Empowering Recruiters with Data-Driven Decision-Making & AI

[00:00:00] Hey, what's going on everyone? Ryan Leary here from Work Defined. You know, if there was one thing that I could change about recruiting, it would probably be the amazingly awful candidate experience that job seekers have to endure at one of the most stressful times in their life. Hiring teams, it is time to step up. You've got to create an experience that is memorable, fast and efficient.

[00:00:28] And you can do that with Indeed Smart Sourcing. Check them out online at Indeed.com or just Google Indeed Smart Sourcing.

[00:00:37] Oh my goodness, bad touching, harassment, sex, violence, fraud, threats, all things that could have been avoided if you had FAMA. Stop hiring dangerous people.

[00:00:56] That's all. FAMA.io.

[00:01:09] Hey, this is William Tencup and Ryan Leary, and you are listening and hopefully watching the You Should Know podcast.

[00:01:16] We have Dara Brenner on today, and our topic that we're going to be discussing today is empowering recruiters to do more with data and analytics.

[00:01:25] Kind of a nice big kind of plot of land that we can do some fun stuff with.

[00:01:29] That is a lot.

[00:01:30] That is a lot. Ryan, how are you doing today?

[00:01:32] I'm wonderful, man. This is my first episode back in, I think, right?

[00:01:38] Yeah.

[00:01:39] And I'm excited. This is going to be a good episode.

[00:01:42] Dara Ryan recently went on vacation with his in-laws, which I don't think is…

[00:01:46] Oh, will you stop it? They can hear this.

[00:01:50] I don't think that's technically…

[00:01:52] Uh-huh. Yeah.

[00:01:53] I don't think that's technically a vacation, but anyway.

[00:01:56] No, actually, this was good and refreshing.

[00:02:00] The challenging part was we had adjoining rooms, Dara, with my brother-in-law.

[00:02:06] So that was tough because for the most part it was good.

[00:02:10] That door stays locked.

[00:02:11] Until the door opens up.

[00:02:13] And you're like…

[00:02:13] Yeah.

[00:02:15] That door stays locked.

[00:02:15] Outside of that, this is a wonderful, wonderful vacation.

[00:02:18] I call security and I get that a lot.

[00:02:21] I get that thing broken.

[00:02:23] Dara, would you do us a favor?

[00:02:26] Introduce yourself to the audience.

[00:02:28] Sure.

[00:02:29] I'm Dara Brenner.

[00:02:30] Super happy to be here.

[00:02:31] So thank you, William and Ryan.

[00:02:33] I am Chief Product Officer at Employee.

[00:02:37] And I've been at Employee about…

[00:02:40] I guess getting close to six months now.

[00:02:43] Oh, cool.

[00:02:44] So long enough to know better.

[00:02:45] That's what you're saying.

[00:02:46] I hope so.

[00:02:46] Yeah, yeah.

[00:02:47] Yes, that's the bit.

[00:02:48] Six months is like, what?

[00:02:50] Wait a minute.

[00:02:51] I didn't realize.

[00:02:52] Six months and she's still there.

[00:02:54] So that's good.

[00:02:54] Yeah, yeah, yeah.

[00:02:55] I mean…

[00:02:56] I should have asked different questions when I was being recruited.

[00:03:00] Well, let's talk about this topic, empowering recruiters to do more with data analytics.

[00:03:05] What are you seeing right now in the market?

[00:03:07] What are the questions that are being asked of you in terms of product?

[00:03:11] And how do you see kind of where you can make them kind of the biggest bang in their worlds around data and analytics?

[00:03:22] So, it's a great question.

[00:03:24] And I think that data and analytics has been swirling around the industry for quite some time now.

[00:03:31] People are trying to land on it and say, what does it really mean?

[00:03:34] I mean, I remember years ago, people would say, oh, we've got analytics or we've got BI and what they really just had were reports.

[00:03:41] Right.

[00:03:41] And so, it's really kind of less about reporting the news and really more about enabling the user with what to do with that news or, in some cases, just doing it for them.

[00:03:55] So, as I think about data and analytics, and one of the reasons I was so excited to join Employee is we have 22,000 customers.

[00:04:06] 22,000 customers have a lot of data.

[00:04:10] And what's really interesting is 22,000 customers from SMB all the way up to enterprise and everything in between.

[00:04:19] So, any place I've been before, we were very focused in a specific area.

[00:04:25] And so, we could talk about that particular enterprise group of customers or that mid-market group of customers.

[00:04:32] But the reality is that if I'm a customer in a specific industry or I'm a customer in a specific region of the country or the world or even in a specific size segment, it's relevant to me regardless of the size of the companies that I'm getting the data from.

[00:04:50] It's relevant to me to understand what other people in that industry or that part of the world are actually seeing so that I can compare myself.

[00:05:00] So, a good example would be, let's say, for example, I want to see how I'm doing in the electric and gas industry relative to time to hire.

[00:05:11] And I happen to be an electric and gas company in the Midwest of the United States.

[00:05:18] I would like to see how my time to hire compares to other people in that region and other people in that industry because that's who I'm competing with for those really good resources.

[00:05:30] So, if I find out that time to hire for them on average is half the time it is for me to hire, I've got a problem.

[00:05:39] So, that's the news that's being reported.

[00:05:41] And that's something that we can do at Employee because we go from SMB up to Enterprise.

[00:05:46] But the second step that I was referring to before, which is what do I do with said news, is I need the application and then tell me, okay, so if your time to hire is double what other people in your industry and region are seeing, means you're probably losing really good candidates.

[00:06:04] What are you going to do about it?

[00:06:06] The system needs to say, hey, I'm evaluating your application steps and I'm realizing, or even your recruitment steps, and I'm realizing that you've got three times the number of steps to get that person hired as the average of the industry and region that you're in.

[00:06:23] And all of a sudden, I have to start thinking, yes, that's what I thought I wanted.

[00:06:27] But if I'm losing good candidates because of that, let me take an action.

[00:06:31] So, that's where I think data and analytics really provide you with interesting information but also allow you to actually make good decisions as to how to improve those metrics for yourself moving forward.

[00:06:45] And most importantly, so that you can get access to the absolute best talent before your competitors do.

[00:06:52] A lot of questions here, Dara.

[00:06:55] You just opened up a lot of questions.

[00:06:57] Let's start with – okay, let's start with size of company.

[00:07:01] So, you have clients, right?

[00:07:04] And I know this isn't necessarily about employee but the topic.

[00:07:07] So, are you seeing a difference on how SMBs – and I guess, Juan, how are you defining SMB?

[00:07:14] But are you seeing a difference between the way that SMBs and large enterprises are looking at the data and then acting on that?

[00:07:22] Absolutely.

[00:07:22] You know, everybody defines SMB differently.

[00:07:25] I hate to put a number out there because I think it's really more about need and what is the need of said SMB versus said mid-market versus enterprise.

[00:07:37] And so, I think that we tend to use size as a proxy for complexity.

[00:07:43] There are smaller organizations that would be considered SMB that have more complex needs.

[00:07:50] But what I'm referring to here, Ryan, is really kind of those that have less complex needs than their larger counterparts.

[00:07:58] And so, that's how I would define it.

[00:08:00] I would say that the process of recruiting is very similar regardless of the size of the organization.

[00:08:11] And I think that's where I struggle in this industry because people just assume it's that much harder for an enterprise customer than an SMB customer.

[00:08:19] I don't think that's true.

[00:08:20] I think especially as you think about having more resources as you are a larger organization to be able to do some of these things.

[00:08:27] The people in the SMB world, they have to do it all themselves.

[00:08:30] They're really jack-of-all-trade.

[00:08:32] And so, they need the same type of data as their larger counterparts.

[00:08:36] And they need the same information in terms of how to best hone their processes so that they can get access to those same best candidates.

[00:08:48] So, a good example that I like to use is imagine if you are a small retail organization in rural Nebraska, as an example.

[00:08:57] And you're competing against the larger retailer that's down the street.

[00:09:01] You're probably competing for the same resources because you're in rural Nebraska.

[00:09:04] Your needs are the same.

[00:09:07] But if you don't have the same capabilities, if you don't have the same data that you have access to to make the right decisions as that large retailer, that large retailer is going to beat you out for the resource every single time.

[00:09:19] And so, for us at Employee, again, while we use size as a proxy for complexity, we don't necessarily focus only on size.

[00:09:30] I joke all the time that maybe size doesn't matter.

[00:09:33] That in most cases, it's really the needs of the organization.

[00:09:38] And you need to find a vendor, and we pride ourselves as an employee on this, that can provide for your needs regardless of what they are, regardless of your industry, regardless of your size, regardless of where you are in the world, regardless of the type of candidate that you're going after, and so on and so forth.

[00:09:56] I love that you've tied data to action.

[00:09:59] I think this is a huge miss for a lot of people for a lot of years is, okay, here's your data.

[00:10:04] Here's your dashboard.

[00:10:05] Here's your report.

[00:10:06] It's like, you know, I'm busy.

[00:10:09] So, and oh, by the way, I'm not great at data.

[00:10:13] How do we teach recruiting folks, talent acquisition professionals to storytell around data, especially as it relates to the business results?

[00:10:25] Hi there.

[00:10:25] I'm Peter Zollman.

[00:10:26] I'm a co-host of the Inside Job Boards and Recruitment Marketplaces podcast.

[00:10:31] And I'm Steven Rothberg, and I guess that makes me the other co-host.

[00:10:34] Every other week, we're joined by guests from the world's leading job sites.

[00:10:38] Together, we analyze news about general niche and aggregator job board and recruitment marketplaces sites.

[00:10:45] Make sure you sign up and subscribe today.

[00:10:49] Because I think they're in their own heads.

[00:10:52] They're in their own, you know, they're in their funnel.

[00:10:54] And they're thinking about what they have to do, which is fantastic.

[00:10:57] And you're giving them data, insights, and action.

[00:11:00] Great.

[00:11:01] They still got to tell a story to somebody at some point.

[00:11:04] Yeah.

[00:11:05] So what do you think the connectivity is to storytelling business results?

[00:11:11] Yeah.

[00:11:12] I think that the most important thing that they have to figure out first is what story are they trying to tell?

[00:11:19] Right?

[00:11:19] And so I can boil that down to what metrics matter to them.

[00:11:24] Right?

[00:11:25] Just because I can reel off time to fill, cost to hire, time to replacement, performance in job, like all of those things.

[00:11:34] That doesn't mean that all of those things are important to every single organization.

[00:11:38] So the first thing they need to figure out is what are the metrics that matter to them?

[00:11:42] And what story are they trying to tell?

[00:11:44] So ultimately, what is their charter or their goal?

[00:11:47] Once they have that, then they need to work with their vendor to figure out how do I get access to that data so that I can tell the story that I want to.

[00:11:56] So I truly believe that you shouldn't be using the data to tell the story.

[00:12:01] You should be using the data to help you with the story that you're trying to tell.

[00:12:06] And then ensure that the data is doing that.

[00:12:09] And if not, then that's where your action takes place.

[00:12:12] That's when you go in and say, what can I do to make sure that what my charter is is being accomplished based on how I have my organization structure, based on how I have my processes structure and so on.

[00:12:25] And then use that data as a validation that you're getting to what it is that you're trying to get to.

[00:12:31] That, to me, makes a little bit more sense than just using the data to tell the story.

[00:12:36] That watersheds from the business goals.

[00:12:39] So the business goal is to whatever that is.

[00:12:42] Then the metric should be aligned with the business goal.

[00:12:46] And then you go find the data, make sure the data tells that story.

[00:12:50] And if it's not congruent, then you've got places to fix to make it congruent.

[00:12:55] Right.

[00:12:56] Right.

[00:12:57] Okay.

[00:12:57] Yeah.

[00:12:57] I mean, because when I think about even just product management holistically and what I do every day, I've been spending a lot of time with my team.

[00:13:05] And again, only six months in now, but I'm spending a lot of time with the team saying, okay, so what are our business goals?

[00:13:10] Well, our business goals are the same as anyone else's business goal, right?

[00:13:14] You want to drive retention.

[00:13:15] You want to drive margins and you want to drive growth.

[00:13:18] It's fairly simple, right?

[00:13:19] So everything that we're doing in product management should be around those three goals.

[00:13:24] So if I'm a recruiter or I'm a talent acquisition professional, I need to understand what are the goals of the organization and then how do I contribute to those goals?

[00:13:34] And by doing that, you can cascade down to every recruiter and every sourcer and every hiring manager, to be honest, within the organization to make sure that they understand what they do every day aligns with the overall business goals.

[00:13:49] I always worry when you do it in reverse and you say, this is what we're trying to do.

[00:13:54] This is what the metrics are telling us.

[00:13:56] And this is the story that it's telling because it could be disconnected completely from where the company is trying to go.

[00:14:01] You're being awfully kind by saying could be.

[00:14:06] Very generous.

[00:14:07] Very generous of you.

[00:14:09] I feel like she's seen a couple of stories.

[00:14:11] Very generous.

[00:14:12] There's a few stories here.

[00:14:13] Yeah.

[00:14:14] Yeah.

[00:14:14] Yeah.

[00:14:14] So, Derek, what are you seeing today around recruiting leaders or talent leaders?

[00:14:25] What are they doing to leverage this data to take their hiring strategies forward?

[00:14:31] Honestly, Ryan, I don't know if they're doing enough.

[00:14:33] Now, I'm sure there are pockets for sure.

[00:14:36] And when you go to the HR Tech Conference at the end of September, there's going to be people that are going to get up on stage and talk about what they're doing.

[00:14:43] And so I think there's really good opportunities to learn from some leaders that are doing some really interesting things.

[00:14:50] But my problem with applications, such as employees or anyone else's, is that if what you're doing for me makes me do more work, I have a problem.

[00:15:03] Because my primary job is not using the ATS or using the recruitment marketing solution.

[00:15:09] My primary job is finding the absolute best talent and getting them into the organization.

[00:15:13] And so that could be sourcing.

[00:15:15] That could be talking to the candidates, getting them to understand why this is the best organization, vetting them, and so on and so forth.

[00:15:24] All of that doesn't happen in the application.

[00:15:27] The application is something that helps drive all of that.

[00:15:30] And so whether it's the data or, and I'm going to shift a little bit to AI because I'm sure you all are going to want to talk to me about that anyway.

[00:15:38] But whether it's data or AI, in my mind, those things have to find a way to be additive without making a recruiter's job harder or making them have to learn something.

[00:15:52] So one of the challenges that I see today is whether it's data and analytics or dashboards, whether it's an AI capability, as an example, and I'm using the word capability on purpose, because when it's a capability or when it's a specific dashboard, we make recruiters come to us and learn how to use it and how that fits in with their bigger responsibilities, if you will.

[00:16:19] That's the wrong approach.

[00:16:20] I saw a tweet at some point, and I should really give credit.

[00:16:25] I should remember who it is that tweeted this, but I'm paraphrasing, but she said, we're doing AI wrong because what we're doing is we're having AI do art and writing.

[00:16:36] And the reality is, is I like to do art and writing.

[00:16:40] I need the AI to do my laundry and my dishes so that I can do the art and writing.

[00:16:44] And so it's absolutely true that what we as vendors need to do is we need to find a way to have the application do the things that a system can do best so that we can free up the recruiter from doing what they do best.

[00:16:59] And what they do best is the conversation and the sourcing and finding the best employees for their organization.

[00:17:06] And so I truly believe when I said AI is a capability, people think about AI as something you turn on or they think about something that you buy.

[00:17:17] In employ, we think about AI as an inherent part of our DNA or data as an inherent part of our DNA.

[00:17:26] It needs to just be there and do its thing, do my dishes, do my laundry so that I can focus over here.

[00:17:33] And so I think there are people that are maybe doing some of that, but if the system itself, Ryan, is making me learn, then that's a big problem because I've already lost – I've lost the plot to be honest.

[00:17:48] Do you think that's just because this is new to a lot of people and they're – what's the word I'm looking for here?

[00:17:56] Change management?

[00:17:58] No, the – it's the newness.

[00:18:02] Oh, man, I'm a total lack of words here.

[00:18:05] The shiny bells, the fun stuff that AI can do, right?

[00:18:10] The creative stuff, the fun stuff.

[00:18:13] Once we get past that, do you think all of this settles down and we – in talent acquisition at least – then it becomes a little more deployable inside of talent acquisition groups?

[00:18:25] I think so.

[00:18:26] I mean I think there's definitely the opportunity if – as a hiring manager, I've got to hire somebody.

[00:18:32] The last thing I want to do is write a job description.

[00:18:35] I don't know about y'all, but it's definitely the last thing I want to do.

[00:18:38] So what do I do now?

[00:18:38] I go to ChatGPT.

[00:18:40] I put in, you know, please create a job description for me for a product manager one or whatever the case may be.

[00:18:47] It creates it and bam, I've got my job description.

[00:18:51] So I can do that, right?

[00:18:53] And I can do that exact same thing within the application itself.

[00:18:59] And that's interesting.

[00:19:01] But is that really moving the needle?

[00:19:03] Because I can do it outside as well as I can do it inside.

[00:19:05] So what's it really doing for me?

[00:19:06] So a lot of what everybody in the space is doing right now is they just want to be able to plant that AI slide – I mean flag.

[00:19:15] And so they're like, I've got to find something.

[00:19:17] And if it's automatic job description generator, I'm going to plant that flag.

[00:19:21] Again, it's something I can do in ChatGPT and just copy and paste, right?

[00:19:26] And so we have to move past the idea of AI for the sexy piece, AI for the planting the flag to say, yes, I'm doing it too.

[00:19:35] AI for the I can charge for this or it's a capability.

[00:19:39] And really move back into how do we use AI to solve real business problems that can't be solved in any other way?

[00:19:47] And I think one of the things that I've really talked to my team about a lot and the engineering team as well is let's stop thinking about things as features and talking about them as features.

[00:19:59] Let's always talk about in terms of what business problem is this thing solving.

[00:20:04] It's product management 101, so I'm not saying anything that doesn't exist.

[00:20:09] But a lot of times as product professionals, we have to go back and say, are we really listening to product 101?

[00:20:16] Are we really focusing on the business problem that we're trying to solve?

[00:20:20] And by the way, we have customers that speak in solutions as well.

[00:20:25] If I go to talk to one of my customers and say, hey, tell me how you're doing.

[00:20:29] Tell me what you want.

[00:20:30] They're going to say, I want to take this button on the left hand side of screen, move it to the right and make it purple.

[00:20:37] And I could say, great, let me go do that.

[00:20:39] Or I can say, OK, take me back.

[00:20:42] What business problem are you trying to solve?

[00:20:44] So I may have a better way to do that.

[00:20:46] And it may be using AI and it may be with data as an inherent part of it or it may be something else.

[00:20:53] But if we don't think in solutions and we think of ways or solutions to business problems specifically, we wind up getting to the answer too quickly and ultimately either cause more friction.

[00:21:08] Because it's a thing that they now have to learn or we don't truly, truly solve the business problem or the pain point that they were trying to solve.

[00:21:16] Darrell, I want to take you in a slightly different direction around data hygiene and data literacy.

[00:21:24] So hygiene, a couple of years ago, probably you could say that a lot of folks in our industry were scared of the data that they were sitting on not being great data.

[00:21:38] So there was kind of a fear of trust of the data.

[00:21:43] I don't know if we're over that or not.

[00:21:44] Let's just talk about that.

[00:21:46] And there's also the other thing is around literacy is about doing something with the data.

[00:21:51] So them understanding kind of data and kind of educating them as to what is their data?

[00:21:59] What are they sitting on?

[00:22:00] I think there's something to be done there.

[00:22:03] I just want to get your take on both.

[00:22:04] Yeah, I think it's fair.

[00:22:06] I mean, you know, garbage in, garbage out.

[00:22:08] Right.

[00:22:09] And so I do think that that fear was real and probably still is real.

[00:22:16] Because as I talked about before, if you make it hard for the recruiter because you have to do something systematically, then they're going to take the path of least resistance.

[00:22:26] And the path of least resistance is going to ultimately lead to bad data.

[00:22:29] I mean, it just and it's not just recruiters, by the way.

[00:22:32] It's pretty much anybody.

[00:22:34] It's everybody.

[00:22:35] Exactly.

[00:22:36] And so I think that's fair.

[00:22:38] So I think the first thing is to ensure as clean a data as possible, you have to make the experience as simple as possible.

[00:22:46] Make it something that's really easy to do so that they don't make it.

[00:22:51] So it's not arduous in that they do it correctly.

[00:22:53] And therefore, the data going in is more accurate.

[00:22:56] The other piece that's important is in terms of what do you do with the data?

[00:23:01] Again, I think that the idea that you could take your data and benchmark it provides more relevance to it.

[00:23:10] Right.

[00:23:11] So if it's kind of like, you know, you have your child.

[00:23:15] I'm trying to come up with an analogy on the fly.

[00:23:18] And the child comes back and says, hey, I got a B on this paper.

[00:23:24] And you go, oh, that's great.

[00:23:25] That's awesome.

[00:23:26] And that's all you ask.

[00:23:27] You're done.

[00:23:27] But if you had the rest of the data and you found out that every single other person in that class got an A, you might have a slightly different perspective.

[00:23:36] Or if everyone else got a D, you might have a different perspective.

[00:23:40] So I think the data in context really provides some value to you that you can action on.

[00:23:48] Have you ever been to a webinar where the topic was great, but there wasn't enough time to ask questions or have a dialogue to learn more?

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[00:24:00] We feature HR practitioners, leaders, and founders of HR, payroll, and workplace innovation and transformation sharing their insights and lessons learned from the trenches.

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[00:24:20] Hey, it's Bob Pulver, host to you, podcast.

[00:24:22] Human-centric AI, AI-driven transformation, hiring for skills and potential, dynamic workforce ecosystems, responsible innovation.

[00:24:32] These are some of the themes my expert guests and I chat about, and we certainly geek out on the details.

[00:24:37] Nothing too technical.

[00:24:38] I hope you check it out.

[00:24:40] The data outside of context doesn't.

[00:24:43] And so that's one of the things, like I said, when I was excited to join Employee because 22,000 customers, it gives you context that can actually help you make really good decisions with that data.

[00:24:54] It's hard for me if somebody said, hey, my cost of hire for said job is $5,000.

[00:25:03] Is that too much or is that too little?

[00:25:05] I'd be like, I don't know.

[00:25:08] Put it tight.

[00:25:08] Yeah.

[00:25:09] How can I tell you?

[00:25:10] Yes.

[00:25:11] Yeah, exactly.

[00:25:12] So asking a recruiter to understand if that metric means anything is unfair.

[00:25:18] And so you have to give that recruiter as much context as possible or business person or talent acquisition leader, as much context as possible to understand where they fit relative to other people, other industries and so on and so forth that are relevant to them.

[00:25:35] So let's talk about recruiters and data real quick.

[00:25:38] So how involved or how deep should a recruiter, maybe in your opinion or maybe what you're seeing in the market, how deep are they getting involved with?

[00:25:48] How involved are they with making decisions based on data?

[00:25:52] Is that their job?

[00:25:54] Data-driven recruiting.

[00:25:56] Yes.

[00:25:57] I think it is.

[00:25:58] Yeah.

[00:25:59] Let me clarify that because that's one.

[00:26:01] Yeah.

[00:26:05] So, yes, data-driven.

[00:26:07] Got it.

[00:26:08] Point taken.

[00:26:09] But should a recruiter be able to just sit at their desk and be a recruiter and not have to look at all of this information and make decisions?

[00:26:19] Should they just go after the people and build that relationship?

[00:26:24] So I would agree, data-driven recruiting.

[00:26:27] I would say data-driven everything, right?

[00:26:30] Yeah, true.

[00:26:30] Because I kind of believe I don't know how you make any decisions without data, right?

[00:26:34] Every single day we're processing data in our heads to make decisions.

[00:26:39] Right.

[00:26:39] The challenge is, Ryan, and we talked about it, is how do you not make it difficult, right?

[00:26:45] So to your point, I just want to recruit.

[00:26:48] I just want to hire.

[00:26:49] I just want to talk to people.

[00:26:50] So how do we make it such that it is just inherently part of what they do every day?

[00:26:56] So when I need the data, show it to me.

[00:26:59] Don't make me go somewhere, right?

[00:27:01] Don't make me dig.

[00:27:04] Don't make me create spreadsheets.

[00:27:06] Don't make me do any of that.

[00:27:07] Don't make me evaluate a whole bunch of metrics.

[00:27:09] But if I need something at the point in time when I need it, let's use as an example, I'm going to put together an offer, which is great.

[00:27:20] And I am hiring a project manager in a specific part of the country, then I have to figure out, okay, so what do I need to hire?

[00:27:32] What do I need to bring that person in at?

[00:27:33] What's the right salary?

[00:27:35] Don't make me dig around into salary structures and guides and the Bureau of Labor Statistics and everything to try and figure out what the right salary is.

[00:27:48] Bring that to me.

[00:27:49] Like I'm about to create an offer, you know, come up with 22,000 customers and say, you know, a project manager who looks like this and whose job description looks like this and has this type of experience because we have all of that data.

[00:28:03] Typically in this part of the country gets offered this type of range of salary.

[00:28:07] Make it really easy for me so that I don't have to go looking.

[00:28:12] Again, do my laundry and my dishes so that I can make the offer, do the thing that I like to do, the art and the writing.

[00:28:19] And so I think that's really the thing is, yes, they need to be making decisions based on data.

[00:28:25] But if you make it hard, they're not going to do it.

[00:28:29] I think what you're describing, and I want to make sure I get this right, especially for the audience, is intuition.

[00:28:34] That the data is intuitive as to your next need.

[00:28:39] Yeah.

[00:28:40] Right?

[00:28:40] So you just have closed off a conversation.

[00:28:44] Looks like you're going to make an offer.

[00:28:46] Hi, I'm Stephen Rothberg.

[00:28:48] And I'm Jeanette Leeds.

[00:28:49] And together, we're the co-hosts of the High Volume Hiring Podcast.

[00:28:53] Are you involved in hiring dozens or even hundreds of employees a year?

[00:28:56] If so, you know that the typical sourcing tools, tactics, and strategies, they just don't scale.

[00:29:03] Yeah.

[00:29:03] Our bi-weekly podcast features news, tips, case studies, and interviews with the world's leading experts about the good, the bad, and the ugly when it comes to high volume hiring.

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[00:29:16] Then the data then comes to you.

[00:29:19] You don't necessarily have to do something.

[00:29:21] It's once you take a step in a process, the data then renders itself and says, do you want to do this?

[00:29:29] Do you want to do this?

[00:29:30] Are you trying to search for this?

[00:29:31] Et cetera.

[00:29:32] Exactly.

[00:29:32] So there's kind of an intuitive nature of rendering data, again, where they are at the point in which they need it.

[00:29:39] Okay.

[00:29:39] That's right.

[00:29:40] I'm down.

[00:29:40] Yeah.

[00:29:40] And it goes back to reporting the news versus, again, not only interpreting the news for me, but help me figure out what to do with the data.

[00:29:49] So have it come at me when I need it.

[00:29:52] And by the way, if I already know what I'm going to offer this person in this particular scenario, it's not a bad thing to have the range come up and say, hey, this is what you were planning.

[00:30:03] You were planning on offering this person $100,000.

[00:30:06] The range for this person is actually lower or higher, whatever the case may be, because then they can have the conversation with the hiring manager and say, hey, I have this data and it's brought to them.

[00:30:18] But, yes, I mean, intuitiveness is the perfect word for it, because if I've got to go look for it, if I've got to get off the phone, if I've got to take myself away from the thing that I am paid to do every day, then I'm not going to do it.

[00:30:33] I'm just not going to do it.

[00:30:34] Something specifically to fix right there is internal comp.

[00:30:38] There's a huge disconnect, right?

[00:30:41] So, again, I know what I want to offer them.

[00:30:44] I might even know market data competitively, et cetera.

[00:30:49] So I might know all those things.

[00:30:51] But if I don't know internal comp and most recruiters don't, they don't even talk to the compensation folks.

[00:30:57] They're over in a cave somewhere in the middle of them.

[00:31:00] And so they don't even know what their structure is, and it creates inequities.

[00:31:04] So we go out and offer this one person, okay, whatever that is.

[00:31:08] And all of a sudden, everybody else that's internal is not being paid that.

[00:31:12] And so there's something to be reconciled there with data, which is a little bit outside of my pay grade.

[00:31:18] But I know it's a problem.

[00:31:19] Mine as well, William.

[00:31:19] Mine as well.

[00:31:20] I know it's a problem.

[00:31:21] I don't know how to fix it.

[00:31:22] I know there's a problem.

[00:31:23] Anyhow, Ryan, you were going to ask a question.

[00:31:25] No, I was going to more comment on what you were saying.

[00:31:30] I mean, it's one of these things where you create the inequity in pay.

[00:31:35] Yeah.

[00:31:36] And it just creates a whole other can of problems, right?

[00:31:42] A can.

[00:31:42] That we go.

[00:31:42] Yeah.

[00:31:44] Recruiters want to close the deal.

[00:31:46] They're like salespeople.

[00:31:47] They will offer.

[00:31:48] And recruiters are generally.

[00:31:51] They want to close the deal.

[00:31:52] It's no big deal.

[00:31:52] No, they want to close.

[00:31:53] But they're generally on the side of the candidate.

[00:31:56] Yeah.

[00:31:56] All great.

[00:31:58] Not knowing the date of internal comp creates future problems, if not current problems.

[00:32:05] And again, not anything we need to fix today.

[00:32:08] Right.

[00:32:08] Well, and to your point, though, so most cases, and it's above my pay grade as well, but in most cases, you know, the internal compensation information is coming from somebody, right?

[00:32:20] They're paying somebody to provide all that information, which is awesome.

[00:32:24] But if the recruiter is handed data that says, but let me show you specifically across 22,000 customers in these industries and so on and so forth, then it's potentially the opportunity to go back and have a broader conversation with the compensation organization to say, hey, look, I know that this person is a little bit higher.

[00:32:45] But in this particular case, we need to pay that in order to close the deal.

[00:32:51] And so I think, again, it just makes the talent acquisition staff as good of a business partner within the company as all the other functions of HR.

[00:33:02] I think salary, knowing someone's salary is almost the same as political affiliations in the workplace.

[00:33:11] It is.

[00:33:11] It is.

[00:33:12] I think it's that.

[00:33:15] It really is.

[00:33:16] Like, you shouldn't know what I'm making.

[00:33:18] I hear you, but that's why that's actually how inequities have been hidden.

[00:33:25] It is.

[00:33:25] Yes.

[00:33:26] Let's talk a little bit more about intuition.

[00:33:28] So I was driving a car.

[00:33:30] I drive a lot of old cars.

[00:33:32] And I was driving somebody else's car the other day, and it had those things where if you got in another lane, it shook.

[00:33:39] Yeah.

[00:33:40] And so I –

[00:33:41] I thought you said it had roll-up windows or something.

[00:33:44] Yeah, exactly.

[00:33:45] It was on the opposite.

[00:33:45] So I'm driving, and all of a sudden, I'm like bouncing in between whatever, and it keeps shaking and beeping and stuff like that.

[00:33:52] And my wife's like, what the hell are you doing?

[00:33:53] And I'm like, yeah, I don't like any of this.

[00:33:56] I just like to drive.

[00:33:57] I don't need any of this stuff, which I think is probably how recruiters feel about data and about intuitiveness and, again, a lot of these things that we're talking about.

[00:34:07] How do we get them to that point faster?

[00:34:10] Yeah, it's interesting.

[00:34:11] I love the guardrails kind of approach, but you have to be careful, right?

[00:34:19] Because just like you, you're going to say, no, no, no, no, no.

[00:34:22] I know what I'm doing.

[00:34:23] I don't want this.

[00:34:25] And so I think there's a couple of examples.

[00:34:30] I mean, I'll give you an example that we're kind of thinking through and working through right now, but one of the biggest challenges, one of the challenges, there's a lot of big challenges, but one of the challenges that recruiters face is that they have a ton of postings that they're working at any given time, right?

[00:34:48] Nobody's working on just one or two.

[00:34:50] They're generally working 25, 30, 50, whatever the case may be, right?

[00:34:55] And so what happens is when you're working that many, it's really tough to spend a lot of time and really kind of nurture and wow any particular candidate because you don't have that amount of time.

[00:35:08] And especially it becomes problematic if you've got a candidate who's awesome that's just going slowly through the process because your hiring manager isn't answering and getting scheduled quickly enough or they're not getting references back to you quickly enough or whatever the processes are.

[00:35:26] And in certain cases, now the market's a little bit different now, but as the market starts to heat up again or with very unique hiring situations, as a recruiter, you don't want to lose somebody who you think is really, really good.

[00:35:40] But your brain is focusing on 30, 40, 50 different job postings.

[00:35:46] And so the question is, is there a way for the application to try and again, do the work for you, watch those candidates, find the signals that may indicate that said candidate might be starting to lose interest for whatever reason.

[00:36:03] Maybe it's because there's a ton of jobs for that type of candidate.

[00:36:06] Maybe it's really hot, whatever the case may be.

[00:36:09] Find that out.

[00:36:11] And based on that, let me know, like give me like a flashing thing.

[00:36:16] It's not vibrating, but a flashing thing that says, warning Will Robinson, you may have a problem.

[00:36:23] What I love about that is, sorry to interrupt, Derek, but I've noticed on a lot of websites lately, and it's been going on forever, but I'm really clueless.

[00:36:33] So when I'm about to X out of a website, it pops something up.

[00:36:39] It does.

[00:36:40] Right?

[00:36:40] Right.

[00:36:40] It's like, I'm done.

[00:36:42] I've already, I've lost, I lost interest five minutes ago.

[00:36:44] And I'm just, I'm about to X out and all of a sudden it's like, hey, before you leave, blah, blah, blah.

[00:36:50] How do you attract candidates that way?

[00:36:54] And follow their, when they start to diminish their interest and then get them reengaged.

[00:37:00] Yep.

[00:37:01] Yeah.

[00:37:01] And there's, and there's kind of two ways to do that.

[00:37:03] Right.

[00:37:04] And this goes to your guardrail situation.

[00:37:05] So there's kind of the flashing light or the vibrating because I'm getting out of my lane kind of thing that says, hey, I know you've got 50 other things you're thinking about, but like there's a problem over here.

[00:37:14] Look, and that person can either then reengage with the, with the specific candidate, which makes total sense.

[00:37:22] Or again, getting the system to work for you.

[00:37:26] Is there a way for the system when it does that for you to configure it such that it says, hey, do this set of things when this particular scenario happens?

[00:37:36] Right.

[00:37:37] So it goes back to your place, your situation where I can either be alerted and do, and do something, or I can let the system do something for me.

[00:37:46] Now it would be, I guess the equivalent in your world would be, hey, when I start to go off into the other lane and it's vibrating, pull me back, which would drive you crazy.

[00:37:54] But give them choices.

[00:37:55] And when you give them choices, again, maybe they want the vibrating that comes out of the lane so that they can do something, something themselves, or maybe they're used to driving old cars and they got this.

[00:38:08] Right.

[00:38:08] But that's going to be the key.

[00:38:10] You're not going to be able to find a one size fits all.

[00:38:14] But if you make it intuitive enough, to your point with intuitiveness, then people that see the value will leverage that value and it'll make them better and more efficient.

[00:38:26] And people who don't, that's okay too because they have their processes.

[00:38:30] Right.

[00:38:30] I mean, there's a big difference and it gets overused and it's a cliche, but there's a big difference between a baby boomer and a zoomer.

[00:38:38] Right.

[00:38:38] There just is.

[00:38:39] Right.

[00:38:39] They want to work differently.

[00:38:40] And so you've got to create a solution that can handle all the different styles so that you don't get them.

[00:38:47] Okay.

[00:38:48] So final question on my side, Tara.

[00:38:52] I don't know that you can actually put a number to this and this is maybe more opinion than fact.

[00:38:57] I'm going to just preface the whole thing by saying I'm going to put you on the spot here.

[00:39:02] Good thing to show us about data.

[00:39:04] Yeah.

[00:39:04] Good thing to show us about data.

[00:39:05] That's true.

[00:39:06] That's true.

[00:39:06] So it makes sense.

[00:39:08] It makes sense.

[00:39:08] Yeah.

[00:39:09] I'm present.

[00:39:10] I'm listening.

[00:39:12] What?

[00:39:13] And this is in my mind.

[00:39:15] This is something I often think about.

[00:39:18] What percentage of a recruiter's job can be automated or influenced by AI?

[00:39:26] And then on the flip side of that, how much of it should be influenced?

[00:39:32] What percentage do you put there?

[00:39:33] So here's my perspective.

[00:39:37] Oh, I feel a political answer coming.

[00:39:39] Yeah.

[00:39:40] So I could give you a percentage, Ryan, but I would say the recruiter's job is to find and hire the absolute best talent in the industry, right?

[00:39:55] In the market.

[00:39:55] And so that's their job.

[00:39:57] 100% of the time.

[00:39:59] And so if that's their job, then that's what they need to be focusing on.

[00:40:04] And AI just needs to help facilitate that.

[00:40:06] Same with data, right?

[00:40:07] So it's not like you're going to offload something.

[00:40:10] So if you've got 50 requisitions, as an example, you've got to find 50 of the absolute best people out there and get them hired.

[00:40:17] AI is not going to do that for you.

[00:40:19] What they're going to do is, like I said before, not to overuse the analogy, but they're going to do the dishes and the laundry.

[00:40:24] So you don't have to worry about that stuff.

[00:40:28] I would hope that ultimately that's where we get.

[00:40:31] So in that case, they're doing 100% of their job.

[00:40:34] The question that you're asking is today.

[00:40:37] Today, when they can't focus 100% of their time on doing what they really need to do best, which is finding the absolute best talent, how much can AI or data offload it?

[00:40:48] I would hope that we would get to 50, 60% so that they can focus on the most important thing and then 100% of their job is focused on that.

[00:40:57] So it's a little bit of a political answer, but I think ultimately it's like everything else.

[00:41:05] I mean there are technology innovations that we've seen over the course of our three lifetimes that somebody had to do way back when.

[00:41:13] Now they don't have to do it anymore, so they're not focusing on it.

[00:41:17] So 100% of their efforts are on something else.

[00:41:19] And Ryan, I think that the automation versus augmentation splits that up a little bit.

[00:41:28] Last question for me is – it's actually on the analytics side – is questions from customers and prospects.

[00:41:35] Are you hearing more about quality of hire?

[00:41:39] Absolutely.

[00:41:40] Yeah, absolutely.

[00:41:41] How are we defining quality of hire?

[00:41:43] Yeah, I mean I think especially in this market.

[00:41:46] Yeah, okay.

[00:41:47] So it's hard if in a perfect world, the way I would define quality of hire is somebody who achieves the expectations or higher than the expectations set for that person over the first 90, 120, 365 days.

[00:42:10] I might spill that.

[00:42:12] And so measuring somebody who comes in and how they do and how they succeed helps you then feed back into the recruitment process what you're looking for.

[00:42:23] Because at the end of the day, it's not about bringing people in.

[00:42:27] It's about bringing people in that move the needle, that help you improve retention, drive growth, and improve margins.

[00:42:35] And if you do that, and you can do that repeatedly, and those people stay and stay engaged and can use internal mobility to move up through the organization, then you've won.

[00:42:45] So to me – and probably part of it is just started my whole world, my whole experience out in HR, got my MBA in HR, so that's who I am.

[00:42:55] I'm always thinking about kind of the whole process, the entire employee lifecycle.

[00:43:00] And to me, it's about the impact that that candidate or that hire has post-hire and from their entire tenure that really defines the quality of the hire.

[00:43:11] I like that.

[00:43:12] I've used – for years I've used the speed to high productivity.

[00:43:16] Right.

[00:43:16] So basically, how fast can we get them to a high productivity level?

[00:43:22] And keep them there.

[00:43:23] And keep them there.

[00:43:23] Because you can get them there, and then they fall off, right?

[00:43:26] And then the engagement is a problem.

[00:43:27] That's right.

[00:43:28] And so, yeah.

[00:43:29] So, I mean – and I do think that part of the talent acquisitions team's responsibility is not only about making sure they're setting proper expectations throughout the talent acquisition process,

[00:43:38] but also making sure that that handoff, that onboarding piece is done super well so that that employee is engaged and excited and doesn't want to be anywhere else.

[00:43:51] And then at that point, it becomes the HR team's responsibility to make sure that that continues.

[00:43:57] Love it.

[00:43:58] Jobs Mike walks off stage.

[00:44:00] Dara, thank you so much for coming on the show.

[00:44:02] Thank you for breaking things down.

[00:44:03] It's been a wonderful subject, and you've been a wonderful guest.

[00:44:07] Well, thank you, Ryan.

[00:44:07] Thank you, William.

[00:44:08] I appreciate it.

[00:44:09] Would love to come back anytime.