But while unicorns have multiplied, the number going public has fallen. According to data compiled by University of Florida professor Jay Ritter, VC-backed IPOs peaked in 2021 at 156 but have averaged just 22 per year since, the worst stretch since the 2008 financial crisis. That raises a pressing question: what’s going to happen to all these unicorns?

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Most unicorns have taken funding from venture capitalists (VCs), so understanding their future means understanding how venture works. VCs raise money from investors (known as limited partners or LPs) and invest in high-potential startups, aiming to return the money with an attractive gain. A typical VC fund runs on a 10-year cycle and targets annual returns of 20% or more.

Returns in venture are famously skewed: one breakout company can return more than all the others combined. The 700 current unicorns represent many of the top bets VCs have made over the past decade. But because investors need liquidity eventually, there’s pressure to exit through IPO or acquisition. And since VC success is measured in annualized returns, time is the enemy. The longer a company takes to exit, the lower the return.

It’s not clear how many unicorns can make the leap to the public markets. Even if venture-backed IPOs returned to their 2021 peak of 156 per year, it would take four and a half years for the existing group to IPO. If the pace is more in line with the 10-year average of 64, it would take over a decade. That’s a crowded runway for the unicorns looking to takeoff.

The capital required only reinforces this bottleneck. U.S. unicorns are collectively valued at $2.9 trillion, but that’s based on their last private funding rounds. Investors in those rounds expect a return, which means the IPO valuations would need to be even higher. In bull markets, IPOs often price 30% - 100% above the last private round, and companies typically float 10% - 20% of their equity. Therefore, taking all unicorns public would require raising between $400 billion and $1.2 trillion. For context, from 1980 to 2024, U.S. VC-backed IPOs raised a total of $367 billion. Unless the IPO market dramatically accelerates, there simply isn’t enough capital to go around.

The math is similarly challenging for the other VC-backed exit: acquisition. Getting acquired can be a big win. For example, cybersecurity startup Wiz was recently acquired by Google for $32B. But only a few dozen companies are both willing and able to pay the $2B+ price tag required to make a unicorn acquisition a success for investors. Here too, there is a long line of unicorns chasing a limited number of exits.

What about staying private indefinitely? For most unicorns, that’s going to be a tough transition. The entire VC model is built around an exit. The playbook is to spend your way into growth, capture market share, and become a category leader. Profitability is deferred in favor of speed.

But managing a company for long-term private ownership is a completely different game. Valuation is no longer driven by growth rates and market potential, but by a multiple of earnings. Making the transition from the venture-backed path to indefinitely private is rough and likely irreversible as priorities change and culture shifts.

Ironically, the unicorns best suited to stay private are often the ones that would generate the most IPO interest. Take SpaceX, for example. SpaceX’s founder Elon Musk has made no secret of his distaste for public markets, citing the short-term pressures they impose (pressures he’s dealt with firsthand as CEO of Tesla). SpaceX enjoys an enormous cost advantage in terms of sending payload to orbit and has used this advantage to develop proprietary technologies such as Starlink, a global satellite-internet service that is orders of magnitude more efficient than running physical cables to rural areas. Musk also happens to be the richest man in the world and is obsessed with SpaceX’s mission of sending humans to Mars.

Given SpaceX’s strong business and Musk’s vast fortune, it likely faces little pressure to go public. At the same time, shares of SpaceX are among the most popular on secondary markets. SpaceX illustrates the position of many top unicorns, but most do not share these advantages.

Let’s do a brief recap. The number of unicorns is at an all time high, and most of them are on a clock because they can’t stay private forever and their VC investors need liquidity at some point. At the same time, the number of IPOs is at a two-decade low, but even a normal amount of IPO and acquisition capacity is unable to absorb so many deals. For these reasons, I think we’re about to see a rush for the exit. That means heighted competition among unicorns looking for liquidity but it also suggests many unicorns will get crowded out.

I doubt the savvy entrepreneurs and investors behind these companies will simply walk away from what they believe are billion-dollar businesses. If traditional exits aren’t available, they’ll look for other ways to access public capital.

One possible path is a resurgence of the Special Purpose Acquisition Company (SPAC). A few years ago SPACs exploded in popularity because they offer a quicker path to public markets with less scrutiny than a classic IPO. There were 237 SPAC launches in 2020 and a staggering 613 in 2021 Collectively, that 2021 cohort raised $144 billion, so this could be a meaningful path to liquidity.

But the SPAC brand has took a serious hit because the returns from the last wave were horrendous. For example, the 3-year buy-and-hold average for the 2021 cohort was -73%. Merging with a SPAC might be the only option available for some, but it’s probably not the first choice for anyone and it will be an uphill battle to convince investors that this time will be different.

Another route could be crowdsourcing through direct appeals to retail investors. Take Pacaso, a fractional vacation home ownership startup valued at $1.5B in 2021. They’re currently offering mom-and-pop investors the chance to “get in early at a high-growth company,” by buying shares directly at the low price of $2.90. They’ve gone so far as to advertise directly to retail investors on TV, which is how I found about the opportunity.

On the crowdfunding page, Pacaso is heavily implying that an IPO is imminent. But why would a company that could raise from institutional investors or is actually close to IPO go through the trouble of courting thousands of small individual investors? If this is such a great opportunity, why aren’t Pacaso’s past venture investors taking up the entire round?

Maybe there is an ideological bent, an interest in helping the little guy, but to me it seems desperate. What Pacaso is doing might become more and more common as unicorns unable to exit run low on cash and get creative in their search for liquidity.

So what does all this mean for VCs and their LPs? There will certainly amazing businesses among the current crop of unicorns that will see good exits. But many are likely facing markdowns. The aggregate outcome depends on three factors:

1. The ratio of companies that can IPO or secure a strong acquisition

2. The premium that these “good exits” command over the most recent valuation

3. The “residual value” that can be recovered from unicorns that don’t get a good exit

Here’s what that looks like on the back of an envelop. Currently, unicorns are valued at $2.9 trillion. In bull markets, IPOs might fetch a 100% premium over the last private round. In tougher conditions, the premium may shrink to zero. For companies that don’t exit, a 50% average markdown is in line with historical outcomes (e.g. a disappointing acquisition at a value below the last funding round to stave off complete failure).

This chart illustrates how the total exit value of the unicorn class changes based on a range of exit premiums and the percent of unicorns that get a good exit:

Historically 10 - 15% of unicorns have gone public and another 10 - 15% have been acquired. But it seems likely that high levels of competition will drive down both exit premiums and the number of unicorns able to exit well.

Even if 30% of today’s unicorns exit and fetch a 100% premium, the total exit value would be $2.8 trillion. These favorable assumptions get us just below breakeven. If 30% exit and the average premium is just 30%, there would be roughly $800 billion in write-downs. And in a downturn scenario — say only 20% exit, with no premium — the write-downs could reach $1.2 trillion.

While the runup in unicorns is a testament to the rapid pace of technological development, the proliferation creates a new challenge as these companies seek an exit. The hyperbolic writer in me is tempted to point out that a massive wave of markdowns could even cause investors to be wary of the VC asset class, which might lead to a pullback in the category and a rethinking of the growth-at-all-costs VC model. But I think that’s going a bit too far. All I can say for sure is that it’s going to be an interesting few years as the biggest and best startups of the past decade attempt to find their way into the public markets.

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My first job out of college was at Bain & Company. When people asked what I did, I told them I was a consultant. Nobody had a clue what that meant. Some would roll with it, others would ask questions. But I could always see the same thought behind their confused eyes: you’re 23, what advice could you possibly give a business?

This question belies a widespread misunderstanding about a large industry. In 2024, Bain, McKinsey, and Boston Consulting Group – the “Big 3” – had roughly $36 billion in revenue. Yet most people I meet still don’t understand what consultants actually do or why companies hire them.

In this post, I’ll share my perspective on where and how consultants create value. I’ll also cover a scenario where companies sometimes get it wrong.

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Situational Expertise

My second case at Bain was a big one. The client, a Fortune 500 company, had stalled after decades of success. Their core business had started to plateau. They had gone through several unsuccessful attempts to buy growth in adjacent markets and, as a result, expenses had ballooned to a level that threatened the business. They needed to cut $2 billion in costs just to stay alive. It was the largest cost cutting effort this company had ever been through, and only the second of its 30-year history.

Now imagine being the executive tasked with leading this effort. You’ve never done anything like this before. The complexity is staggering. There are countless ways it could go wrong, and a major mistake could derail your career and possibly end the company. Enter Bain.

The partners on our case had spent decades guiding large organizations through similar transformations (the polite euphemism of the time). They had seen this dozens of times before. They knew what works, what fails, and how things typically break. For the client navigating this high-stakes situation for the first time, their experience was golden.

This example illustrates a dynamic that creates the need for consultants. While this cost-cutting case was unique for our client, at any point in time at least a dozen other large companies are going through a similar transformation. This is just one example of a situation that is rare for any one company but common in the economy. Other examples include merger integrations, adjacent market strategies, and international expansions. Consultants create value by accumulating and sharing expertise in these situations that no single company could develop on its own.

This dates back to the origins of the industry, with the OG management consulting firm: McKinsey & Company. As detailed in The Firm by Duff McDanold, McKinsey’s early success was shaped by business historian Alfred D. Chandler Jr., particularly his work detailing how, in the early 20^th century, companies like DuPont, General Motors, and Sears Roebuck moved from centralized functional structures—where a single executive team managed all lines of business—to multidivisional models, where each business unit operated semi-independently. The new structure better aligned strategy with execution and helped companies manage complexity as they scaled.

Beginning in the 1940s, McKinsey took this insight to market and built deep expertise in helping companies like General Foods and AT&T make the leap. The value they offered wasn’t invention, it was translation and application.

Specialized Tools

Building situational expertise naturally leads to a second form of value creation: specialized tools. Consulting firms walk a line between client confidentiality and leverage. Clients won’t let you share their confidential information, but at the same time the whole game is taking something you’ve learned with one client and using it to help another. The solution is to distill your work down into a framework or tool that helps new clients benefit from your past work without revealing proprietary details.

Take the Net Promoter Score (NPS). If you’ve ever been asked, “On a scale of 1 – 10, how likely are you to recommend our company to a friend or colleague?” you’ve encountered it. NPS was created in 2003 by Bain partner Fred Reichheld, who was searching for the best way to measure customer loyalty. Surveys at the time were long and had low response rates. By working with multiple clients facing the same challenge, Reichheld developed a single-question metric that solved a major issue in quantifying customer loyalty and is now an industry standard.

NPS is just one example. McKinsey & Company helped create the Universal Product Code that underpins barcodes. BCG rose to prominence with the “experience curve,” the insight that a manufacturing firm’s costs linearly decrease each time production doubles. BCG discovered this relationship in 1966 while working with a semiconductor manufacturer and today it’s a staple in operations planning.

Beyond these well-known examples, each consulting firm has dozens of lesser-known tools and frameworks. Prior to Bain I studied physics, and next to the concrete mathematical models I learned in school, these frameworks looked squishy. But my perspective changed after starting my own company. I spent years building management tools from scratch, only to discover the Entrepreneurial Operating System. A few hours spent reading this book beat years building my own system from scratch. I tossed mine out and never looked back.

Talent Arbitrage

I wasn’t sure I wanted to work at Bain when I got the job offer. I started college thinking I’d pursue a career in science, but by my fourth year I was looking for something more practical. Senior year I interviewed broadly. Consulting appealed to me as a nice way to build a generalist skillset while learning more about a variety of industries. Even after getting an offer, I wasn’t committed to that path. But as soon as I met my potential start class at Bain, I was sold.

I did a quick spin through LinkedIn to see where my fellow analysts are now. I didn’t find everyone, but at least a two dozen are currently the CEO of a company they founded. The largest among these is Faire, which was last valued at $12B (and several others joined Faire early and are on the executive team.) Others stayed at Bain and became partners. Many more are now C-level executives at high-growth companies like Turo or are partners at venture capital firms. This is all from a few classes at one office, which illustrates how the junior levels of my Bain office were stacked with an absurd level of talent.

As successful as my peers are today, nobody had any special wisdom to offer businesses fresh out of college. That’s also not the role of the junior consultants. Companies weren’t paying for our brilliance. Our job was to provide leverage for the partners. We were working full 60-hour weeks collecting data, synthesizing research, and making models all to help scale the experience of the partners.

The honest truth is that the companies I did cases for simply could not hire this level of young talent. We weren’t interested in a traditional corporate path. The Fortune 500 rarely offers the fast-track roles that appeal to the ambitious overachievers that end up at the Big 3. But sometimes you need to throw a handful of intense type-A personalities at an urgent problem. For many companies the only option is to hire a consulting firm.

Some companies can attract this kind of talent, but only need it occasionally. Take private equity, for example. Every major consulting firm has a group that does diligence work for PE clients. PE is a common next step for consultants, so why do these firms pay Bain at all? Because their need is cyclical. During a deal-heavy phase, they might need a team to work around the clock vetting acquisition targets. Once a deal closes, the workload shifts in-house. Bain fills the surge capacity.

You can also see this dynamic in reverse. At Bain, we noticed a handful of large, fast-growing tech companies never brought in consulting firms. In 2013 this included Google and Facebook. Today it likely includes the hottest, fast-growing private tech firms. These companies happened to be attractive destinations for people in my class after they did two years at Bain. It seems like no coincidence that the set of large companies that DIDN’T hire consulting firms for project work almost perfectly overlapped with the set of companies that were actually capable of hiring the young talent away to do “Business Operations,” which is essentially internal consulting.

Aligning the Organization

One of the first things I learned at Bain was that corporations don’t make decisions, people do. Every strategic move comes down to individuals with different perspectives, incentives, and egos trying to agree on a path forward.

That’s easier said than done. Navigating internal politics is hard, especially at large companies where senior leaders are often savvy, opinionated, and good at politics. Some organizations manage this tension well, often because they are led by an exceptional entrepreneur. But other companies struggle, which is a huge issue because the ability to align around a specific course of action is fundamental to business success. Consultants can help by creating the structure, process, and evidence needed to bring leaders into alignment.

This source of value is perhaps the most misunderstood. The cynic sees a consulting firm as a million-dollar rubber stamp that does a bunch of fake work to justify the conclusion it was hired to reach. But that misses the tremendous difficulty and importance of getting the leaders of a company on the same page around a major course of action. The alternative is statis and inaction, which is a killer. Furthermore, I have seen with my own eyes a Bain team pushing back on the theory of the case sponsor because, after thorough investigation, they legitimately didn’t think it was the right thing for the company.

Helping a company reach alignment on a sound strategy is hard, unglamorous work. But it matters. And when done well, it unlocks everything that follows.

When Does Hiring Consultants go Wrong?

To be clear, this isn’t a blanket defense of every consulting project. There are absolutely times when hiring consultants is the wrong move.

One common failure mode? Trying to outsource strategic direction. Large companies, especially those without the founder at the helm, can end up adrift. Strangled by the weight of bureaucracy, they are coasting and not sure how to get the wind back in the sails. At this point, it can be tempting to turn to outside consultants. Creating a strong vision for a major enterprise is not easy. Maybe we should see what McKinsey has to say? This is a mistake.

Setting the vision for a large corporation is extremely difficult. If the leadership team doesn’t have a point of view, hiring a firm won’t fix that. The content of your strategy can’t be outsourced. Consultants can help run a process to align your company. They can also do legwork that’s useful in terms of evaluating your options. But if you use consultants to generate the high-level direction for your company, you’re in trouble.

Another time consultants aren’t much help? When you’re doing something truly new. That’s why high-growth companies often avoid them. If you’re the leading company in a new market encountering issues for the very first time, consultants can’t leverage situational expertise to help your business. It doesn’t exist yet.

I hope this helps shed some light on why the Big 3 consulting firms have had so much success. Consultants aren’t a substitute for vision or leadership, and they’re not right for every problem. But used wisely, they can help companies move faster, think more clearly, and act with conviction when it matters most.

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This is Part 2 in a three-part series on the economics of recurring revenue business models. If you’re already familiar with the key metrics mentioned, you don’t need to read Part 1.

Previously we looked at the four metrics that define a recurring revenue business: ARR, NRR, CAC, and CLTV. These metrics give you a snapshot of your company’s size, stickiness, acquisition efficiency, and long-term value.

In this post, we’ll look at how these metrics work together to govern the growth of a recurring revenue business. We’ll connect these metrics through simple but powerful equations that reveal how fast your company can grow, what’s holding it back, and what to focus on to unlock growth.

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Sales and NRR Determine Company Size

Recurring revenue businesses grow in one of two ways: they acquire a new customer or retain and existing one. This means that over a set time period, your current ARR equals last period’s ARR multiplied by Net Revenue Retention (NRR), plus any new ARR from sales.

For example, if you start the year with $1M in ARR, retain 90% of that ($900k), and add $500k in new ARR through sales, you end the year at $1.4M in ARR. This simple formula can be used to project the future size of a business.

Suppose two companies operate in the same market and have similar margins:

• Company A adds $1M in ARR annually with a 90% NRR.

• Company B adds $2M in ARR annually with a 75% NRR.

Which company ends up bigger?

We answer by calculating the Long-Term ARR (LT ARR), the steady-state ARR when the business stops growing. This happens when sales exactly offset losses from churn. Plugging that condition into the growth equation and solving for LT ARR gives us:

This equation tells us that a company will grow until its ARR equals its sales divided by (1-NRR)1. So for our example above:

• Company A LT ARR: $1M ÷ (1 – 0.90) = $10M

• Company B LT ARR: $2M ÷ (1 – 0.75) = $8M

Company B has double the sales, but Company A ends up larger because its customers stick around longer. Retention beats acquisition.

Customer Economics Determine Sales

So far we’ve assumed sales is constant, but over time sales capacity is a function of customer economics – the cost to acquire a customer and the value that customer brings over time.

Customer economics are measured by CAC and CLTV:

To understand the profitability of a new customer, we combine these metrics into a ratio:

CLTV/CAC tells you how many dollars of margin you earn for every dollar spent acquiring customers. A ratio of 2 means you generate $2 of gross profit for every $1 spent on sales and marketing. For high-growth startups, a typical benchmark is 3.

In theory, a business should acquire customers until CLTV/CAC = 1 for the marginal customer, which is the break-even point that maximizes profitability. In practice, this rarely happens.

One major constraint is cash flow. Customer acquisition costs are paid upfront, but the value of a customer accrues over time. With a solid NRR, that time period might be 5 or 10 years. This is why CAC Payback Period—how long it takes to recover the customer acquisition cost—often factors into how quickly a business can scale:

A second common constraint is execution bandwidth. If the product solves a major problem in a growing market, you might have more opportunities than your sales team can handle. Especially for early-stage companies, growth is often gated by how quickly you can scale your team. It takes time to hire and ramp up talent that fits with your culture.

CLTV/CAC should guide how you invest in growth. I suggest the following process:

1. Calculate CLTV/CAC for each sales or marketing channel

2. Invest in channels proportional to CLTV/CAC

a. Increase spending on top-performing channels; often, one outperforms the rest by a wide margin

b. Reduce or eliminate spend on any channel with a low relative CLTV/CAC, especially if CLTV/CAC < 1 which means you’re spending more than you’re making

3. Repeat until CLTV/CAC is equal across channels at some benchmark that is above 1

Get Sticky Then Scale

If you look closely at the growth formulas we’ve covered, you’ll notice a common thread: both include (1 - NRR) in the denominator. That means NRR has an outsized impact on both the long-term size of your business and the value of each customer.

To see why, let’s first consider LT ARR. It increases linearly with sales, so if you grow your sales by 10%, your LT ARR grows by 10% too. But LT ARR grows inversely with (1 - NRR), which creates a multiplier effect as NRR improves.

Here’s how that multiplier plays out at different retention levels:

As NRR approaches 100%, even small improvements have a massive impact. For example, increasing NRR from 80% to 90% doubles your potential size. Going back to our earlier example, Company A has a 10x multiplier, while Company B has only 4x. That’s why A ends up larger despite having half the annual sales.

This same dynamic applies to CLTV/CAC because it also scales with 1/(1 - NRR). But the true importance of NRR is seen when we understand that these effects are related because CLTV/CAC drives sales, which also shows up in the LT ARR formula. The net result is that the long-term size of a recurring revenue business is proportional to 1 / (1 - NRR)². That’s why nothing powers a recurring revenue business more than a high NRR.

Companies with high NRR are said to be sticky, and sticky businesses enjoy a number of compounding advantages:

• They earn more lifetime revenue per customer

• They can afford to spend more on acquisition

• Their customers are happier and more likely to refer others (the most powerful form of marketing)

• They see higher returns from product investment

In short: stickiness creates a virtuous cycle. It’s the single most important attribute of a successful recurring revenue business.

Crossing 100% NRR: Hypergrowth

There’s one big caveat to everything we’ve covered so far: the math changes when NRR exceeds 100%. Remember, NRR includes expansion revenue—upgrades, add-ons, price increases, etc.—which means it can actually go beyond 100%.

A triple-digit NRR is the sign of a product solving a critical problem in a large market. It’s common for high-growth startups with strong product-market fit to hit 110%, 120%, or even higher. Mathematically, NRR > 100% means your business is growing without adding any new customers. It’s the hallmark of hypergrowth.

Crossing the 100% threshold is like stepping across a discontinuity in the growth math. In theory, a company with positive contribution margin and triple-digit NRR would be justified in paying any price to acquire a new customer because the value of that customer compounds indefinitely. At this point, the limits to growth become more about external constraints, for example the total addressable market and competitive dynamics.

Hypergrowth is the realm of companies with rocket-ship trajectories with sky-high valuations. When NRR is above 100%, it’s time to go big:

• Invest heavily in sales

• Raise capital, likely VC money if your total addressable market is >$1B

• Scale as fast as before your window of opportunity closes

Of course, hypergrowth can’t last forever. U.S. GDP grows at about 3% per year, so if your NRR held steady above 103%, you’d eventually outgrow the economy itself. But some companies manage to sustain hypergrowth for years or even over a decade. It’s rare, but hypergrowth is exactly how to build a massive recurring revenue business.

Conclusion

As we’ve seen, growth in recurring revenue businesses comes down to stickiness. Whether you’re modeling Long-Term ARR or evaluating your customer acquisition strategy, NRR sits at the center of your success. It’s the lever that multiplies everything else about your business.

If your NRR is low, no amount of sales will get you where you want to go. If it’s high, your business starts to compound. And if it’s over 100%, you’re in hypergrowth.

In the next and final post in this series, we’ll dig into the practical side by exploring proven strategies to boost NRR.

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What do ChatGPT, Netflix, Pacific Gas & Electric, and your neighborhood gym have in common? They all rely on recurring revenue. Their business models are built around customers making consistent, periodic payments in exchange for ongoing access to a product or service.

From tech platforms to utility companies to fitness clubs, recurring revenue is big business. Subscription services alone generated nearly $600 billion in revenue in 2024, with expectations to approach $1 trillion by 2028. Entrepreneurs and investors love these models because they have predictable income streams, compounding growth, and higher company valuations.

As the founding CEO of Certified Employee-Owned, I built a recurring revenue business from the ground up. I became fascinated by the underlying economics that drive this model. Beneath the surface, all recurring revenue companies share the same fundamental metrics and are governed by simple equations with profound implications.

This is the first in a three-part series breaking down the core economics of recurring revenue businesses. In Part 2, I’ll introduce the fundamental equation that governs recurring revenue growth. And in Part 3 I’ll outline five strategies to increase retention and supercharge your recurring revenue business. The metrics covered in this post are:

• Annualized Recurring Revenue (ARR)

• Net Revenue Retention (NRR)

• Customer Acquisition Cost (CAC)

• Lifetime Customer Value (LTCV or LTC)

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Annualized Recurring Revenue (ARR)

For a recurring revenue business with year-long contracts, the best measure of size is Annualized Recurring Revenue (ARR). ARR is the total annualized subscription revenue at a specific point in time. If a contract spans multiple years, you divide the total value by the contract length to calculate its contribution to ARR. For example, a two-year contract worth $20,000 adds $10,000 to ARR.

ARR gives a more accurate picture of the size of a business than revenue, especially in cases where billing is lumpy. If the above two-year contract were billed upfront, it would generate $20,000 in revenue in the first year and $0 in the next, a misleading drop. ARR smooths out billing fluctuations and reflects the true size of the business.

For businesses with monthly contracts, for example streaming services, the equivalent metric is Monthly Recurring Revenue (MRR). The underlying business concepts are the same. Just substitute MRR for ARR throughout this and future posts.

Net Revenue Retention (NRR)

For a business to grow, customers must be retained. There are many ways to measure retention, but the most fundamental is Net Revenue Retention (NRR). Conceptually NRR measures the change in ARR from existing customers over a period of time. It quantifies stickiness, the ability to build enduring relationships with customers. NRR is calculated as the new ARR of all renewed contracts divided by the prior ARR of all contracts up for renewal.

We can break NRR out more explicitly by factoring in expansion (upsells, price increases) and churn (lost or downgraded customers) to get the equivalent formula:

NRR is calculated over a period of time, typically a month, a quarter, or a year. Here’s a simple example. Suppose two customers are up for renewal in January, each having a $10,000 ARR contract. Customer 1 renews for a slight increase of $11,000 in ARR and Customer 2 churns. We start with an ARR of $20,000, lose $10,000 to churn, and gain $1,000 in expansion. Our NRR for January is $11,000/$20,000 = 55%.

In the part two, I’ll cover why I think NRR is the most important retention metric, but before moving on, it’s worth noting a few other helpful retention metrics:

• Gross Revenue Retention (GRR) = (Starting ARR - Churn) ÷ Starting ARR
  Measures retention excluding expansion. Since it ignores upsell revenue, GRR is always capped at 100%, making it a clearer view of pure customer retention.

• Logo Retention = Renewed Customers ÷ Customers Up for Renewal
  Measures the percentage of customers who renewed, regardless of deal size. Logo retention is a good complement to NRR, especially for understanding retention across smaller customers.

• NRR ex-Multi-Year Agreements
  Some teams calculate NRR excluding multi-year contracts, especially if renewal anniversaries cluster at certain times. This can provide a cleaner month-to-month signal.

• Churn Rate = Churn ÷ Starting ARR
  The percentage of ARR lost from cancellations or downgrades. Churn Rate is often approximated as 1 - NRR, though this is inaccurate when expansion revenue is significant. Many companies track churn separately to keep closer tabs on losses specifically.

Customer Acquisition Cost (CAC)

Customer Acquisition Cost (CAC) measures the average amount you spend to acquire a new customer. It's calculated by dividing total sales and marketing expenses by the number of new customers acquired during the same period. For example, if your company spent $100,000 on sales and marketing in Q1 and brought in 50 new customers, your CAC for Q1 is $2,000.

CAC can also be broken down by acquisition channel (e.g. paid search, outbound sales, content marketing, or referrals) to reveal which channels are most efficient. If you haven’t segmented CAC by channel before, the results might surprise you. Typically, one or two channels significantly outperform the rest.

Customer Lifetime Value (CLTV)

Customer Lifetime Value (CLTV or sometimes just LTV) measures the total value a customer brings to your business over the course of the relationship. The most accurate way to calculate CLTV starts with contribution margin: the revenue from a customer minus the direct costs to serve them. Once you have the average contribution margin, divide by (1 - NRR):

Why divide by (1 - NRR)? Imagine a customer generates $1,000 in margin in year one and your NRR is 50%. You’d expect $500 in year two, $250 in year three, and so on. This forms a geometric series that sums to $2,000. More generally, each year you expect to lose (1 - NRR) of last year’s value, so the payout sequence converges to first year margin divided by (1 - NRR).

This version ignores the time-value of money. In my experience, most businesses operate on a short enough time frame that discounting is not important. But if you have very long relationships, it’s easy enough to add a discount factor. You simply determine your weighted average cost of capital (WACC) and the CLTV calculation becomes:

Another common shortcut when calculating CLTV is to use average revenue per customer instead of contribution margin. This is reasonable in models like SaaS, where the marginal cost to serve an additional customer is very low. But if serving each customer carries a meaningful cost, say 10% or more of their revenue, you should stick with contribution margin.

Conclusion

There are plenty of metrics you can use to analyze a recurring revenue business. But if I could only pick four, it would be ARR, NRR, CAC, and LTV. Together, they tell you how big your business is, how sticky it is, how efficiently you’re acquiring customers, and how much value each customer brings.

As companies grow, they often track these metrics by customer segment since performance can vary dramatically across segments. Tracking them consistently over time (and ideally by cohort) offers a much clearer picture of long-term health than any one-time snapshot.

In the next post, we’ll look at how these metrics come together in a simple but powerful equation that governs growth in recurring revenue businesses. And in Part 3, we’ll explore actionable strategies to improve retention and drive sustainable, compounding growth.

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Between late 2013 and early 2014, I bought 19 Bitcoin for about $7,000. They appreciated an average of 100% per year, and by early 2017 I was thrilled with the returns so I sold all but three. After riding another boom-bust cycle, I fully cashed out in 2020 for a total exit of around $80,000. You’d think an 11x return that helped fund the down payment on our first home would be a blessing. Instead, my wife Blair jokingly calls Bitcoin, “the worst thing that’s happened to our relationship.”

My wife hates Bitcoin because every time the price goes up, she has to hear me talk about what could have been. I am not alone. Whenever Bitcoin surges, social media floods with stories of fortunes almost made. Bitcoin regret is tragic because it is inevitable. Unlike other assets, Bitcoin’s value depends on circulation. This means regret over selling too soon is built into its very nature.

In this post I quantify the total amount of Bitcoin regret by calculating the value of all hypothetical Bitcoin fortunes. At the March 24th closing price of $88,283.29, the forgone profits of all Bitcoin sold since 2014 was over 272 trillion dollars (yes trillion with a “t”.) I also introduce a new metric, the Bitcoin Regret Index, defined as the ratio of the hypothetical profit of past sales to the current value of all Bitcoin. Currently this index sits at 172. In other words, for every dollar of wealth today, there are 172 dollars worth of regret.

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The Rise and Fall and Rise of Bitcoin

Bitcoin’s volatility is legendary. Every few years it skyrockets, for example jumping from $13 to $1,000 in 2013, from $4,000 to $19,000 in 2017, and from $17,000 to over $100,000 between 2022 and 2024. But crashes are just as brutal, with drops of 83% in April 2013, 84% in 2017-2018, and 53% in May 2021.

To make this more concrete, let’s look at how my investment did (or would have done.) 19 BTC were worth $23,522 at the end of 2023, $950 in the middle of 2014, $367,555 in December 2017, just $126,065 a year later in December 2018, $1.31M in November 2020, $342,000 in December 2021, over $2M at the peak last year, and a bit over $1.6M today. That’s quite the emotional roller coaster.

Volatility is part of Bitcoin’s draw. There are true believers who are all-in on the vision of an alternative reserve currency outside the control of any government, in fact that’s what got me interested in 2013. But many people buying are simply looking to get rich quickly. The extreme appreciation of Bitcoin drives headlines, attention, and speculation in a cycle that amplifies both the highs and lows.

Every time Bitcoin reaches a new high, there’s a particular genre of story that begins to circulate on social media wherein someone would have made a fortune if only they had held their Bitcoin. Perhaps the most famous is of Laszlo Hanyecz, who on May 22, 2010 performed the first ever transaction with Bitcoin, paying someone 10,000 BTC for a pizza. Today the value of the coins used to buy that pizza is close to one billion dollars. Laszlo is the most extreme of an entire genre of post, and it’s likely that for every story shared there are hundreds kept private.

Bitcoin Regret is Unavoidable

The fundamental case for Bitcoin is that it’s a new decentralized reserve currency. Proponents like that Bitcoin operates without any one group or individual in control (subject to a few key assumptions) and that the number of possible BTC is fixed, making it inflation proof.

Currencies serve two main roles: storing value and enabling exchange. As a potential new currency, Bitcoin’s worth depends on circulation. If liquidity dries up, so does its value. If Laszlo never used his coins to buy that pizza, perhaps someone else would have been in his shoes. But if no one had ever spent their BTC, it wouldn’t be worth anything today.

This makes Bitcoin fundamentally different from other types of investments. When you buy stock in a company, you’re buying an ownership claim on a business that has underlying value because it produces something useful at a profit (or will some day in the future.) That value can increase without shares changing hands, for example the way startup stock grows between funding rounds or the way a family business increases in value while remaining closely held. But BTC must trade hands to be valuable, and each transaction has a buyer and a seller.

Quantifying Bitcoin Regret: The Bitcoin Regret Index

While the supply of BTC is fixed, the dreams of everyone who has owned or thought about buying Bitcoin have no theoretical limit. But the most clear case of Bitcoin regret is selling and then watching the value climb. For example, selling at $2,000 and watching the price climb to $100,000 generates $98,000 in regret: “if only I had held, I’d be that much richer.” A concrete way to quantify the total amount of Bitcoin regret is to calculate the forgone increase in value of all BTC ever sold. Normalizing this by Bitcoin’s current market cap gives us the Bitcoin Regret Index.

A full treatment probably involves looking at the Bitcoin ledger. But we can get a first pass by looking at the price data from Yahoo Finance and the Bitcoin supply data from BiTBO. Starting with the earliest available date - September 17, 2014 - I estimate the current value of all BTC ever sold to be roughly $346T. Subtracting out the proceeds of the sales yields $272T in forgone gains. For reference, the total value of all S&P 500 companies is currently around $51T. In other words, hypothetical Bitcoin gains are over five times the value of the 500 most important American businesses. This explains the prevalence of Bitcoin regret stories.

How does Bitcoin regret compare to actual Bitcoin wealth? Roughly 19.9M BTC have been mined, but nearly 3.8M have been lost. With a current supply of around 16M and a price of $88k, the ratio of hypothetical to actual Bitcoin wealth, stands at 172. In other words, for every dollar of BTC value today, there are $172 dollars of regret. For every person who made a million dollars with Bitcoin there are well over a hundred who could have, but didn’t.

Using the same data, I calculated the value of the Bitcoin Regret Index over time. Much like the price of Bitcoin, regret is highly volatile. Surprisingly, Bitcoin regret is not bound by zero and has even been negative at times. What does that mean? When the Bitcoin Regret Index is zero it means the volume-weighted average profit of all transactions since the start of my data is zero. Deep drops and negative values of the index probably equate to times when social media is quiet about Bitcoin regret. It might not be a surprise that bottoms in the index tend to correspond to great times to have bought BTC (but some of that is mechanical.)

A deeper analysis would likely find a higher value of the Bitcoin Regret Index. First of all, my data begin in late 2014, when the price was already north of $450. Second, Yahoo Finance’s volume data are pulled from the exchanges, but many Bitcoins change hands directly. Finally, I am sticking to situations where people actually owned and sold BTC, but I’m certain the fantasizing around Bitcoin wealth extends to “what ifs” that involve hypothetical purchases.

It pains me to see the stories of Bitcoin regret on social media because I know the feeling. My wife is joking about Bitcoin being the worst thing that happened to our relationship, but for some selling Bitcoin might actually be their biggest regret in life. If you’re feeling Bitcoin regret, I hope it helps to know that not only are you not alone, but that’s simply the way it has to be. Bitcoin’s value exists because people sold. If everyone had diamond hands, Bitcoin wouldn’t be what it is today and there would be nothing to regret. This aspect of Bitcoin is reminiscent of a Greek tragedy, which is why I think Bitcoin is a regret machine.

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I first encountered employee ownership in 2014 as a Stanford PhD student. It struck me as a no-brainer: employees with an equity stake have the incentive to work harder and smarter, making both the business and the employees better off. My research into the 6,300 employee-owned companies led me to a simple question: why don’t more people know about this?

A conversation with a friend led us to launch Certified Employee-Owned in 2017 with the goal of promoting employee ownership. Over the next seven years we certified 700 companies, nearly 10% of all U.S. employee-owned businesses. By 2024, it was time for new leadership, so we transitioned leadership in October.

Stepping back from the day-to-day responsibilities of running a company has given me time to reflect on the big picture. This post codifies what I learned over a decade of thinking about employee ownership. It’s organized around what I see as the three major questions in the field:

• Why does employee ownership matter?

• What does “employee ownership” mean

• What aren’t there more employee-owned companies?

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Why Does Employee Ownership Matter?

Employee ownership benefits both employees and businesses. It boosts performance while broadening who shares in capitalism’s gains. It strengthens companies and grows the economic pie.

First, let’s looks at the benefits to employees. In 2021, Harvard Business School professor Ethan Rouen and I asked: what if every company was 30% employee-owned? We found median household wealth would nearly double, from $121,760 to $230,076, with the largest gains for lower-income households. For the bottom 20% of earners, median wealth would quadruple.

Our simulation is supported by the stories I have of employee-owned companies turning front-line employees into millionaires. One example is Cathy Burch, who earned regular stock contributions working the register and stocking shelves at the employee-owned grocery chain WinCo Foods. After 23 years of service, the company had done well and Cathy’s stock was worth almost $1 million.

Beyond financial success, employee ownership creates a better working environment that benefits employees from their first day on the job. Ownership fosters an employee-centric workplace. It strengthens relationships and amplifies employee voice. It reduces turnover, which leads to stronger relationships and better teamwork.

Employee ownership is also great for business. It’s simple to understand why: employees who own stock have a direct financial incentive in working harder and smarter to help their company succeed. Giving employees upside via equity is how tech companies attract and retain the most in demand talent and has long been part of how public companies incentivize their management teams.

But does it work when everyone has ownership, not just key employees? Academics have studied the performance of companies practicing broad-based employee ownership and found a positive correlation with key metrics including sales and profitability. Recently the private equity group KKR has been making broad-based employee ownership a standard part of deals in their industrials practice. Pete Stavros, KKR’s co-head of global private equity, has even launched a new non-profit, Ownership Works, to help expand the practice to other private equity firms. The fact that some of the most sophisticated investors in the world are making employee ownership part of their deals is a strong endorsement that broad-based employee ownership can improve company performance.

What Does “Employee Ownership” Mean?

Launching Certified Employee-Owned, we faced a big question: what does “employee ownership” mean? The first step in defining our certification standards was to talk to over 250 people in the space including advocates, service providers, and companies. It became clear that the big idea behind employee ownership is to distribute the rights and responsibilities of business ownership more broadly. But we saw that meant different things to different people. My experience over the years has reinforced the initial idea that ownership can be decomposed into three main concepts:

1. Ownership and Money

The first major aspect of ownership is money. Technically the owners of a company’s stock have the right to the profits leftover after all debts and obligations have been paid, for example through dividends or the surplus funds in a liquidation. In the world of employee ownership, this means ensuring that employees have a stake in the company's financial success, often through mechanisms like Employee Stock Ownership Plans (ESOPs), Employee Stock Purchase Plans (ESPPs), stock options, or profit-sharing.

2. Ownership and Decision Making

Ownership is also about decision making. In the employee ownership world, this means practices such as open-book management and inclusive strategic planning that empower employees to contribute to the company's direction and daily operations. This participatory approach can lead to increased engagement, innovation, and a stronger sense of ownership among employees, ultimately enhancing company performance. The process of helping people see how their actions as employees can lead to better outcomes as owners is often referred to as the process of “building ownership culture.”

3. Ownership and Governance

Governance is the final aspect of ownership. Governance is about formal rights to have a say in significant company decisions, for example electing or having representation on the board of directors. Employee involvement in governance is less common, but for some corners of the space such as the worker cooperative movement, this is critical.

After 10 years, I’ve seen varying perspectives on the relative importance of these dimensions. Some focus entirely on the financial dimension, which is understandable given the importance of money. Others see decision making as equally important, arguing that it’s critical for both improved company performance and a better working environment. Many disagreements in the field stem from differing perspectives on the importance of the dimensions of ownership.

Rather than searching for a “correct” balance, I’ve come to accept that ownership means different things to different people. The employee ownership community is small, much smaller that it should be. Rather than argue about fine differences among friends, my focus has always been on finding common ground and working together to grow the community. That’s why we set Certified EO’s standards around financial ownership, to create the biggest possible tent.

Why Aren’t There More Employee-Owned Companies?

According to the National Center for Employee Ownership, in 2014 there were 6,718 ESOPs and today they estimate there are 6,548. If employee ownership is such a good idea, why aren’t there more employee-owned companies?

I laid out the most common theories roughly one year ago and I think they are still the leading hypotheses on why we are stuck:

1) Lack of Awareness: Many individuals, including business owners, might be unaware of the employee ownership model. Increasing awareness among this group could be the key lever to growth as nearly all employee-owned companies a created by owner-initiated transactions.

2) Transaction Cost and Complexity: Establishing employee ownership, particularly through Employee Stock Ownership Plans (ESOPs), involves significant expenses and regulatory complexities. Perhaps the cost and complexity of conversion is turning owners off. Simplifying these processes could make adoption more accessible, especially for smaller companies.

3) Financing Hurdles: Financing a transition to employee ownership typically requires large amounts of seller financing, where owners are paid over time. This can make the transition to employee ownership less attractive than alternatives such as a sale to private equity or a strategic buyer, where the majority of even all of the purchase price will be paid at close.

4) Limits on Existing Structures: Traditional models like ESOPs may not suit all businesses. The flat number of ESOPs over time might simply indicate the structure has saturated its niche. Perhaps, for example, the price that an ESOP is able to offer is not competitive outside of a narrow range of industries and enterprise values. It’s possible that alternative structures such as Employee Ownership Trusts (EOTs) are required to expand the number of employee-owned companies by opening up new market segments.

While I think all four of these theories have some role to play, it is my opinion #3 and #4 are the most important levers. From what I’ve seen, the majority of the time an owner chooses to transition the company to an ESOP, the structure used by roughly 95% of employee-owned companies today, a big part of the decision is concern over what will happen to the company once they are gone. I’ve personally spoken with hundreds of sellers who chose employee ownership because they wanted to preserve their legacy and reward the people who helped make their company successful. Those stories are extremely inspiring, but they often involve owners taking a smaller sale price and shouldering additional risk. I love and appreciate the owners who make this values-based decision, but the space would grow faster if conversion was the most attractive option to owners viewing the sale strictly through a financial lens.

In my opinion, what’s needed are big ideas on how to expand the category in ways that make employee ownership the most attractive option in more transactions. Luckily there are already a number of promising ideas in the works. Private equity funds such as Mosaic Capital Partners and Apis & Heritage are raising dedicated pools of capital and offering owners better terms. They’re winning deals in competitive bidding environments and converting companies that otherwise wouldn’t have become employee-owned. Expanding ESOPs is looking at how to create a new incentive that will make a small ESOP a logical consideration in every company transaction. And the rise of ESOP holding companies is starting a conversation about how to grow the space through the purchase of existing businesses rather than conversions.

Reflecting on 10 years thinking about employee ownership I think we’re temporarily stuck, but we have solid fundamentals and we’re in a good position to grow. The idea of employee ownership is fundamentally sound. We have 50+ years of experience with the ESOP to guide us, and we have a major recent endorsement of the concept from sophisticated investors. Employee ownership works, and we have several interesting ideas on how to expand the category. I’m excited to see what the next ten years brings.

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