Ok, I'm wicked excited about this one.
I'm moderating an Abacum webinar on legacy FP&A tools versus AI-native platforms, and we're getting into the weeds. The question isn't "should finance use AI?" Everyone's past that, and the answer is yes. The question is whether your planning architecture was actually built for it, or just has AI bolted on top.
We'll have operators in the room who've lived both sides. And they come with real examples and use cases.
If you've sat through a demo where the words "AI-powered" appeared seventeen times, but nothing made your job easier, come hang. This one will impress and inform.
Where’d All the 130’s Go?
A few weeks ago I disclosed that net dollar retention disclosures increasingly suck.
This week I want to talk about the numbers (when they are available) and how they’ve changed.
What does “good” even look like these days? And where’d all the 130s go?
I went through every quarterly filing for 95 public software companies going back to early 2020, forcing me to upgrade my Claude plan in the process (I do it bc I luv you). The data set covers more than ~1,500 quarterly disclosures of NDR or NRR or DBNR or whatever the company chooses to call it. The headline finding is that the 130% club used to have 18 members and now has just two (Palantir and Figma).
The collapse went like this:
And net net, the median has compressed by 13 percentage points since 2021.
But medians can be misleading. So I wanted to check the quartiles.
I expected to see a K-shape. A K-shaped recovery is when the strong get stronger, the weak get crushed, and the middle hollows out.
Source: Corporate Finance Institute
You read this macro-econ-VC-twitter-thread-babble all the time post-2021: "software is bifurcating," "the gap between the best and the rest is widening," "the top quartile is decoupling."
A K-shape is a succinct story that attempts to explain both the index compression and the AI-enabled outperformance of certain names in one story.
And yet…
The data set didn't give me that. Woops.
What actually happened is that the top quartile, median, and bottom quartiles all moved down in lockstep.
The interquartile spread was 18-20 points at peak, and has compressed to 10-13 points today.
P75: 116% (-14 pts): from 130% 2020-Q1 2022, to 116% today
P50: 110% (-13 pts): from 123% in Q4 2021, to 110% today
P25: 103% (-10 pts): from 113% in Q2 2021, to 103% today
Another way to look at it is everyone moved down a floor.
The top quartile moved down to around the median and the median went to the bottom quartile and the bottom quartile found out hell has a 10 point basement.
The one exception is the very, very top tail, above the 75th percentile, which is where the plus 130% club used to live… that penthouse got wiped out completely.
And Remember, the Pool of Disclosers is Smaller Than It Used to Be
The denominator done changed, which makes you wonder what the “real” median is if the median is relative to those who choose to disclose.
The following companies sat around or below the median when they chose to switch up their disclosures (or kill them entirely):
Fastly discontinued quarterly NRR disclosure in Q1 2024. Their last quarterly DBNR was 121%. They still report LTM NRR (113% as of Q1 2026)
CrowdStrike was at +120% before pivoting to qualitative language like 'slightly below our benchmark' starting in mid-2023. Specific numbers since then have surfaced on select earnings calls (115% in Q3 FY25, 112% in Q4 FY25)
DocuSign was at ~125% NDR peak in 2021, dropped to ~99%, then stopped specific quarterly disclosure around 2023
So the pool of companies that feeds the published median is pretty biased. While median is mathematically correct, it is the median of the survivors. If you add back conservative estimates for the dropouts, the real median is probably 6 to 8 points lower than what gets passed around.
In my estimation, the aggregate compression isn't 13 points, but closer to 19 or 21.
The Peak to Trough Hall of Pain
Here are the most violent declines in the dataset, restricted to companies that have stayed publishing.
Snowflake's 53-point drop from 177 to 124 is the most extreme revenue retention compression in public software history. To put that in perspective, Snowflake was claiming the highest NDR of any S-1 filing I've ever seen, then printed a quarterly decline averaging about 4 points per quarter for three straight years. To give them credit though, that was probably an aberration and they are still very much top quartile, despite the decline. The majority of companies would welcome their current NDR.
Is there a trend as to which types of companies bled the most poker chips?
Consumption priced companies (Snowflake, Twilio) had the highest highs, but lost more than seat-based companies because customers could throttle usage faster than it would take to renegotiate licenses upon renewal.
Productivity based companies that are tightly linked to dev and product teams (Asana, Monday) got slammed by tech layoffs in 2022-2023.
And companies in marketplaces with SMB exposure (BILL, Toast) stayed depressed because their customers were depressed based on the state of the economy.
What’s 10 Points of NDR Worth?
Multiples are a function of revenue growth and durability. Points of growth are worth more than points of profit (2 to 3x more), and long term profit points are worth more than short-term ones.
NDR is what makes growth durable, because it comes from customers you've already paid to acquire. A company doing 120% NDR is already grinding out 20% growth before it lands a new logo. A company at 90% NDR is working itself out of a 10% hole before the year kicks off.
So the rule of thumb for an NDR move is something like this.
Every 10 points of NDR is worth around a turn of forward revenue multiple, more or less.
To be more specific, based on my regressions, one point of NDR is worth about 0.07x at lower growth rates (below 20%) and closer to 0.18x at higher growth rates (above 30%).
Net net, the faster you're growing, the more the market pays for the durability NDR provides, because there's actually growth there worth retaining.
Let's run the numbers on a $250M NTM revenue business.
Today they're trading at 8x forward revenue, which puts enterprise value at $2 billion.
A ten-point NDR improvement adds about a turn of multiple, so they re-rate from 8x to 9x.
New EV: $2.25 billion.
So $250M of upside from ten points of NDR
And that's just the immediate uptick. If the higher NDR also leads analysts to bump their out-year growth assumptions (which it usually does, because higher NDR means future growth is more achievable), the multiple gets a second bump. Now you're talking about closer to $500M of value created over the next 18 months.
And this is exactly why the disclosure decay we covered last week matters. Workday, Fastly, and CrowdStrike aren't hiding NDR because they want to save paper. They're hiding it because each point of NDR maps to real enterprise value, and they'd rather not show the market a number that's been moving the wrong way.
Opacity is a valuation decision in and of itself.
And there’s no version of this where you hide the number because the number got better.
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 (12 companies) Modern data stack, AI/ML platforms, vector and analytics infra, GPU compute. Software-native by design.
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.
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 (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
