Every forecast I've ever built started the same way: exporting a CSV from NetSuite and praying nothing changed since this morning. The joys of static data.
I'm sure you know the pain - ERP actuals over here, CRM pipeline over there, and a hiring plan in a Google Sheet someone on the People team owns.
By the time you've stitched it all together, the numbers are stale and you've spent your week as a data plumber instead of a finance leader.
Cool, cool, cool.
This is why I think it's worth knowing that Abacum just became a Built for NetSuite SuiteApp. This is a pretty big deal as it is the first FP&A platform live in the NetSuite App right now.
Abacum sits on top of NetSuite and gives you connected planning, forecasting, reporting, and scenario modeling against live data. Strava, PostHog, and JG Wentworth already run their planning on it.
If you recognize yourself in the first sentence of this email, it's probably worth a look.
My Pizza Shop is Using AI
Last Sunday I wrote that AI is redrawing the lines we bucket companies into. When deciding who you benchmark yourself against, it's becoming more nuanced depending on what you sell, how you sell it, and how you monetize.
Today I want to go the other way. The smallest possible business. A pizza place up the road from me.
Every Friday we get pizza, often from a place called Bocci’s that sits a few minutes from the house. We call in often enough that I can usually match the face to whomever’s voice is picking up the phone.
I called in last Friday and said, “Can I get a Grandma’s Pizza with sausage and sun-dried tomatoes.”
The voice said,
“Got it. One Grandma’s Pizza with sausage and sun-dried tomatoes. That’ll be ready for you in about 22 minutes. Is there anything else I can do for you?”
I almost hung up, but something felt slightly uncanny about the whole exchange, so I doubled back and said, “Hey, do you mind reading back what I ordered? It’s a specialty pizza and I was annoying and added a few things to it.”
The voice said,
“No problem. You ordered a Grandma’s Pizza with sausage and sun-dried tomatoes… and I told you it would be ready for you in 22 minutes.”
That repeat of the exact 22 minute estimate to chef it up was too perfect. A real person would have just rounded to twenty.
I said OK thanks, and the voice replied,
“Cool. See you soon.”
Cool? Idk. Maybe it is a person.
My Man Tony is at the oven
I drove over (about 22 minutes) later and walked in to find the owner Tony working the oven himself, with nobody manning the counter and nobody on the phone. So I straight up asked him if it was AI.
“Oh, did you call in to place the order? Yeah, it actually is.”
He told me a company reached out promising to cut his costs, and he was super skeptical. the pitch took a few rounds before he let them in. Now keep in mind, we’re in Naples, Florida, where half the restaurants don’t even use DoorDash. This is not a part of the country that you’d describe as tech forward.
He said the vendor flew over from Europe and sat inside his pizza shop for eight hours one Friday, recording the whole rhythm of the place. They downloaded the menu, the way his daughter answered calls, and the cadence of how many customers call during a busy evening. They took all of that back home and trained the thing on it.
I had to ask… This couldn’t have been cheap.
“Twelve thousand upfront.
Five hundred a month.”
I told him that’s actually a lot for a single-location business that sells dough with sauce for twenty bucks a pop. He hadn’t moved the prices in years.
“Paid for itself in the first month.”
He explained that he went from six phone lines down to one, and that he stopped paying high schoolers who in his words were transient and semi-dependable on a good day. The AI doesn’t call out sick the morning of a Friday rush, and it makes fewer mistakes on a complicated order, it doesn’t check its Tik Tok constantly, and it also happens to speak 16 languages.
“I speak Italian pretty good. This thing speaks it better. Plus 15 more. You’d be surprised how many people want to order in Spanish or Portuguese.”
Oh, and the voice on the phone, the one that had sounded familiar to me, was his daughter’s.
The AI had cloned it (with her permission of course) by listening to how she took calls in the shop one day, and “Cool, see you soon” turned out to be exactly the way she actually talked to regulars. It mimicked her mannerisms.
She leaves for college in the fall.
So what
A few things to chew on as a finance person walking back to the car with a steaming hot Grandma’s Pizza (with correctly applied sausage and sun dried tomatoes) attempting to big brain my way through the unit economics of the whole situation.
First, a vendor flying from Europe to install $12k of software in Naples (not the one in Italy) does not pencil well from a CAC standpoint. Either they're burning cash to get reference customers, or this becomes a remote install over Zoom within 18 months. My money's on the second one.
Second, the payback math for the owner is faster than almost anything I see on the tech operator side when software is purchased. Twelve thousand upfront and six grand a year in opex is not nada for a single-location pizza joint, but he’s recouping it on labor he doesn’t have to schedule and orders he doesn’t have to remake, and the benefit rolls straight into operating margin. This is not a multi-quarter recoup, let alone multi-year. The speed to not only value, but break even, blew my mind.
Most of the AI conversation in finance circles is about PE portco margin expansion and customer support cost takeout at the next B2B tech company. It’s very much sequestered into investor backed circles. That stuff matters (and I write about it all the time). And yet, the pizza shop is a more wide ranging application. The same tool (that just paid for itself) applies to nail salons, physical therapy locations, independent insurance brokers, solopreneur media companies, and the dry cleaner two doors down. These are businesses that by definition are not venture-capital-backable, and make up the actual fabric of the world we engage with.
Yes, two semi-dependable people who used to answer the phones don’t have those jobs anymore. Tony mentioned to me that he was always trying to hire for those spots anyway and nobody wanted them. And he’s now making pizza, which is the thing he likes a lot more than managing people, and he’s doing it at a higher margin than before.
All of those things are true at the same time, and which one you weight more heavily probably says more about you than the pure math.
I have no idea what valuation multiple pizza shops trade at, and I'd guess most of them never trade at all. And while AI can't make pizza, it’s making it a better time to own a pizza shop than ever before.
Plus, they got my order right.
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 (17 companies)
Endpoint, network, IAM, security operations. The CISO budget.
CrowdStrike, Palo Alto Networks, Fortinet, Cloudflare, Zscaler, Okta, SentinelOne, SailPoint, CyberArk, Check Point, Qualys, Tenable, Rapid7, Varonis, Rubrik, Mitek, OneSpan
2. Data & AI Infrastructure (12 companies)
Modern data stack, AI/ML platforms, vector and analytics infra, GPU compute. Software-native by design. The legacy hardware names (HPE, NetApp, Lumen, Rackspace, Cisco) that used to live in the old “Cloud Platforms” bucket are gone.
Snowflake, Arista Networks, Equinix, CoreWeave, MongoDB, DigitalOcean, Elastic, Akamai, Fastly, Teradata, C3.ai, Cerebras
3. Dev Tools & Observability (10 companies)
Anything bought out of the engineering budget. The observability names used to live separately. Combined them with dev tools because they’re sold to the same buyer through the same procurement motion.
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) (18 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, Semrush, Five9, Zeta Global, Wix, Sprout Social, ON24, Yext, Criteo
6. Vertical SaaS (16 companies)
Software built for a specific industry without take-rate or transaction economics. This bucket used to include Toast, Olo, and Shopify. They don’t belong here. They make money on transaction volume, not seat licenses or SaaS usage. They’re in #7 now.
Palantir, Autodesk, Veeva, Aspen Technology, Samsara, ServiceTitan, Guidewire, Tyler Technologies, Doximity, Procore, AppFolio, CCC Intelligent Solutions, Blackbaud, nCino, CareCloud, CS Disco
7. Take-Rate Platforms (19 companies)
Marketplaces and commerce platforms that earn money on transaction volume. This is a new bucket. It’s the single biggest reason your old Vertical SaaS median was hard to use, especially if you’re a hospitality or commerce CFO trying to find your comp set.
Uber, Airbnb, Shopify, MercadoLibre, DoorDash, eBay, Zillow, CarGurus, Instacart, Etsy, Toast, Lyft, Opendoor, StubHub, Olo, Upwork, Udemy, Ethos, Fiverr
8. Payments & Money Movement (11 companies)
The rails. Payment processors, payment infrastructure, B2B payments, treasury. Volume game, utility margins. Used to be jumbled in with consumer fintech in a 28-company FinTech bucket. Now they’re on their own.
Intuit, Fiserv, Adyen, PayPal, Block, Shift4, BILL, Clearwater Analytics, Flywire, Marqeta, Lightspeed
9. Consumer Fintech, Lending & Crypto (16 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, MoneyLion, LendingClub
Please check out our data partner, Koyfin. It’s dope.
Wishing you trade at a high revenue and EBITDA multiple,
CJ
