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felt cute, might delete
The world is a big place.
I’m reminded of its girth whenever I leave my office, put pants on, and get on a plane to have dinner with finance folks.
(edit: I guess I should technically say I put my pants on before leaving my office)
I look at Mostly Media as a media company first and foremost - a niche, intensely focused purveyor of CFO content. Doing that well gives us the opportunity for fun side quests like dinners, happy hours, and even recruiting.

The dinners and happy hours have been an amazing offshoot. Because they aren’t our main thing, we don’t have to force-feed them down people’s throats. If you’re an event company, you gotta do a lot of events, and inevitably the quality will slip. We can be selective about how many we host and who’s in the room, only doing about 10 per year.
My wife actually runs the events for me. She used to work in marketing at a tech company, and now she’s an unpaid laborer in the family business. (Don’t worry. She spent whatever profits we made on this trip on handbags, apparently made of rare Mayan gold.)
She came to the dinner and happy hour this time and laughed her ass off when a few people asked to take pictures together. I’m persona non grata in our home, yet score high with the middle-aged male finance director demo. As Pop Up Bagels likes to say, not famous, but known.
I know many of you imagine me to be an imposing 6-foot-5, but I’m a svelte 5-foot-8 on a good day. And apparently the majority of our readers are towering infernos who make me look like a small child in pictures.

Shout-out to Andy from Goalhanger, and the London Eye!
Adding London to the event rotation has been a happy surprise
(Also, I love when people in the UK randomly use “happy days” as a modifier to any sentence. It truly makes me happy.)
Bad day to be a Noam

but who ordered the Lemonades?
The world is a big place. It’s also a thirsty place.
On Thursday night alone we deleted 118 Noam beers. While not Miller lattes, serviceable in their substance.
We usually have about a 90% attendance rate at our dinners. Well above industry standards, which are closer to 40% or 50% (as a metrics man I benchmark this). But the inevitable last-minute regrets have a different flavor depending on where we are in the world.
In the US, people say something basic, like their kid is sick, they’re stuck at work, or a board call is running long.
In London, it’s dramatic.
My wife has fallen and broken her ankle.
My mother-in-law is stuck at the bottom of a well.
The local museum is burning down and I’m a volunteer fire fighter.
A dinosaur ate my cat.
Brits are more artful, and poetic, in the decline.
And yet once everyone sits down, a lot of the convos sound familiar.
I remember reading The World Is Flat by Thomas Friedman in high school. It was about how the internet was creating an increasingly global economy. A “no shit” statement today, but back then it was a deep thought.
I think the world is also becoming flatter when it comes to investor-backed companies and what we expect from the finance leaders who run them. I was amazed at how many series A, B and C companies I met who are experiencing the same day to day dynamics as those in the US. We consume the same media, wrestle with the same hiring problems, and apparently receive the same AI generated emails from our investors.
But first, grapes.
I am long grapes
The world is a big place. It’s also a fruitful place.
One of the attendees at dinner was the CFO of a company that owns the IP for growing a specific class of grapes.
The type of grapes you buy at the supermarket to eat, not the type you make wine with.
They license that IP to farmers and collect royalties on sales. And get ready for this - as a family owned business, they do hundreds of millions of dollars in revenue per year, at a 75% profit margin.

In an AI first world, I am long grapes.
Another guy at the happy hour told me he was “in the piss and shit business”, more formally referred to as fertilizer. He walked me through how he forecasted the supply chain for cow manure, which was something I didn’t realize I was passionate about until I had consumed three Noams.
Another attendee led FP&A at a company that made cleaning brushes for dental businesses. People have teeth all over the world, and apparently there’s always room for a better tooth mousetrap.
I spend so much time talking about software that I occasionally need someone to bring me back to reality and explain the economics of a grape, the logistics of cow shit, or the dental hardware business to me. There are a lot of ways to make money if you know your niche and are willing to roll your sleeves up.
And yet in a software world, I can barely change a lightbulb.
(One reader asked my wife if I was handy around the house, and she embarrassingly told him about the time I tried to throw out our living room lamp because I thought it was broken, rather than changing the lightbulb)
And then I found a handful of Series B and C CFOs laughing, crying, and raging about their investors, which took me back to familiar ground.
Investor slop
The world is a big place. It’s also a sloppy place.
My biggest takeaway from this trip was the prevalence of work slop. It knows no geographical borders.
As one CFO remarked at dinner, “AI did not have time to write us a shorter letter or build us a smaller model.”
I was also introduced to a remix of work slop: investor slop.
Multiple finance leaders complained about the obviously AI-generated emails they now receive from their investors. What used to be a slightly frustrating human question about an unremarkable working capital fluctuation becomes infuriating when you can tell AI both plucked it out of the financials and wrote the message asking you to explain it.
One CFO described a recent exchange that went something like this:
Investor: “Why is your cash balance $500K higher than projected?”
CFO: “It’s a working capital movement caused by timing. Normal for the business. Also, we have an $80M cost base.”
Investor: “OK, and why did you prepay $50K for this conference?”
CFO: “Did I mention we have an $80M cost base?”
Junk ass AI emails about junk ass AI “discoveries” are a villian we must vanquish.
As someone exclaimed (over a Noam):
“Like, am I not good enough for your human brain time?”
Better Lucky Than Good
The world is a big place. It’s also not a fair place.
Getting out into the world and hearing about people’s career journeys reminds me how massive a role luck plays in a career.
I’ve always been envious of people whose résumés suggest they only pick winners. Like my friend Sid, who went from CA during the perpetual license era, to AWS during the cloud boom, to Hubspot during the SaaS explosion, to Databricks during the consumption revolution. If I were a betting man, I’d take Sid to the track and watch the ponies run.
Part of that is judgment. Part of it is luck. And part of it is preferential attachment: one great opportunity gives you access to an even better opportunity.
Like when a boxer beats a good guy and then gets to fight an even better guy. When you win a few times, you start getting calls that other people don’t.
(Of course, I’ve also known plenty of headscratching CMOs who’ve defied gravity, continually falling upwards through a series of 18 month stints. So the system does have some bugs.)
But there are many, many good finance professionals who haven’t had that same luck. There are many who have had absolute shit luck, despite doing exceptional work. And so, so many who had a deal at the 2 yard line and saw it implode.
Always the bridesmaid, never the bride.
I didn’t appreciate how common that was until I started meeting people all over the country and the world who work in the same profession. A résumé without a big exit never tells you how close someone got, or how much of the outcome was outside their control.
We have plenty of sayings about luck. The harder I work, the luckier I get (Gary Player). Luck is when preparation meets opportunity (Seneca… I think? Or was it Samuel L. Jackson?).
We also have lots of sayings about being close but no cigar, or how close only counts in horseshoes and hand grenades.
In finance, it helps to say yes to the right opportunity, under the right CEO, in the right macro environment, under the right moon.
Because good luck being the CFO of a once booming micromobility business during COVID. You could build a hell of a business and still have nobody riding the scooters to an office they no longer go to.
Big dawg gotta eat
The world is a big place. It’s also a hungry place.
I was having lunch at a pretty nice restaurant in London when I witnessed something inspiring.
The guy next to us was hosting a man and a woman who seemed to be colleagues. He was pitching some sort of business partnership, and he was going for it. Ideas, compliments, data points. Coming at them left, right, and center.
He wanted their business.
Eventually, they seemed to agree on something. His two guests had to leave early, so they shook hands, agreed on next steps, and left him to grab the check.
As they hit the door, this man reached over and finished whatever morsels of food they’d left on their plates.
Just gobbled it up. Zero shame. Grin on his face.
Sales is an eat-what-you-kill job.
This man was hungry. He had that dawg in him. Would hire him 10 out of 10 times, table manners aside.
So go close that deal today. No matter where you are in the world. No matter if it’s a sales deal for $5,000 or an M&A transaction for $500 million.
Take the deal off the table. Get that shit on your resume. Get that preferential attachment rolling in your favor.
And wash it down with a frosty Noam.
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












