William Uranga | Dir TA @ Spokeo | Shally's Alley

Hey everybody, all right, happy Friday! You are live on Shally's Alley. It is the end of June for some people, the beginning of the summer. Um, for me it's literally the middle of the summer and it is hot out there, but definitely the last Friday of the month. Next week is the 4th of July, so for a lot of us it's going to be a short week. Um, for some of us it might be a really long week, long days anyway. So in advance of the celebration, happy Fourth of July to all my listeners in the United States. Uh, we will have that on Thursday, and then Friday it's kind of commonplace for people to take the day off, so we are not going to have a live Shally's Alley. We are going to be rebroadcasting one of my favorite episodes, which really means I'm just going to pick one at random because I can't play favorites. They're all good, so you know how that goes. But what are we talking about today? Well, so today I am super excited to have been able to land a whale of a guest. Uh, this one's been one I've been trying to reel in for, for quite some time now. We've been friends for, for years. I've known William in the space. I knew him before we actually worked together, but we in fact worked together when he was at TiVo. And I gotta tell you, William is probably the most pragmatic, strategic mind that I've ever had the pleasure of working with. As a consultant early on, I learned a lot from his approach to pragmatic decision-making, like kind of cutting to, you know, get rid of the fluff, cut to what's really— what the real impact is going to be. I talk a lot about Talk to the outcomes, not talk to the steps. But I, in part, I learned that from, from William and his analytical sense. And he's just been someone that, that I've always looked up to for a strategic mind. Currently, he is working with a company you might recognize called Spokeo. William, can you tell us a little bit about Spokeo for those of you who don't know what it is?

Sure. Happy Friday to you, Shelley, and your audience in advance. Happy holidays also for those who enjoy the Fourth of July. And yes, we go back a few years, a few hair follicles for me, folks. And Spokeo is simply a company that specializes in digital identity search. So whatever people have as far as a digital presence, Spokeo makes that searchable. It does it on the consumer front from its website and now also offers enterprise solutions for law enforcement, credit and collections. We'll be getting into real estate and e-commerce down the road. So a lot of, um, what we call web application building, a lot of security because we deal with very, very large amounts of datasets, about, you know, easily 2 or 3 petabytes worth. Everything's offered on a subscription basis or for the enterprise standpoint, seat licensing. And it's a great company of about, it's about 17 years old. Privately held, founder-led, and about 200 people in, uh, in staff.

Yeah, so, uh, and it's, it's, it's a tool actually I use, I train recruiters on. Um, it was, it was a tool I've been using for sourcing for a while. But can you explain to me why— how is it that you always end up finding work at companies that I am a customer of? I mean, you know, going back to like TiVo, I was a big TiVo user back in the day and you were there for a long time. Then Groupon, uh, you know, I was a big user of Groupon, GoDaddy, and Spoke. Like, you just go into all these companies that I like to, um, I like to actually use as a customer. How do you, how do you do that? Are you just following me around or what?

You know, I, I guess maybe there's something I know about you that, that I haven't admitted. No, you know, there's a lot of different elements, I suppose, in, in our lives that you kind of look and there's, there's serendipitous, uh, effects You know, for me, I was adopted and raised in Fremont and pretty much in the Silicon San Francisco Bay Area. So you get exposed to those companies. And I was blessed to be able to have some of those good, those opportunities and cut my teeth as both an individual contributing recruiter and then also in leadership roles and got to work with some great people. Great minds, very interesting times. And I've always been interested in technology, maybe a frustrated product manager or wannabe engineer. So the neat thing, as you know, in being recruiting and sourcing is you get to hunt for those people, you get to engage, you get to find out what makes them tick. And the opportunity to put some of these people into some really interesting combinations of teams and departments and new functions and initiatives just has all kinds of great stories that you get to talk about, shop about. Some of them are challenges and problems that we've had to, you and I, certainly brainstorm. But I guess part of it has always been starting small. And as a result of that—

These are high-growth companies.

The asking of the so what about, so what does this mean? How do we get this done and how do we scale it? Has been kind of part of my vocab. Since we met. Go back a little bit of history. I think you and I met right about the point where I was doing retained search in the Bay Area, and it was an invite over LinkedIn, I thought. And I thought initially— this is the funny part— I thought LinkedIn was one of those cults. Initially, I thought, okay, this is another pyramid scheme. I'm going to get snookered.

Right, right.

It wasn't that at all, obviously.

But yeah, it was early on.

And after, as a result, that kind of just started to have the curiosity mindset that you had and say, well, I wonder how this works. How does this apply? And that has just also really served me well, your curiosity that you kind of make very infectious to people that want to learn.

Yeah, exactly. But you have just done really well with high growth, bringing companies up from being, you know, smaller to like just going through either a big shift or a big change or a big growth spurt and everything. So talking about something that is really second nature to you, which is scalability, you've always been really good at figuring out what's going to scale ahead of others, like before others have have figured out how to scale. You're like at the forefront of getting into things that are going to scale well. What's your secret for that? Or is there— how do you know what's going to scale, I guess is what I'm asking?

Well, I guess I've heard it say for some of those popular military leaders, I tend to have more metal in the back than on the front. And so, and I don't mean necessarily that people shot me or launched stuff at me, but some of it's just, some lessons that got really, really drilled into me at various different points in my career path. One of it was, you know, get really, really clear on what needs to be delivered and also understand the why. And it isn't that the why itself doesn't make a difference in how you get it done, but it does add to the burn, the drive, the relentlessness or continuous improvement mindset that I didn't initially understand, but quickly adopted because I got to work for people that were just relentless on saying, I need more, I need better.

But what is it that you really want?

That's what you really, really want. What do you really want? And not being satisfied with however good you have it, knowing that, I mean, you know, as we're only good as our last hire or last quarter. And just realizing that that's part of the nature of business, particularly here, you know, in the West. And so from that standpoint, that's one thing. And then the second thing was the relentless pursuit of— I won't say perfection, but improvement— also taught me that in order to get things done, it was really hard. I found when you had other people that had other agendas, sometimes it was budget concerns, sometimes it was other alliances. So the one thing I adopted in that continuous mindset was, well, let's run tests. Let's do an experiment was the key word. And particularly with the people that were naysayers about things, I said, you know, you may be right. So let's do this little micro test. And if it fails, at least we learn something from it. It could have been why I was—

instead of fighting the naysayers, you actually went to them and said, all right, why is this going to fail? Let's figure it out.

Have somebody to show as a shining example of what it could be, you know, the positive. Got I'd love to have that, but sometimes you didn't even have that. So I started with, let's just do an experiment. And if I'm wrong, then whoa, at least we didn't sell the house. But if I'm right or if you're right, then let's double down on that and let's push it. And that grew more support, more partnerships, and more wins as a result of that. And then we've done it also being also ruthless in right-sizing things, you know, taking away stuff from your team members because they're your teammates, because they're not using it or haven't used it.

Oh man, that's a huge one.

There's no sense in putting good money just for people to—

Yeah, you know how many organizations do that? Yeah, it's not just LinkedIn. I mean, everybody likes to poo-poo on LinkedIn when, you know, you've got 10 recruiters and only one uses it. It's kind of an easy target, but the same thing applies to, other, many other technologies. I mean, if you went through all the effort to, you know, bring on board a CRM and nobody's really using it, maybe you just don't really need a CRM. You thought you did, but you don't. Why isn't anybody using it? Is it leading to that outcome that you want? Yeah.

So that brings up another point related to what you just said there, Shelley. You know, getting to understand why people are not using or using something or supporting something becomes really, really helpful. It could be your own staff person. It could be a team that you're trying to partner with on some hiring assignment, you know, to understand why people didn't do something or don't support something becomes pretty important. It could be that they didn't understand what the steps were, or it could be that they don't understand the motivation or the purpose behind that. And how you then choose to engage them becomes very important. If you don't understand that, you could misprescribe a solution or come in at the wrong way. And unnecessarily make it adversarial or something worse in that sense. So that's something else I've learned is to ask those questions about why is it you don't see it my way, or why don't you agree, or why are you not treating your feedback? And when you engage with that sort of honest curiosity, I think it tends to disarm a lot of the perceived power struggles or value struggles that usually aren't there. Usually, people are aligned with wanting good outcomes, particularly as it relates to their team or initiative, their, their year. And, you know, if you paint, if you position yourself to be supporting them, as opposed to being the powers that be, it goes a lot, lot farther, a lot better.

That's, uh, that's really insightful. Yeah, I'm glad I, glad I asked about that. So there's also, as, as you, as you get ahead of the curve and get into go into, um, not being reactive, right? So there's, there's this reactionary seat that, that recruiters tend to fall into or get pushed into, if whatever, not trying to assign any kind of blame here, but for whatever reason, getting into a very transactional, a daily wheel kind of situation where you're just grinding and grinding and grinding. It's really difficult to get out of that. But one of the things that you, that you need to do to get out of it and have that help your success is pipelining. And so that's something that you've always brought into your, your teams is smart pipelining. And I think you have a way of explaining what the pipeline for, because it could be, it could be overwhelming if you haven't started this. Like, where do I start? What do we pipe? What do we pipeline for? You can make the mistake of pipelining for the most urgent recs, which may actually not be the best way to pipeline. And we've talked about this before, where you want to pipeline for not the freaky one-off recs, where, you know, you're never going to be able to recycle those candidates. You want to pipeline for the bread and butter, I think is what you call it. Right?

Yeah. So that's a good way of putting it. You know, aligning with, with, with the business, uh, you know, that most businesses look at, you know, smartly, kind of the 80/20 rule. What, what can I do 20% that will get me 80% of the way there? And you focus on the major stuff. And, uh, because if you're not aligned with the business, um, I don't know who's underwriting your budget, but it, that, that gravy train just won't last long. And so to be attuned to that, to make sure you're sized correctly, that you're using the right tools. Once you have that and understand what you need to really deliver on, then you have the choice to help define what your scope of what you're gonna actually deliver. Well, with these resources, these tools, this budget, and this sort of time period, I can deliver X, Y, and Z. If you want A, B, and C, That's a relevant discussion, but that's separate. And then you certainly also need to do the same thing within your team to say, okay, we have this, this exercise that we need to go through and recommit to what we're going to do. And for us, you know, here at Spokeo, we realized that it was just not fun posting and praying and grinding out just-in-time sourcing. There's, there's a time and place to do that, maybe initially, But there's no virtue in doing that day in and day out. And I've found most of the recruiters and people I get to work with don't find that life-giving. They don't find it exciting or challenging. And so to keep that, you know, the positive elements of that, you know, prioritize and say, well, what are the key things we'd just rather not be doing? Some of it's administration, some of it's this grinding stuff. This is where we came up with both pipelining on it. And, you know, one example we have that we use a lot is we tend to hire full-stack developers. They're versatile. We need different levels as far as experience and capabilities. And we could post and get a whole bunch of them, but it seemed to then just kind of kick us back in the teeth in a bad way when we had all these thousands of people that had applied to other roles that we were doing nothing with. I mean, what's the point of having a reservoir of static resumes if you don't go back and revisit them because, as you know, people change, people grow, people take on different capabilities and roles. It could then become viable candidates in the future if only we checked. So that's where we started doing the pipelining, not the role by role or profile by profile of each candidate or applicant, but we started to do keywords, degrees, and other things that were salient to certain positions, such as the web app. And we had, I don't know, about 4,500, I think was last count, for the web app positions that were now ready to, with a push of a button, to be able to start communication. Hey, you had applied here at Spoke at one time. We even engaged in the conversation. We have some more positions open for you. We'd love to talk with you about them. You know, is now a good time, or are you interested in that? And then be, of course, being prepared because not everybody's gonna stay in the same place. Some may want to, oh, I wanna do more backend or I'm now a product manager. Good, allow for those sort of responses to come in so that you're getting your pipeline smarter and smarter as time goes on, rather than just hoping and paying unnecessarily agency fees or other just-in-time solutions that will always cost you more than if you just spent the time knowing what your company needs you to deliver on time and time again and getting really, really good at that, such as with the pipeline.

So, getting really good at that one pipeline that is meat and potato— bread and butter, meat and potatoes. Sorry, kind of lost my analogies there. But then, here's the trick, right? So, when you have— well, forget about where to put it. People always struggle with, do we need a CRM? Do we not? I don't want to get tied into a tool. You could have a pipeline in a CRM, you could have a pipeline in a spreadsheet. I mean, I've put together spreadsheets in Excel that can be, that can be used as pipelines. However, the one thing that you do need to do, no matter what, even if you put this— even if you use crayons and a notepad, um, is you've got to figure out what these— who these people are in a way. And so what are your recommendations for how to tag candidates? And what I'm asking for really is like the structure from a higher plane of thinking. What is the composition or the elements of tagging that make it useful? Because I ask because you can very easily go in the direction of way too many tags, and then you have this tag cloud of 20,000 tags, some of which may be redundant or repetitive or unnecessary. And tagging becomes useless. But you can also have tags that are so simple or so encompassing that they're also useless because engineering, for example, could be, right? So what's your philosophy on how to structure tagging to organize these?

Yeah, that's a great question because you can get to the point where you're slicing water.

Yeah, that's a good one.

And then that's where you meet yourself saying, what am I doing with my life? 1:00 AM in the morning. So we have— before we started that particular pipeline project with the web app, we started to say, well, what are the things that we need to have as minimums to consider a person? So out of the— we looked at everybody that had applied to these roles and some were— had no business applying. You wondered what job they even read. And then there were some that were closer. So we looked to put in the ones that would get as close as to what the hiring managers had typically looked for in a profile. So for our particular example, I'm not saying this is prescriptive. It's going to vary from anybody's company organizational situation. But in our particular environment, they needed to have a computer software, computer science related degree.

Okay.

If not, so that, that, that meant Information systems wasn't good enough, William, I was told.

Okay.

Or, you know, data analytics wasn't good enough, but computer engineering, computer applications. So we got that down. So that was, that was one thing we said, okay, well, we'll put that in there. And we figured out a Boolean search for that. And I see people say, hey guys, still use Boolean? I said, I don't want a tool if I can't control my search terms, I have a trust issue.

Right, 'cause you don't know what they're gonna do.

With that algorithm. And maybe I'll get over it over, you know, still as I get older, but I still like being able to do that. So we developed that. And then once we found the right set, we created a tag for that, a short, simple one. I think it was 2 or 3 letters or a word. Okay, we got that one taken care of. And then what was the next one for us? For that, it gets a little bit harder because ours is high availability and large web applications. We have to think of, well, what would that be? We started to think of the amount of data that would be used or accessed to this. For us, I mentioned 2 or 3 petabytes, so we set a minimum since we were going to be building pipelines of 5 terabytes or more, and that really starts to shrink that down. Then after that, we were looking for— I'm trying to remember what the other tag was.

I'm hearing a pattern here of— sorry, I'll let you continue, but I'm hearing a pattern here of using tags that are once again really related to your outcomes. You could tag a bunch of other things, but those tags don't mean anything about what you're looking for. So really focus only on the tags that lead to the outcomes that you want. If everyone has to have this particular degree, then that becomes a tag. The names of 20 other unrelated degrees are not necessary to tag, right?

That is correct. Of course, and there's some that overlap. There's some that are good for a couple positions and you allow for that. But the moment you realize that there's a better tag that gets you what you're really after, then you go with that one. You always go with the greatest number for that because you always do refining later on. The only other thing that I think besides qualifying, meaning a hiring bar, is the nature in which either they applied, so source-related, or in which they left your, your vetting or consideration workflow. So did they withdraw? Or did they miss not getting a high enough score in the hacker rank, which would be true in our situation. You wanna message those people differently. And if you don't have a tag, it's really hard to be able to message that uniquely.

Hey, because of that slice of, yeah.

Right, we're so sorry that you didn't quite make the cut for the hacker rank, but if you're up for trying it again, we've got another exercise and we have an opportunity for you to be considered. Because just because you failed once doesn't mean you'll, you're always gonna be a failure in our book. Because if that's the case, then you just might as well stick with posting and praying, right? You just see who applies and deal with that. This is kind of almost very trying to do positive reinforcement that just because we said no, it's more of a no, not now for this role. So the fact that we invite them back to be considered again, if it's good timing for them and of interest, if they haven't switched their career trajectory, then, then great. It should be— they should be messaged differently. I think that's sort of, ah, somebody gets me, or somebody understands and appreciates where I was and what I tried, goes a lot farther than just saying, hey, you want a job?

Ready to— ready to get back to work?

Yeah, ready to get back to work?

Apply here.

Right.

Apply now. Okay. All right. So once you get past the pipelining and tagging, now you have useful lists, however you, you know, whether they're crayons or spreadsheets or right advanced CRMs. Now, um, what's the next evolution? Is it— do you move into, into predictive information? Is that competitive intelligence? What's the next step? What's the next evolution in that?

Well, I think that can go in a couple of ways. I mean, one, you need to think about your selection cycle that you do put people through. It should look a little bit different for people returning or being recontacted than for first-time folks. I'll submit. But then I think there's also good intel that can come in from competitors. Now, the example I gave you from web app, I mean, we think we compete with everybody for full-stack developers, you know, for large-scale websites. So that doesn't have a true evil empire, like I'm trying to go beat up Oracle or some other company. If I could just hire them from Oracle, I win, you know, in my goals. It's much broader than that. So I don't have a list of companies to go steal from or beat up. I mean, I know who tends to hire full-stack people.

Right, right, right, right, right. Yeah. But in other words, if they do petabytes—

I'm interested.

Right. And that could be healthcare, biology, scientific research, or something we don't even know about yet, you know, AI, whatever. Yeah. So it doesn't matter.

It's really fun. I mean, I remember, you know, we were— I might have shared this story with you back then, but at TiVo, we were looking for, of all things, IP attorneys. And we just couldn't— we couldn't find the right people. And what hit it for us was there was one person we said, that was a great interview. We looked at the profile, the person had actually been a software engineer one too many years in undergrad, stopped and switched and got their law license. So we ended up looking for somebody that had a JD, but that had been working for or gotten even their computer science undergrad degree. They were the most creative, looking for opportunities versus, you know, boogeyman behind every clause type of thing. And that's what we were looking for. We were looking for somebody that could see opportunities and craft agreements that would license our technology in a fair bilateral way between companies. And that was great. Then after that, it was like taking candy from a kid. It was just, it was so easy to find that. That made all the searching and all the tagging you know, breeze after that. So it's amazing when you're very clear on what your purpose or your goal is. That is, that's a great moment.

That's a skills transfer thing, right? Or is that—

yes, in discovering, it's discovering what they, what they call themselves. Or what they did, right?

Right, right, right, right.

Yeah, not, not how you and I call it. I mean, we can call them what it is. They had a lot of money, they could switch degrees. I couldn't. That's why I majored in poli sci, I guess. But anyway, um, but you know, whatever they call— whatever we call them is irrelevant. It's what do they call themselves and what drives them. And when you have that understood, um, man, you're, you know, you get to be the, the hero for them because you hired—

because you know them better than most other Yeah, it's interesting. So you, you might not have a list of target companies, and you might not have a clearly defined target job title in a way. Um, and it's almost like, you know, there's, there's, there's not even— there may be some common skills, macro commonalities, but it's the micro commonalities that are not skill specific that are really significant. Like, for example, some time ago You might have heard of a company called Bridgewater. They're a big fund management company, one of the largest, and their founder and CEO, Ray Dalio, had a very specific way of recruiting people that had particular potential, but hadn't necessarily— like, we think you're going to do it, but you haven't done it yet kind of thing. And I was brought in to try to ideate ways that we could identify people with that potential using keyword analysis. And it's one of the things that led to my revelation around using cloud analysis. It's sort of built into that. But what I'm trying to get at is it was nearly impossible to find any kind of common event. Some people were gold medalists. Some people were scholars or had won, you know, huge merit scholarships. Some people were— just went to the right school and worked their, you know, behinds off. But there were all these different reasons why it— you know, some people enlisted in the military and, and had a military trajectory that was far more accelerated than expected. So it's, it's not how they, it's not how they started. It's basically what we were looking for was the speed of the trajectory. So if you went to college and, you know, went from zero year to graduation year and you just followed along taking all the courses and you graduated and you're magna cum laude, yay for you. But that's not what we were looking for. We're looking for the one that finished in 3 years and won a merit scholarship. And, you know, and then got selected for Young Entrepreneur of the Year, stuff like that. It's just like this, this— and you can't really search for that in keywords, but you can search.

Yeah, I think for me, when I hear— when you— I hear you share that, uh, I think there's a question behind that. Why did you have to finish it in 3 years rather than 5? Or why did it take me 5 years and my roommate 7 to get our degrees that were standard report.

Yeah, that's the question.

What was going on? But, but if you— and if you don't stop and ask that, then, then I would miss it too, right? Um, but I, I— what the— it wasn't just that we found those keywords for, uh, the intellectual property, uh, IP attorney. It was that it really became an aha as we interviewed them and asked the questions. Well, yeah, well, why didn't you get—

what was the transition?

Why did you leave your CS degree and go get Yeah, you know, law license. And that was the interesting thing. That was the— that's where we said, oh, there's this curiosity gene, if you will, that they just— and you could see the questions they were asking, almost more questions during our interviews than we were of them, um, in that sense. They were just naturally curious. And that's something, um, you know, you can't train people on that. There's not a course. Well, maybe if you're a lawyer, maybe asking questions and cross—

yeah, investigative techniques. But I think I know playing. So it's the same with this, with this concept that Ray Dalio wanted us to identify, which is why did you want to win a gold medal? Of all the things that you wanted to do, you know, you were pursuing a degree, you were working a job. And so why did you choose that? And how did you stay the course despite injuries? And despite, you know, why did you join the military and then proceed to like break all the rules and become an officer in record time? Why did you decide to go to this school when you were like— and that source is, I don't want to say intangible because it is. We did figure out patterns of ways to identify it and used it and implemented it. But it taught me a lot about words that are hidden in plain sight. For example, one of the patterns that I recognized was that people used different tenses for the same word in their description. For example, engineered, engineering, engineers, manager, managed, managing. So there, it's not just manager, it's manager, managed, managing. If they have all three of those, then it's an indicator that that's kind of like a trajectory.

Right.

It's just, it's just one silly example. There's others and you can combine those, right?

Like, it also indicates who drives them.

Exactly right. Exactly.

So why did you get your degree in 3 years? Well, because my, my father passed away and I was going to have enough to do a luxurious 4-year. Got it. Okay. Well, I'm not saying that's what we started, you know.

Yeah, yeah. But it's—

but you understand that there was, there was a driving need that they were willing to do it. And man, if you can unlock that in anybody, what the drive is, and make that their need that they will adopt for themselves, get out of their way. I mean, you, right, you can't teach them that enough. It's almost like what you're talking about is, is a version of Moneyball.

Yeah, it was really like that. It was really like, it was completely, it was patterns that were there but nobody was seeing them because the obvious patterns were too obscuring. The obvious patterns were like, you know, the typical search that you do, years of experience, pedigree of company, type of school, type of degree, all these searches, these are macro commonalities. It's the little micro ones that were not very obvious. And that's exactly what Moneyball is. It's identifying those tiny little, you know, the kid had a bum leg, therefore, he developed a way better pitching arm than all the other kids, and that was his advantage. So he became, you know, this like ace pitcher that learned how to pitch to everybody based on their batting style. So they adapted to their batting style. How do you figure that out? Well, I figured that out because they had a bum knee, right? And it's like, oh, okay, so this guy's going to be the next world-class pitcher because they had a bum knee. Well, not everybody with a bum knee is going to become a world-class pitcher, right? So the Moneyball equation is Definitely the right—

yeah, when you find that— we're doing that, we're seeing this a little bit right now with, um, I say playing around in loose term, with AI, with these large datasets and models. And, um, I'll have some people say, well, I just, I just wanted to do this. I'm like, well, you got to get curious and break it down into, you know, good script writing. Usually the smaller script, you put those together then you get something really interesting for a first draft or a yield of what you're looking for. And that's forcing people to think, okay, well, I need to think about my audience. I need to think about the format. I need to think about my style. I need to think about— I need to ask, have it, AI, ask me questions so that I— they can be better informed of what I'm—

You can learn from them asking the questions.

Right. So there's all kinds of stuff that We're almost back at this again in using AI and what it could do. I mean, yes, I know we can write, it can find better job descriptions. That's interesting, but that's the low-hanging stuff. If we're going to get some of the complicated stuff, let alone what it can do for pipelining and discovering trends and other analytics, we got to get really good at making these observations of, like you said, these keywords and note these small these patterns, they can have a bigger yield. And that's what it'll take. It'll take that small pattern, find a bigger yield for us, and then you look like the smartest person in the room for having done it, but you start out small.

Interesting. That's something that computers, machines are particularly good at. I'm really fond of an explanation that a friend and colleague gave me when I was exploring different solutions for AI. And I wanted to understand why everybody kept calling it AI when it really isn't. And I have since stopped arguing and stopped saying it's not AI because there's no point in that. It is an AI, but there's just no point in me saying that because it's just become like it's trying to tell somebody it's not a Kleenex, it's a paper tissue, or it's not a Xerox, it's a photocopy. Like, you know, at some point you just give up. It is an AI, but I wanted to understand why we kept calling it that. And the explanation that he gave me was really, it was one of the most succinct explanations just stuck with me. He said, look, if you make a nail hammering robot, that robot is going to hammer nails faster and better than any human ever could. But don't ask it to serve you tea. So machines are really good at some things, and this is one of them: identifying unexplained or unknown or hitherto unseen patterns in massive amounts of data. So that's truly— so in other words, the, the end of the anecdote is it's not AI, it's just that they, that the machines are able to analyze so much data that the vastness of the amount of data that it looks like AI, right? Because they're making these inferences that humans could make that it would take a lot longer for humans to make. It would take years or decades or hundreds of years. So these AIs, quote unquote, are just analysis that are discovering new vaccines or new diseases or new cures for diseases because it was always there. We just never had enough data to look through.

Right. So now we have, you know, hopefully this will get applied to, you know, pipelining. I know it is in some, in some senses coming up with rules and, and key steps. But you know what, what it will do for us is it'll allow us to move away from the more manual stuff of finding connections and data points. And it'll allow us to think about the next level up of what does that mean?

What do we do with that?

But to do that, we almost have to get in again and relearn the art of asking some of those, those obvious questions and the whys to find other drivers so that we can make that next level better. So we'll continue. I mean, it's like the buggy whip industry. Eventually it went, you know, Bye-bye. And nobody rides horses around except for fun, I guess, unless you're at the racetrack. But, you know, this similar thing will happen with recruiting. And it'll be fine as long as we continue to have that curiosity about, you know, how can we work on higher-valued work? If it's just about finding 2 resumes for customer care, You know, shoot, that's the things that we can let our partners, our agencies do, and they'll do that great for a lower price point. But our value and where we keep making a big difference in our organizations for our executives and our hiring managers, our clients, is taking care of presenting those great options to find great talent and do so in a timely, cost-effective, and at-scale manner. So as long as we keep focusing on that, you know, things will continue to change, but we'll just roll right with it. We'll figure it out. It's part of our kind of our determinative spirit of things.

Yeah, yeah, that's the curiosity component, I think, which is absolutely critical. So it's a kind of competitive intelligence. Last or second to last question that I wanted to— almost last question I want to ask you is about Scrum. You mentioned something about adopting, recruiting, about recruiting, about your team, your process adopting or absorbing or learning from the Scrum that developers do. And what my understanding of it is, Scrum is essentially kind of like, it's similar to in business school, just-in-time or manufacturing, you know, inventory control and manufacturing, things like that. So how, but what are you doing How is recruiting learning from—

how are we— what are we adopting? And how are we adopting that?

So yeah, whatever you can share, that's not, you know, sure.

So, you know, this started back in, you know, when we met at TiVo. I, I wanted to— I had too many hiring managers say, I don't, don't bring up HR lingo. They just they didn't like it, they didn't want to hear it, they cringed, they didn't bother to learn it or appreciate it. And, you know, I could have gathered up my marbles and gone home and, you know, sucked on my thumb or something, I suppose. But, um, I decided to say, well, what are they doing that's so great that they don't want to learn any of the HR or recruiting lingo? And I just— that kind of opened up this whole world to Scrum methodology and Agile thinking. And I thought, well, gosh. And then the other thing is, I took a course in project management, which I was around technical project managers from Space Systems Loral, and I mean, smart people, and heard how they were applying project management with that. That sounds a lot like there's a lot of different terminologies.

Yeah.

And so I just made a personal intention that slowly but surely, as much as my team could change with me, we were going to start changing how we looked and referred to things so that it was more natural for engineering hiring managers and the other business people too. And we just did that. We explained that this kickoff meeting is like our sprint planning. And so we started to use those sort of analogies. And then we realized, man, there's a lot of wasted effort and cycle in recruiting. So we started to do standup meetings.

Okay, I've heard that.

We started to award points to our hiring projects as far as difficulty and stuff like that. And we started to talk about how, you know, from a Scrum team, the interview team really is like a Scrum team and why and so forth. So I adopted that pretty quickly. And then I realized, oh, if I'm going to be a Scrum Master, I need to also think about how do I start removing the blockers and the difficulties for my fellow staff, which you would assume would be the case of any people leader for their team. I just hadn't thought about it in engineering terminology. So we started to take on those sort of elements as well. So now We're here, present-day Spokeo, we're starting to run our recruiting projects. We haven't gotten to the actual hiring project itself, but the recruiting-related projects that are not offer-related, we'll say, whether it's recruitment marketing or pipelining, we're starting to run those in 2-week sprints. We scope them out, we have discussions about the difficulty value, we have a rubric using the Fibonacci code for that. And it's just been great to know and compare notes with other project managers and other engineers saying, oh, well, here's how we do that. There's lots of documentation out there on how the engineering do it and how they're trying to improve their velocity of completion and everything else. I mean, that's exactly what recruiters want too. They wanna be able to complete, increase their mastery of certain skills. How can I, how can William help them realize that as well? So that's been something that's been part and parcel to how I've looked at it. And admittedly, it is biased towards software engineering. You know, there's other companies that do waterfall and stuff like that, or Kanban, and those all have, you know, good benefits too. But this is something that I've done with my teams. It's been once they understand it, really, really embraced and start, you know, they start talking that way. And it's the greatest thing when you start having both candidates and hiring managers start wondering, you know, who's the real hiring manager. And when they think your recruiter is actually the possible hiring manager, particularly if you're a candidate, That to me is kind of such a huge accomplishment and the biggest compliment that they understand the business, they understand the tech stack, they understand the history, the culture, and they are so well representing your client, the hiring manager of the company, that hey, they get all the credit. It makes for a great candidate experience. And the bond between the hiring manager and the recruiter can't get much stronger than that.

When you said the Fibonacci sequence, that's that sort of like, it's like a, like that, you know, like the previous two numbers added up lead to the third one. So it's kind of like a— so in, in that analysis, you're saying that the, the points system was following that? So the further they were out from the center, right?

So let me back up a half step. I realized I could have, I could have rushed a little bit.

That was just, it was just me. I felt like I was a half step behind.

We're looking, uh, we assume that, uh, right now our hiring searches take up a certain percentage of our, our time, uh, over a week. The other portion, we try to figure out how many points can we fit into that in a given 2-week sprint to complete things. And we have a rubric, uh, that we stole from our engineer, one of our engineering, uh, Scrum teams. And it goes from one, hey, really easy, there's no QA, just get it done one day type of thing, all the way down to, I think the max we've gone is 13, where you got to have executive approval, QA sign-off. It would be probably super complex, redesigning our core website. It is. And that probably even includes multiple sprints. It probably will have to be broken down. 13 may even be— when you get that large number, you probably start thinking about how do I break it into smaller chunks.

That makes sense. That makes sense. Okay. Yeah, because of the complexity or, or the time involved, or the—

yeah, and the number of parties that have to be involved in approving that. I mean, you know, for example, some of our Scrum teams do not have design at all because it's all backend related. Cool. Okay, well, that simplifies a lot, you know, saves resources. And that's why design is more of a shared resource versus having to be on each and every team, as opposed to at least on the software side. So you want QA on every team, you want DevOps in every team. Those are, those are required, you know, stakeholders or participants.

Yeah, although it seems like you're complicating it, you're actually simplifying it because you're reducing, uh, all alternative steps that would have cause delays and have sending, shipping something off and then having it shipped back versus just— yeah, that makes sense. Um, so last question, and I know we, we're, we're pretty close on time here, but I did want to ask you this. You were talking about, uh, and, and I— this is part of me just kind of, um, being evil to a certain degree because I feel the pain and I want other people to see, uh, to see someone else feeling the pain. You just got into recording content for training your, your team, and you're delivering the content asynchronously. So in other words, you're recording video content to be played back. And I hear a lot from clients, they go, oh, we have our own internal training, we're going to do our internal training. Everybody thinks that they can do their own asynchronous video training until what happened with you happens to them. So can you share with us exactly how painful it is to actually really do asynchronous training? It's not a matter of just, I'm just gonna get in front of the camera and record some, you know, videos. Yay!

Well, there's a couple lines of thought. One, the people that get up and present the news or something like that regularly, they make it look easy. And I guess that's why you've got professionals and they have an earning capacity that is in the stratosphere. Good for them.

Right.

You can tell I'm—

Getting in front of a camera for them is like you and I writing an email.

No, I can barely do a stick figure, Shelley. So, yeah. But draw a stick figure. The other one is there's also doing it well enough doing something well enough so that it is useful and absorbed or adopted.

Yep.

As one knows. So those are the two tensions. Otherwise, you know, the worse you want it, the worse you get it, I guess, is the other thing. So not wanting to, not realizing I can't attain the former, but having—

Right, you're not a highly trained professional actor and you're not a highly trained professional educator. So getting in front of the camera is difficult, putting together material in a succinct enough manner that's edited enough.

Can I baffle? No, I can't.

Yeah. That retains their interest. It keeps it going. It's really hard to do content that sticks. It's really hard for learning asynchronously. It's just—

So what I had to do, what I was doing is I was delivering it live and that had its own problems. I had it, well, but it was solved because I had, I had at one point a bigger team. Somebody could run the scheduling, somebody could herd cats and get everybody in the room. And, but now that we're, everybody in this day and age from productivity is being asked to do more with, in some cases less. I figured this is one of the things that I need to pay the piper now, pay the pound of flesh or the pain now. But the idea is it should last me for the next 2 or 3 years because I'll be able to automate it to a certain extent. And so I'm taking what we refer to as our 100 series for interview training workshop, about 4 different modules. And rather than present it in an hour and a half or 2 hours, I'm breaking it into like 15-minute chunks. And am going to be recording it on a video platform, specific video platform that can track who watches it and also deliver a quiz for retention and for—

Remediated learning activities.

Right. And so doing that, it's interesting because I'm kind of a little bit of back in school again in the sense of You know, it's one thing for me to deliver it, but it's another thing to actually script what I'm going to say. And you think about your words a little bit differently when you do that. I mean, this is going to live on for a little bit, and I need to have the content reviewed and approved. So there's some steps doing that. But the wonderful thing, and this kind of brings it back to AI, or sorry, machine learning, is there's a whole bunch of what I'm stuck for an alternative way of presenting something or wording something. You know, ChatGPT, Claude, whatever, you know, you're using is wonderful to get you through that, over that hump of different ways to present. Or I want this style to be replicated throughout the whole module. And I'll go through and find out quickly as opposed to having to painfully do that yourself or take up your marketing person's time doing it. It's amazing what you can get done. So I have by the end of this year, all 4 modules in addition to recruiting, in addition to leading a team and other deliverables to have those 4 modules done for that. But I think it'll be fun. We're gonna probably put everybody back through it, even if they completed it a year or 2 ago, because I don't know why, I mean, they do it for, harassment training and other critical things, why not do it for interviewing and hiring? People should have refresher courses, even if they've been doing it for 20 years, like some of us.

Yeah, it's always good. It's like, I like to say, it's kind of like, pick whatever your favorite athlete is, Federer, the tennis player, right? He still has coaches. Well, not anymore because he's retired. But he, you know, even at the top of his game, he had a swing coach, he had a fitness coach, he still had coaches because you need to constantly revisit the fundamentals, even like putting one step forward in front of the other. Because if you forget that or you do that wrong, it leads to all kinds of other things getting messed up, mixed up, or not working well later on. And we just sort of take it for granted. So it's a really good idea to refresh those Every once in a while, just take a course on how to step. You might think you don't need it, but—

You're so true, Shallon, when you say that. I think, one, if you always think that I— if you wake up every morning and say, one, not only am I grateful that I have another day, but there's something out there for me to learn, that feeding that curiosity, life will never get boring for you. Two, it keeps me realizing, grounded, that I don't know everything. And I know that, but it's great to have that remind. And then 3, it's good to get outside your own head. And like you mentioned, with the coaches, they can spot stuff that I'm way just too close to see. And that's something that other tools— and yeah, because you're right, that's the benefit of having a Scrum team, if you will, or coaches to point that out. So yeah, great, great points.

Well, man, it's been a pleasure as usual, uh, to chat with you. I'd love to have you back on the show. Uh, before I let you go though, I do have to ask you the same two questions I ask all my guests. Uh-oh. Okay, the first one of those is who should be our next guest?

Oh my goodness.

And it's okay if it's someone we already invited. Just, well, so do you think we should interview next? They may not even accept the invite. Right, but I'll try.

Well, I, I think this guy's a real, um, uh, kind of a, a pillar in, in the recruiting community. I'd be surprised, um, uh, if not. And I'd have to say John Vasilica.

Oh, good one.

Yeah, if you haven't, um, he's always thinking of something new or something that he's heard, and he's noodling on it. And I love doing that, whether he's doing it on LinkedIn or actually teaching a workshop or presenting somewhere. Yeah, that would be, that'd be, that'd be a done and done.

I'm gonna invite him, see if he gets, see if he takes the bait. And then the last question is for people that watch this as a replay, which is a majority of folks because they can't be online at 1 in the afternoon on a Friday. Leave them with something that you think that they should think about? So not, not words of wisdom, but a question that, that drives wisdom in themselves, you know? Like, what do you want them to mull over, chew on, think about after they watch this?

Uh, also a good question. So I would— this is gonna sound really, really meta, But I do mean it for, you know, as intended, particularly for the recruiting and sourcing and HR community. What's your purpose? Why are you doing what you're doing right now? Not just that somebody gave you the job or you live in Hotlanta, or in my case here, the Phoenix Valley, where it's drier. You know, why are you doing what you're doing? Your perspective on that shows up in how you do what you do. And I think that's worth examining. I think everybody's life is worth— has great value, that we're all here for a purpose. But if you, the individual, aren't dialed into what that is, I think you're missing out on some of the fun in understanding that.

Deep. All right, I like it. Great way to close. Yeah, man, thanks a lot for being on the show, everybody. Uh, skip next week or watch the replay if you, um, if you want. The week after that, we have ABQ, Amy Beth Quinn, coming on board to, uh, meet with us.

Another—

until then, yeah, another good one. I have some— got some doozies in here. I'm starting to catch the big whales.

There you go.

Until then, please be kind to yourselves and take care.