind of the job searching side of things. If they haven't,

William Tincup: 1:08

Corey, at this point, I really feel sorry.

Cory Stahle: 1:11

You know what I'm saying? I know. I know. It's, uh, it's pretty rough out there. It's like that progressive.

William Tincup: 1:15

What is that? You've been living under a rock for, you know? Yeah. But anyway,

Cory Stahle: 1:19

I'm sorry to interrupt. Uh, yeah, no, no, uh, you know, so yeah, I mean, you talk about Indeed, you know, a lot of people think, you know, jobs, job postings, and certainly that's a big part of what we do, you know, and kind of coming up with the, you know, those kind of connecting kind of this hiring between employers and job seekers, uh, but we also have a lot of other functions. And I get to fortunately sit in one of those kind of little bit of a different kind of fun things that we do at Indeed, which is I work with what's called the hiring lab. Which is a group and team of economists that uses the data that we have on Indeed to really kind of track what's going on in the labor market and in the economy kind of broadly. And so that's kind of as we talk about, um, stuff today, you know, that's kind of where that's coming from is we're looking at kind of what the trends have been on Indeed and in hiring in recent months, in recent years, and then looking at kind of where those trends might lead us going forward. So

William Tincup: 2:13

ADP has, um, and they've done this for years is because they have payroll. So they can kind of tell people, uh, with the data that they're sitting on kind of what's going on with payroll, what's going on with, you know, those types of things. And y'all are sitting on a massive amount of doc, of data as well in terms of who's hiring, uh, who's posting job ads, et cetera. Could you, could you tell us a little bit more about the data that y'all sit on?

Cory Stahle: 2:40

Yeah, so I mean, yeah, great, that's, uh, so we got a lot of different data. I would say that with the economic data that we're looking at, a lot of what we're looking at is job postings, you know, so this isn't necessarily proprietary data in terms of, you know, these are job postings that job seekers can go on to look at, Bye bye. Bye bye. But if you want to kind of get a good feel for what's going on, on the larger picture, it would be pretty difficult to comb through millions and millions of these different job postings and to break them out, you know, into, you know, this sector is hiring more, this sector is hiring less. That's something that would be very, very hard to do as a job seeker or an employer or somebody working in recruiting or HR. So that's what we really do is we take. That data we have, we take the data, you know, the job postings that employers are posting, and from that we can tell a lot about, you know, our sector's posting more or less than they used to. You know, we can also look at, within a job posting, we can see, you know, how much are employers asking for. Generative AI type of tools, you know, how much are employers advertising pay in a job posting and is that pay going up or down? So there are a lot of economic indicators that we can actually draw out of job postings, you know, as we start to kind of roll up and look at those at a higher

William Tincup: 3:54

level. I love that because you can give people, you could paint a picture about skills and comp in a way that. Other folks that don't have that data set. Um, can you, uh, can you, can you kind of talk to about traffic? Like again, there's the posting itself and then there's obviously people that then go into the posting and do something. Is there, is there data there that's meaningful or even candidate flow that kind of go further into people that have applied, etc. Is that useful to the, uh, to the audience?

Cory Stahle: 4:26

Yeah, so that is something that we've done a little bit of work and, uh, just clarify, I think if I understand correctly, you're asking about kind of people who click on job postings, you know, kind of the flow of job seekers. So yeah, so that is something that we've looked at historically. I know one example of that recently is we looked at The share of clicks on job postings, you know, so we're looking at people who are interested in job postings in the United States. In this case, we were looking and we wanted to see how many people who are clicking on a specific job posting were living overseas, you know, so we can kind of track foreign interest and kind of get an idea. You know, we're not necessarily getting into the U. S. You know, really individualized, personalized data, like we take protecting personal information really important, you know, really seriously, um, but again, we kind of roll up, we look at the aggregate level and, you know, we can then have trends like we see that, you know, during the pandemic, foreign interest, you know, in U. S. jobs, you know, was pretty flat, but we've seen that actually grow pretty significantly in the last couple years, and I think that's been a A pretty significant part of the story behind the resilience in the labor market, you know, so we can really take these things and we kind of tie them together and get a picture, you know, and on the job posting side, we have public data sets that we publish, one's called the Indeed Job Postings Index, so that's what allows us to kind of track how job postings are moving, anybody can access that. Um, on our website, which is hiringlab. org, um, and then we also have what's called the Indeed Wage Tracker, which I alluded to earlier, which gives a measurement of kind of how wages and comp and job postings are changing over time. Um, so again, just helps us to create more of a full picture than I think government statistics, uh, Right. You know, where, where they kind of maybe, maybe can lack sometimes, especially in terms of frequency. So.

William Tincup: 6:14

I love it. I love it. First of all, I love that you're doing something with the data to help both the larger audience, but also the folks that, that are indeed customers as well, like giving them some insight into all of those things, skills and wages and everything. It's like. Because left to their own, they, they might not know that these macro issues are at play. So I, first of all, I just love that. Um, let's talk a little bit about the report. Uh, the, the 24 US jobs and hiring, uh, trends report. What, um, if we were to roll this up for folks with short attention spans, which would be pretty much everybody, um, what are some of the things that you'd like to highlight?

Cory Stahle: 6:56

Yeah, so I think, you know, kind of the, uh, quick rundown of it, I think, setting the stage for this report, I think is important to understand as we were kind of preparing this report, we were looking at what was going on, kind of in this macro economy, this current economic environment, and kind of what's happened in 2023, and what's been really interesting is, you know, we've seen a lot of different discussions in 2023, you know, we've heard, you know, a lot of discussions around return for rural. return to office, generative AI, you know, how that's going to maybe change the labor market. You know, so there's been a lot of things to talk about that have happened in 2023, but I think it's important to note what did not happen in 2023, or at least, you know, this far. We've still got, you know, a month left here, but, uh, but what we haven't seen based on the data we're looking at is a recession. You know, we saw so many people, you know, making recession calls at the end of last year. And as we, you know, have gone through the year, the labor market and by and large the whole economy has been pretty resilient. And so really that's kind of the setting the stage for this report. So the report basically is saying, okay, what are the trends that we saw in 2023 that need to carry forward into 2024? You know, what is it that, you know, employers need to do in terms of their demand? You know, what do we need to do in terms of attracting people, you know, into the labor market? You know, what does. Quitch rates and like the dynamics of the labor market look like in a healthy labor market. You know, and then also we talked a little bit about wage growth. And I think also generative AI, you know, is another trend that we'll really be watching. So that's kind of the stage. So I would say in short, uh, you know, here's kind of the, the wrap up really, really short version. You know, what is it that needs to go right in 2024? We need to continue to see, you know, strong demand from employers, more people entering into the labor market. And we need to, you know, keep our eye on some of these emerging things like generative AI.

William Tincup: 8:50

So, um, and I'm just probably done in the, in the report, but did you, are we bifurcating between hourly and corporate or salaried? Uh, in any way, because I, you know, what I hear usually from practitioners is more, uh, on the hourly side, it's, it is, it's, it's very difficult and seems to be getting more difficult, but on the salaried side, depending on the job or wherever, uh, not as hard. There's like a, and again, that's just, you're sitting on data. I'm sitting on anecdotes. So. I'm going to trust you more than they trust me, but basically, basically it's kind of like the story of like, okay, if you're hiring for salary, you're going to have some options. Got it. If you're hiring for hourly, you've got to re your, your options are limited and you've got to raise the pay.

Cory Stahle: 9:39

Yeah. Yeah. And I think ultimately, you know, as we look across indeed, about 50%, a little over 50 percent of jobs now include a salary, you know, partially as a result of different kind of pay transparency regulations in some states. Um, but that means that there are almost 50 percent that still do not contain a salary. So we don't necessarily have like the breakout, um, in those terms, but what we do look at is we kind of look at, and I think this is another interesting proxy. We look at. The breakout between jobs that are more likely to be done remote versus the ones that are less likely to be be done remote and I would say there's probably, you know, some comparison there to be made lower remote jobs tend to be a little more skilled labor, probably a little more, you know, in person hourly type jobs, whereas some of those highly remote jobs tend to be more kind of knowledge worker type positions, um, and potentially more salary jobs. And I think as we kind of Talk about that. We have seen, you know, really the bifurcation this year in the labor market, you know, where coming out of the pandemic, you know, all sectors rose together. They all sunk together during the pandemic and they all rose together. But what we've seen in 2023 is kind of this, this kind of fork in the road to some degree. You know, we've seen software development jobs drop below their pre pandemic levels, whereas we've seen, you know, jobs in areas like nursing and construction and manufacturing, you know, keep, you know, pretty high levels of employer demand, you know, which ultimately I think relates back to what you're talking about in terms of the difficulty to find workers, um, as, you know, there's just, you know, stronger demand that we're seeing in some of those type of low remote in person type of opportunities. So

William Tincup: 11:21

did y'all, uh, just as a matter of a point of honor, is did y'all? Do this report last year at

Cory Stahle: 11:27

the year? We did actually. Yeah. So we did produce kind of five trends to watch last year. Um, so that is something that people can kind of go back and look. I would say last year we kind of focused on a long term outlook. Um, kind of the takeaway was. You know, demographics are really weighing on the United States labor market and population in general. You know, we have this aging population, which over the course of decades, unless something changes, you know, with the birth rate, we're going to see labor markets continue to be pretty tight over the coming decades. You know, business cycle might go up and down, but if you have fewer workers to take jobs, that's going to create different challenges in the future. So we took that long term perspective, but for this year, we kind of wanted to focus. Uh, mainly on 2024, so, so yeah, so we have kind of this kind of long look last year versus the short look this year, which I think in some ways they kind of make a good pairing of reading together. Oh, a

William Tincup: 12:24

hundred percent. I always love it when people go back and look at like their predictions. And, and analyze their prediction, they're very critical, analyze their predictions like, yeah, wildly missed. Uh, yep. Got that one. Right. Got that one. Right. This one's kind of, we're still, you know, we're still almost there. Um, so, so first of all, where's the, uh, where's the report located? It's obviously on the

Cory Stahle: 12:47

website. Yeah, yeah, so people can go to hiringlab. org. Um, and so we've got not only this report, um, but also like the data sets I've mentioned. So like hiringlab. org slash data, um, is a, a data portal where people can kind of do their own analysis, so to speak, and you can kind of sort. And I love getting in there and just playing with it because you can look how job postings are different between different states. You can look how they're different between different like metropolitan areas. You can look how job postings vary by different sectors, you know, we've talked a lot about sectors, you know, and so it can be really helpful to go in there and to, you know, kind of slice up the data that you want. Um, but yeah, as far as these reports, they're all just up on hiringlab. org along with a lot of other research that we publish using this data pretty regularly. Oh, I

William Tincup: 13:33

love that. And I love, I especially love the fact that they can, um, use your, use your aggregate data to then model and think about themselves. Cause that's, you know, that's, that's for them, especially, uh, CH, uh, CHROs and VPs of talent. They're trying to figure out forecasts. They're trying to figure out like what is going on. We know what's going on within the four walls of our company, but we have no idea what's going on the four walls of the United States. And so we might learn some things at an industry event or something like that, but to actually play, I say play, to actually use aggregate data in this way, it's just, first of all, Thanks, because I don't know of anybody else that does that, that allows people to, to look and kind of do their own research, uh, based on such a large data set.

Cory Stahle: 14:24

Yeah, no, I love it. I think it's absolutely amazing, and I would say from a past life, I used to work as an economist in state government, um, and a big part of my job With that was to go around to different businesses and HR, you know, and heads of talent and to, you know, kind of talk about the tools that the government had available, you know, and things that were out there, um, for businesses to use and the government has some really, really good, you know, really great tools as you look at it, but then Now coming into my position within Indeed, I'm saying, you know, there's a lot of value in the government tools, but it's really hard to beat the scale and the size of what we're doing at Indeed because, you know, you talk about, you know, the government data, you know, we, there's been a lot of discussion lately about government data surveys, having problems with response rates, and the result has been a little bit of noisiness in the series, and then you compare that to the Indeed data where we're talking about basically, you know, Any job posting that's on the internet, it's probably being included in this analysis. You know, we're really pulling together a near universe of jobs, so you can get like a really good picture. You don't have to worry about surveys. And not only that, but I mean, I can look at what the data was doing, you know, last Friday, rather than waiting, you know, three or four months. Because I know one of the biggest employment data sets, you know, in from the government is the QCEW data. And that's kind of the best census overall universe of employment. But that tends to have like a six month delay, you know, in six months when you're trying to forecast and kind of think into the next quarter, you know, can be so yeah. So I think there's just so much value. In these data sets, in these high frequency, you know, based off of, you know, large amounts of data, you know, and rolling that up, and I know for kind of the even more technical users, you know, we also have all this data shared on GitHub, you know, so we've got the job postings index, the Indeed wage tracker, and all of that, so, you know, if there's anybody who is doing that type of modeling you mentioned and wants to pull it into whatever coding language or environment they're working with, you know, we make that easy to do as well with our GitHub.

William Tincup: 16:30

I love that. So now or in the future, do you see a position for generative AI, uh, to help folks, uh, with prompts in terms of you've given them keys to the kingdom, the aggregate data that, uh, that is just wonderful, but they still might not know. I mean, I'm assuming you actually, you know, have a degree, maybe have studied this stuff for a while, you know, might be pretty good at it, you know, this, that, and the other, but also, The average practitioner might not be as skilled at like, okay, they have access to this data, but then what questions should they be asking or what pro I'm thinking of prompts in terms of generative AI, it's like, what prompts should I be asking this to get, to tease out The things that I should really know.

Cory Stahle: 17:19

Yeah, no, those are some great questions, I think. Unfortunately, I do have the degrees and I do have the skills, you know, to do the economics piece. I think, you know, we're still in an era where I don't know that I'd feel comfortable accounting myself as an expert in writing prompts, you know, in generative AI. Um, but I think that is one of the nice things about what we do with the hiringlab. org slash data. I mean, it's really as simple as kind of just changing the drop down boxes, you know, selecting, you know, the sectors you want. And I think that that type of kind of democratization of data, making it easy so that, yeah, like, I mean, AI can do some great things, you know, it can really help with some of the coding and all that. But I think also just making sure that, like, we're telling a story. With these data visualizations and making it so easy that, you know, when somebody clicks on to the site, they say, oh, I see clearly what this data is representing. You know, that's really something that we try to aim for anytime we publish a graph, anytime we publish any of that stuff. So, um, I think, I think, you know, generative AI is going to be a part of potentially helping people to Right. Pull out some different trends, um, but right now, you know, we're doing our best to, you know, make those trends as clear as possible, um, in a way that you can kind of compare and really say, you know, if you're working in healthcare and you want to see how healthcare is stacking up against retail in terms of hiring, you know, you just drop down that box, uh, you know, in that, uh, Kind of in that dashboard and you can see, hey, you know, healthcare or nursing, whatever part of healthcare you're looking at is maybe a little stronger than retail right now and you can make those comparisons kind of in the blink of an eye without having to kind of go through that iterative process with a generative AI tool. Right,

William Tincup: 18:58

right, right. When people start a kind of a research product, uh, project, I'm always fascinated with like the thesis or, or kind of the, like what we're, what's, what are we starting with? What do we think will happen? And then at the end of the reporting process and the data's back there going through it, uh, things that surprised them or things that they kind of invalidated, like, eh, is that Trex? Um, was there anything like that there for you? Like when you started this, I, when you started with this idea, did you already have kind of an idea of how it would play out? And if so, or if not, is there anything that kind of, I wouldn't say shocked, but was there anything that just kind of came out of left field for you?

Cory Stahle: 19:40

Yeah, I think, you know, we've talked a little bit briefly about kind of the five trends to watch, you know, we've talked a lot about kind of the macroeconomic trends, but kind of carrying forward off your last question with generative AI, I think it's been a really interesting thing. So in addition to this trends report, we've also done some recent work on generative AI and the Potential exposure and impact we see that having in the labor market, um, you know, and so what we kind of did is I think we approached it very, very differently. You know, if you want to get into some, uh, some theory and methodology, you know, I'm, I'm willing to talk all day about that stuff because, uh, graphs methodology, you know, those are all my, you know, happy nerd words. Uh, but, you know, as we look at generative AI and as we started kind of look, um, Understanding and saying, okay, how can we measure the impact potentially of some of these tools? What we realized was that a lot of the existing methodologies out there were really focused on saying you have a job that's called a janitor. You know, janitors use brooms, janitor of AI can't You know, use a broom. So, you know, therefore, generative AI, you know, is not going to take that job or piece of that job. What we did, though, that was different in our research, is we looked at the skills in every single job, you know, everything from the janitor to the computer programmer. And what we found was really, really interesting. So this gets to your point. I know this is kind of a long build up to your, to your question here as far as something that was surprising, but what I thought was really surprising was kind of two things. First, we saw that as we analyzed every skill across 55 million job postings, what we saw was that every job has at least some skills that chatGPT or generative AI is potentially able to do, you know, to some degree or another. Um, so I thought that was really, really interesting. You know, you think about how, you know, pretty much every job now we use email, you know, email is an area where generative AI, you know, is really poised to potentially, you know, revolutionize and transform the way we're doing that. And so it's kind of interesting to think that, you know, it's not necessarily a question of can an entire job be. You know, change or transform, but really like, what are the skills within an individual job, you know, and what we found, you know, from this research again was that, you know, while every single job was exposed to some degree or another, some were obviously exposed more than others. ChatGPT, you know, these generative AI tools are better at writing code than they are at, you know, doing, you know, care, you know, personal care type of home health tasks or something. Um, but I think the other thing then, and this is kind of the second thing I think that's really surprising, especially given everything we've heard about generative AI this year, is as we looked across job postings, what we found was that, It was about 0. 05 ish percent of job postings were actually asking for generative AI type of technologies just even a few weeks ago. You know, so I think it's interesting that, you know, a lot of the coverage has been, hey, you know, generative AI is coming, you know, it's this huge thing, it's transforming everything already. And, and certainly like it's grown pretty fast this year, but it is still like a super, super small fraction of the number of jobs that we're seeing. Like employers are still not, by and large, asking for people. With these types of skills, and we also haven't necessarily seen a massive drop off postings of, you know, ChatGPT taking jobs or anything away that oftentimes, you know, the media fears. So that to me, you know, is kind of surprising, but also maybe I'm just a little bit of an optimist because I think that these technologies are so early. on in the game that we can really shape them into what we want them to be and help them to be things that, you know, enhance our productivity, you know, and aren't things that, you know, really hurt us, but we need to be aware of, you know, what their capabilities are to be able to do that. It's,

William Tincup: 23:32

it's really, it's really interesting because it reminds me of the beginning of the internet. So there was this whole pre, uh, I'd say pre monetization of the internet, uh, that was going on with message boards and whatnot. And then there was a, there was a time in which the internet then basically, uh, kind of took over. And it was the buildup to that that's fascinating because it feels a lot like generative AI in that people just had no idea. They knew that it was coming. They knew to impact, in this case, their life or their business or whatever, but they didn't know how, and I feel a lot of the times when I talk to people about generative AI, they're like, I know I should pay attention to it, I'm tinkering with it, Um, it's interesting, but I still don't know if it gets to your point. It's like, I still don't know. Like, is that a job or is that a skill within another job? And, uh, and where do those things lie? And does it, does that just kind of play out over time? Um, I did want to ask you a question, uh, that's a little bit similar, but. But, but, but, uh, but different. Have, have, have y'all seen an uptick in generative eye either on the candidates side of things in terms of creating personalized resumes to fit jobs? Or is that something you could see? Or generative eye in terms of job postings? Is that, well, you know, for me, on the outside looking at, again, anecdotes, your data. Okay, so we're different. Um, it seems like if I were a candidate. I would, I could apply to more jobs and do it more personally using the keywords that are in the job description, job posting, and then mirror them with the, my resume. Um, it seems like I could do that as a candidate. I don't know if that's true. The other part of that, if I were on the corporate side or the hiring side, it seems like I could create a much better job posting. Now I don't, I don't know if either of those are true or if you have any insight into either of those.

Cory Stahle: 25:45

Yeah, no, I think those are both great questions. I know on the, the research side of things and what we've done within my team of economists, I know we've focused a lot to this point, you know, really on the job posting side of things, you know, really looking to see kind of what employers are, what their reaction is, you know, our employers are adopting and that's where that, you know, 0. 05 ish percent number comes from. Um, you know, so we look at that, but I, but I do think that you also, you know, you bring up a good point that there are a lot of other things beyond just. Having it mentioned as a skill and other areas, you know, where it could potentially have an impact. And I know that, you know, indeed, um, you know, is doing a lot, you know, we've used artificial intelligence tools now for years as part of kind of our matching and trying to, you know, align people together. I can't necessarily, I'm not the person to speak to the specifics exactly of what we're doing with all that. But I know that, um, I've seen a lot of things that we're doing to you. Make the job posting better to make it easier for people who are posting jobs, because certainly, you know, generative AI has a lot of promise for, you know, as you talked about, you know, creating better aligned resumes, creating job postings easier, and maybe job postings that, you know, better attract the type of worker that, uh, the employer actually is trying to attract, you know, so I think that those are going to be really interesting things To really watch over the coming years, I mean, as you kind of said, you know, nobody really knows like we're so early in this, but I think you've pointed out two things that are really going to be interesting, um, as we kind of move forward with this is to see how people adopt this and people, how people use these types of technologies. So

William Tincup: 27:19

last question is what's the, what's the next thing you're researching? What do you get a little dramatic foreshadowing? What are you, what are you looking

Cory Stahle: 27:26

into next? Yeah. So, I mean, so again, we have a team of economists kind of all over the world. Um, you know, so we have economists and, you know, Germany and England and, um, you know, just kind of everywhere, even in Japan. And so we are constantly researching a bunch of different topics. Uh, for me in the U S though, I would say I can speak probably personally to what I'm working on right now. I think really looking at some of the kind of skills. pieces of job postings. You know what we've done with the generative AI piece and really unpacking the skills is something that we had kind of done a little bit and we've kind of looked at in the past. Um, but it's something that I think we still feel like there's more to look at and to really help people to understand, you know, what skills look like over time, you know, and especially, um, how. The strength of skills has changed over time, you know, just because an employer puts a skill in a job posting doesn't necessarily mean that it's, you know, a must have skill. And, you know, this is like, I need to have this, right? And so like, trying to unpack, you know, it's like, what are kind of the mandatory skills, you know, versus kind of the nice to have skills, you know, so we're kind of looking into that, also looking into like educational requirements, you know, trying to understand how those are changing over time. We've, we've seen a lot Uh, discussion around states and other companies, you know, dropping the requirements for education, um, in their job postings and for their jobs. And that's something that we're looking to see, um, if that bears out in the Indeed data as well, so that we can kind of track how those are changing. Because I think, ultimately, all of these are good. Measures for, you know, kind of the recruiting intensity in the labor market and where things are and where, you know, HR and talent attraction is right now. And so I think those are just a few of the things that are kind of at the top of my mind. Yeah, it's,

William Tincup: 29:14

it's interesting because it's opening the funnel. You know, if you ban the box, uh, with, with felons, you're going to have 70 million people that, that, okay, so you've, you've, you've opened the funnel there. If you've, again, take degrees away, you're opening the funnel there. Uh, and if you add compensation, To those things, you're going to get a higher uptick of people because it's not a, you know, guessing game in terms of what the, what the job pays. So it's like opening up those funnels. What are all the different levers we can pull to open the funnel so that you get more candid flow and then you can qualify them as you go through the process. So it indeed does a wonderful job of being able and letting their customers have the ability to have some qualifying questions. Being able to push people through, et cetera, but, uh, this has been fantastic, Corey. Thank you so much for your time.

Cory Stahle: 30:09

Yeah. Thanks for having me. Anytime I can get on and talk about methodology and graphs and stuff, you know, I'm going to be happy. So.

William Tincup: 30:16

And for the audience, it's a hiringlab. org. Please go there because there's just some great information there. And uh, thanks again for listening until next time.