No one should ever build a dashboard again: key lessons for HR leaders

An operating partner wants to know why revenue per employee trails the rest of the portfolio. The QBR is two weeks out, and the turnover figure on the slide is one the HR leader already knows is wrong. Cole Napper has spent his career on both sides of that conversation. As HRBench's Chief People Intelligence Officer, a title he believes is the first of its kind, and the author of the book People Analytics, he argues that the discipline HR trained in is collapsing into something smaller and sharper.

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What is people intelligence, and why does it matter now?

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Four analytics disciplines are folding into two layers. Napper's "tree of value" once separated human capital data into people analytics, workforce planning, talent intelligence, and behavioral science. His People Intelligence Manifesto argues those branches now collapse into a data layer and an intelligence layer, with nothing else left standing. Mid-market companies gain the edge, because they can build AI-native from day one instead of taking apart four legacy functions first. "I don't ever think another human being should have to build a dashboard," Napper says. The work HR long treated as a luxury, in his telling, was a commodity the whole time.

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Why do HR and the board talk past each other on metrics?

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Because the same word carries different meanings, and often opposite emotions. Turnover has close to a hundred definitions, and HR rarely states which one is on the slide. Napper adds a second trap beyond the definition problem. "There's also a valence issue to it. Are you talking about this as a good thing or a bad thing?" Finance may read turnover as cost reduction while HR reads it as lost talent. His fix: define the term and get the metrics agreed at the executive level before the meeting, then tie each one to a live business initiative instead of pushing unrequested data across the table.

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Should HR be nervous about revenue per employee?

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Only if it walks in without a story. The metric makes HR queasy for a plain reason. "If you start to look at revenue per employee, the easiest way to fix it is layoffs." Napper traces most of the recent pressure to investors rather than the C-suite, because the number is easy to benchmark across a portfolio. The counter to a layoff reflex is a workforce-planning narrative that explains why the extra headcount produces future return.

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Is benchmarking enough to defend a headcount plan?

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No. It opens the conversation and settles nothing. "Benchmarking helps get you in the door. It helps you understand the lay of the land, but it's only the opening overture." A company carrying twice the employees of a same-revenue peer draws investor pressure unless it can show why that structure produces higher margins or faster growth later. Without that narrative, Napper is blunt: paying more for the sake of being different no longer flies.

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Is it okay if your HR data is reliably wrong?

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Yes, as long as the error is consistent and tracked. "If I know that my turnover is always 2% inflated, and it's reliably 2% inflated, and I track it over time, being reliably wrong is fine." Reliability is survivable. Validity is where the real danger sits: measuring the wrong thing entirely, the difference between counting animals and counting jaguars. In most board settings the precision that matters is direction. Better, the same, or worse is usually answer enough.

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The mindset upstream of the metrics

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Every answer traces back to one move: understand the business before bringing the data. Napper puts the whole discipline downstream of a single mindset, that HR serves the business and is not walled off from it.

Logan (00:02): But hey, I'm here with Cole Napper. He is the leading people intelligence or people analytics influencer. And you said you're like the king of the nerds, or the guy out there. But is there anything you would like the audience to know about you?

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Cole Napper (00:26): Yeah, so I've recently started HRBench, the Chief People Intelligence Officer. I believe that's the first one in the world, and I'm pretty excited to be kind of on the cutting edge in that respect. But also have my own podcast called Directionally Correct and a newsletter under the same name, Data Driven HR Academy. I published a book called People Analytics last year. And I do a variety of things to kind of give back to the community globally. So I love this space, love this type of work, and I appreciate you having me on, Logan.

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Logan (00:56): What are you doing currently to give back to the community?

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Cole Napper (01:00): Yeah. So historically I led the Dallas Fort Worth people analytics meetup ever since 2018. And then for a brief stint, for about a year, was leading, as the community chair for the Society of People Analytics, which led all of the meetups globally, which I thought was pretty cool. And then now, more recently, still get back in Dallas, but I'm doing some stuff with the Boston one and the San Francisco Bay area one as well.

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Logan (01:15): Yeah, that'll be good events. Now, I know we prepared a few items to chat through, but since talking about doing this episode and now you've released the People Intelligence Manifesto, and for our audience, they may not realize totally that there's this whole underground world in people analytics that's shifting around and moving. I kind of wanted to give you a chance to chat through some of the things you believe, because the People Intelligence Manifesto is a good section of kind of where things are going with analytics, and that's kind of going to be the foundation for what we talk about today for the HR folks. Anything you want to comment there? I have a few questions as we get through that, but...

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Cole Napper (02:20): Yeah, so the thesis kind of came from, in the third chapter of my book, I coined this concept, the tree of value, which is people analytics, workforce planning, talent intelligence, and behavioral science being four branches of a tree that are nourished by the roots of the tree, which is the value of human capital labor data. And in more mature HR functions at larger organizations, typically those are four distinct functions. And the thesis with the people intelligence is the tree is collapsing into one. And there's essentially going to be a data layer and an intelligence layer, and that's all that's left. In the world, organizations, at least in the near term, I see, because of the impacts of being in the post-ZIRP era and AI more generally impacting them, I think smaller and medium sized organizations are incredibly well positioned, because they're actually going to be able to, instead of deconstructing these types of functions and reconstructing them around that intelligence that I talk about in there, they can just build it out of the gate for the first time. And so some of the things that I talk about in the manifesto is, I don't ever think another human being should have to build a dashboard.

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Cole Napper (03:35): Like, I just think that AI should be more equipped to do that. I think all the analytics that I've been talking about for years has been positioned more as a luxury item, when in reality it's been a commodity for quite some time. And I go through and I list about 10 of these different bullet points of the NFS, but the last is, I actually don't think that these beliefs are controversial at all. I think they're shared by most of the leaders in the field. The leaders in the field are just kind of concerned to say it out loud. So I said, hey, I'll say it for you. Because I really think that this isn't the future, this is the present. And I'm trying to help equip people to be ready for the present and for the future.

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Logan (04:16): Yeah. So in some of those bullets, you said that the overall HR operating model is consolidating as one of your beliefs. I kind of wanted to touch on that, because I guess that was a new concept for me. And what you're thinking when you say that HR operating model, is it like specifically within the analytics function, or just overall from CHRO all the way down?

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Cole Napper (04:34): Yeah, the overall, from the CHRO all the way down. There was a model, the most widely adopted... there's lots of HR operating models out there, but the most widely adopted one is called the Ulrich model, for Dave Ulrich, who's at the University of Michigan, which talks about breaking HR into HR business partners, centers of excellence, and then shared services. And essentially AI is eating shared services before our eyes, right? And it's the COEs and shared services, even before AI, were kind of collapsing into one already. And so it was already kind of breaking down. And I just see a world where you have to have these kind of data native, AI native, data-driven HR individuals that probably constitute what we meant by HR business partners, although most HR business partners actually didn't kind of embody that mission. They mostly were working on things like employee relations. And then you're going to have the AI tech stack that's going to enable that happening. And that's going to replace a lot of what is shared services, but also a lot of the core functions of those centers of excellence, where you still might have a true subject matter expert, one or two of them at an organization. But most organizations, especially in that small to medium size, a lot of that's going to get collapsed just into pure technology.

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Logan (06:01): Yeah, and in a lot of the mid-market companies, some of them are going to be really small HR departments anyways already. And then the other thing, thinking through that, you have in there also, you talk a bit about HR becoming AI native. And now you're talking about HR, or AI, taking a shared services portion. And this is probably going to be on the little more controversial side, I would imagine. Like, how do you see HR needing to adopt and become AI native?

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Cole Napper (06:39): Yeah, I actually don't think it's all that controversial, at least on the shared services side. No one ever wanted to do shared services. It was really hard. Like, they have these ticketing systems, you have ways of trying to deal with high volume requests. Many tools, pre-generative AI, already said they had AI that could help with a lot of these things. A lot of times it didn't work all that well. But I think no one wanted to have to invest in headcount to have to do this. And frankly, the people who were doing it, it's a very kind of thankless job. It's not a whole lot of fun. It doesn't pay particularly well. And so I think most folks would agree this is probably a net positive, right? I don't think it's super controversial in that respect.

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Cole Napper (07:21): The reality is, being AI native, what that means is: can you construct this out of the box to do the work without needing to necessarily have a logic based system for things getting done, rather than a human based system of tickets and somebody pulling them and that type of thing? And just, I don't know, it's just not a lot of fun, to be honest with you.

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Logan (07:43): Yeah, but that's actually really hard for most organizations, because you have to think through... and even pre-AI, like you think of employee onboarding, for instance, how fragmented, and good at some places, not good at most places, it is. Like, you have to think through that entire process to make something happen. I mean, there's good debate around whether that's going to even be possible, because you have to do all that work first.

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Cole Napper (07:53): Correct. And I think that there are very kind of key touch points in the entirety of the employee lifecycle. And frankly, you need good data at all of those points too to do this effectively. I've said that ever since AI came out, the key unlock for HR more generally is just this concept of knowledge management. So a huge part of what makes onboarding challenging, or a lot of these touch points challenging, is just: where does the knowledge reside to be able to get the information you need to get on board? And where's your laptop coming from? Where do I need to do my payroll? How do I get things sent to my house? If we're sending company swag, who's ordering the swag? Is there a backlog? And just having all of that knowledge in one place, to have that exist for an organization, has always been really challenging. I mean, I go back, I remember in the beginning of my career, but it was at the tail end of other people's career, they even had things like Lotus Notes that went out and managed some of this stuff. And then it was, I'm blanking on the name of the tool, but it was like another tool that did a lot of this stuff. And then a lot of folks have tried to pilot things like Jira and Atlassian and ServiceNow and things like that nowadays. But I think frankly the space of knowledge management is where AI is going to be a key unlock for organizations.

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Logan (09:28): Yeah, I had experience with Lotus at the start of my career. And I knew somebody that worked at IBM, and they were like, we don't even use Lotus at that time. And I was like, wow, okay. So yeah, that's funny. Okay, so some of the main stuff we wanted to get into: I actually wanted to talk to you about our mutual contact, Ryan May, who we had on the show. She said we should chat with Cole about some of the data fallacies that you run through, which is actually what we wanted to chat with today. But in talking through that, we came up with a couple different items, and I wanted to start with, not defining metrics or anything like that, but why HR practitioners, when they go to their board, struggle talking about metrics with the board. Because everybody thinks of stuff differently. And just one example of that is turnover. When I've talked with HR leaders, it's always turnover and referenced as just turnover, but often they mean voluntary turnover. And when I first started in HR tech years ago, I didn't realize that distinction. And so that's kind of an example of a nuance that maybe not everybody in the business talks about. So how would you advise HR leaders to start thinking about the metrics that they're doing and framing them in a way that maybe other people in the business aren't, in that every day?

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Cole Napper (11:10): Correct. Yeah, I think... and first of all, I want to say Ryan May is a gem. I'm so glad you had her on, and I'm glad you guys got to know each other a little bit. But the thing I think about is what I call HR speak. It's these kinds of words and terms that have grown up in the HR function. And if you're in kind of the cool kids club of HR, everybody kind of uses the same lingo. But when you're talking to business leaders, not only is it confusing, but sometimes off-putting, because you're not using the language in the way that they talk about how the business operates. And so the example you used of turnover is a good one, because there's probably, even within the business context, not just with HR words and terms, there's probably a hundred different ways of defining turnover, right? And so being very clear upfront about... then there's also a valence issue to it. And what I mean by that is, are you talking about this as a good thing or a bad thing, right? A lot of times business leaders, when they're talking about turnover right now, they're talking about it as a positive, because they're seeing it as a cost reduction measure, whereas HR is usually talking about it as a negative, because they're trying to retain. And it's not just voluntary turnover, sometimes it's even called regrettable turnover, which is, these are folks that we wanted to make sure they stayed and they left anyway. And regrettable turnover is the worst kind of turnover, truly is bad for any organization. However, again, if you're not defining your words and terms at the executive level, you really can be talking past one another.

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Logan (12:28): Yeah, why would... I guess, what's the trend? Are you seeing the trend of turnover being positive more and more right now?

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Cole Napper (12:58): I don't necessarily know if it's positive. It's insofar as, like, business leaders are talking about how is this impacting business operations and the cost of doing business, right? And so turnover can obviously have a negative impact on business operations, but it also can have sometimes a positive impact on the cost of doing business, right? And so again, it's about understanding the context of the situation. And when I've coached HR leaders on this in the past, and frankly had to do it myself at some of the midsize organizations I worked at, like Motive and Booster Fuels, we were talking at the board level and the C-suite level, it was always about understanding the context before trying to bring the data. I think a lot of HR leaders go wrong when they try to push the data out, like, hey, we're data driven too, here's a bunch of data that you didn't ask for, and they just push it out. And then they don't understand the context of why this might be influencing a different outcome than what they think. And then they kind of run afoul of either different executives or the board, because either they're saying something that runs counter to another fact that's being presented by operations or by finance, and they haven't aligned on that appropriately, or they just don't understand the context of what's going on in the business at the time.

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Logan (14:12): So then how do you start with that, before just taking a bunch of data out of your HRIS? So you have your HRIS reps probably telling you, we've got these really great dashboards, you should start pulling these numbers and putting them in decks and showing them, like every software tool does that. Like, I'm in marketing, the marketing vendors are constantly wanting you to do that, but they don't know whether or not it's actually hitting revenues. So how do you advise teams to start with understanding what to start pushing first?

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Cole Napper (14:50): So one thing I would say is getting into the cadence of doing business. Most organizations have some version of MBRs or QBRs that are going on, or monthly check-ins, or there could be things like OKRs. I know we're throwing a bunch of acronyms out there, if people aren't familiar with those. Getting into those monthly business updates, or the quarterly ones, or getting key HR metrics that are agreed upon at the executive level into how the organization is evaluating its performance, are really key aspects of it. Because usually what's being done, like let's say for the sake of argument you have a monthly business review, is you're not just reporting out data from marketing, from finance, from HR, just for the sake of doing it. It's usually tied to key business initiatives that are going on at the time. And so you want to be able to tie your metrics, not just at the organizational level or just for the sake of sharing it, but to that key business initiative, to say: is this enabling it? Is it hindering it? Is it making it more efficient? Or whatever is kind of the key thing that's going on at that moment.

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Logan (15:55): Yeah, that's often... I've talked with... so our PE audience is definitely going to know the QBRs, because they all have to go through them. And that's always a stressor for everybody in preparing them. But I've talked to a few leaders, it's come up quite a lot, where it's: learn the business. And everybody has different tactics of advice, like... and it's learn how the business makes money, is really the advice that people give. Like, Ryan May's was, go talk, you know, go ask every department their three priorities, which is spectacular advice, because that's super tactical to kind of understand what it is that they're interested in, and how you can then start framing it within the context of your HR department, if you will.

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Cole Napper (16:51): Yeah, my riff on this... and again, everybody's got their own, and I think most of them are pretty valid... is: can you explain to your CEO how your business makes money, and not feel embarrassed? If you're skittish about it, if you feel like you're not confident in it, you don't know the business well enough.

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Logan (16:54): Yeah. And that's a good segue into one of the probably more popular things. It's always been a popular measure, but I've heard that HR sometimes... our audience might shy away from it, and that's revenue per employee. That's clearly, in the last, I don't know, probably since '22, mid '22, when that's become at the forefront of everybody trying to optimize that in the post-ZIRP era. And then AI is just accelerating it, like gasoline on a fire. I guess my question to you, because you've probably talked with a lot of teams about this and helping them understand it within the business: why do some teams shy away from revenue per employee, or why is that sometimes a scary thing to start being at the forefront of?

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Cole Napper (18:08): Yeah, I think there's a few levels to it. And I would say this may be somewhat of a historical artifact, because I think HR is kind of getting with the program too, so this isn't as big an issue as it used to be. But I think the first level is just, financial terms sometimes made HR queasy, right? They didn't necessarily feel like they understood it or owned it, and therefore they didn't want to be held accountable to it. And anytime HR creates a measure, there's also a degree of accountability associated with it. And so there was kind of that hesitance associated with it. Another is, typically if you start to look at revenue per employee, the easiest way to fix it is layoffs. And so HR doesn't necessarily want to just be the driver of layoff after layoff after layoff, because the numbers don't necessarily add up correctly. But a lot of the pressure has come in the last few years from investors, not from the C-suite itself. And it's because this is an easy way to benchmark yourself amongst firms. And frankly, getting this data has become easier too, and therefore more people are benchmarking it than they used to. But some... I actually want to give HR credit to a certain extent, that some organizations have really started to kind of champion this and adopt this. You used to be kind of persona non grata if you tried to share this at an HR conference. But workforce planning teams, and I've been a part of a few of those in the past, have always championed this metric. And I think workforce planning has been a very hot topic over the last few years. And so I think that that mindset has kind of been winning out in this debate.

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Logan (19:35): Yeah, and it is interesting, because it is the fastest way to fix it, is layoffs. And that's the unfortunate thing, I think, that you're seeing a lot of right now. I mean, you just can't get away with that. But the benchmarking piece of that is interesting, because everybody wants to benchmark: what's good, in the context of how everybody else is doing? And I have... something I had thought about was, like, the benchmarking, to me, seems like it's a standard. It's like, what's the standard, or the best practice? And I kind of have a theory, or at least from my marketing experience, best practices or standards are for amateurs. It's for, like, at the start. Like, that's kind of my take. And that you actually need something custom for your business. So even the benchmarking of revenue per employee, yours could be higher or lower, but your profitability makes sense. Like, what is your opinion on benchmarking stuff like that externally?

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Cole Napper (20:46): Yeah, I think it's important and under-misunderstood at times. And so one of my thoughts on this is, let's take... I try to be really practical when I talk about these things. It's like, there's company A and there's company B, and they have essentially the same revenue, they have the same business model, but for whatever reason, company B has twice as many employees. You're going to have a lot of investor pressure right now for company B to get with the program of company A. They're going to be seen as non-competitive. However, that's only if this thing doesn't happen. And what is this? It's if company B doesn't have a compelling narrative about why their doubling of the number of employees is going to bring future returns to investors. And PE firms are especially sensitive to this, and saying, if you don't have a compelling vision for the future about why you're going to make more money, why margins are going to go up, why... maybe even though you have more employees, maybe you have a higher margin for some reason, because maybe your cost structure is lower on a per employee basis, or something along those lines. And so the reason, like, benchmarking helps get you in the door, it helps you understand the lay of the land, but it's only the opening overture. Really, the devil is in the details about getting into it. It's like, we've actually made a strategic decision why our business model is different at company B. But if you're just different for the sake of being different, and you're just costing more for the sake of costing more, that's not going to fly anymore.

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Logan (22:01): Yeah, so something interesting on that is... so when I chatted with Nicole Logue on the podcast, she had mentioned, when we were talking about learning the business and learning how the business makes money and understanding this workforce planning piece and where the product roadmap is going and where the company is going, you could say something to the effect of: the reason we're double the headcount is because we have this particular innovation coming. So, like, we're tweaking our own AI model, for instance, we need these 20 cutting edge developers who are on this particular model, and so our headcount is going to be higher because of what the roadmap is doing. And that's, like, a level of understanding the business that I would assume helps make that conversation significantly better.

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Cole Napper (23:03): Yep. That's exactly the narrative that I was talking about, is like, can, do you have a narrative? Do you have a vision for the future that you can paint to investors to show why you're going to grow? And the other thing is, revenue per employee, and any benchmark, is a point in time measure. The reality is, where do you stand in the trend? And so perhaps you and another organization are roughly at the same point now, but are you growing on a faster trajectory or a lower trajectory than they are? Is your employee headcount growing faster or smaller? And all these different variables, like, if you project them out into the future, to the person that you just mentioned a second ago, I suspect their revenue looks like it's about the hockey stick, because they've made those investments as an organization. And that's what investors want to see: they want to see a return to the cost structure investments that they're making.

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Logan (24:09): Yeah, I guess the question then becomes, how do you paint that picture when people want to see strategic shifts mid-quarter, like shorter than 90 day cycles? So something else that's interesting: there was just Dreamdata, which is a marketing analytics company, just put out a study of the sales cycle length. So they did the time from first marketing impression to closed won revenue. And to me, that seems like a big deal, even if you're thinking HR strategic shifts and just business shifts. It's like 280 days, nearly a year. And so my thought was, like, you can't possibly make a strategic shift in one quarter, because the data says you couldn't know for almost a year if that's going to hit revenue. Now, if you're a consumer company, that's a little different. But if you're a B2B company... so how do you think about those types of things in that narrative of strategic shifts, and how long that trajectory is going to be?

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Cole Napper (25:19): Yeah. Well, I think that's where organizations are always sort of in tension with, sometimes their leadership, but sometimes their investors. There's a difference between an edict and a plan. An edict is: you need to fix things this quarter. A plan is: how are we going to reduce our 280 day sales cycle time to 90 days, to get it to where we can move on a quarter to quarter basis and really make these changes? Right? And that's the difference. And sometimes edicts are unrealistic, and even plans can be unrealistic, but at least you can kind of vet and falsify a plan, whereas an edict is just like, if you don't do this, we're shutting the whole thing down or something like that. And frankly, it may be completely unrealistic. And this is why data and benchmarking sometimes is the great equalizer, to say, look, we don't have a plan for changing 280 to a lower number, we can't hit these targets, and if we can't hit these targets, we're never going to live up to this edict.

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Logan (26:25): Yeah, yeah, sometimes that link... there are some things you just can't shift and change. Okay, so the last item I wanted to talk through is when your data is wrong, or dirty, or messy. And something that's interesting, that I don't know if I agree with right now, and that's: you can't layer on AI onto dirty data. And I actually think you can. And the reason being is because you can have AI clean it for you. And so actually it's a perfect use case for dirty data, because you can say, hey, we know that here's all the test information, here's things you should look out for, clean this up for me and put it out. But the question that I had come up with: your data has been consistently wrong for years... and is that okay?

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Cole Napper (27:00): Well... yes and no. And I agree with the point you made about using AI. There's different use cases for AI too, right? And AI can be used as kind of a processing unit for making your data better. But it's also kind of that... just that layer that some people just slap on top of their poor data, and then they get poor results, and AI is not going to fix that. No. But the reality is, to your point about, if you have bad data, if it's reliably wrong, is that okay? And I would say reliability and validity are kind of these two concepts that always come into play when you're using data. If you're reliably wrong and we can track against that... so, like, let's use an example of turnover. If I know that my turnover is always kind of 2% inflated, and it's reliably 2% inflated, and I track it over time, being reliably wrong is fine. Because I can at least do the mental math to say, okay, subtract by 2% every time, and I know what it is reliably in the future. But if you're just reliably... I think we even talked in prep for this, where you're reliably... you're trying to understand animals, and you say, well, I'm actually trying to measure jaguars, and another one's measuring elephants. You know, that's a level of unreliability that we cannot deal with, is like, are we measuring animals? Are we measuring jaguars? And if we're measuring jaguars, that's an unacceptable level that doesn't have the validity associated with it. And frankly, we kind of joked about this, this is the genesis of the name of my podcast, Directionally Correct.

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Cole Napper (29:07): Directionally correct is kind of this recognition that we know we're never going to have perfect data. So perhaps just being directional in nature, and being as close to accurate as you can get, might be the best we will ever be.

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Logan (29:20): Yeah. So on the 2%, like, if you know you're 2% wrong all the time... why would you sit and leave that as 2% wrong, rather than fix it? Like, why would something like that be unfixable?

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Cole Napper (29:37): Yeah, I mean, sometimes there's systemic limitations in how you do the data. So an example might be backdating of quit dates, right? So quit dates get logged in a system, but for whatever reason they only get logged, like, the month after the quarter closes, right? And so then you go back and you have what I call then "then as of then" and "then as of now" problems. And "then as of then" was, at the end of the quarter, this is what we reported to the board, and it was wrong, it was reliably wrong, but it was wrong because we don't have all those backdates. And then if you report on that in the future, that's "then as of now," right? And so you go back and you put those backdates into your data, you correct the data to what it should be in actuality, but at the time you reported it, it was wrong. But it was reliably wrong.

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Logan (30:30): Okay, I'm trying to see if I can digest that. But that's all good.

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Cole Napper (30:34): These are the things that you have to deal with when you're a data person. I'm sorry, I haven't found simpler ways of explaining it.

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Logan (30:40): Yeah, and sometimes complexity is okay. Like, often... I had a leader at one time that would always tell me, Logan, your stuff's just too complex. And I'm like, but the simplicity loses everything in nuance. And so sometimes...

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Cole Napper (30:55): Yeah. When you see the same thing in hiring, it's like no call, no shows in hiring. So people are expected to show up. A human being is reflected in our headcount numbers, because they're being put into the payroll system. Everything, every box is checked, but a certain percentage of people just never show up. Right? And so your headcount numbers are always wrong, but they're reliably wrong, because you know that a certain number of people just are going to no call, no show and never be there.

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Logan (31:25): Yeah, okay, gotcha. Okay, is there anything, any advice you would give to people in communicating that when they're hit with questions in a board meeting? Because often our leaders will be in QBRs or board meetings and having to chat through this.

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Cole Napper (31:45): Yeah, I think it's just being very aware, because there are times when extreme levels of precision are needed, but most times they're not. Right? And so it's about understanding... again, this going back to that kind of term "directionally correct"... is it getting better, is it staying the same, or is it getting worse? Right? Knowing the answer to whether it fits into one of those three categories is mostly the precision you need in HR. There are times where you truly do need to round to the second and the third decimal point, but that's pretty rare.

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Logan (31:57): Yeah, that makes sense. And, well, good. I don't think I have any other questions on that.

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Cole Napper (32:36): Yeah, well, I'm curious, from your perspective, Logan: how do you see precision playing a role, from your experience, kind of on the marketing side? Because a lot of people that do this type of work say, you know, what we do in HR with data is just what marketing was doing 15 or 20 years ago when it came to customers. And so do you see some of these things playing a role?

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Logan (32:45): Ugh, yeah, the question is, do you have another hour? This is a huge debate. So, actually, something interesting. The big debate in marketing is always attribution. So there's... well, there's two debates, I think... well, there's many debates, but I boil things down into two things. There's data collection, which for whatever reason seems to be a problem for a lot of marketing teams. And I don't think it's that difficult. And so it's collecting all the data and getting the right stamps. Often it's because you're stuck with a system like Salesforce, or that doesn't seem to like to do things well between digital marketing stuff and normal marketing things. And so then you have, like, a HubSpot layer on top of it, and these tool integrations that then seem to have issues. Then there's an attribution problem, where it's like, what happened first, and where do we give credit, so that we can then spend money on top? And that debate has raged for a decade, 15 years. And actually, the interesting thing, people are scrapping it altogether now. I just thought... and this year they basically, like, CMOs are saying, you know, we're just done, it's an unsolvable problem, and we're just going to look at the pie as a whole. Which, actually, you have some influencers... he's not doing marketing now, but, like, Chris Walker was talking about that in, like, 2018. And so actually it's becoming your... everybody was trying to be as precise as possible, because average CMO tenure had gotten to, like, 18 months, because the sales team didn't have enough leads, didn't have enough good pipeline. And so then they would fire the CMOs. Because typically in a business, the closer you are to revenue, the more secure you are. So marketing steps back from revenue. So it acts before, normally, the sales team, because the sales team is much closer to revenue. So it's actually moving more into your "directionally correct," which is where I live. Like, here at HRBench, I'm testing a kind of newish format out, where we do inbound, midbound, outbound. So we just... three general buckets, and then layer budget onto three buckets so that it's directionally correct. And we're not going to worry about first impression to closed won, because it's a messy timeline. You probably have a lot of fun in marketing data.

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Cole Napper (35:40): Absolutely. And actually, strangely, my last job, I sat in marketing for some reason. And so that was very eye-opening for me. But one of the things that most people find surprising is, usually one of the biggest line item spends in HR is recruitment marketing. And so that's actually a huge budget issue when it comes to investing in things like LinkedIn and Indeed and all the different channels for bringing in, especially in high volume kind of blue collar or service sector roles. Recruitment marketing can be one of the biggest spends for HR.

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Logan (36:12): Yeah, that makes a lot of sense. And what's interesting, I find, is that your external brand, which often sits in marketing, has a huge influence on whether or not you can effectively recruit. So if you think of some of the bigger companies... I'll just take some SaaS companies. If you think of Anthropic right now, their marketing team... well, the product team is the marketing team right now. That's not entirely true, but since their product is so cool, that's helping fuel... that obviously makes it good, but they probably have people knocking at the door wanting to work there because of that. Then you have... like, we had our customer Kim Ray on the podcast. They have a whole marketing set around culture, and that helps drive it. So there's this whole brand piece that sits in there. I think that companies forget that the whole thing is marketing. Like, marketing isn't, like, a department. Like, everything drives marketing. Traditional marketing... we're getting really in the weeds here for all of our eight hour folks, but it's good for you to know, it is pertinent, because recruiting people, you can partner with the marketing team to do it, to help improve that, because of your external brand. So, I don't know if you're familiar with the four Ps, Cole.

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Logan (37:41): I love old marketing models, because it was pre all of this, like, shift strategies every quarter, all of this stuff. The CMO, or the marketing function, had come up with the four Ps. So it's product, place, price and... people, I think, now that I'm doing it. But anyways. Yeah.

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Cole Napper (38:01): You committed. You had to fill out the last one.

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Logan (38:04): But all of that sat under marketing. It was, like, marketing and operations, like how they envisioned it in the 60s and 70s. So, Mad Men era of marketing. And now marketing really handles, like, it's promotion. Yeah, product, price, place, promotion. So most marketing teams only handle promotion. And so that has created a lot of strife in companies, because product is a huge function of, like, are you going to do stuff, how you get it out to people, so that whole function.

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Cole Napper (38:37): Yeah. And you see a lot of these concepts of, like, forward deployed engineers nowadays, which is essentially acting as, like, a product function on the ground with customers, which used to be the job of marketing.

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Logan (38:50): What's... correct. But that's to say that all of that model would have helped recruit people when it all sat within one section, to think about it holistically. And now, if you think of, like, Coca-Cola, I bet you Coca-Cola has more of that function now than, like, a Salesforce, for instance, because they're very traditional. Like, they're, like, the top marketing company of all time. One of them.

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Cole Napper (39:00): Yeah. And, I mean, they make everything off of their brand, you know? Yeah.

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Logan (39:20): Yeah. And then, to your point about recruiting marketing budget, it's probably very easy for them to recruit a relative, because they're just spectacular. I don't know if they're regarded that way, but as far as a marketer is concerned, you know, the Coca-Cola brand is huge.

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Cole Napper (39:36): Yeah, I used to... I mean, I just had Sue Lam, who is the VP of people analytics and culture at Coca-Cola, on my podcast. So that'll be coming out soon, and she talks about some of this.

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Logan (39:49): Nice, that's awesome. So, well, good, Cole. Hopefully... I'm curious if people are going to stick around for that whole monologue. But, yeah, always.

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Cole Napper (40:02): I'm sure they will, Logan. I'm sure they will.

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Logan (40:05): It's an interesting dilemma for the marketers out there. And HR folks have to recruit marketers and understand why they're recruiting them, and they're turning over in 18 months, or the business wants to move on from them in 18 months, which is very, very common now, unfortunately. Cole, is there anything else you would like to leave the audience with, in how they can improve their skills in the boardroom?

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Cole Napper (40:35): Yeah. I created an academy called the Data Driven HR Academy, just for this reason. And one of the things I say in there is: upstream of all of the skills is the right mindset. You've got to have the right mindset. First of all, you are a part of the business. You serve the business. You're not excluded from the business. And then it's about understanding: how is the inflection point of using your human capital to drive business results? And then, what is the data that's going to support tracking that and making that happen and enacting change in the real world? And then going through and building all those skills to understand that. So I think that's what I would leave folks with. And I really appreciate you bringing me on to have this conversation today, Logan.

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Logan (41:21): Yeah, it was great. And I will leave one comment. So we actually talked with Tim Williams on the podcast, and he does a lot of work with... he was at P&G for a very long time. And he talks about making the decision to lead. And that's what he helps coach HR folks: you know, make the decision to lead. And that's... you're saying the exact same thing. Make the decision first, and then let everything else follow.

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Cole Napper (41:43): Yeah. Absolutely, couldn't agree more.

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Logan (41:51): Awesome. Well, thanks a lot. It was a pleasure to have you on, and I appreciate you asking about the marketing. I could go on forever, but all good. Actually, that would probably be a good episode: all the data fallacies in marketing. Thanks a lot.

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Cole Napper (41:59): Hey, maybe we'll do another one on it sometime. Absolutely. Well, thanks for having me, Logan.