The Science of Scaling: The 3 Steps to Knowing You're Ready. Podcast with Mark Roberge - Sales Outsourcing Company | Outsourced Sales Team Services

Sell Like A Leader – Episode 41

David Kreiger sits down with Mark Roberge, Co-Founder and Managing Partner of Stage 2 Capital, to break down the three-step framework behind his new book, The Science of Scaling — a data-driven answer to the question most founders never rigorously address: when to scale.

In this episode, they cover:

Why the metrics most founders use to “prove” product-market fit are actually the wrong ones
The three-variable formula for building your own leading indicator of retention
The real reason churn gets blamed on customer success and product, when the sales team is usually the root cause
Why hiring in a “lump sum” after a fundraise breaks companies, and the pacing model Mark recommends instead
Two moats built for the AI era: an AI-native org chart, and a “trust moat” in the vertical markets everyone else is ignoring

If you’ve told your board you’re ready to scale without a defensible reason why, this conversation gives you the full framework to get one.

All proceeds from Mark’s new book, The Science of Scaling, go to mental health research at McLean Hospital. Sell Like A Leader is giving away 15 copies — DM David Kreiger on LinkedIn to grab one.

About Mark Roberge

Mark is the Co-Founder and Managing Partner of Stage 2 Capital, the first venture fund backed by over 1,000 top sales and marketing executives.

He’s the Founding Chief Revenue Officer at HubSpot, where he scaled the company from zero revenue to a successful IPO.

Mark is also a Senior Lecturer at Harvard Business School, where he has spent over a decade teaching sales, marketing, and entrepreneurship.

Podcast Key Takeaways

Product-market fit is about retention. If your product is selling but customers are quietly churning, you don’t have product-market fit; you have market-message fit, and Mark calls that an infomercial.
Churn is usually a sales problem, not a CS problem. Boards default to blaming customer success and product for churn, but the root cause is typically who sales chose to sell to and what they promised to close the deal.
A real moat survives being copied. If a handful of great engineers could rebuild your product in under nine months, it was never a moat; it was a feature. What holds up is how efficiently you run internally and whether customers trust you enough to stop shopping around.
The metric everyone tracks is a lagging indicator. Churn shows up at the board meeting a year after the decisions that caused it. Leaders who build their own leading indicator of retention early are running their business five quarters ahead of competitors still waiting on quarterly numbers.

Connects

Connect with Mark Roberge: https://www.linkedin.com/in/markroberge/

Connect with David Kreiger: https://www.linkedin.com/in/davidkreiger

Subscribe to the podcast and follow our Podcast LinkedIn page so you don’t miss any episodes!

Transcript

David: Welcome back to another episode of the Sell Like A Leader podcast, the podcast for revenue leaders who are on a mission to cultivate a high-performing sales team within their organization. I'm your host, David Kreiger, president of SalesRoads, America's most trusted sales outsourcing and appointment-setting firm.

Today, I am super excited to have an amazing revenue leader — I'm sure you all know him — Mark Roberge. Mark is the co-founder and managing partner of Stage 2 Capital, the first venture-backed fund by over 1,000 top sales and marketing executives. He is the founding CRO of HubSpot, where he scaled the company from zero revenue to a successful IPO, and he is now a senior lecturer at HBS, Harvard Business School, where he's spent over a decade teaching sales, marketing, and entrepreneurship. Mark is the best-selling author of The Sales Acceleration Formula, which I'm sure most of you have read. [00:01:00] I use many of its principles in building SalesRoads myself. And today, we're going to dive into his newest book, The Science of Scaling, which lays out a data-driven framework for founders and executives on when and how fast to scale.

And all the proceeds from this book are donated to mental health research at McLean Hospital. And before we get started — I know my intro has already been long enough — we have a quick gift for our listeners. We'll be giving away 15 copies of Mark's book, so if you'd like one, just direct message me on LinkedIn, and the first 15 people to reach out will get a book sent over to them.

Mark, welcome to the show.

Mark: Wow, David. Thank you, times two. That's a beautiful intro. I appreciate that, and again, thank you for the support on the book and the cause.

David: It's an amazing book and a really important cause. Excited to do it. So let's dive into some of the meat of the book. Basically, I think the framework — I'd argue — is that true scaling happens in three sequential steps: product market fit, go-to-market fit, and growth and moat.

[00:02:00] So let's go through each one, one by one here. I think most people do think they understand product market fit, but you argue that most don't have a clear definition of what product market fit actually is. I think you even ask some of your students at HBS to define it, and they have trouble doing it.

So I'd like to dive into how you would define product market fit, and what are some of the common mistakes you see founders make when they're trying to determine whether they actually have it?

Mark: Absolutely. I'll kind of provide some overarching context around it, Dave, and then get precisely to your question. So this particular work — I never really intended to write a book. It's just that I'm very involved in the startup ecosystem from many dimensions.

[00:03:00] Most of my work these days is at a venture capital firm. I probably look at about 1,000 startups a quarter, so I get to see the pattern recognition and what's happening. I'm involved in about 75 board meetings a quarter, so you get to see what's happening in the founder's mindset and the sales leader's mindset.

And then I'm also blessed to be invited to speak about these things, whether it's at South by Southwest, SaaStr, Inbound, or whatever. So every year I'll do two or three fresh perspectives on what's happening, and then repeat it across various venues. I've done, whatever, 40 or 50 of those fresh perspectives over the last decade.

This particular one, The Science of Scaling, was first spoken about at the 2019 SaaStr Conference, and it just went viral. I think it was one of the most-watched videos for many years from SaaStr's speeches, which is just a signal that you've stumbled across something timeless and abstract.

[00:04:00] And I just kept writing about it. We did a bunch of classes and cases about it at HBS. It's taught a lot at different business schools. People kept asking me to talk about it, and then eventually it was like, "Can you write this up?" So that's how this work came about — just so people know, it's not something I thought about last month. This is just a natural progression. It's crazy how much unnecessary failure occurs in any business because of a lack of rigor on these two critical questions, David: when to scale, and how fast. And how little rigor exists — it's crazy how many classes there are in college on, you know, remember your Wharton days, David, on accruing and recognizing revenue.

Really detailed frameworks and formulas. No questions about what net income is. And the same set of rigor doesn't exist for when to scale and how fast. That's just a massive hole in our business community and our startup ecosystem, okay? [00:05:00] So that's the framing there.

I would say, if you tackle the first question — when are you ready to scale — I think a good answer is product market fit, and we hear it quite a bit, to your point, David. Thank you, Eric Ries and The Lean Startup. Thank you, Steve Blank, and his work at the beginning of the century that led to product market fit and evolved our startup ecosystem for the better, forever.

We no longer built products in a basement and then tried to sell them. We built them hand-in-hand with customers. Beautiful. And I think this work, product market fit, came out of that. But to your observation, David — when I do this at every single speech this year, it's like: okay, product market fit. I go out into the audience, and I have one person on the right side whisper to me what product market fit is, and then I go to the left side and have them whisper to me what product market fit is.

No one has ever matched. Now, if I did that for net income, they probably would match, right? [00:06:00] So the most common answers I get are something like "100,000 in revenue," or "100 customers," or "100 inbound leads a month" — those are when I have product market fit. And you can define it however you want.

I would argue with high conviction that those are wrong. Because then my second question will be, "Okay, fine, I'll give you all three. I'll give you 100,000 in revenue. I'll give you 100 customers. I'll give you 100 inbound leads a month. What happens if you have all that, and half the customers are churning and stop using the product? Do you have product market fit?" And the CEO will say, "No, but I'll just listen to what the issue is and adapt the product to address it." And then I'll ask, "How do you know when you've done it?" And he or she will say, "When they stop churning." And I'll say, "Exactly." So that's kind of a moment for them — product market fit is not about whether your thing is selling.

That's market message fit. Those are infomercials. [00:07:00] Product market fit, qualitatively, is when your product delivers the value that you promised to the market, and it's best quantified by retention.

David: I think it's a beautiful framing, and I think the pushback, which you address in your book, is: "Well, Mark, that's great, but it takes a year — if we've got them on a contract — to know whether they're going to churn or not, and I've got investors who need me to scale. They need me to move on. So the metric that's much easier to quantify is those three you talked about." So how do you respond to startup founders who say, "Listen, I love what you're saying, Mark, it makes a lot of sense, but I can't wait a year to figure out whether I've got good retention or not"?

Mark: Yeah, you can't sit here and say, "Okay, we did all the things The Lean Startup said. We have this hypothesis around a problem we're solving. We got four design partners. They love the product. And now, according to Mark, we have to define [00:08:00] product market fit as retention. We just signed up 10 customers — I can't wait a year." So that's where we have to define our own leading indicator of retention. This has definitely been a favorite part of the work for the ecosystem.

To David's point — the leading indicator of retention is something we can observe, usually in the first month of a customer's experience with our business, our service, our product, whatever. If that occurs, they'll be with us forever and keep renewing; if it doesn't, they'll likely churn.

This is one of those cool things where there's no universal definition. It's a genuine entrepreneurial moment of creativity, where we get to think about what it should be for our own business. I'll scaffold it a bit with a formula: P percent of customers do E event every T time. So now the design decision is framed down to three variables — P, E, and T. [00:09:00] Let's use a couple of fairly well-documented examples from practice. Let's start with Slack. So many people use Slack — Slack's would be "80% of customers send 2,000 team messages every month." It's beautiful.

Imagine if Stewart had acted like most founders and said, "Hey, we got our product working, we have four design partners, our next North Star metric is to get to a million in revenue." You can imagine everything that follows — hiring a ton of salespeople, spending a lot on marketing, raising a ton of VC, whatever. Versus if he'd stood up and said, "Our next North Star metric is to have 80% of our customers send 2,000 team messages every month."

Such a better anchor. Now, as I say that, I just want to clarify for everyone — this isn't about going slow. This is about going as fast as possible on the right metric at the right time. I find that most founders I meet, half of them are going too fast too early, and half of them are going too slow [00:10:00] and too late and are going to miss the window.

This is about tuning it to your business context and the underlying performance of the business. So that's the LIR — leading indicator of retention: P percent of customers do E event every T time.

David: So now, thinking like some of the founders and challenging this — where they struggle is: okay, I'm going after these metrics, but the problem is I've got a few salespeople trying to close deals, and I've got some individuals doing customer success trying to get customers to hit these metrics, and they're blaming each other, saying "we're not doing this."

So I think you talk about an interesting way to make the first sales hire, or to make sure you're going after the right metrics in this regard. Could you walk us through that for our listeners?

Mark: Yeah, aligning these things is really critical. So let's [00:11:00] stay with the Slack example — we're saying, "Okay, our first North Star metric is to get 80% of our customers to send 2,000 team messages every month." A lot of times you'll end up with a salesperson in charge of acquiring the revenue, and a customer success person in charge of making them successful.

I have to parachute into a lot of companies where churn becomes a problem, and the first place the board usually looks to blame is the customer success team and the rigor of the onboarding. The second place they look to blame is the product team and the rigor of the product. They don't often think about the sales team, and that's usually the root cause of the churn.

If you have 50% churn, that's really bad, but it also means 50% of your customers are actually working. And if you acquired customers the same way, with the same expectations, as that good 50%, you'd have a massive retention success. [00:12:00] So most of these deficiencies in retention — the leading indicator of retention — are caused by who the salesperson chooses to sell to, and the expectations they set.

At scale, one underutilized but very successful compensation plan to align this is to pay the salesperson half of their commission when the customer signs the contract and wires the money, and half when the customer achieves the leading indicator of retention. So in Slack's case, that would be: you get half when they wire the money, and half when they send 2,000 team messages in their first month.

And your first thought is, "I don't want to turn my salesperson into a CSM." You're not. The CSM is still there, still doing their job. It's just that the salesperson is going to set the expectation and talk to the people necessary to make sure that LIR comes to life. A common thing is that salespeople don't need to talk to IT to get a contract signed, but if you don't get IT on board, if there's any setup [00:13:00] necessary, the CSM is screwed. So that's an example of them talking to IT if they're compensated accordingly. And you'll often find, in Slack's case, that you can try the product for free — the salespeople will get the customer to send 2,000 team messages a month before they even sign the contract. What better way to align these things?

And just to click back one more time on your question, David — in the early going, sometimes you don't have a CSM and an AE; you're just dealing with your first go-to-market hire. It's advantageous to have someone who's full cycle, doing both sides, at least in the early going, to eliminate this misalignment.

David: Yeah, let's talk about that person, because I think that's a step startups either stumble into — and get lucky because they've got the right person wanting to do it — or they build this out [00:14:00] once they get a little bit of a green light on what they think is product market fit.

They hire the big sales team. They have a whole customer success team. A better path is to find who you call "process builders." Can we talk a little bit about how you've seen the best companies identify who those people are? How do you hire them, and what is their role within the organization that creates some magic there?

Mark: Yep, good. Let's frame out that whole question, because so far I think what we've established is that we started the conversation around "when are you ready to scale," and we've settled on product market fit as a good answer — it just needs to be defined well, and is best measured by retention and codified with the leading indicator of retention.

That's our first goal, and we define that for our business and go at it. David alluded earlier that you're not ready to scale once you have that — you have to do the second step, which is go-to-market fit. Because all you've proven at the [00:15:00] product market fit phase is that when you go out and acquire another 10 customers this month, the majority of them will see value in your product.

You haven't proven that you can acquire, onboard, and serve those customers profitably — nor should you have. In the first phase of the business, the famous Paul Graham, founder of Y Combinator, says, "Do unscalable things early." That's what he means here — even Elon doesn't get it right every time. It's very hard to invent a product that delivers the value you intended as the inventor. So you need to focus all of your energy in the first phase of the business on helping customers see that value, even if it means flying to their office to onboard them when they're paying you $50 a month.

That's what we had to do. But once we've achieved that, we're not ready to scale, because we need to prove — that's not scale. We need to prove that we can acquire those customers and serve them, and that's what we call go-to-market fit. Once you have [00:16:00] go-to-market fit, then we move into growth and moat.

Now, it turns out that as you navigate those different phases of preparing for scale and entering scale mode, the decisions on your go-to-market system design change. The way you sell, the way you generate demand, the way you price your product, the way you define your ICP, the way you hire your team — which is what Dave is asking about.

The optimal hire at the growth phase is very different from the optimal hire at the pursuit of product market fit phase, as well as the pursuit of go-to-market fit. The number one salesperson at Workday is a terrible first hire for you. They're a really good growth hire, depending on your context, but a terrible first hire — because the number one salesperson at Workday is number one because they came into the Workday office, went through the 30-day training, went through all the commission planning, the pitch decks, and the sales playbook, had their manager help them, had all the Gong recordings coaching them, were given a territory and a commission, and [00:17:00] went out and cranked.

You don't have any of that. You don't even know who you should be selling to in the product market fit phase. So you need someone who's almost half product manager — able to see patterns and communicate with engineers to accelerate your learning — and half account executive, comfortable talking about money and commercials and running discovery processes.

So that's our ideal first hire. To David's point, once we achieve product market fit and we're in go-to-market fit, we need someone who can build that process. Where do you find these people? In the product market fit phase, usually a good place to find the person is your favorite Series B through Series D funded business with a similar go-to-market motion — find their first reps, because they're miserable right now.

They were there when there were six people in the room. They were sitting with the founders. It was so fun. Now they're in the factory. They have a manager and a comp plan. They miss the old days, [00:18:00] and some of them are meant to be career first go-to-market people. That's their purpose in the ecosystem, which is great. So that's a good place to find that product market fit person.

Then, once you move into the go-to-market-fit phase and need a process builder, usually it's someone who was recently promoted to management — say, in the last six to 12 months — again, probably at a Series B through Series E company. Because what happened there is they're not far enough removed from carrying a quota that they've forgotten how to do it, since they'll still need to do some belly-to-belly work. But they've had a taste of systematization, management, and process development.

Hopefully, as we've pointed out, as you move from product market fit to go-to-market fit to growth and moat, the optimal design considerations of the go-to-market system — price, demand gen, sales methodology, ICP, commissioning — all change for the sales rep. [00:19:00] And what we've just walked through is how that changes for the go-to-market hire, and where to find them.

David: That's a great way of redefining the first two stages, which I think a lot of people think they know, but don't realize — and most get wrong. I'd love to move to the third phase, which I think —

Mark: Yeah.

David: — people, at least with the words, might think they understand, but I think it's a really important phase that you've defined in the book: growth and moat.

So first, can you define that stage for our listeners?

Mark: Yeah. So now, okay, we have product market fit and go-to-market fit, and we've calculated them using our own data, and calibrated them for the context of our business. In some cases we're under a lot of pressure to blitzscale because of a category, so we've set those numbers accordingly, because we've got to go fast. Half the entrepreneurs go too slow — so we've done that. Now it's, how fast should we go? We're ready to scale. And the first [00:20:00] pothole is easy to fix and very intuitive when you think about it.

Usually what happens is: okay, cool, we have product market fit, we have go-to-market fit, let's go raise a round of funding, or let's go tell the board what this year's going to look like — let's go from 3 million to 12 million. They sell that to the next round of investors and get a good valuation, or they sell it to the board, and they're like, "Okay, cool, that means we need to add 9 million in revenue. Each rep does 800,000 in ARR, so I just do the math — I need 12 reps." And they hire 12 reps next week, and that's the scale process.

That is ridiculously broken and causes a ton of unnecessary failure, because there's absolutely no awareness or consideration of the operational flywheels necessary to hire 12 reps successfully. Let's start on the recruiting front. You could probably argue that the more qualified first interviews you do per hire, the better quality hire you'll get.

If you do [00:21:00] two high-quality interviews and hire one person, you're probably going to make a worse hire than if you did 10 high-quality interviews and hired one person. So if we're trying to do 10 qualified phone screens per hire, and we're going to make 12 hires, we need 120 good, qualified resumes and first screens.

Who's doing that? Who's doing the interviews? And then who's ramping the rep, and how are we 12x'ing demand gen overnight? These are the operational flywheels. So the fix is easy: you don't think about scale, at this moment, as a lump-sum hiring event right after a fundraise, or a lump-sum hiring event at the beginning of a fiscal year.

Scale is about establishing a pace. It's not "let's hire 12 reps and see how 2026 goes." It's "let's hire three reps every other month." Okay, so now we do that, and that allows us to build these operational flywheels. And we're going to do that forever — you're always going to be hiring if you're scaling that way.

If you're B2C — and I know most folks here are B2B — but if you're [00:22:00] B2C, it's more like just investing in marketing. You're just going to scale that forever, throttling the pacing up and down depending on the signals of the business. So this measure of product market fit and measure of go-to-market fit now become our speedometer, telling us if we're going too fast or too slow.

So now we're going to hire three reps every other month for two quarters, and watch these indicators. If they go red, we're going to intervene immediately and fix them, hopefully within a week, and get back on track. But if they stay green, we can throttle up to four reps every other month for six months, then throttle up to eight reps every other month for six months.

And now you've become a unicorn in a very calculated way, monitoring along the way — because most people monitor whether they're going too fast or too slow using the board meeting's churn results, which come six weeks after the numbers were put in, and the churn itself is a result of work you were doing a year ago.

So when you operationalize the business with these frameworks, [00:23:00] you're running your business usually about five quarters ahead of your competitor.

David: Yeah, and I think you bring up a good point about the recruiting side. But also, when you're recruiting and hiring 10 people, I think people underestimate — especially for a new product, and a company that hasn't built out a whole training program — the training and coaching side.

Bringing 10 people on when you probably don't have that infrastructure — it's really hard to coach and train 10 people all at once. Right there, you can create a nightmare where you bring in some of the right reps but don't give them the right kind of support to be successful.

Mark: Totally.

David: And I think another part of this — which you talk about in the book — is pricing and competitive positioning at this stage. Can you talk a little bit about where founders go wrong there, and how it plays into this phase?

Mark: Yeah. Pricing is another aspect of the go-to-market system, [00:24:00] and it does need to adapt through each phase. In the pursuit of product market fit, it's rare that pricing is that important. The only time it's absolutely mission-critical is if the entire innovation of the business is a pricing innovation, which is rare.

Sometimes it happens. But usually your main innovation is some new value creation for the customer that isn't associated with the price. So my guidance in the pursuit of product market fit is: price for commitment, not profitability. We don't want price to be a friction point toward achieving our leading indicator of retention and product market fit.

At the same time, I don't want to just give stuff away for free, because I want the customer to be committed. So usually, at this point, we're trying to find our first five design customers, and we'll be doing great discovery. "We're working on this problem — can you tell me how you're thinking about it?" [00:25:00] And then we'll navigate to something like, "You know what, you're the 19th head of RevOps I've spoken to, and all of your peers are saying exactly what you just said. In fact, we're building what you just described, and it's coming out in August, and we'll be charging $50,000 a year for it — but we're just looking for our first four design partners to use it, and in exchange we'll give them a 90% discount for the first year.

So would you like to sign a letter of intent today, with a $1,000 deposit, to be one of those four design partners and get a 90% discount on the product for the first year?" I think that's beautiful, because just asking that eliminates so much false positive — people who say, "Oh, if you build that, I'll definitely buy it." All right, do it — buy it right now. Prove it to me. And if they're not going to sign the LOI, that's when the real objections come out, and I can address them, as you know very well, David. And now I have my four design partners, and I've picked early adopters who will be good [00:26:00] design partners for me.

Now, when we move to the go-to-market-fit phase, we're trying to prove that we can acquire and serve those customers profitably, so price becomes crucial to get right. A simple way to think about price design there is through three lenses. The first lens: what does the price need to be for your unit economics to work? What do you predict the acquisition cost to be, and where does the price need to land for you to have a business? The second lens: how does the customer think about ROI? At this price, do they see it as a very strong ROI purchase? The third lens: what's the substitute price? How would the customer solve that same problem a different way — hiring a different consultant, buying a different set of products, whatever — and how much would that cost them? That usually gets you pretty close to an optimal price. Then, as you move to the growth and moat phase, [00:27:00] price can be both a proponent and a detriment to your moat. People don't really — especially in this day and age of AI — realize how weak most moats actually are.

Oftentimes, when we're making investments and we ask founders, "What is your moat?" they talk about a feature. My follow-up question is: let's say you're right, and that feature kills it, and you win all the customers in the next three months. Obviously your competition's going to find out and build the feature. So how long would it take them to build it? If the answer is less than nine months, that's not a moat. The real answer you need is: if five awesome engineers from MIT or Stanford, or wherever, copied your product, how do you still win?

The higher you set the price, the more attractive a target you become for a copycat. We're seeing a lot of that right now — it's a very attractive [00:28:00] moment for copycat entrepreneurs. The fast follower has usually won over the last few decades. Slack, Zoom, and Salesforce were not the first versions of those categories.

It's usually the fast follower that wins, and a lot of VCs and founders don't get that. There's definitely a first-mover advantage — get the most money, get the best engineers, get the best logos, the whole blitzscaling mentality. It's definitely real, but it doesn't win most of the time.

The fast-follower copycats win most of the time. And this is a moment for fast-follower copycats, because there's a big "why now" with AI — copycatting is easy with accelerated product development cycles. The first wave of AI companies raised at such a high valuation that they're in what's called "ACV jail."

They can't lower their price, because they're trying to grow into the valuation they promised. And third, they don't have much of a moat. So yeah, it's a pretty interesting time for copycat technologists.

David: Yeah, and with that — [00:29:00] you can also have copycats of the copycats, so it's an interesting time.

Mark: Yeah, keep going, David, on that — that's an important point. I want to jam on that with you.

David: Yeah, I'll try to tee it up, and maybe it's the direction you're going. Moat can be about pricing against the first movers, which is an interesting perspective, and I think people need to think about that as they're building their product.

But what are the other moats? Because if you're just trying to create a moat by undercutting price, you're going to get undercut too — because everyone can be an entrepreneur now, as we all know, and there are going to be so many people jumping in. So how does this not just become a race to the bottom for customers?

Mark: Yeah, this is all good. Another important piece of The Science of Scaling — timeless rigor — is that it creates first principles by which to build these new AI operating systems. I think we're essentially replicating how it was done three years ago, but the [00:30:00] real innovations happen when you pull all this stuff down to its studs and rethink it. That's going to be the real work.

This is kind of what you're getting at, David, on the moat front. A couple of example moats: on the thread we were just pulling on with copycats — I think a good moat is how you run your business internally in an AI-first way. The companies that were started two, three, four years ago as AI-native companies built themselves too much like the SaaS businesses of 2015.

That was partly because of a lack of understanding of how all this stuff could be done — and we still don't fully know. We've had massive advancements in product development, but we haven't seen the same advancements in sales yet. That's coming this year. For example, we're seeing a movement toward much more athletic, full-cycle roles, where we're reversing the specialization trend we saw in R&D and go-to-market.

In 1985, there was one role: engineer. In 1985, there was one role: salesperson. [00:31:00] By 2010, R&D had PMs, front-end engineers, back-end engineers, designers, data scientists, data analysts — and now you're starting to see it go back, with PMs being asked to do more. The same thing is happening in go-to-market.

We used to just have "rep." Now we have SDR, AE, AM, CSM, RevOps, whatever. We're starting to go back. Specialization has advantages, but it also has a cost — starting with a terrible experience for the buyer. AI can elevate an individual to do all the roles more than adequately. It's really hard for an established company to retool its people and restaff around that.

So if you can operate from the ground up in a very AI-first, efficient way, and pass some of those efficiencies onto your customer, that will be a moat. There's a lot of meat on the bone right now — a copycat can come in and literally charge 70% less for the same product.

The copycat of the copycat doesn't have as much meat on the [00:32:00] bone. If the copycat runs their internal org in a highly efficient way, maybe someone else can come in and undercut them by 5 or 10% more — but it's just not going to move the needle. So, to zoom back — one of your moats is how you operate your internal organization in building and distributing product in a highly AI-first, efficient way.

That's a moat. Another moat: I'm pretty bullish on vertical AI software in non-tech end markets. That was a lot right there — we felt like we were back at Wharton, David, right?

Like, "Welcome to class, we're going to talk about vertical AI software in non-technical end markets."

All right, let me drill into that a little bit. Vertical software has had a great decade — Toast and ServiceTitan are some of the best names. Toast is for restaurants. Even in that huge win, when you go to your favorite restaurant that's running on Toast, [00:33:00] they still use other software. They use Toast for the POS and a lot of the back-office stuff, but they use other tools too. They don't file their taxes with Toast. Some of them don't even pay their staff through Toast. There's other tech in the mix.

In this next era of vertical AI, because R&D cycles are so rapid, you can imagine that the whole tech stack will end up on one vendor. If you're a small manufacturing plant in Nebraska, a regional bank in Minneapolis — we have a roofing company as a portfolio company, and it turns out roofing has some very unique workflows, like drone photos.

And that's a reason why ServiceTitan isn't ideal for them. So there's an opportunity to pick these smaller markets that had historically been too small for a venture-scale outcome, but are now very attractive because the ACV potential is 4x, since you become the entire tech stack.

And when you have the full [00:34:00] operating system — say, in roofing — where everything talks to everything else, you can call it whatever you want: a brand moat, a trust moat, whatever. These roofers talk to each other. If everyone trusts Mike's Roofing Company, and Mike is using your vendor, they don't really care who's cheaper or who has what feature.

They just care that it's working for the best in the industry, and that it's going to work for them too. It's Michael Porter's "barriers to entry" brand moat from the 1970s, one way to put it. I think a lot of folks in the ecosystem are calling it a "trust moat" right now.

David: All right, two interesting moats — a lot to unpack here. I want to start with the first one, the org chart moat. The argument is really about being able to either build your company from the ground up as AI-first, or do what Jack Dorsey is trying to do at Box — re-engineer an existing company to become AI-first.

So first, let's [00:35:00] talk about this from your point of view, because I think a lot of people are trying to understand what this actually means.

What is an AI-first org chart? We can talk about it just on the go-to-market side, or —

Mark: I love it, man, and it's so good for your audience too. I want you thinking about this too, because you've always been a thought leader on this stuff, and I'd love to hear your take. I have a lot of conviction on this, because I've been sitting in board meetings all through Q4 and Q1, and I'd hear, "Oh, we're so AI-native in our sales team." And I'm like, "Really? Why? You're writing emails with ChatGPT? How do you know?"

So that's really what I've been pushing everyone on: can we measure this? Here's a cool thought process. There's this focus on go-to-market — how do we know if we're AI-native, and what's the opportunity?

The end goal is productivity per rep. So say you've got Bob and Susie, who've been with us [00:36:00] for three years, and every quarter they've each closed $250K of business, within 5%. We know that's the bar. We're AI-native if Bob and Susie start closing $500K all of a sudden. Now we can argue about LTV and all that, but say productivity doubles.

Let's pull that back one level. There's a first-principles formula that's not talked about much — the revenue velocity formula. It's the number of active opportunities a rep is working, times the close rate, times the ACV, divided by the sales cycle. Say they're actively running 20 opportunities, the historic close rate is 10%, so they'll close two of them. The ACV is $100,000, so that's $200,000, and the sales cycle is two quarters, so that's $100,000 per quarter. That's the revenue velocity formula, and it leads to PPR. So the question is: of those four variables — close rate, ACV, number of opportunities, and sales [00:37:00] cycle — which is easiest to double?

Don't go after ACV, because if you double the price, that's a whole different story — it might push you into a completely different target market. You can't mess with that too much. Close rate and sales cycle depend on buyer processes, and buyers haven't adapted their buying processes with AI as fast as sales has.

You're dependent on the buyer there, and that's become a major bottleneck. Number of opportunities is completely in your control, as a salesperson and as a sales team. That's the one that's easiest to double. So our goal is to double PPR by doubling the number of active sales opportunities in the revenue velocity formula. How can we double the number of active sales opportunities without compromising the quality of our sales efforts? The next pullback, algebraically, is selling time. Selling time is defined as the percentage of a week a rep spends in front of a customer, [00:38:00] and over the last two decades, best-in-class has been around 30%.

With today's AI, that can be pushed to 60%. So: measure selling time. Most teams aren't even measuring it. Every sales team should measure selling time, and then double it. If you can double selling time and keep everything else constant, your rep will double their PPR. That basically creates your roadmap for AI in sales.

How do we double selling time? Automate the CRM updates. Automate meeting generation. Automate demo, wireframe, and sales room creation. All the things people are talking about — but let's frame them within this formula to measure how AI-native we actually are in sales.

I think the top 5% of teams in tech sales this year will get to 60% selling time, and will double PPR for their legacy reps.

David: And with that, in the short term, it's a lot about using these tools efficiently and building them into [00:39:00] your tech stack. A lot of companies are trying to do that, and some are succeeding in hitting that 60% metric — I love how you frame it, as you always do, with a clear metric to go after.

How do we move past that? You touched on it — there are all these different roles that were created to increase selling time. The SDR role was created to increase selling time for the AE. But AI does give us opportunities to rethink the org chart now.

As people optimize their tools — updating CRMs, doing research more effectively, doing follow-up more thoughtfully and quickly — are there other aspects of how we rethink the org chart of the last 15 years, and how that aligns with the metric of increasing selling time?

Mark: Yeah, I think we reverse the specialization [00:40:00] curve, and SDR, AE, and AM collapse into one role. The AI should be good enough to get that one person to perform better. Let's take that specialization decision — the advantage of having an SDR, AE, AM, and CSM is that it lets us put the right skill in the right role.

When you're coming out of college, you don't go into strategic accounts — you go into SDR. If you're more relationship-oriented and technical, you might be a better AM. If you're really good at closing and discovery, we don't want to waste that skill on onboarding — we want you selling.

Those are great. Now, the disadvantages: number one, terrible buyer experience. I meet John the SDR, then I meet Mary the AE, and now I'm passed off to Greg the AM — it's terrible. And furthermore, the internal handoffs are so hard. You end up with local maximums — SDR solving for meetings, AE solving for revenue, CSM solving for LTV — and they're [00:41:00] all misaligned.

Today's AI causes the advantage of specialization to go away. So if you're scaling, what you should do is keep running your legacy teams — your SDR plus AE plus AM — and see how that performs. Run a test team of two or three full-cycle AEs in some territory, and keep working on them with AI until they outperform the legacy model. Then phase out the legacy model.

David: Yeah, I think for us — and I've been trying to think through this a lot — AI obviously helps AEs do a lot of these tasks they didn't have time for before, in a much more efficient way. What I think companies are running up against — and maybe it's a legacy thing, maybe it's the number of salespeople who have this disposition, maybe it's who we're thinking of as who we should hire as salespeople, and maybe we have to rethink that a little — but I think companies, first of all, obviously struggle [00:42:00] using these tools and figuring out how to do it. We'll get to a point where we've figured it out and there's a roadmap, but it's also about focus.

The AE is still going to need to be pushed. Prospecting is still very difficult, even with AI tools. We've got to push them to do that, and there's some friction there. Customer success is a bit of a different animal too, and in some ways you need to hire a different type of person who can really use these tools and processes differently, and be that "super salesperson."

Mark: Right.

David: And in some ways, I love the formula you laid out, but I'm curious — going back to some of your original metrics — if you just focus on selling time as the lever, I think there are some challenges, because you're now taking on three roles, and you've still got all these other things to do, even if you can do them more efficiently.

With that formula, if you can [00:43:00] factor in close rate — and I get it, buyers are part of that equation — but if you can factor in close rate, and then the magic number we started the conversation with, retention — because the buyer journey, as you said, is broken or more challenged by this — but if you have one person doing the SDR role, the selling, and the customer success, hopefully retention will go up. So I'm wondering if there's a way to layer in those other two variables.

Mark: I love that — you're right, I've got to think about that. Thank you for challenging me on it. It's so ironic — the call I had right before this one was with a guy named Fred Reichheld, who invented NPS. We had an awesome talk about this stuff, and now he's working on codifying referrals in the same way.

I think you're absolutely right — that's the downside of specialization: no SDR is going to book a crappy meeting if they have to sell it themselves, and no [00:44:00] salesperson is going to sign a crappy customer if they have to deal with them post-sale. That's the beautiful flywheel you're pointing to, David.

I've got to figure that into the math. I think it's somewhat captured in PPR if you measure productivity per rep on an LTV basis. And then there's also the viral coefficient — the referral piece — which is another hidden gem. How much money and time has gone into codifying outbound, versus almost none into codifying referral generation? Imagine if all the work that went into SalesLoft and Outreach had gone into similar systems for generating referrals. That's a big opportunity, especially as we move toward this full-cycle role.

David: Awesome — we could jam on this for a lot longer, Mark, this is such great stuff. But I've got to move us toward a close. I love to ask our guests a few rapid-fire questions — mind if I shoot a few at you?

Mark: Yep, let's go.

David: All right — what is one thing people don't give enough [00:45:00] value or attention to in leadership?

Mark: Understanding the personal motivations behind growth, career, and even wealth. My favorite thing is, once someone's been with me for three months and started drinking the Kool-Aid, kind of losing memory of their last employer — just sitting down and asking: what gets you up in the morning? What do you need money for? What do you need your career for? The answers that come out of that are amazing.

My favorite one was a gentleman who had an autistic child who needed to get into a private school, and he couldn't afford it. We rooted his sales call metrics in that goal, and we got there.

Sorry, that wasn't rapid, but that's the big one for me.

David: I'll give a non-rapid response too, because I think it's such an important thing, and I haven't heard anyone talk about it — the "why" behind whatever we're doing. In business school, one of my professors had us write down what job we wanted to get out of the program. [00:46:00] Then we'd say it, and he'd just ask us "why" — five times — to get to the essence of it. I love that.

Mark: That's awesome.

David: What is one skill you'd advise everyone in sales to master — leaders or non-leaders?

Mark: Consultative selling. Good discovery. Walk into a wedding or a random room this weekend, walk up to a stranger, and see how long you can ask them questions without them getting annoyed and feeling interrogated.

David: Love it. Favorite business, leadership, or sales book?

Mark: Qualified Sales Leader, by my mentor, John McMahon.

David: Favorite quote, mantra, or saying that inspires you as a sales leader?

Mark: I think Ben Franklin said, "When you're finished changing, you're finished."

David: Love that. And lastly, what's the most important goal or project you're working on right now?

Mark: The book you're contributing to, and mental health. I think we've come a long way in fighting the stigma, but we still have work to do. When you find out someone recovered from cancer 10 years ago and you're interviewing them, you probably elevate your perception of them. [00:47:00] If you find out they struggled with serious mental health issues 10 years ago, you might have concerns — which isn't fair.

They're both diseases, often genetic, and yet people have to suffer in silence over one of them. I've been a direct caretaker, and I've also been a patient, and not everyone can be that transparent because of the stigma. That's the big thing I'm working on now. I also think, as a tech community, we're over-investing in building and profiting from AI, and under-investing in helping society adapt. We need to find ways to balance that a bit.

This is my small way of doing that right now, with this book and the donation, and I'll do more later. I just hope everyone can find their own small way to contribute — all the little ways add up.

David: I think that's beautiful, Mark. I do think we glorify tech leaders and their successes, but we don't think about the sacrifices they're making, sometimes to their own mental wellbeing, because they feel that's what's expected of them in order to succeed.

That's a real difficulty. [00:48:00] So I appreciate the work you're doing, and the support you're giving there.

Mark: Great.

David: Thanks again, Mark — this was a fantastic conversation. I really appreciate your perspective. You always bring such a rigorous thought process to things people take for granted, things we take at face value, and you unpack them in ways that challenge us all to think better and do our work better.

So thank you for all the work you've done on this, and for coming on the show to share your perspective. And Mark — again, we're giving away 15 copies of your book, so please hit me up on LinkedIn and we'll ship one out to you.

But Mark, if people want to learn more about your work, and more about the book, how do they find it?

Mark: I'm most active on LinkedIn. I'm trying to get TikTok going for the younger crowd, so you can check me out there too — it's funnier videos. But yeah, LinkedIn is where I put out most of my content, so check me out there. Thank you.

David: Wonderful. Thanks again, Mark, and thank you [00:49:00] to everyone tuning in today. Until next episode — happy selling.

Mark: Thanks, David.