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Every decision a company makes is a financial decision. So finance isn't just the function that reports on the business; it's where the business gets decided.
Which makes it worth asking where your finance team's hours actually go. Reconciling last month. Chasing receipts. Policing a $30 subscription. All of it backward-looking.
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What’s my terminal value?
Wow, what a morbid title! Should we do a wellness check on CJ?
Author’s note: Up until the last sec, this piece was originally titled “Mr. Highway’s Thinking About the End” after the song by A Day to Remember. If you want to scare your neighbors, play this out of the Sonos on Halloween.
Why am I talking about then end? Fear not - I'm only talking about terminal values. I've got discounted cash flow models on the mind.
In fact, on an incredibly positive note, here is a sentence that borders on farcical:
I had a chance to speak to MBA students at Harvard Business School on Tuesday about a case study they wrote on me and my media company.
(BAHAHA how is that a real thing)
The premise of the case is I've just exited the company I'm CFO at, and I'm at an inflection point. With my first transaction under my belt, should I:
Go the serial startup CFO route, clip a good salary, negotiate for healthy equity that may or may not be worth a bunch of money, and keep leveling up the swings at each stop (maybe even becoming a public company CFO someday???)
Bet on myself, send fire emails, drop dope podcasts, and become a media mogul?
As part of the analysis, the students take my projected career earnings under each scenario and risk-adjust them back to an expected financial outcome.
Essentially, they're building a DCF on my career.
My biggest takeaways from the discussion were:
I should have more life insurance.
Each career path is really a bet on a different part of the DCF model.
A word on DCFs
One of the most common ways to value a business is using the discounted cash flow method, which takes the projected future cash flows of a business and adjusts them back to today's value based on assumptions around growth and risk.
If you want a primer on why they are kinda sorta broken but also the least worst form of government, read this piece I wrote a long time ago.
Startups are particularly difficult to value using DCFs because so much of their valuation depends on cash flows beyond the explicit forecast period (meaning the actual years you label and try to ascribe a revenue and profit number to).
In high-growth models, 75% or more of the value ends up sitting in this chunky "terminal value" bucket, which is like a catch-all for everything that happens after year five, seven, or ten year forecasted period (however long you decide to stretch the model out for).
I've always had a gripe with this.
The overwhelming majority of the company's value is determined by an assumption that is so far removed from the reality of today. It's false precision to assume you can mathematically surmise what a business that makes no profits today will look like in ten years when it theoretically does.
Anyway, the serial startup CFO route has a similar dependency on the future.
The startup CFO route
Many of you are aspiring to this route or living it today. I've interviewed some CFOs who are even on their sixth or seventh startup. Like holy shit, how did your heart and liver survive?
(Spoiler: maybe 25% (?) of their at-bats ended in FU money.)
OK, say you have a salary of $300K per year. You can model that out with small increases to your comp each year, perhaps with a nice bump whenever you jump to a bigger company. Those are your "profit" and growth assumptions in the named years of the DCF today.
But the really exciting numbers come from the equity grants. That's your ticket to the candy factory.
Every time you join a new startup, you're negotiating for a chunk of ownership in something that you hope will be worth considerably more in five to seven years. The students modeled market-rate CFO equity grants between 0.75% and 1.5% of company ownership over a single-digit number of career at-bats, also making assumptions about what the companies were worth upon entry and exit.
If you play your cards right, you can keep taking larger swings as your resume improves, eventually landing at a company that sells for billions or goes public.
Unfortunately, the payout doesn't always come from the company where you did your best work or spent the most time. In fact, no one knows!
As I wrote in my CFO Dispatches from London piece, "In finance, it helps to say yes to the right opportunity, under the right CEO, in the right macro environment, under the right moon."
This is going to sound so incredibly tone deaf to people who don’t work in finance: while a $300K salary is a nice way to make a living, if you're playing the serial startup CFO game, you're doing it with the expectation that one of those equity packages will eventually change your life. In fact, you’re probably spending most of that $300K on living expenses, and planning to retire off your equity (you'd be the poorest rich person in Marin County with that salary).
So there’s an enormous amount of financial value sitting in a bucket that you don't know the size of, or when / if you'll get to access it.
My current business is a weird little animal
The business I run today has a very different cash flow profile.
It throws off profits at a reliable clip. My profit margins look like a great SaaS company's gross margins. And while I don't have the recurring revenue mechanics of a software business, I have pretty good visibility into what the next twelve to eighteen months should look like. At the moment, I can contractually see 14 months into the future.
Our cost structure is also pretty simple. We don't need a team of engineers to maintain our product (I use exactly ten fingers and one 2022 Lenovo Thinkpad), and our capex budget is basically whatever I spend on microphones, cameras, and things that make my studio look like a man-child’s dream loft (just bought an old Paramore vinyl to hang, get at me).
So as the CFO, CEO, Walter’s dad, and reader of mean YouTube comments, I have a pretty intimate understanding of the business.
Most importantly, I don't need to raise money to keep the lights on. The business generates enough cash to pay its expenses, hire people, and fund new initiatives without asking a VC for permission (like launching recruiting and tech guide reports).
Some might (perhaps pejoratively) call this a "lifestyle business." That's fine. It also generates more money than I made from my salary as CFO (the smaller, but predictable profits in the serial CFO DCF model).
My business’ terminal value, however, is cloudy as hell.
What's a business worth when the product is a person?
A meaningful portion of my business is predicated on people wanting to hear what I have to say about finance.
This is flattering, strange, and a fairly inconvenient variable to plug into a valuation model. How do you value my commentary and longevity? Will I always be in tune with the times?
Advertisers want to reach the audience I've spent six years earning the privilege of speaking to. They are looking for trusted distribution.
And I'm a fairly critical component of that relationship.
What happens if I stop writing?
What happens if I decide I've had enough of talking about net revenue retention and take up competitive wiffle ball?
Could I hire somebody else to write the newsletter and host the podcast without materially impacting the audience or advertising revenue?
Prob not.
It's also possible that the business is worth more today than it would be under new ownership because I'm willing to do a bunch of things that a typical buyer would have to pay somebody else to do (and already did for free for +3 years).
If I sell the company, somebody needs to replace my role in the business. Maybe that's one person, maybe it's six because I’m a superhero in a belt and suspenders. Either way, it's an expense, and an acquirer isn't going to value my earnings without considering the cost of keeping the product running.
Which creates a pretty interesting valuation conundrum.
I might own a business that's worth substantially more to me than it is to somebody who wants to buy it.
I essentially have a "bond" that pays me an "annuity" for as long as I choose to hold it.
75/25 inversion
In the HBS case, students were assigning roughly 75% of the value of my media company to the cash flows generated during the medium-term forecast period, with 25% or less in the terminal value bucket.
That was pretty much the exact inverse of how they looked at the startup CFO gamble.
I found this fascinating because I've spent most of my professional life working at companies where the big financial payday was always expected to happen several years down the road.
A few times I did not get it. One time I did get it. And many times I’ve been disillusioned by the uncertainty, lack of control, and general fuckery that happens when there are big pots of money on the line.
Then I discovered there's something very appealing about owning a business that generates cash you can actually take out of the company (like, why not today!) without asking anyone for permission or death wrestling ogres over escrows and earnouts.
TANGENT: AI terminal values
I've also been thinking about all of this in the context of the staggering amounts of capital flowing into AI companies.
For the most capital-intensive businesses in the AI ecosystem, a huge amount of today's investment is predicated on what the economics will look like once they've accumulated enough infrastructure, customers, and market share to start generating substantial free cash flows (negating the 10% chance it zaps us all from existence).
The next five to seven years will involve historic amounts of capital expenditures, losses, and follow-on financing (and from where I don't know) just to get to that point.
The underlying bet is that once those companies reach sufficient scale, they'll be able to turn on the profit spigot (took me 6 tries to spell that word, even with the help of spell check).
And when they do, the amounts of cash generated will (maybe) justify the billions, or trillions, of dollars investors assigned them along the way. But until then, a lot of that investment is sitting in infrastructure and other assets on the balance sheet. And all of the valuation is sitting in the terminal bucket.
Actually, with all the capital investment required to get there, it's like a levered 100% bet on the terminal bucket… (If that was a thing you could model in a DCF. I'm just making up valuation shit today, stay with me.)
And the assumptions required to make those valuations work are pretty whack (or should I say WACC hahaha).
You need to forecast what customers will be willing to pay for AI services several years from now (as if we can forecast what products Anthropic will drop tomorrow, nuking an entire category of jobs),
What it will cost to serve them (using the world's most tricky usage based pricing models),
How quickly the underlying hardware will depreciate and need to be replaced (shit runs hot), and
How much of the profit pool will be captured by the companies making these enormous investments today (reg capture?)
And I have a hard enough time figuring out the appropriate terminal multiple for a newsletter about EBITDA that produces lots of EBITDA.
It's all a gamble anyway
Any career choice is a gamble. No risk-it-no-biscuit.
Had I taken another CFO job (maybe at one of the hypergrowth AI companies I just referenced), I'd be arguing about hiring plans, negotiating equity with new hires, and selfishly checking Carta periodically to see how much money I might theoretically have after we exit (don’t lie: we all run the exit waterfall scenario for fun on nights and weekends).
Instead I spend my day as an observer, indexing that same profession. It's very meta.
I'm really happy (and thankful) with how things have worked out so far.
One of the questions the students had was how long I could keep doing this. I'm 35 years old and will need to continually reinvent myself to stay fresh.
But in many ways writing and podcasting reminds me of playing wiffleball on Cape Cod with my friends Danny and Jack growing up.
We'd play for hours in the hot sun, making up rules and arguing about the ghost runners who may or may not have been tagged out. When I pitched, I’d push my hat to the side and pretend I was the D-Train, Dontrelle Willis, with his signature exaggerated windup. Nobody was concerned about how many innings we'd played. We'd just keep going until somebody's parents screamed loud enough.
But how’d he get the leg so high
I've started to think about my business the same way.
I'm trying to create the conditions that allow me to keep playing for the sake of playing.
The startup CFO game is a series of finite games. The media game is an infinite game.
(I'm also home for dinner with my family every night, and I have a treadmill next to my desk so I can log miles between meetings. These are meaningful perks that don't show up in the HBS students' discounted cash flow analysis.)
So yea, I don't really give a shit how many innings there are.
If I get to keep playing a game I enjoy, holding a bond I can directly impact, I'm happy to keep taking at-bats until somebody calls me in for dinner.
Weekly Valuation and Efficiency Metrics
Revenue Multiples
Revenue multiples are a shortcut to compare valuations across the technology landscape, where companies may not yet be profitable. The most standard timeframe for revenue multiple comparison is on a “Next Twelve Months” (NTM Revenue) basis.
NTM is a generous cut, as it gives a company “credit” for a full “rolling” future year. It also puts all companies on equal footing, regardless of their fiscal year end and quarterly seasonality.
However, not all technology sectors or monetization strategies receive the same “credit” on their forward revenue, which operators should be aware of when they create comp sets for their own companies. That is why I break them out as separate “indexes”.
Reasons may include:
Recurring mix of revenue
Stickiness of revenue
Average contract size
Cost of revenue delivery
Criticality of solution
Total Addressable Market potential
From a macro perspective, multiples trend higher in low interest environments, and vice versa.
Multiples shown are calculated by taking the Enterprise Value / NTM revenue.
Enterprise Value is calculated as: Market Capitalization + Total Debt - Cash
Market Cap fluctuates with share price day to day, while Total Debt and Cash are taken from the most recent quarterly financial statements available. That’s why we share this report each week - to keep up with changes in the stock market, and to update for quarterly earnings reports when they drop.
Historically, a 10x NTM Revenue multiple has been viewed as a “premium” valuation reserved for the best of the best companies.
Efficiency
Companies that can do more with less tend to earn higher valuations.
Three of the most common and consistently publicly available metrics to measure efficiency include:
CAC Payback Period: How many months does it take to recoup the cost of acquiring a customer?
CAC Payback Period is measured as Sales and Marketing costs divided by Revenue Additions, and adjusted by Gross Margin.
Here’s how I do it:
Sales and Marketing costs are measured on a TTM basis, but lagged by one quarter (so you skip a quarter, then sum the trailing four quarters of costs). This timeframe smooths for seasonality and recognizes the lead time required to generate pipeline.
Revenue is measured as the year-on-year change in the most recent quarter’s sales (so for Q2 of 2024 you’d subtract out Q2 of 2023’s revenue to get the increase), and then multiplied by four to arrive at an annualized revenue increase (e.g., ARR Additions).
Gross margin is taken as a % from the most recent quarter (e.g., 82%) to represent the current cost to serve a customer
Revenue per Employee: On a per head basis, how much in sales does the company generate each year? The rule of thumb is public companies should be doing north of $450k per employee at scale. This is simple division. And I believe it cuts through all the noise - there’s nowhere to hide.
Revenue per Employee is calculated as: (TTM Revenue / Total Current Employees)
Rule of 40: How does a company balance topline growth with bottom line efficiency? It’s the sum of the company’s revenue growth rate and EBITDA Margin. Netting the two should get you above 40 to pass the test.
Rule of 40 is calculated as: TTM Revenue Growth % + TTM Adjusted EBITDA Margin %
A few other notes on efficiency metrics:
Net Dollar Retention is another great measure of efficiency, but many companies have stopped quoting it as an exact number, choosing instead to disclose if it’s above or below a threshold once a year. It’s also uncommon for some types of companies, like marketplaces, to report it at all.
Most public companies don’t report net new ARR, and not all revenue is “recurring”, so I’m doing my best to approximate using changes in reported GAAP revenue. I admit this is a “stricter” view, as it is measuring change in net revenue.
OPEX
Decreasing your OPEX relative to revenue demonstrates Operating Leverage, and leaves more dollars to drop to the bottom line, as companies strive to achieve +25% profitability at scale.
The most common buckets companies put their operating costs into are:
Cost of Goods Sold: Customer Support employees, infrastructure to host your business in the cloud, API tolls, and banking fees if you are a FinTech.
Sales & Marketing: Sales and Marketing employees, advertising spend, demand gen spend, events, conferences, tools.
Research & Development: Product and Engineering employees, development expenses, tools.
General & Administrative: Finance, HR, and IT employees… and everything else. Or as I like to call myself “Strategic Backoffice Overhead.”
All of these are taken on a Gaap basis and therefore INCLUDE stock based comp, a non cash expense.
Companies Included
1. Security & Identity (16 companies) Endpoint, network, IAM, security operations. The CISO budget.
CrowdStrike, Palo Alto Networks, Fortinet, Cloudflare, Zscaler, Okta, SentinelOne, SailPoint, Check Point, Qualys, Tenable, Rapid7, Varonis, Rubrik, Mitek, OneSpan
2. Data & AI Infrastructure (13 companies) Modern data stack, AI/ML platforms, vector and analytics infra, GPU compute. Software-native by design.
Snowflake, Arista Networks, Equinix, CoreWeave, MongoDB, DigitalOcean, Nebius, Elastic, Akamai, Fastly, Teradata, C3.ai, Cerebras
3. Dev Tools & Observability (10 companies) Anything bought out of the engineering budget.
Datadog, Atlassian, Figma, Dynatrace, Nutanix, GitLab, UiPath, JFrog, AvePoint, PagerDuty
4. Horizontal SaaS & Back Office (18 companies) Software sold across industries to ops, HR, finance, and collaboration teams. Not vertical-specific.
Oracle, ServiceNow, Workday, ADP, Paychex, Paycom, Paylocity, Zoom, DocuSign, Navan, monday.com, Asana, Workiva, BlackLine, RingCentral, 8x8, Box, Dropbox
5. GTM (MarTech & SalesTech) (16 companies) Anything bought out of the revenue org. Marketing automation, sales engagement, CRM, ad tech, customer experience.
Salesforce, Adobe, HubSpot, The Trade Desk, Twilio, Klaviyo, Braze, ZoomInfo, Freshworks, Amplitude, Five9, Zeta Global, Wix, Sprout Social, Yext, Criteo
6. Vertical SaaS (15 companies) Software built for a specific industry without take-rate or transaction economics.
Palantir, Autodesk, Veeva, Samsara, ServiceTitan, Guidewire, Tyler Technologies, Doximity, Procore, AppFolio, CCC Intelligent Solutions, Blackbaud, nCino, CareCloud, CS Disco
7. Take-Rate Platforms (18 companies) Marketplaces and commerce platforms that earn money on transaction volume.
Uber, Airbnb, Shopify, MercadoLibre, DoorDash, eBay, Zillow, CarGurus, Instacart, Etsy, Toast, Lyft, Opendoor, StubHub, Upwork, Coursera, Ethos, Fiverr
8. Payments & Money Movement (10 companies) The rails. Payment processors, payment infrastructure, B2B payments, treasury. Volume game, utility margins.
Intuit, Fiserv, Adyen, PayPal, Block, Shift4, BILL, Flywire, Marqeta, Lightspeed
9. Consumer Fintech, Lending & Crypto (15 companies) The front-end. Consumer-facing financial apps, BNPL, lending platforms, crypto exchanges. CAC-driven, marketing-heavy, totally different unit economics from #8.
Coinbase, Robinhood, SoFi, Chime, Affirm, Upstart, Circle, Bullish, Figure, Klarna, Sezzle, Gemini, Blend, Remitly, LendingClub
Please check out our data partner, Koyfin. It’s dope.
Wishing you trade at a high revenue and EBITDA multiple,
CJ
