Each week, we interview proven leaders from our network, to learn from their experiences, and share their Talent Attraction and Candidate Experience stories with you.

• Our mission is to promote the accomplishments of our guests
• Highlight the companies where they work and the services, and products that they offer
• Share success stories from their experiences and, most importantly
• Provide strategies for job seekers and advice to talent seeking to accelerate their careers.
  

Today’s guest is Dan Quigg, CEO – Public Insight. In our chat with Dan, he shares:

• his experiences starting, operating, and exiting several software businesses
• keys to success over the course of those start-up experiences
  • Find great talent
  • Become a market expert
  • Disrupt the currently accepted model
• as well as lessons learned over the course of those start-up experiences
  • Always be in learning mode
  • Focus on what you really love to do
• a detailed overview of his latest venture, Public Insight, and demo of its flagship product TalentView

Summary transcript of our interview below:

Ron Laneve: Hello and Welcome to Episode 16 of the Bell Falls Search Focus on Talent video series. As we restart the series, we are thrilled to feature today’s guest.

[00:00:16] He was a client of ours, in the past, and we’ve recently reunited and have been, talking about his new venture, which we’ll feature here in a couple of minutes. He started his career at KPMG and then went on to found and successfully exit several software companies here in the Cleveland area.

[00:00:35] His latest venture could not be more relevant given the current state of the job market and its focused on data analytics. We are thrilled, to have today with us, Dan Quigg, founder and CEO of the Public Insight Corporation. Dan, thank you very much for being here. 

[00:00:51] Dan Quigg: Hey, my pleasure, Ron. Great to chat with you.

[00:00:54] Ron Laneve: I added a brief introduction of your background, but could you talk our listeners through, your career evolution from KPMG and then, the companies you started focusing on how and why, for each one, did you do that? It’s fortunate for a person to start and found and run maybe one software company or one successful company in their career. You’ve done it several times. So where does that, bug come from? 

Dan’s Career Path and Start-up Experiences

[00:01:21] Dan Quigg: My wife and I always talk about that when I left KPMG, I was on a, pretty fast partner track and, had two little kids at home and left KPMG, with the, thinking that, oh, I can go out and create my own revenue. And, boy, it’s a lot harder. 

[00:01:37] I was a, run of the mill CPA. I did a lot of different things as a CPAs. So how I got involved in the tech side of things is, I was dating my wife long distance and, she lived in the Chicago area. I lived here in Cleveland and, I was trying to evoke a transfer to KPMG in Chicago office.

[00:01:58] That didn’t work out. So I went to work for a, an up and coming CPA firm, called Sikich Gardner. Now, if the name Sikich rings a bell, it’s because Sikich LLP is now one of the largest CPA firms in the country. And Jim Sikich hired me. I think I was employee number seven or something like that in Aurora, Illinois.

[00:02:21] And, Jim’s firm was very progressive as a small CPA firm, and they had started developing a computer practice and, I’m really dating myself, but I was like a duck to water. I got, the first compact C. O. M. P. A. Q. They were bought by H. P. Yeah, you’re probably dating myself totally, but it was like a sewing machine and it had a 10 megabyte hard drive and floppy disks. 

[00:02:47] And I just sat there immersed in Lotus 1-2-3 and spreadsheets. So the rest of the job stunk, I hated it. I was a tax manager. But I love doing the computer consulting side. My wife agreed to come back with me to Cleveland and I rejoined KPMG and then started the, along with two other guys at the time we called it the small systems consulting practice.

[00:03:11] And, doing all my other stuff with audits and leading audits and, our department, the middle market department did a little of everything. I was on the tax side, I was on the audit side, I, did all our department scheduling, but I really loved the technology side.

[00:03:26] I asked to do it full time, they said, we’ll let you do it full time and, it never really materialized. I took the leap of faith to, go, and start a, consulting firm really focusing in on what we now call ERP. But, at the time it was, basically financial accounting.

[00:03:46] So that was 1988. That was a company called, Business Systems Consultants. I’ll condense the rest of the story, which is, started involved in consulting, did a, had worked up pretty marquee list of ERP consulting clients, Babcock and Wilcox, Dana Corporation, a lot of the Playhouse Square, Cleveland Orchestra, Cleveland Ballet, back then ballet.

[00:04:13] And, hired a guy who you guys know, to be, co lead with me on the technology side. He was very gifted software developer and we chewed around, what would be a kind of a missing ingredient in the ERP space. And, we came across really what we now call financial analytics, which is, corporate budgeting and planning. We did, an allocation software and we literally built this product kind of sight unseen, and I remember like it was yesterday walking down Technology Boulevard in Irvine, California, and just shopping it and, stopped in at the time it was called State of the Art. It’s now SAGE and then, what was then called Platinum Software, which is now called Epicor. So SAGE was at 99 technology and Epicor was at 56 technology. So literally they’re like right down the street.

[00:05:06] And SAGE or Epicor said, we want this, we’ll give you an exclusive. And, after multiple meetings, it wasn’t quite that simple. And so that’s what formed what we, named OmniVista. So OmniVista was, financial budgeting and planning. And it was white labeled products, so it was white labeled by, by Epicor, what’s now called Epicor, again Platinum Software, a product called Active Planner.

[00:05:33] They also white labeled our allocations product, because it was better than the one they had developed. They gave us exclusive, so we didn’t have to hire salespeople. We had sales engineers. Really were a support function, white label type product.

[00:05:48] We ended up selling that business to Best Software. And Best was then bought by SAGE. I was, head of analytics for that division. So the path for me has always been, I love data, I’m a data nerd so the things that have worked out well. Had been where it’s a data business or really our data derivative business.

[00:06:08] The things that have not gone well for me have been when I’ve gotten outside of the, of that scope.

[00:06:15] So I worked for Best slash SAGE for 2 years, then, then started a, radiology software company. I was approached by an attorney, Joe Lo Presti, who since passed away, who was Principal of McDonald Hopkins. And Joe and a guy by the name of David Dahl, who is with McDonald, Investments, wanted to talk to me about joining this teleradiology, radiology information system spinoff.

[00:06:41] Which you guys know is RISLogic and, had two employees, a handful of customers, and I knew zero about radiology. Probably still do, but I think I faked it well. And learned a lot. And, RISLogic became the third most recognized brand name in its space. Market leader in outpatient radiology.

[00:07:04] We had four of the top ten chains. So that was in, 2003. And we sold that business in 2005 to Merge Healthcare, which is still around. They’re bought by IBM and, I worked for Merge for a couple years and then, started in this odyssey of some not so successful startups that I’m happy to get into.

[00:07:29] I’m not happy to get into, but I don’t want to make it sound like, I have somehow this, magic pill that makes startups successful. I, like I said, I’ve had my share of stinkers too.

[00:07:37] No, I appreciate that.

[00:07:39] Ron Laneve: One question that comes to mind is from the transition to from, Best and SAGE and I’ll say a financial analysis software to the radiology business. What was the biggest thing that you brought to the table to make that business successful? 

[00:07:59] Dan Quigg: I definitely think first is finding the right people to engage with.

Dan’s Keys to Success

[00:08:03] And as you guys know. We didn’t know people in the space. And, we had worked with, with you and your partner Stuart to, to help fill those roles. And, so we had a little bit of money to work with, but not a lot. So clearly, it’s getting the right people in and, I don’t remember which guys, which people you guys placed, but I know it was, a laundry list of really credible talent, and, there’s no way that RIS Logic would not have been successful without the right people. So that was clearly number 1, probably numbers 2 and 3 would be, learning the space and not accepting the status quo. As an example, because I knew nothing about radiology, I was the perfect outsider.

[00:08:51] And the first thing, one of the first things I did was hire a consultant to tell me why people buy my product, and she said, radiology information systems, they, they, and by the way, so the first person I hired is now my Chief Marketing Officer at Public Insight, Christy Hawkins, Christy Boehm at the time.

[00:09:11] And Christy was in the exact same boat as me. She came out of retail. So we hired this consultant who said, people buy radiology information systems because it saves money on film and because it’s, essentially digital information.

[00:09:27] So it’s a cost savers. But then I went to our early customers, which predated me and I asked them, why did you buy RIS Logic and they said, oh, it gets us 1 more MRI through the door a day. And I go, that’s not what the consultant told me. The consultant told me, oh, it saves costs.

[00:09:53] One of the things we, we did was we looked at then the ROI on that. So he said, okay, if it’s one more MRI through the door a day, MRI costs this, there’s no increment, there’s minimal incremental costs of doing that. So what’s the ROI on our current price point? And I determined it was about four weeks.

[00:10:10] So I quickly, and then I looked at what the competitive products were pricing out in hospitals, cause this was still outpatient and they were, anywhere between 5 and 50 times the price. We literally increased our price point tenfold and said, no, this is the space we’re in, we changed our tagline, which to accelerating productivity, which again, credit Christy, I think somebody along the line had that.

[00:10:40] And, we went to town as a improved workflow solution rather than a cost. And that’s that was probably the 2nd big thing is not accepting the status quo and really digging into what makes people buy your platform or your product. So we, we were able to then get some good traction.

[00:11:01] We got some, another key thing is getting early marquee wins. So getting the right early adopters, the right customers, and we’re experiencing that now as Public Insight. There are people that early customers and partners that are, really helping to, not only use our application we call it really a platform and we’ll talk about this in a minute, but they’re really helping us to define what this is going to look like, how it can add value. We’re still learning. So not that dissimilar from RIS Logic in that, we’re in a, a neophyte space, HR tech still in a neophyte space and we’re still learning ways that we can do things that are different and not just the status quo. And, we’re still in that. That realm.

[00:11:47] Ron Laneve: So I appreciate that. I want to talk about lessons learned a little bit, because you talked about, I’ve had some successes, excuse me, and I’ve had some stinkers and I’m sure there’s in retrospect, you probably dissected those and realize, hey, there’s probably, here’s some things that we probably could have done different. Maybe not. 

[00:12:03] One of the things as it as I’ve gotten to know you better more recently, and as I hear you talk today, one of the things that clearly comes out is your, humility, around who you are and how you’ve approached business versus, I’ve been around a lot of startups in my career, probably over 100 now, whether I’ve worked inside of them or I’ve helped them. 

[00:12:23] And the number 1 thing that I’ve seen happen to founders is they get their egos get too large and they become a little bit arrogant in, what they know about their business and what they know about their product and what they know about, the mark, what they think the market should, why the market should want what they sell, versus everything you just said.

[00:12:44] Hey, I’m learning about this radiology space. I’m going to go figure it out. I’m not going to tell them what they want. I’m going to figure out how to make our product fit. So I appreciate that. In addition to that, which I think is just core to who you are, but what other lessons learned along the way, can you mention, from your experiences starting up and trying to run software businesses?

Dan’s Lessons Learned

[00:13:06] Dan Quigg: Yeah, whether good or bad. Yeah, I have an ego like everybody else. And, my ego has gotten in the way. In fact, the, the stinkers that I’ve had have been as a direct result of my ego. So I, I thought at the time I exited one thing I didn’t as so we sold our little ERP consulting business, that wasn’t much of a return, so I’d had three essentially successful exits by, investor return and so forth. 

[00:13:37] At 2005 I thought I had the magic pill to success. And I should add that one of the things that I did it at the time I took a break in 2005 and went and latched on to micro enterprise development around the world.

[00:13:53] And that was 1 of the biggest. Most challenging fun things that I’d done. So I became a advisor to Opportunity International, which is the third large at the time, anyway, the third largest micro-finance in the world. So I went to Rwanda, I went to Ghana, I went to Nicaragua, I went to, India, just, observing entrepreneurs in developing countries struggling. Micro finance had evolved quite a bit at the time. It wasn’t just making loans, the whole Grameen model, to micro insurance, micro, enterprise development. 

[00:14:31] So the problem with micro-finance Has been that you don’t really move the needle in GDP, but if you do enterprise development where you’re increasing yield, you’re increasing efficiency, you do expand GDP. So that’s the whole big thing. So I did that for a period of time, but then thought, okay, I got the startup bug again.

[00:14:51] And so got involved in a series of companies, which probably I had no business being in, but because I was so smart and, they needed my magic pill and my wisdom, I joined them, one was in the golf space, knew nothing and the other than I’m a crappy golfer.

[00:15:10] Knew really nothing about it again, tried to learn it. And that did not go well, especially when the 2008 2009 recession hit. Got involved in telecom, trying to help a company in the telecom space that did not go well. So I, I definitely had a, and I firmly believe that, God has providence to use the easy things to for the right purpose. So there’s good coming out of bad, at the time, I probably wouldn’t have said that. So really having those lessons learned, the lessons learned were, a, I don’t know as much as I think I do. So there’s always I’m always in learning mode.

[00:15:50] Like you guys, we just talked about is, there’s always things I can learn. And there’s always ways to impact a business. The 2nd is to stick to the things that I love doing, and, so I’m back in the data business. I like to say, I’m doing the same thing I did when I was in my twenties. 

[00:16:07] I’m back to data, back to the analytics side, pure analytics, HR Analytics is a good fun space to be in right now. Who’d have thought that we’d launch a company in March of 2020 at the start of a pandemic. But, that’s what happened. And to some level, our technology and our insights benefit from an unstable labor market. So I’m not complaining too much about that. 

[00:16:33] Ron Laneve: No, that’s a super transition. So can we dive into Public Insight? You know what it is, why you started it. I’m fascinated by the data you guys are releasing and how it compares to maybe other data sources around the job market that are out there. 

Dan’s Latest Venture: Public Insight and TalentView

[00:16:52] Dan Quigg: Yeah, so Public Insight actually an example of the things that I already described of, my hubris thinking that I could make even back then.

[00:17:02] So Public Insight been around actually 10 years amazingly. And the neat thing about Public Insight is the mission hasn’t changed at all. So our mission was to create actionable insights out of public and market data. So our name fits, and It fits today.

[00:17:23] The, I think the rule that we broke, is. Is great ideas and great technology in search of an application. So we. We started out as, our mission was to make communities more informed was our first stab at it. So we built a, a tool to help communities benchmark themselves across a number of different, criteria.

[00:17:52] We’re a strange company in that we are venture funded, we’re private, but we also have a nonprofit research firm that has a stake in us. And early on, anyway, we had a lot of collaboration with government leaders. Bill Currin, who is former mayor of Hudson, is an investor in our company. He’s been on our board, and he’s still an advisor. And, Bill saw the vision from day one and is still a tremendous advocate, tremendous friend. 

[00:18:20] We went into education and, we created a, higher ed benchmarking application and actually it was a freemium model.

[00:18:28] You could get in and you could use pieces of it for free. And at one point we had something like 500-600 colleges using it. And except the problem was 98% of them were free. So we’ve struggled for the first six, seven years of just trying to get traction and, get enough paying customers to, keep the lights on and we’ve been at, we, at different points in time, we’re at death’s door, two minute warning of business life.

[00:19:00] So the big change for us was, we were helping do some projects that were helping colleges to do more market based planning and, colleges traditionally are not agile to what the market dictates and that’s changing pretty rapidly. so we started looking at market based data around helping colleges with.

[00:19:23] Whether it’s program planning, whether it’s, institutional, assessment, and that’s really what started then us building a market based talent platform. 

[00:19:35] And so that’s how Talent View, which is our HR talent acquisition solution was born was really around providing market data to colleges and institutions. And, that’s still a space for us. But then we realize, oh, we built this now we can actually sell it into. And partner with HR technology firms.

[00:19:59] And so that’s how we got into the, this side of the space. And, now we’ve got, people that are willing to pay, premium for this type of market data, because it’s, it’s actually helping them to drive business forward. 

[00:20:14] Ron Laneve: Can you talk about this? And I know it’s pretty large, but the scope of the data that you are collecting, analyzing and then re-presenting. 

[00:20:25] Dan Quigg: Yeah, and in fact, that’s the way that we describe it. 

[00:20:27] We collect a lot of different data from, primarily from, the leading job board, at least one of the leading job boards in the U. S. and really the world and Indeed, Glass Door, which are owned by Recruit Holdings. We made a decision early on that we were going to concentrate on predictable sources of data.

[00:20:48] A lot of the approach has been and still is to get high volume, but low efficacy of the data. So they will go out and they will, get every, whether it’s every job board, every corporate site and say, we’ve got 10 gazillion job postings. Our approach is, that’s table stakes, especially when you consider that 40% of job postings are repostings.

[00:21:17] What we wanted to find out was not just the volume of job postings, but the movement. The effectiveness of that data. And we do the same thing with every piece of data. So we look at not just the numbers, but we look at the movement and the change in those numbers to see, what’s really happening.

[00:21:37] So we take a lot of data in, the talent acquisition side of things to paint a picture of, whether it’s supply demand, whether it’s effectiveness in, in delivering results, whether it’s all the way to compensation and, bands of compensation for the right role. We’ve got a whole workforce satisfaction or employer reputation component, all of which are predicated around, not just getting the numbers, but getting the story behind the numbers.

[00:22:11] And so we have 153 market metrics today and It’s going to be growing sizably. So think of a market metric as something like, job posting volume, but it might be something like fill rate, open rate, open days, all the way to, on the compensation side, it might be min compensation, max, range to supply demand ratios, to net promoter score. 

[00:22:37] So how can we take those market metrics and make them actionable for people to, whether it’s growing a business, improving a business. What we didn’t know back then is that how people use our data. So people use our data a lot more in sales than we would have guessed.

[00:22:57] It’s probably the number one driver is. What employers are hiring, what employers are not effective in hiring. What are the reasons they’re not effective? How do they compare to their peers, what could they do differently? Those are all things that are part of the mix. 

[00:23:12] Ron Laneve: Yeah, I’ve gotten a glimpse into it and it’s pretty impressive and pretty, pretty powerful. I hope to find a way to capitalize on it before everyone else in our space does, but we’ll see. I’m still working on that.

[00:23:22] I’ve been really studying your jobs report over the last few months, and I think it’s significantly better than what we get through the news and other resources. I’ll just say that. Can you talk about what makes it more valuable and more, useful, to the market compared to the other reports that come out?

[00:23:43] Dan Quigg: A lot of competing thoughts that came to mind there. And I want to, I’m partly I’m hesitating because I want to say it the right way. I think it’s 2 primary reasons. So we, just we’re looking at efficacy of data. We’re applying that same efficacy to the insights that we can generate. For example, things like reposting rates, things like open and fill, we call it a presumed fill rate. It’s not a true fill rate. it’s an inferred fill rate based on ad expiration.

[00:24:16] But when you look at it over millions of jobs, it’s actually pretty, pretty accurate. If you look at any single job, it’s we filled that internally. Of course, but when you look, when you bubble all that up, it’s pretty effective at measuring how we have are unique to us. In fact, we white label a couple of those metrics to TA Tech, which is the leading job board leading association for job boards. And Peter Weddle, who, leads that organization, looked at some of the data that we had and said, oh, that’s unique we’d like to put a wrapper on that. And so they call it the Talent Market Barometer. 

[00:24:57] So it’s just data that we’ve, insights that we’ve created that, other people haven’t. And I’m sure, there’s other people that create their insights that other, we haven’t. So there’s lots of things that we’re still learning.

[00:25:09] The other part of that, so the other part is that government data is simply not either accurate or out of context. So give you an example, pick up the Wall Street talks about the JOLTS data, right? Job openings and Labor Turnover Survey and what people don’t realize is the JOLTS data is based off a survey data. we’ve found that polls aren’t always the most accurate barometer. The JOLTS data is really, in my mind, very questionable, especially the job openings number. 

[00:25:43] So we’ve been sitting here at 30, 30 plus percent above pre pandemic levels and in, in a place where we haven’t been in ever, right? So we’ve been in record territory, even with the latest number, which I think is 9. 6 million job openings. It’s still 28% above pre pandemic. And this was a very stable number. So we’re in a different market now. We’re in a, we’re in a market where, how do you define a job opening?

[00:26:14] Are, are people sitting on the fence saying, in a survey that, we have these job openings now, according to the jolts survey, you have to say, I’m advertising for this. And, but I think people are, more and more you’re hearing about ghost postings. I don’t think that number is accurate. And if you compare it to the job postings number, job postings are around 20% below last year. And they just don’t, they don’t connect. 

[00:26:40] So I like to think that the market lens is a better indicator of what’s going on in our labor market, then the BLS numbers, I just don’t, I don’t think they’re reflective of what’s going on. Now, let’s put the reality in its perspective, right? That we have, very low unemployment. The average fill rates have been the, what we calculate fill rates has been in the upper fifties, cross composite, but people are holding on to old jobs.

[00:27:09] Now that’s changed a little bit. So if we look at a now a metric of 15 month moving average, and that number has been in the 120 day range. So 4 months of open postings, that’s I don’t think that’s a healthy number. Now, I don’t have the benefit of 10 years of data to look at that. But I just think that jobs are not getting filled as timely, and the real root of the problem is labor force participation is still not, still not where it needs to be. 

[00:27:38] Overall given, if you jack up interest rates, inflation goes up, hiring goes down, unemployment goes back up. Throw out the old script. The old script’s gone right now. This is the normal script behind, inflation, almost recession, soft landing, we still have a pretty healthy labor market. We have low unemployment. but I think the pandemic did was it caused people to get out of the market altogether. So it’s not reflected in the unemployment rate because they’re not active job seekers.

[00:28:14] So our labor force participation is, it’s starting to creep back up, but I haven’t looked at it. In the last week or two, mid 60s ish. And there’s pockets of people that are not in the game. And therefore, and with record low unemployment, these jobs are just going to sit idle. So I don’t think we have record job openings. I just don’t see it. Because if we do, they’re, like cicadas waiting to come out in, three years and, they’ll come out. Who knows? I don’t know. But, I don’t see evidence that we have that many job openings because they’re not being advertised as such.

[00:28:52] Ron Laneve: Yeah, and I think anecdotally given the uncertainty from the news around the economy and is there a recession? Is there not a recession? When’s it coming? I think a lot of these jobs are posted and I and I think a lot of these companies are just either waiting for the absolute perfect candidate or they’re just paralyzed and not making any decision. Again, at least that’s just my anecdotal perspective from the business I’m in and what I see. But that’s why, your data is so powerful because it’s empirical and not anecdotal. 

[00:29:24] Dan Quigg: Yeah, 1 of the metrics we look at that it’s 1 of the ones that TA Tech looks at is supply demand ratio. And we look at it by major industry. And what it’s based on is the number of people advertising resumes. It’s not a perfect metric. I want to make sure I say that. But so what the way we best base it on is unique postings against resumes, unique resumes. it’s based on resumes published on indeed to resume abstracts.

[00:29:52] So we take that job title, we bubble it all up by, by industry, and then we compare. And if you look at that, you look at the technology area, the finance insurance area, they’ve come back to what I like to call parity, which is, one job seeker for one job posting. 

[00:30:10] Right now that’s come back to 1-1, whereas health care, is still essentially 20%, 20 to 30% oversubscribed, if that’s the right word, where there’s so many more job postings out there than there are candidates.

[00:30:25] And, people like me that are getting older and, I’m going to need, I’m going to need healthcare workers, right? We’re in a world of hurt if we don’t get more people into healthcare, and to some level, I read this, manufacturing too, I think. So some of the technology jobs that’s going to ebb and flow finance insurance, high and low high and it averages out the parity.

[00:30:48] I think health care is a more permanent problem. And, until we take steps to get people into health care, it’s, as we continue to age as a culture. It’s going to be, more and more challenging. 

[00:31:03] So Public Insight’s our company name. Talent View is our platform name. We are, of course, evolving our platform as, as we learn things. So Talent View is really Two types of uses, two forms of how people use it.

[00:31:17] One is, is people license our data. That’s probably the vast majority of our revenues is through our data licensing. What I will show you here is our self service analytics. So self service analytics is some ways like our job report. So it’s ways of showing how you could use the data.

[00:31:39] So we do use our self service analytics as a way to sell our data, but we also have direct licensors of our self service analytics. 

[00:31:48] I’ll just show you a couple quick examples, and then we can take it from there. So what we call our self service analytics, application is TalentView Interactive. So TalentView Interactive is, a suite of dashboards. We build our multi dimensional data structure and our BI approach around Microsoft, so Power BI, Power Query. Now I’ll wrap stuff under Microsoft Fabric. So what you’re looking at here is a, essentially embedded Power BI, and what we do is we take those 153 market metrics I talk about, And we present them in a different in different ways.

[00:32:27] So here, for example, is, one of the suite of dashboards that we have called posting volume summary. And, let’s say that we were looking at nurses. So right now we’re looking at the last 6 months of activity for nurses. So let’s say we’re looking at nurses in Ohio where we are and the way I like to describe these tables essentially their power tables. So they’re taking data and presenting it in a tabular form. We like to provide people with different visuals to do different explanations. So here, looking at nurses in Ohio, we can, we could start to gain a sense for the volume of activity in the state 

[00:33:13] We let people dictate what time horizon that they want to look at. So here we’re looking at the activity for nurses for the last six months. And we can already start to draw some inferences such as, 10% urgency rate, the mid posted compensation, how many ads are being advertised for a given nurse.

[00:33:32] So this is what we call an openings to postings ratio. So there’s actually 70,000 job openings. But we also could look at this in a, time trended form. So take a second. So let’s do the last, calendar months because we have a partial month right now. Here we’re, looking at the activity since February, we’ve had a pretty significant jump in nursing positions in Ohio, we could see that we’ve got an increase in urgency rate. So this just different ways to look at the data. 

[00:34:10] Ron Laneve: That’s 44,000 unique postings for nursing jobs in 6 months.

[00:34:17] Dan Quigg: So define unique Ron, so I just want to make sure because, we define unique might be different than what you define unique. 

[00:34:24] Ron Laneve: How do you define unique? 

[00:34:26] Dan Quigg: So what we call we, so we call all postings versus net postings. Let me go back to the table. So all postings Is 44,000 net postings, which is net of repostings. I would consider that a unique job.

[00:34:40] Ron Laneve: So that’s what I meant to. 

[00:34:42] Dan Quigg: Okay, whereas this is unique to, we eliminate duplicates, but it’s not net of repostings, 

[00:34:48] Ron Laneve: But those include repostings got it. Yep. So the 25,000 still pretty significant number. 

[00:34:54] Dan Quigg: Yeah, no, when we look at the job status. We’re starting with the 25,000, so we’re looking at and saying, okay, 23, it’s 23 here. Of those, the average fill days. So we’re looking at the disposition of those 23, 53 is the average fill days. 85 is the average open days of the last 6 months. 63% have been filled. 37% have not been filled. And so you could start to get a picture for how these things are moving and again, we might look at this time trended. So we might look at how the fill days are moving. Now fill days, typically you want to go and look at it over a long period of time. So let’s look at, let’s say 18 months and look at the fill days and see, okay, is it trending so that 53 days, where does that sit?

[00:35:47] Let’s yeah, this 82 might be because we don’t have a super high percentage of these being filled yet from July. We did have a pretty significant decline in April. So it was getting better. Maybe by quarter might be a little better way of looking at it. So you can get a picture of where things are at. And, from a, how easy it is to fill these jobs or not?

[00:36:12] Ron Laneve: And the beauty, from my seeing this before 1st, it’s, as you saw earlier, it’s all industries, manufacturing, nursing, software, marketing, everything. So it’s not, it’s very agnostic in that regards. And then secondly, from a potential use case perspective, internal recruiting teams could use this to make the case for, requesting more advertising dollars or re or requesting more, new methods to go to market to find talent because the open rate’s so long and it’s so competitive against all the other open roles in the market. Leadership within talent management, could use it to, again, make the case for more investment or, for potentially more internal training and education. Talent, recruiting companies could use this for, understanding, where the jobs are open and drill down to, the company level? The use cases are for everybody in the talent market are just, pretty amazing from my perspective. 

[00:37:16] Dan Quigg: Yeah. 1 of the good things about our platform and in our self service analytics is it’s very flexible. One of the downsides about our application is it’s very flexible.

[00:37:26] Changing a presentation. This case, I changed it to a company view. Let’s say grouped by job title. So so now I’ve got the mix by company. You can group it by any number of different things. And we’re going to be adding more in the fall release. Some cool things around the when to pay for an ad, when not to pay for an ad, what types of things you put in an ad, those are things that we’re trying to improve and build on, but another area where we add, some pretty interesting metrics is around, where do you advertise the pay range at, And, a few months ago when the, states started requiring, pay transparency and then indeed required it, we started doing things like saying, is it, and I’ll show you something that’s, it was a fun revelation to myself is if I say that salary is estimated, Indeed automatically puts a, a range. You see how this number is almost always at 23.5. So when they impute they basically take that midpoint and, do a plus or minus from that midpoint, whereas if the employer always discloses it, you have a much wider range.

[00:38:47] In this case, actually, it’s lower range, so it’s a much tighter range, which is unusual. Some of the other, technology, it might be bigger. strategies around compensation also play a role here. 

[00:39:00] We love the whole, employer reputation area because what’s moving the needle with people is less about compensation and more about, numerous other things such as work life balance, culture, uses of remote strategies, when it’s and a lot of things that get sussed out through, that employer reputation component.

[00:39:24] Ron Laneve: Yeah, I think it’s just a fascinating product and I can’t wait to, to see how the market embraces it because I think it can make a big difference for a lot of companies. Thank you for sharing. So appreciate your time today, Dan and been great to reconnect. 

[00:39:39] Dan Quigg: And, happy to help in any way. And, of course I’m a big fan of you guys. Good luck to you guys. And let me know if there’s anything else we can do. 

August 16, 2023