Headcount Data can make or break your QBR
Podcast Overview
-
Eric Guidice: Episode 17, HeadCount Experts, I'm Eric Guidice, joined, of course, by none other than Chris Mannion. This week we're talking about the QBR. And not just for TA or finance, but the QBR in general and how all the moving parts come together and create what is the retrospective of a quarterly performance and then how to build an action plan once you've reported on everything and need to fix it. And right before we started the episode, Chris and I were talking about the bluff framework, which we talked about in episode 16 about getting promoted and getting promoted means being able to speak to an executive with the bottom line up front, not bluffing like all in. But what's funny about that, just to open, is the QBR that I have on Unicorn. So I released a QBR for recruiting on Unicorn Talent, which is a community for recruiting leaders. Completely free. It's 8 or 12 slides, but it uses the bluff framework. The first two slides, and I'll flash it up on the screen, the first two slides are, did we hit or did we not hit. And if you think about every department doing that in a one hour meeting, let's say eight to ten departments, if you can get that statement done for a chief or a board level person within the first 15 to 20 minutes of the meeting, you can then prioritize what's going to happen and what to focus on and what has the biggest impact and how to shift it through the rest of the meeting using the rest of each individual department's QBR data to kind of build that case and talk about it and support arguments and distribute accountability, etc. And so if you're interested in the recruiting side, specifically Unicorn Talent, that is what I used. That has been downloaded almost a thousand times now, the QBR, and I've used it personally at about a hundred companies when I was a consultant to articulate the recruiting team hit the goals or didn't hit their goals, and we know why they did or didn't hit those goals with supporting data that helps distribute accountability. So that's my opener. Where's your standout QBR experience? Where were you most on the hook for doing a QBR in your career?
Chris Mannion: Yeah, I think there's like three big experiences I think I had when I was leading the innovation team at Wayfair. So one was actually the internal QBR that our head of TA used to run. Every one of me and my peers had to create a QBR and then report up to the head of TA to let know how our business units were going. So my team of 40, like what we were prioritizing, where we performing. Then every six months we'd have a... I was called an STO. It was like a six monthly QBR, up to the CEO where we'd report on the state of recruiting and the whole recruiting business unit. It included everyone that, including non-recruiters as well. There was about 800 people. And so I had to prepare the analysis and the reports for that geared to what the CEO and the CFO cared about. And then I think the most interesting one was sitting on the other side of the table, receiving the QBRs from our vendors, given I was responsible for our vendor spend and just getting so frustrated that they would kind of go off in these wild tangents around things we didn't care about. And we said, well, why don't you just fix the problems that we have raised? So a lot of different experiences, happy to get into this. Probably a little less structured than I think your framework. I think for those listening, go and download that framework. Use that if you're doing your first QBR, but you're happy to share some war stories from time in the trenches as well.
Eric Guidice: Yeah, for all the data points of like work didn't work, that framework kind of cuts through the middle. So there's going to be a lot of situations where you need to add slides or add context or maybe take some things away. But we both kind of started in the TA side of a QBR, which I think we should just kind of zoom out and talk a little bit about like what the QBR is to the business. Like why does a leader, whether it's to your individual organization of talent or finance or ops or sales, why does the individual leader care? Why does a chief care? And like what's the whole purpose of dedicating all these resources to do this work? And what is an outcome that the business gets when the QBR process is working well? I think let's start there. Do you have a take on why they're doing it? If you had to explain it to my grandma like what's the point of you doing all this work every quarter, one week every quarter, four weeks, a month salary, why are we spending that across all these different analysts and rec ops and rev ops and sales ops and people ops, like why is everyone pulling this stuff?
Chris Mannion: So if you look at the fundamentals of a business, it's revenue and cost and revenue minus cost gives you profit. So there's only two variables that you're controlling for. How can you increase revenue? How can you decrease costs? And the talent decision is where those two meet. Like how can you get the maximum productivity and revenue for the business while minimizing the cost? And the key component of that is TA. It's like, can you hire the best people without overspending while hitting your goals in order to drive the business? Like that's the fundamentals of business 101. And so the TA QBR is arguably the most important business driver and the most important meeting that you're going to have. And without clear data and clear recommendations coming from the subject matter expert, which is the head of TA or a more senior leader within a talent organization, the executive team are not really going to have that decision making capability. So I think getting that right is so key because it drives everything else in the business. Like the sales team, the product team, the engineering team can't do their job if they don't have the right people in place. And talent and TA especially really drive that to get them in place. And coming from the business and from an MBA background, I think that's how I look at things. Obviously you've kind of grown up within the organization. Any kind of different perspective there?
Eric Guidice: Not a different perspective, I agree, but I think we're coming to a place where talent was the bottleneck of getting additional productivity for a cost into a business where we're entering this era where people can go buy this productivity in the form of the AI softwares or agents etc. And so the TA team is the human component of adding the productivity. There's another cost structure that's going out there that is the AI additions and then there's the greater business that cares about the execution of all these resources in totality. So for example, if you're a finance person, you care about spend from people and attrition and, you know, sellers creating revenue. You also care about the AI tools that help with, you know, automating sales emails or updating information in Salesforce and you're balancing the costs of those things together. Now, I don't think they happen out of the same line item on the P and L. And so you're going to have the talent story or the talent QBR meeting the rev ops, you know, these individual kind of decentralized talent spheres and I think that is going to be the forward-looking change, right? So like I have a few, I have the QBR on unicorn, I have like a guide to building a QBR on the headcount website in the blog. These frameworks are going to change as far as like these are all how it's been done so far and I'm not sure we really know where it's gonna go quite yet, but I'm ear to the ground with my customers and just in the market of like, how during the QBR process are people accounting for and controlling spend versus ROI on some of these tools, because where that bottleneck happened in talent and humans actually creating productivity for value, you had consistent measurement and you had predictability and you had a unified definition of ROI. And so if you're going to decentralize that work and you're going to take the head of sales interpretation of ROI, the head of CS interpretation of ROI, the head of engineering interpretation of AI, you're going to start to see these different... You know, if you're going to use Claude for all your junior engineers while your senior engineers are writing less code or doing less architecture because they're reviewing all this other, there's a difference. So you have to now figure out, and I think this is going to be kind of when I talk about the QBR as it relates to headcount data, you're going to have to figure out how to unify across all these different decentralized value creators. What is the actual cost? What is the actual ROI? And how is it recognized as a business? I have some thoughts about how it's gonna go, but that's where I think the change is going, is moving from a talent-centric humans create costs and create productivity in a centralized, evenly measured, kind of QBR centric around talent to that being one part of a puzzle that then comes out of multiple cost centers with this decentralized approach to adding productivity for dollars. So don't know if you're in the work you're doing. I could imagine that, so for those who don't know what Chris is doing, he is at the forefront of educating, training, and consulting the companies how to infuse AI in the headcount TA and general people ops process. So I have to assume you're starting to see some markers here and maybe I'm on, maybe I'm off, but what do you think?
Chris Mannion: No, I think that's right. And I think one of the challenges that I think we're seeing right now, and I'm hearing a lot from senior talent leaders, is you have this kind of general market message of AI is coming, it's going to improve productivity by replacing headcount. And that's the general message. You look at any headline news. So CEOs are seeing that news and then thinking how do I apply that to my organization? If I can get 80 percent of the productivity for 20 percent of the cost, then can I lay off 80 percent of my workforce and still maintain the same level of output? We know that's not true, but that's the message they're getting. And it's a strong marketing message and it's compelling. Especially if you're a publicly traded company where your investors are kind of really pushing you to get those productivity gains that have been promised by AI. And so the challenge comes when that actually rolls down to the people that implement it, generally the talent team. Like, how can you get me more productivity from fewer people? And so I think that the kind of approach and we...
Eric Guidice: Yeah, you see the layoffs with block. Block and snap, right? They both laid off and got like crazy bumps in the stock price.
Chris Mannion: We even used to... Yeah. And that's the world that we're living in. That's what's expected. But the people that are going to implement it, they have to actually make sure it works. Because if you go through these huge layoffs and then productivity drops and the share price drops, then everyone's going to be pointing the fingers at the people that implemented it, not on the ultimate decision makers who forward some kind of marketing message. So it's a very kind of one-sided view. And the way I like to tackle it is actually how we used to tackle all data-driven projects with hypothesis early on and then like more of test driven approach where you say, okay, well, let's assume that if we implement Claude code for our junior engineers to build on your example, and we can automate all of the kind of junior code monkey work that we would normally hire, you know, someone who is a year or two out of undergrad to do. And that's going to free up productivity for our senior engineers who then won't have to go in and do all the manual code reviews. Know where this is going, but let's see if that actually works. Let's pick one team. Let's set this hypothesis and say, if we give this team no more junior engineers, can they maintain productivity if we give them more access to AI? And what is the ultimate cost of that? And you can do like an A-B test and you can say, okay, this team had this change. We actually implemented this change here. We didn't here what was the result in three or six months later. I think that's where the QBR becomes really valuable because then you can tell the story and say, well, six months ago we said, let's test this out. This is what we implemented. This was the result. And therefore we recommend or don't recommend rolling this out to the rest of the organization. And having that data and that story is so much more powerful than coming up with hypothetical excuses as to why it may or may not work. And your CEO is not going to listen to that, but they are going to look at the data and if you can prove that something does or doesn't work. That's going to be much more valuable. So very, very kind strong opinions on that because I think there is a, I think a huge push to maybe make changes very quickly. Whereas taking a little bit longer and seeing which changes are worth implementing is going to help to avoid this kind of boom and bust hiring and layoff cycle that I think we've faced over the last few years. That's ultimately one of the main goals of what I'm trying to do is try and get away from that cycle and make better informed data driven decisions instead of more kind of vibe based decisions.
Eric Guidice: I think there's also like a, I don't want to say security, but there's in the routine of having a quarterly check-in, right? Like I think the market is moving much faster now than it has in the past in terms of how fast the AI is able to improve or expand its functionality or productivity within your business. So if you don't have these kind of preset check-ins, you're not going to have this opportunity to do what you said and evaluate kind of the impact and do it. So like, I think the best analogy I have in this framework is climate versus weather, right? It's rainy today, it's sunny today. We're getting AI, we're not getting AI, we're doing this thing, we're not doing this thing. But what is the trend over time is kind of one reason why the QBR exists. And it's one of the data points within, but it's the most frequent or it's like a planned cycle, but it's not the only, just like you said, you're going to have to see things. You might get a nice burst in a quarter that looks really good, but over time in the engineering example, you might notice that the senior engineers have a shift in other markers. You know, what I tell when I was a consultant, what I would tell each business to do is have for their different departments, or you could organize it by project, or you can organize it by person is having like some level of what is the North Star metric and then the supporting kind of three things that create that metric. And so in a QBR setting or in a board meeting, which is usually my context for joining, the board doesn't really have the attention span or real interest of diving much deeper unless there's a major problem or a major success and then you want to have all the information to back it up. But if you have eight execs each with three different things that are driving to this North Star metric, what you're doing at the board level is you're looking, how are my North Star metrics doing? And you're investigating your deltas from the average. In some board meetings, if everything's going well, you're just kind of spot asking people questions to see if they know what they're talking about. Separate topics, separate topics. But in the world of like, how does the AI work? The anecdotal things that I've heard, depending on who you're reading on LinkedIn or what news organizations you follow is like, you'll see big whiplashes. Companies fire, stock price goes up, but then they have to rehire or companies claim AI but there's people behind it. And so there's this idea that you're bringing to the table that I think most people should do. It's like the JCPenney example of like they just changed all their prices to not be .99 and then it like almost killed their business. Great case study. Anyway, the point is, is like if you don't look at a period of time about the climate of what your changes are doing and you don't have a unified metric either as a business or as a department to like help track that metric over time, then you're not going to be able to diagnose these longer form diseases that might be, you know, killing you over time or helping you over time. And that's kind of, I think, the QBR's purpose. I think if you're an analyst pulling information like, why am I doing this? Or you're consolidating or synthesizing the information down to three points, but your department is so much more than that, why are you doing it? It's for that reason. It's this big manage up melting pot of all this information to try to get to those 15 or 25 metrics that are super important to the health of a business.
Chris Mannion: Yeah, I think one of the kind of additional benefits that you touched on a little bit is actually the history of every decision that has happened in the past as well with that quarterly check-in, you know, every three months you're to have a pulse check on how things are going. The metrics are going to be presented. The decisions are going to be recorded and that narrative over time really plays out. So you do this every three months, over two years, you've got eight data points. You've got eight narratives. You can actually throw them into Claude and say, how has the business changed over time? What we used to do is just read through them before we created a new sheet. And what you can do then, because we know execs have a short memory because they have so much going on. If a question comes up in the meeting and says, well, I'll give you one example. We were trying to fix recruiter productivity by implementing new processes and new approaches. And so we kind of invested in a few different areas. And then we were looking at ramping up the overall capacity requirements across the team. And we'd already kind of reduced the number of recruiters to fit this kind of new model. In discussing whether we, in the QBR and discussing how we were actually going to increase the overall hiring velocity, the recommendation we came forward with was, well, let's build the process first and then backfill it with the recruiters so that we can actually scale effectively. To which the CHRO pushed back and said, well, why don't we just hire more recruiters? And we can kind of look back then and say, well, two years ago when we did that, we actually flatlined and we went negative. So let's learn from the mistakes. It happened because of this. This is what we did instead. Let's do this first. And that's a very quick way to get by and it doesn't involve kind of weeks and back and forth. In the meeting, you can just point to the number and say, this is the decision we made, this is the reason for it, so therefore this is what we're going to do moving forward.
Eric Guidice: I'm gonna go on a slight tangent here. Just from being in the room of people receiving the QBR with no real company contact. So I've had the privilege of being on the investor side and being in the room of the other board members receiving the information from their portfolio company and reacting to it. And it's such an interesting way to see this information can you just no one really ever gets to see that unless you're on that side of the business and so being an operator my whole life being on this other side of being kind of a part of like the capital is it was very interesting and the discussion particularly if you're a company that has outside investment and you're not a majority owner, which is most companies, there isn't like a person making the call for the company, it's this consensus decision, is there's a balance between the output and the quality, which I think our generation has popularly named it in shitification, but you can get, like the business numbers can change where the quality of the production is different in one way or another. So when you add an AI component to it in the engineering space as a software provider myself, like I am watching what the agent development process does to the folks on my team who are doing architecture and are trying to plan things out. And we as a team just have decided to keep a more traditional architectural approach to how we leverage this information. And we do it at the sacrifice of my more senior engineers doing the maximum output all the time because we're still in this part of the product development where like the product has to be so overwhelmingly good, right? We're like creating a category or we're creating a product that hasn't really existed before to a degree that we're trying to do it. So we can't make those sacrifices this early where companies that have a external pressure to be worth a billion dollars, right? You're trying to turn over every stone and the thing that works at 60 percent if it's good enough, like if it really is that market mover, it's gonna go do it. And so there's this like, I can see being on the board side how some of this information, I don't think it gets talked about enough as people kind of come up. You know, last week we talked about getting promoted. And it really just means, are you ready to get to the next step? And the closer you get to the next step, the more you are balancing outcomes with inputs versus being really good at a specific input. And so the QBR is kind of like a way that will you'll get the you know it's like taking blood work you don't get it every day unless I know that there's these new tech companies doing it. But nonetheless you get this blood work to see like is am I healthy and you're like all right well do I have the lifestyle I want and am I willing to make the health sacrifices to be active to party or to whatever people want to do, they're willing to make these sacrifices. I think that same logic is being applied to businesses. It's like, are we willing to sacrifice one part of the business's health for this over, for a better return on investment? And that's the QBR will indicate that. And I would say the quality that good leaders in the kind of high growth, invested in space is like they are able to translate the information coming from the QBR, whether it's up, down, or neutral, up to a board who has a different set of goals and then back down to the business, kind of like reset and reinvigorate people around it. Because I think it could be pretty demoralizing if you're creating things for a QBR and you as a contributor think quality is going down, but there's a difference of goals. And I see that a lot in, I don't want to say like, it's like director level management who have a pride in what they're doing and that is being, you know, leveraged for an outcome that's kind of outside their zone. So I see that a lot and the QBR is an indicator of that. If you're doing a QBR really well, then you're going to see that you'll be able to read the tea leaves before it even gets to the decision process. And I think that that's like a cool part, once you start to get good at reading the QBRs and like kind of forest through the trees, that's what's coming out of it. Have you ever experienced that?
Chris Mannion: Oh yeah. So I think this is a mistake that a lot of analysts make when they're going to that stage, they're promoted to that stage where all of a sudden they're producing board level reports. As an analyst, you want to report on the data. Like the story is the data and you explain what the data is saying and what it means and what your recommendation is. In a board level presentation, you're taking a story first and then you're finding the data to support that story. It's not, you're not lying about the data. You're just selectively choosing the supporting data that supports the story and the trajectory that you want to stay. And that, I think that dissonance can be quite hard when you first start doing it. One of the books I used to get for my team when new joiners came to one of my teams was called How to Lie with Statistics. And it's not to teach them how to make up data. It's to help them understand with data, you can select what data you include. You can select how the pattern tells the story. And you can selectively exclude certain parts of it if it doesn't support the story and the hypothesis that you want to share. It's helpful. I think both as creating a QBR that's going to support a decision that needs to be made in order to drive a certain direction. But also just so that you don't get taken by surprise when someone presents a story and they only include 20 percent of the data because they're only kind of including what you want to see. That was actually very helpful when we were working with vendors, when we had access to all the data and they will present the 20 percent that makes them look good. We could actually create a real ROI. And then there's kind of a more in depth conversation, which we won't go into on actually working in government and actually producing data to support government projects, where the decision is not necessarily which is the best project for the ROI in financial terms, but which is the better politically. And that gets into a whole can of worms, which is not quite as, it's a little off focus for I think what we're talking about here, but there definitely are politics involved in these discussions in large organizations. And so I think to be aware of the story you're telling first and then to kind of build the analysis around that story is going to be really helpful. I'd say that, you know, that if you've got an ethical leader who is interested in the data, they're always going to understand the whole picture, but they're probably just going to want to focus on one thing. And if you're not directly in the board meetings on a regular basis, they probably know what the board cares about. And so they're going to actually tailor the message and everything to what the board wants to see, as opposed to reporting on everything that you can potentially report on. There's kind of like bumpers to this where you kind of don't want to over report because that could actually raise more questions and concerns, but you want to make sure that the reporting that you do is accurate and truthful.
Eric Guidice: And there's a company side and there's a ownership side. This is, you know, a lot of people ask me like, what's the best, what should I be asking in an interview when I'm looking at a new job? And I think if you're starting to get to that director level, you're starting to get to that I'm gonna get some board exposure type level. I think a good question is like, what is the goal of the owners of the company? You wouldn't say owners, but like what is the goal of the ownership level of the company? Because it influences what is success, right? And I use extremes to kind of create the point, but like how do you think the QBRs went at Toys R Us? You know what I mean? Or you know, Red Lobster. How do you think those QBRs went for the last few years before they went bankrupt after whatever the business was doing beforehand. It's like there are going to be goals that exist at the company that are, that create an environment where the QBR has its context within the business. And so not to get that to drift too far away from the headcount side of information into this. It's all just good context for why we say the headcount information is important to this. So we started with saying people verse machines as far as like what is the cost for what is the value. But there's a few other things in here that as we like how we do it on top we got about 20 minutes left. Like let's bow it up with some headcount stuff, revenue minus cost equals profit means you have a number, the cost of the business is the heads, and we're going to see a distribution of headcount cost be moved across to machines. This is no different than what happened with like Bell Atlantic, you know what I mean, back in the day or whatever, right? You're going to see machines and so you're going to see the same progression of how that gets absorbed into other parts of the P and L, but it will have an impact on headcount data. So like let's kind of talk about in context, not just of TA, but let's talk about for the rest of the business. What do you think in the abstract of just headcount, what do you think is going to happen to total heads at a company over time? How does the headcount information, will it become more impactful, less impactful to a QBR? What do you think is macro economically if headcount right now is 60 percent or 50 percent of a company? QBR importance? Will it go up? Will it go down? And what do you think is going to be important about reporting on this data in a QBR to a board going forward?
Chris Mannion: Yeah, I don't see any point in a near term where headcount is not important in these results because for most organizations, that's your competitive advantage. You have a strong team and you can out-compete your competitors. If everyone's doing the same thing, the differentiator, assuming everyone has access to the same tools, differentiator is how you apply those tools and the people that apply those tools are your team. And so the strength of the team is going to be your competitive advantage. So I'm still bullish on kind of human driven businesses. I think, you know, they're here to stay for a good while yet. But I think what we are going to see is more scrutiny on the overall cost of maintaining that kind of human headcount. It's, you know, for the last few decades, the cost of human capital has been one of the largest costs on the balance sheet for most organizations, especially where we come from in kind of tech or service based businesses. You don't have huge factories or kind of huge assets that you're purchasing that you're going to amortize over 30 years. Like your biggest asset walks in the door in the morning and walks out the door at the end of the day. So as long as that remains the biggest cost, that's going to be one of the biggest challenges on the QBR. I think to your point where we're going to see changes is what are we doing to either improve the effectiveness and productivity of the number of people that we have in the organization or to drive down the kind of cost component of that and create a more predictable mode of something else that's going to be sustainable for the business. Because again, if your business relies on having the right people and those people move to another company and take all their skill sets with them, then that's a huge threat to the organization. And I'd say the fewer people you have, the more risky that concept is. So if you have 10,000 people and you lose 100 people, it's not a huge deal. If you have a thousand people and you lose 100 people, all of a sudden you're at 90 percent capacity and those hundred people may be a critical part of the business. So I don't think that's lost on boards or lost on executive teams right now. But I would expect that to become more of an issue. So better retention of the employees that you have, that you want to keep and more productivity from the overall employee population using tools such as AI, but also a lot of other automation and approaches there. Hopefully training as well, which is kind of one area that I'm leaning in into to actually help people do more with what they have.
Eric Guidice: For a brief moment there was like this talent density wave, right? I saw it on LinkedIn all the time, which I don't particularly like talent density or quality of hire, not because I don't think that they're important metrics to measure, but they are subjective in interpretation of whatever it is. If it's like measuring, I used to race, I used to do motorcycle racing and there was like grip. Do you have grip? Well what does that mean? It's like yes I do or no I do. It's like do you have quality of hire? Do you have talent density? It's like yes or no. That might mean different things to different companies. That said, I think that the measurement of overall workforce productivity in the same way like I see it, I follow some macro economists who kind of show like the difference in productivity versus wages as a chart. I think there's going to be a very similar, if I'm looking macro at what headcount is, if you're seeing companies like Snap and Block make the attrition and see the stock price go up, now you have a bunch of people in the capital world who are super interested in like, I want my returns to go up by that amount. And so you're gonna have to present, there's only two ways to do it. You can get rid of people or you can lower wages. Both not great for the humans, but the other side of that coin is, can I make these folks more productive? You know, we've been working on that concept at Headcount 365 where like, I can associate a revenue with a seller, but kind of what I'm listening for in the market is like, how do people look at the measurement of individual chairs or the aggregate of the department to say the productivity based on a certain spend is X and now it is Y. And I think if you're reporting on, you know, headcount in any form, QBR or long form, it's like weather versus climate conversation, instead of your North Star being we've hired or we didn't hire or we've met quota or we didn't or we've produced the product that we said that we would or we wouldn't, that might be one of your three supporting North Star metrics. There also the headcount is going to be am I staffed and what is my cost to what is my productivity to cost ratio and how am I trending and what am I doing to reduce or maintain that. I think that's my hypothesis, that's my prediction. I have no evidence of it yet, but I want to say that like the more I hear the capital side of the world talk about this headcount problem that AI will inevitably disrupt is kind of a... that's the theme that I'm pulling out. I don't know if I'm making sense.
Chris Mannion: No, I think we've just seen this happen in practice, right? If you look at the Disney layoffs that were announced just this week, if you track back to like look at all of the what would have been internal QBRs that became public announcements. You know, they have consolidated a bunch of their organization. I think it's mostly marketing from what I gather into kind of one kind of central team and leveraged technology there to improve automation of the things that can be automated. And so they've removed duplicative work and used automation to make other employees more productive. And as part of that, hypothesis there was if we do that, we need fewer people, which is going to reduce overall headcount. You see how a QBR maybe two years ago would have said, this is our roadmap and these are the steps we're going to follow. And every three months there would be a progress check-in. And so probably the last QBR was, okay, we've now reached a point where we are overstaffed in this team because we've consolidated everything. The cost to do this is going to play, it's not an instant cost saving move. But that cost is then going to ramp over the next two years. And overall, it's going to be a net positive return on investment for us. And we're going to lay these people off now, reduce the roles, maybe we would stop backfilling. But it's never a snap decision that's made on a dime. It's all these kind of QBRs that happen over time that are kind of driving towards this point. And I think that's where, talking about to the hypothesis thing, it's if we were to do this thing, this is what I think we could get out of it. This is going to be the cost and this is the payback period. And so you kind of see that playing out and you know it's probably a shock to a lot of people that were impacted by the layoffs at Disney but I'm pretty sure that these conversations have been happening for a long time because they have to be because the business has to operate as a business and reduce its overall cost to compete with everyone else. So not taking any sides but just like looking at the reality of how this plays out in large organizations, I think we can actually tie a lot of these kind of QBR decisions to what we see in the news.
Eric Guidice: Yeah, the centralization, specialization thing, like that's always been, I think the economic climate is going to force people to do this. I think if the trend is constrict or limit so that you increase productivity like centralization there, but you're going to give up specialization of, you know, when I was at Uber, every city had its team and then we moved to regional. And then with regional, we added a whole other central team called localization. There was like all these, but a centralized localization team had different metrics than in city. Because even though language can be translated, you miss context and nuance and all this type of stuff. So depending on what your goals are, and if you're on the capital side of things, like how much profit to be extracted, you're gonna make these waves of investments to remind you why the other one is good. And so I do think that we're gonna see a lot of effort to consolidate and centralize to see the same outcomes and the markers on the QBR become even more important to say, all right, what did we gain from this? And it's going to be the cost to productivity ratio, but then you're going to have all this other information about like the quality of your service and what it does to your customer and until the benefits of the one outweigh or sorry the cost of the one outweigh the benefits of the other you're gonna see that natural wave of what's happening. I actually watched a great YouTube about YouTube channels and the private equity acquisition of YouTube channels and I think it was a very interesting analogy of roll up and centralization. I think it's analogous to what you're saying that Disney is doing and I just think it's a, you know, sometimes a product of like the economic times or this whole focus on productivity and you know everyone's we're gonna get a lot of a lot of debt as a country right now and we're trying to figure out ways to make money out of what we have now so I think that that'll continue to be a theme.
Chris Mannion: And I think it ties back to this whole session on QBRs. And I think what we led with is it's revenue minus cost equals profit. If your goal is to maximize profit, you adjust revenue and cost. And they're not done in isolation. That's why the QBR is so important because you have the timeline of what's happening. And so the decision to do something where you look at ways to reduce costs and you check to see whether it's actually impacting revenue are pretty key. We used to do this quite a lot, even in supply chain. We'd look at, to what extent can you optimize the customer experience and tie customer experience to net new purchases. If you look at your lifetime value of a customer, how much are they gonna buy over the course of their lifetime? And what is the experience at which maximizes the amount of... it sounds very transactional, I think when you put it this way, but this is how business works, right? How do we extract the most money from the customers over the long run for the lowest cost? And that's how you maximize profit. And the way you do that is to optimize for customer experience. And so you kind of draw this line of, what's the worst experience we can give that still gives us the maximum amount of money from the customer. And by worst experience, I mean the lowest cost. So, talking back to the Disney example, what I would imagine those QBRs would have looked at was if we reduce the overall number of people in our marketing team, and we automate some of the processes, what impact is that going to have on the customer experience and what impact will that negative customer experience have on the revenue? And where does that point meet to optimize the cost cut into the revenue loss so that we maximize profitability? And that's why companies hire data scientists, because it's a complex equation. But this is what happens. And 20 years ago, it was offshoring. Before that was leveraged buyouts and roll ups and, know, this is not something new. It's just AI is now the latest interpretation of that. And so, to what extent, if you replace your customer success team with AI agents, are you going to damage your customer relationship? And then those will then leave for your competitors who are not doing that. And therefore you then kind of struggle to compete in business. So that's where, tying back to why the QBR is so important. If you have the right metrics and you've tracked that over time you can understand where you're going and how those things are impacting your business.
Eric Guidice: Yep, and if you're a director, VP, SVP, you're preparing a QBR for your executive or you're even presenting one, I encourage everybody to go try to get with their executive and see how that made it to the board deck. If you're a TA leader and you want to see like the QBR on Unicorn Talent is what I used to run and the first two slides, if you hit or you missed, that's what made the board slide. So you could produce all of this information. You get one slide. Sometimes you get part of a in a board deck. So if you haven't seen how your work is going past the level you're presenting to, to a different audience, then you're missing a core context that might help you... I don't want to say cope, it's not like that serious, but like you have to see, if you're in a meeting and you put together a great presentation and it got passed over, you got something focused on that was like you thought was nothing, you're missing that context as to why. And it really smooths out the operating relationship between the person preparing the QBR and the person consuming it because you're all on the same page about like the, you know, I use the ABCD framework, right? Audience Behavioral Condition Degree. Who is the audience this is really for? What behaviors are we trying to change to what degree? And so that's like... Do that exercise. I'll close with that. I know we're coming up on time, but I'll close with that. If you're producing a QBR, know your North Star metrics. Know what you're actually reporting on and produce your supporting documentation for it. When you do that and you understand how it's being consumed in this weather versus climate executive meeting, that will help you understand what information to provide. Then understanding how it's being used beyond that meeting or in a board deck or in to an investor whatever is the context that will help give you a piece in the effort you put in for the return that you've gotten in that moment it'll help you communicate to your team etc. So that's my closing thoughts. Anything to add? Anything that you would advise folks to do during the QBR process? We're mid-April now so most people are in the mix.
Chris Mannion: Yeah, I'd say building on that point, if you can actually get in the room when the QBR happens, that was like night and day game changing for me when I just first heard what was happening, what questions were being asked, how deep they actually got into the one or two of the 20 topics we presented. And they ignored the other 18. It's just a really good experience and that helped make me better at designing QBR decks and white papers for the future. So that's a really good recommendation.
Eric Guidice: Yeah, agreed. Okay, let's wrap it up. Another fantastic episode of the Head Count Experts. We have some cool guests coming up in the next few weeks. Some CFOs, some heads of headcount, some workforce planning folks. We're gonna have some more outside opinions from different companies from different styles start to join us on the podcast. And so if you're interested in joining, send a note. But either way, stay tuned for another episode of the Head Count Experts and we'll come at you next week.
QBRs missing headcount data are counter productive
Without headcount data the fundamental "pulse check" of organizational health is missing the critical context. For years Labor & labor cost was the singular constraint to add productivity, but now there’s an emerging tension between traditional headcount and AI-driven productivity.
QBRs are the primary forum for evaluating these macroeconomic shifts as they are regular check ins about the “climate” of the businesses productivity.
How QBRs change as AI redefines the productivity relationship
The "Bottom Line Up Front" Advantage
The most effective QBRs respect executive time by addressing the core question within the first 15 minutes: "Did we hit the goal, or did we miss?". By leading with this result and the immediate reason why, leaders can use the remainder of the meeting to focus on action plans, prioritize high-impact fixes, and distribute accountability across the organization.
Weather vs. Climate: Managing the Narrative
Individual quarterly data points represent "weather," while the long-form trend over several years represents the "climate" of the business. A strong QBR narrative uses historical data to prevent "vibe-based" decision-making. When executive memory falters, a well-documented history of past successes and failures serves as a shield, allowing leaders to backfill recommendations with proven data.
Headcount vs. Machine Productivity
We are entering an era headcount is the only source of productivity. With decentralized value creators, analyzing the cost-to-productivity ratio of AI tools alongside traditional talent costs is now part of the conversation.
Board-Level Storytelling
The investor/owner/board leader set’s the context for how businesses operate, and report, on success. Leaders must understand the audience's underlying goals, whether focused on majority ownership returns or high-growth exit strategies, to ensure their QBR metrics align with what success looks like at the highest level.
There’s a growing need for high fidelity headcount financials
In the age of AI, the cost to productivity ratio will become highly scrutinzed as companies look to maximize their profit relationship with productivity.
To accurately position the value of every headcount there must be a clear comparison of the cost of adding productivity to a buissiness, and the QBR it the best leadership tool to do so. Whether you are managing human teams or AI agents, the ability to read the "blood work" of your quarterly performance is what allows you to diagnose and cure organizational inefficiencies before they become fatal.