How Public Companies Calculate Net Dollar Retention (NDR)
7/13/25 Benchmarks for Operators
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This post was inspired by Figma’s S-1 (full breakdown of the IPO filing below)… and the collective aneurysm it gave every metric nerd on the internet .
Figma IPO: S1 Breakdown
The Design software category leader goes public after failed Adobe acquisition in 2022.
The product? Incredible. The revenue growth? Strong. The free cash flow margin? Shit’s real!
But their calculation for Net Dollar Retention (NDR))? They had some of us refreshing the SEC filing to make sure we weren’t misreading.
David Spitz, a fellow metrics super fan, said it best:
“I love you, Figma ❤️ – but what’s with these DUMB retention metrics 🤮 in your S-1?!”
Figma’s NDR clocks in at 132%, but they calculate it… backwards.
“We calculate [NRR] by starting with the ARR as of the date of measurement from all Paid Customers with more than $10k in ARR that were also Paid Customers with more than $10k in ARR as of 12 mos. prior. We then calculate the ARR for those same customers as of 12 mos. prior to the date of measurement (“Previous Period ARR”). We then divide Current Period ARR by Previous Period ARR.”
-Figma S1 Filing
Instead of tracking how last year’s customer cohort performed over 12 months, they start with current customers and look back, only measuring who stuck around. Which means churned customers disappear from the equation entirely.
They also only include customers spending over $10K / year…Which is… fine… I guess.
If you reswizzle it to include the churned customers, which you can deduce from their Gross Dollar Retention rate of 94%, it would most likely be closer to 128% (which is still bonkers good).
Now, none of this discredits the business. Figma is a juggernaut. But it does highlight a broader truth:
NDR has no standard definition. And if you’re not paying attention, it can mislead more than it informs.
Let’s unpack how other public companies play the same game.
What Goes Into NDR?
At a high level, NDR tells you how much more (or less) your existing customers are spending over time. It has four main components:
Starting ARR: Revenue from customers who were active 12 months ago.
Expansion: Upsells, cross-sells, seat increases, price increases… any additional ARR from those same customers.
Contraction: Reductions in spend from customers who stick around but downgrade.
Churn: ARR lost from customers who leave entirely.
The formula:
(Starting ARR + Expansion – Contraction – Churn) ÷ Starting ARR
Of course, this is just the theoretical version. In practice? Every company adds its own flavor.
If you want a template for building your own NDR bridge, you can get it here.
How Public Companies Define NDR
We reviewed over a dozen public SaaS companies, and let’s just say, there is no “one NDR to rule them all.” Some use trailing averages. Others exclude churn-heavy segments. A few even adjust how they define ARR depending on the product line.
Here are a few highlights from our research:
Figma: Only counts customers >$10K in ARR who are still active today. The spend threshold is an attempt to strip out the smaller, more fleeting customers paying for personal use on credit cards. More importantly, if a customer churned in the last 12 months, they’re invisible. That’s how you get to 132% NDR while ducking churn entirely.
Confluent: Uses dual ARR methodologies: Platform revenue from contractual commitments, and Cloud revenue based on annualized consumption from the trailing 3 months.
Klaviyo: Calculates NDR as a weighted average of monthly point-in-time retention rates, smoothing volatility. Excludes usage overages.
Snowflake: Measures NDR over two years, not one. It takes product revenue from a fixed cohort and compares it across a 24-month period, which creates a disconnect between cohort retention and current-year revenue growth.
Same Number, Different Planet
The problem isn’t that any of these approaches are “wrong.” It’s that they’re wildly different, and yet all show up under the same label. A 130% NDR at GitLab (which includes detailed upsell and seat increase reporting) means something very different than a 130% at MongoDB (who annualizes revenue differently depending on if you bought through a sales team or not).
And without footnotes or context, it’s easy to misinterpret what’s actually happening underneath the number.
What Do You Do With It?
If you’re an operator, be precise. Pick a methodology and stick with it. More importantly, disclose it. What you exclude is MORE important than what you include.
If you’re an investor or benchmarking a peer set, dig. Ask questions. Read footnotes. Look at trends, not point-in-time deltas. Honestly, ask for a CRM export and calculate it yourself.
As the Who famously sang: I won’t get fooled again.
BONUS!!! Snowflake’s NDR can be > than growth???
Snowflake calculates Net Revenue Retention using a cohort defined by product usage in the first month of a two-year trailing period, then comparing that cohort’s product revenue in the second year.
That means they’re not looking at a simple “how much did last year’s customers spend 12 months later?” Instead, they’re:
Taking all customers who were active in month 1 of Year 1 (starting two years ago).
Measuring how much those same customers spent in Year 2.
Computing NDR as Year 2 revenue ÷ Year 1 revenue.
Because it spans 24 months, this method:
Includes churned accounts, which lowers retention, but they also smooth the impact of lost customers across a longer time horizon.
Blurs short-term fluctuations, especially useful for usage-based businesses.
Can lead to NDR that trails actual ARR growth in a given calendar year, making it easier for NDR to appear stronger than annual revenue growth, even when using a longer window.
By design, they anchor to a longer historical base, which generally smooths volatility and amplifies trending expansion, but also makes NDR less temporally aligned with current ARR growth.
TL;DR: Multiples are FLAT week-over-week.
Top 10 Medians:
EV / NTM Revenue = 15.8x (DOWN 0.1x w/w)
CAC Payback = 30 months
Rule of 40 = 50%
Revenue per Employee = $463k
Figures for each index are measured at the Median
Median and Top 10 Median are measured across the entire data set, where n = 140
Population Sizes:
Security & Identity = 17
Data Infrastructure & Dev Tools = 11
Cloud Platforms & Infra = 15
Horizontal SaaS & Back office = 19
GTM (MarTech & SalesTech) = 18
Marketplaces & Consumer Platforms = 18
FinTech & Payments = 24
Vertical SaaS = 18
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 Benchmarks
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.
Operating Expenditures
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.
I use a NDR closest to Hubspot's. It's part of a three part churn discussion of revenue, logos, and cohorts.
Hey CJ, I love your metrics breakdowns. I would love to see you do a deeper dive into product metrics and their relationship to org metrics; if at all possible.