Category Archives: SaaS

How to Present an Operating Plan to your Board

I’ve been CEO of two startups and on the board of about ten.  That means I’ve presented a lot of operating plans to boards.  It also means I’ve had a lot of operating plans presented to me.  Frankly, most of the time, I don’t love how they’re presented.  Common problems include:

  • Lack of strategic context: management shows up with a budget more than a plan, and without explaining the strategic thinking (one wonders, if any) behind it.  For a primer, see here.
  • Lack of organizational design: management fails to show the proposed high-level organizational structure and how it supports the strategy.  They fail to show the alternative designs considered and why they settled on the one they’re proposing.
  • A laundry list of goals. OKRs are great.  But you should have a fairly small set – no more than 5 to 7 – and, again, management needs to show how they’re linked to the strategy.

Finance types on the board might view these as simple canapes served before the meal.  I view them as critical strategic context.  But, either way, the one thing on which everyone can agree is that the numbers are always the main course. Thus, in this post, I’m going to focus on how to best present the numbers in an annual operating plan.

Context is King
Strategic context isn’t the only context that’s typically missing.  A good operating plan should present financial context as well.  Your typical VC board member might sit on 8-10 boards, a typical independent on 2 (if they’re still in an operating role), and a professional independent might sit on 3-5.  While these people are generally pretty quantitative, that’s nevertheless a lot of numbers to memorize.  So, present context.  Specifically:

  • One year of history. This year that’s 2021.
  • One year of forecast. This year that’s your 2022 forecast, which is your first through third quarter actuals combined with your fourth-quarter forecast.
  • The proposed operating plan (2023).
  • The trajectory on which the proposed operating plan puts you for the next two years after that (i.e., 2024 and 2025).

The last point is critical for several reasons:

  • The oldest trick in the book is to hit 2023 financial goals (e.g., burn) by failing to invest in the second half of 2023 for growth in 2024.
  • The best way to prevent that is to show the 2024 model teed up by the proposed 2023 plan. That model doesn’t need to be made at the same granularity (e.g., months vs. quarters) or detail (e.g., mapping to GL accounts) as the proposed plan – but it can’t be pure fiction either.  Building this basically requires dovetailing a driver-based model to your proposed operating plan.
  • Showing the model for the out years helps generate board consensus on trajectory. While technically the board is only approving the proposed 2023 operating plan, that plan has a 2024 and 2025 model attached to it.  Thus, it’s pretty hard for the board to say they’re shocked when you begin the 2024 planning discussion using the 2024 model (that’s been shown for two years) as the starting point.

Presenting the Plan in Two Slides
To steal a line from Name That Tune, I think I can present an operating plan in two slides.  Well, as they say on the show:  “Dave, then present that plan!”

  • The first slide is focused on the ARR leaky bucket, metrics derived from ARR, and ARR-related product.ivity measures
  • The second slide is focused on the P&L and related measures.

There are subjective distinctions in play here.  For example, CAC ratio (the S&M cost of a dollar of new ARR) is certainly ARR-related, but it’s also P&L-driven because the S&M cost comes from the P&L.  I did my best to split things in a way that I think is logical and, more importantly, between the two slides I include all of the major things I want to see in an operating plan presentation and, even more importantly, none of the things that I don’t.

Slide 1: The Leaky Bucket of ARR and Related Metrics

Let’s review the lines, starting with the first block, the leaky bucket itself:

  • Starting ARR is the ARR level at the start of a period. The starting water level of the bucket.
  • New ARR is the sum of new logo (aka, new customer) ARR and expansion ARR (i.e., new ARR from existing customers). That amount of “water” the company poured into the bucket.
  • Churn ARR is the sum of ARR lost due to shrinking customers (aka, downsell) and lost customers. The amount of water that leaked out of the bucket.
  • Ending ARR is starting ARR + new ARR – churn ARR. (It’s + churn ARR if you assign a negative sign to churn, which I usually do.)  The ending water level of the bucket.
  • YoY growth % is the year-over-year growth of ending ARR. How fast the water level is changing in the bucket.  If I had to value a SaaS company with only two numbers, they would be ARR and YoY ARR growth rate.  Monthly SaaS companies often have a strong focus on sequential (QoQ) growth, so you can add a row for that too, if desired.

The next block has two rows focused on change in the ARR bucket:

  • Net new ARR = new ARR – churn ARR. The change in water level of the bucket.  Note that some people use “net new” to mean “net new customer” (i.e., new logo) which I find confusing.
  • Burn ratio = cashflow from operations / net new ARR. How much cash you consume to increase the water level of the bucket by $1.  Not to be confused with cash conversion score which is defined as an inception-to-date metric, not a period metric.  This ratio is similar to the CAC ratio, but done on a net-new ARR basis and for all cash consumption, not just S&M expense.

The next block looks at new vs. churn ARR growth as well as the mix within new ARR:

  • YoY growth in new ARR. The rate of growth in water added to the bucket.
  • YoY growth in churn ARR. The rate of growth in water leaking from the bucket.  I like putting them next to each other to see if one is growing faster than the other.
  • Expansion ARR as % of new ARR. Percent of new ARR that comes from existing customers.  The simplest metric to determine if you’re putting correct focus on the existing customer base.  Too low (e.g., 10%) and you’re likely ignoring them.  Too high (e.g., 40%) and people start to wonder why you’re not acquiring more new customers. (In a small-initial-land and big-expand model, this may run much higher than 30-40%, but that also depends on the definition of land – i.e., is the “land” just the first order or the total value of subscriptions acquired in the first 6 or 12 months.)

The next block focuses on retention rates:

  • Net dollar retention = current ARR from year-ago cohort / year-ago ARR from year-ago cohort. As I predicted a few years back, NRR has largely replaced LTV/CAC, because of the flaws with lifetime value (LTV) discussed in my SaaStr 2020 talk, Churn is Dead, Long Live Net Dollar Retention.
  • Gross dollar retention = current ARR from year-ago cohort excluding expansion / year-ago ARR from year-ago cohort. Excluding the offsetting effects of expansion, how much do customer cohorts shrink over a year?
  • Churn rate (ATR-based) = churn ARR/available-to-renew ARR. Percent of ARR that churns measured against only that eligible for renewal and not the entire ARR base.  An important metric for companies that do multi-year deals as putting effectively auto-renewing customers in the denominator damps out

The next block focuses on headcount:

  • Total employees, at end of period.
  • Quota-carrying reps (QCRs) = number of quota-carrying sellers at end of period. Includes those ramping, though I’ve argued that enterprise SaaS could also use a same-store sales metric.  In deeper presentations, you should also look at QCR density.
  • Customer success managers (CSMs) = the number of account managers in customer success. These organizations can explode so I’m always watching ARR/CSM and looking out for stealth CSM-like resources (e.g., customer success architects, technical account managers) that should arguably be included here or tracked in an additional row in deeper reports.
  • Code-committing developers (CCDs) = the number of developers in the company who, as Elon Musk might say, “actually write software.” Like sales, you should watch developer density to ensure organizations don’t get an imbalanced helper/doer ratio.

The final block looks at ARR-based productivity measures:

  • New ARR/ramped rep = new ARR from ramped reps / number of ramped reps. This is roughly “same-store sales [link].”  Almost no one tracks this, but it is one of several sales productivity metrics that I like which circle terminal productivity.  The rep ramp chart’s 4Q+ productivity is another way of getting at it.
  • ARR/CSM = starting ARR/number of CSMs, which measures how much ARR each CSM is managing.  Potentially include stealth CSMs in the form of support roles like technical account manager (TAM) or customer success architects (CSAs).
  • ARR/employee = ending ARR/ending employees, a gross overall measure of employee productivity.

Slide 2: The P&L and Related Metrics

This is a pretty standard, abbreviated SaaS P&L.

The first block is revenue, optionally split by subscription vs. services.

The second block is cost of goods sold.

The third block is gross margin.  It’s important to see both subscription and overall (aka, blended) gross margin for benchmarking purposes.  Subscription gross is margin, by the way, is probably the most overlooked-yet-important SaaS metric.  Bad subscription margins can kill an investment deal faster than a high churn rate.

The fourth block is operating expense (opex) by major category, which is useful for benchmarking.  It’s also useful for what I call glideslope planning, which you can use to agree with the board on a longer-term financial model and the path to get there.

The penultimate block shows a few more SaaS metrics.

  • CAC ratio = S&M cost of a $1 in new ARR
  • CAC payback period  = months of subscription gross profit to repay customer acquisition cost
  • Rule of 40 score = revenue growth rate + free cashflow margin

The last block is just one row:  ending cash.  The oxygen level for any business.  You should let this go negative (in your financial models only!) to indicate the need for future fundraising.

Scenario Comparisons
Finally, part of the planning process is discussing multiple options, often called scenarios.

While scenarios in the strategy sense are usually driven by strategic planning assumptions (e.g., “cheap oil”), in software they are often just different version of a plan optimized for different things:

  • Baseline: the default proposal that management usually thinks best meets all of the various goals and constraints.
  • Growth: an option that optimizes growth typically at the expense or hitting cash, CAC, or S&M expense goals.
  • Profit: an option that optimizes for cash runway, often at the expense of growth, innovation, or customer satisfaction.

Whatever scenarios you pick, and your reasons for picking them, are up to you.  But I want to help you present them in a way that is easy to grasp and compare.

Here’s one way to do that:

I like this hybrid format because it’s pulling only a handful of the most important rows, but laying them out with some historical context and, for each of the three proposed scenarios, showing not only the proposed 2023 plan also the 2024 model associated with it.  This is the kind of slide I want to look at while having a discussion about the relative merits of each scenario.

What’s Missing Here?
You can’t put everything on two slides.  The most important things I’m worried about missing in this format are:

  • Segment analysis: sometimes your business is a blended average of multiple different businesses (e.g., self-serve motion, enterprise motion) and thus it’s less meaningful to analyze the average than to look at its underlying components.  You’ll need to add probably one section per segment in order to address this.
  • Strategic challenges. For example, suppose that you’ve always struggled with enterprise customer CAC.  You may need to add one section focused solely on that.  “Yes, that’s the overall plan, but it’s contingent on getting cost/oppty to $X and the win rate to Y% and here’s the plan to do that.”
  • Zero-based budgeting. In tough times, this is a valuable approach to help CEOs and CFOs squeeze cost out of the business.  It takes more time, but it properly puts focus on overall spend and not simply on year-over-year increments.  In a perfect world, the board wouldn’t need to see any artifacts from the process, but only know that the expense models are tight because every expense was scrutinized using a zero-based budgeting process.

Hopefully this post has given you some ideas on how to better present your next operating plan to your board.  If you have questions or feedback let me know.  And I wish everyone a happy and successful completion of planning season.

You can download the spreadsheet used in this post, here.

Key Takeaways from the 2022 KeyBanc SaaS Metrics Survey

KeyBanc Capital Markets (KBCM) recently published their 13th annual private SaaS company survey.  This post has three purposes:  to let you know it’s out, to provide you with a link so you can get it, and to offer some quick takeaways on skimming through the results.

The first thing to remember about this survey is that it’s private SaaS companies.  Unlike Meritech Public Comps, where you can see metrics for the best [1], public SaaS companies, this private company data is somewhat harder to come by (the only other source that springs to mind is RevOps Squared) and, for most of us, it provides much more realistic comparables than Meritech [2].

The second thing to remember is that there are a lot of smaller companies in the sample:  about 20% of respondents are less than $5M in ARR and about 40% are less than $10M.   (The overall median is $13MM.)  Depending on who you want to compare to, this may be a good or a bad thing.  In addition, for most of the metrics they exclude companies <$5M in ARR from the calculations, which brings up the overall median for that set to $17.6M.

Net:  this is not VC-backed SaaS companies (62% are), this is not IPO-track SaaS companies (presumably some small subset of that 62%).  This is all private SaaS companies, including 22% PE-backed and 13% boostrapped.

One of my new benchmarking themes is that people need to pay more attention to matching their benchmarks with their aspirations. If your aspirations are to raise money from top VCs at a good valuation, my guess is you should be thinking 75th precentile of this data set; if they’re to IPO, you should be thinking 90th.

That said, let’s meet the Joneses, who have median:

  • ARR growth of 31%, lower than I’d hope.
  • Forecast 2022 ARR growth of 36%, so they’re planning to accelerate.  Everyone’s an optimist.
  • Expansion ARR of 46%, higher than I’d hope.
  • Net dollar retention (NDR) of 109%.
  • Customer acquisition cost (CAC) ratio of 1.2 blended, 1.8 new, and 0.6 expansion, in line with my expectations.
  • Gross churn of 14%, in line, perhaps a tad high, relative to my guess.
  • Available to renew (ATR) gross churn of 10%, but it’s hard to understand how ATR rate can be lower than gross churn rate [3].
  • Margin profile of 77% subscription, 73% blended.  In line.
  • Sales and marketing (S&M) expense of 40% of revenues.  They’re frugal, but they’re not growing that fast, either.
  • Free cashflow (FCF) margin of -5%.
  • New ARR per seller of $673K, which I if I understand, is what I’d call sales productivity.
  • Contract length and billing frequency of one year.
  • ARR/FTE of $143K, lower than I’d guess (for public companies it’s nearly double that).
  • Valuation of 6.1x ARR at their most recent round (in 2021 or later).

Since I don’t want to lift too many of their slides, I’ll extract just two.  The first shows S&M spend as a function of growth rate.

If there’s one area where you really need to look at metrics as a function of growth rate, it’s customer acquistion cost and, by extension S&M spend, on the theory that in enterprise SaaS you need to invest up front to grow.  Therefore a high-growth company is theoretically carrying the cost of as-yet-unproductive capacity where as a steady-state one is not.  You can see this pretty clearly here where the sub-20% growth companies spend 27% on S&M, which surprisingly drops to 17% at the 30-40% bucket, but then begins a steady upward march to 59% for those growing faster than 80%.

The second discusses a concept I’ve called The Rule of 56789

Here, KeyBanc is saying roughly what I say, which is [4]:

  • 5 years to $10M (5.6 years, per KCBM)
  • 6 years to $20M (7.1 years, but to $25M)
  • 7 years to $50M (7.6 years)
  • 8 years to $75M (they have no threshold here)
  • 9 years to $100M (9.3 years)

I’m glad they’re now tracking this, along with net burn rate (aka, cash conversion score) though I’d say their implied cash conversion scores are more efficient than I’d guess based on my experience and Bessemer’s data.

Overall, this is a seminal report for SaaS companies.  Every private SaaS company should read it.  Grab yours here.


[1]  In the sense that even a “bad” public SaaS company (dare I suggest Domo or C3 as two of my favorites to scrutinize) was still good enough to get public in the first place and ergo creme de la creme when viewed more broadly.

[2]  As I said in a recent speech, it’s the difference between benchmark off all SAT test takers and Ivy League applicants.  See slide 13 of this presentation.

[3]  KBCM calls this non-renewal rate, but I think it’s 1 – ATR churn.  The reason it’s hard to believe it’s lower is that it should be the same numerator over a smaller denominator.

[4]  I was looking at European 75th percentiles and they are looking at worldwide (but US-weighted) medians

Slides from a CFO Summit on Leading and Lagging Indicators

Just a quick post to share the slides of a presentation on leading, lagging, and predictive indicators that I gave at the recent Foundry CFO Summit.

  • It starts with a discussion of the importance of leading indicators, particularly as we head into an uncertain business environment.
  • It discusses go-to-market funnel and how leading indicators are basically up and lagging ones are down.
  • I observe that we’ve spent 30 years trying to get marketers to focus down-funnel, so we should care before suddently saying, go worry about names or responses.
  • We discuss whether you want to use a metric for prediction or management.  You can’t really pick both.
  • It concludes by suggesting an ICP re-evaluation that’s both qualitative (which use-cases should be more compelling in the new environment) and quantitative (which prospective customers look most like our existing successful ones).
  • The last point begs an interesting riff on what we mean by successful, which is far more of a greased-pig question than most realize.

The slides are here on slideshare, and here on Google Drive.  Thanks to Brian Weisberg for inviting me.

Slides from my SaaStock Dublin Presentation on GTM Efficiency

Just a quick post to share the slides I presented at SaaStock Dublin today on driving go-to-market (GTM) efficiences over the coming 24 months.  I chose this topic because extending runway is on everyone’s mind and — because it’s usually the single largest contributor to overall operating expense — sales & marketing (S&M) is where companies turn to do so.

After a brief review of the problem, I look at two popular approaches that don’t work:

  • The Excel-induced hallucination, where you make seemingly small but unsupported tweaks to your GTM funnel model that result in massive (and totally unrealistic) productivity gains.
  • Everyone for themselves!  A Lord of the Flies approach, which sales usually wins, resulting in too many mouths to feed with too few supporting resources.

Newly hired sales reps waiting for pipeline

What does is work is to adopt a three-musketeers attitude across sales, marketing, customer success, and professional services.  (Yes, there actually were four muskeeters; they picked up d’Artagnan along the way.)

All for one and one for all to maximize ARR

I then run through a punch list of ideas, some obvious and some less so, structured in four groups, about how you can drive GTM efficiency:

  • Work better together
  • Shoot at richer targets
  • Forward-deploy more resources
  • Improve operating efficiency

The slides are embedded below.  Note that the Slideshare previewer sometimes doesn’t mix well with the Balderton fonts, so I uploaded only a PDF to Slideshare.  If you want it in PowerPoint, go to Google drive here.



The Mental Mapping from Annual to Monthly and Usage-Based SaaS Metrics

A guy walks into a bar and orders a $17 Martini.  Is it MRR (monthly recurring revenue)?

The potentially surprising answer:  maybe, and often yes.

  • If he’s a tourist who happened to walk in, then no, it’s not MRR.
  • If he’s lived here for two years and comes in every Thursday for a Martini, yes.  He represents $68 of what I’d call empirical MRR [1].
  • If he just moved in next door, says every Thursday he drinks a Martini, and he’s selected our bar as his new spot, then I’d also say yes.  I might call this intentional MRR, much like signing up for a SaaS service on a month-to-month basis [2].
  • If the bar’s in a club with a $2000 annual membership and a quarterly food and beverage (F&B) minimum of $221, I’d say yes.  It’s contractual MRR.  I’d probably even call it $2,884 of ARR, not MRR, to reflect the annual nature of the contract [3].

I’m writing this post to help readers who (like me) grew up in an annual subscription SaaS world adapt to the new and increasingly popular world of usage-based pricing [4], including month-to-month contracts and variable fees [5].

In this new world, people still use terms like ARR and MRR.  For example:

SaaStr Discussing Snowflake’s ARR

Meritech Showing Implied ARR

But what does this mean in a usage-based world?  Specifically, what does “recur” mean?  Why does the phrase “recurring revenue” appear exactly zero times in Snowflake’s 10-Q?

And what’s the impact on your other SaaS metrics?  What’s your CAC ratio if I don’t know what your ARR is because I don’t know what the recurring means?  What’s your churn rate? What if a customer fluctuates across months: do I count churn each month they shrink and expansion each month they expand?  If ARR is a forward-looking metric [6], what do ARR-based metrics like net dollar retention (NDR) mean [7] in a world without fixed forward commitments?

What Does Recur Mean?
So many questions.  But since I like to start with the basics, let’s go back to our bar and think about Martinis and the meaning of the word recur.  In the annual world, “recur” seemed pretty clearly defined.  Unlike perpetual software license revenue, which was largely one-shot in nature [8], SaaS subscription revenue would recur.  A customer would purchase a subscription to a service for a time period.  At the end of the period the customer could, and usually would, renew the subscription to the service.  Hence, the revenue recurred.

The subscription period varied typically as a function of contract size and target market.  A $200/month product might have a month-to-month contract with monthly billing, whereas a $2,000/month product might have an annual contract with up-front billing, and a $20,000/month product might have a three-year contract with annual billing [9] [10].

The important point here seems forgotten by time:  recur didn’t mean a company gets $10K per month from a $120K annual contract [11].  Recur meant the $120K contract had a fixed duration and periodically came up for renewal [12].  Recur never meant contractual.  The revenue didn’t recur contractually across contract periods.  The fact that it might, however — unlike perpetual license — meant that it recurred.

I’ll say it again.  Recur never meant contractual.  Which is why I think the Martini revenue in the second and third examples is MRR.  There’s no contract that says the guy has to come in every Thursday.  But, empirically, he does.  There’s no binding commitment that our new neighbor will come in every Thursday going forward, but he said he would.  That’s as “recurring” as an annual SaaS renewal.

The Usage-Based Model
To make our Martini bar more reflective of usage-based SaaS, let’s change our example a bit:

  • After a few trial visits, you are no longer admitted to the bar until you sign a contract.
  • The bar sells credits, which you can buy purely à la carte but they now cost $20.
  • If you buy 20 credits or more, they cost $17.  More volume discounts exist beyond that.
  • Overage credits can be purchased at $19, a price designed to incent purchasing more regular credits up front, possibly even hitting the next discount level where they are $16.
  • Unlike many other bars [13], unused credits may be rolled over into the next year’s agreement.

Our customer signs a deal for 52 credits at $884 to cover his weekly Martini.  Some weeks he either brings a friend or has a hamburger and spends two credits, so his monthly credit usage ends up looking like this:

He’s spent 32 credits in the first half of 2022, on pace to spend 64 on the year, well above his 52 credit plan.

What is the MRR?
If you come from the annual world, you might be tempted to break the 52 purchased credits across the year (especially if they don’t rollover) and say his baseline spend is one credit per week, thus 4.3 per month.  At $17/credit, that’s MRR of $73.66.  But he spent 15 credits in 1Q, so you might bill him for 2 overage credits ($38) and then spread that across the three months to arrive at MRR of $86.30.

I think the psychological issue here is that you’re fighting to stay in the MRR mindset, thus allocating the credits by month, and then applying overages as you go along.  You’re doing that, I believe, because you view the baseline as “recurring,” but not the overage.  You’re stuck on MRR, and that’s potentially based on the faulty notion of recurring that’s discussed above.  Now imagine doing this with multiple products and a hybrid pricing model that includes both monthly subscriptions and multiple different consumption fees (e.g., compute, storage, API calls).

Trailing Spend Calculations to the Rescue
Let’s send in the external financial reporting team to save us.  What do they see?  Simple.  They see quarterly revenue of 15 credits x $17/credit = $255 in 1Q22. They would not report it as baseline and overage revenue, but aggregate it to F&B revenue [14].

This is a better way to view things.  The problem is less that it’s usage-based pricing and more that it’s monthly-varying pricing.  Much like our bar, a customer’s monthly spend bounces around so we’re never quite sure what’s fluctuation vs. churn/expansion and we don’t know what they’re going to spend in the future.  MRR thus becomes an inferior unit to quarterly spend.

What is the Net Expansion?
When we think about expansion (or churn) let’s stick with trailing spend and not fuss about trying to first calculate MRR and then see how it changes.  With that in mind, what is customer A’s net expansion in 2Q22?  $34, right?  He spent $289 in 2Q22 and $255 in 1Q22, and the difference is $34.

Wrong.  At least in the traditional SaaS world where the correct answer is unknown.  Why?  Because we don’t have last year’s 2Q21 data in the spreadsheet and in the traditional SaaS world, churn is a year-over-year metric [15].  Monthly SaaS tends to silently slip your brain into a quarter-over-quarter mindset, as you see with metrics like lazy NRR, which is quarterly, compared to NRR, which is annual [16].  Note that this is not a bad thing — in the usage-based world, you need to watch customers and their usage like a hawk — it’s just a different thing if you grew up in the annual SaaS world.

Let’s provide the 1Q21 data we need and then answer the question.

Customer A used 13 credits in 2Q21 and 17 units in 2Q22, so he expanded by 4 units.  But, he negotiated a better price per credit in 2022 ($17 instead of $18) so his spend went from $234 in 2Q21 to $289 in 2Q22, an expansion of $55, reflecting a net expansion rate of 124%.  Had the customer’s spend been the other way around and shrunk to $234 from $289, it would be churn of $55, reflecting a churn rate of 19%, or a net expansion rate of 81% [17].

What is Net Revenue Retention?
Isn’t net expansion rate the same thing as NRR?  Well, as I’m using the terms here, no.  Above, we calculated net expansion rate using year-over-year quarterly spend.  In the traditional world, NRR is supposed to be a year-over-year ARR comparison.  But in the monthly SaaS world, we don’t really have ARR [18], so what can we do?

We can rely on trailing spend calculations to save the day.  For example, we can define NRR, as Snowflake does, to be trailing one-year spend divided by trailing year-before-that spend for customers who started on or before the first month of the year-before-that period.  Here’s how Snowflake says that:

We need more data in our Martini bar example to calculate NRR, so here it is:

Let’s calculate NRR for customer A as of 12/31/22 using the Snowflake NRR formula.  In the trailing year (2022), he spent $1,131.  In the year before that (2021), he spent $936.  Thus NRR is 121% (= 1311/936).

Please note that this makes NRR — and every other metric that substitutes trailing spend for ARR/MRR — more backward looking than their ARR/MRR counterparts.  Why?  Because in an annual subscription world, NRR would compare 2023 to 2022, not 2022 to 2021.  That is, NRR would compare the ARR value of the renewal we signed on 12/31/22 for the coming year (2023) to the one we signed on 12/31/21 for the then-coming year (2022).

Before moving to other topics, let’s quickly review how other leaders calculate NRR.  Twilio defines NRR in line with how I defined net expansion rate, above (i.e., quarter over prior-year quarter).  Note that, oddly, when calculating it for a year instead of comparing two trailing 12-month periods, they instead use a (presumably unweighted) average of the quarterly rates.

Datadog, often described as a usage-based pricing leader (e.g., in the OpenView Usage-based Playbook) seems to rely more heavily on subscriptions than the hype suggests.

Datadog calculates NRR using a rather traditional ARR-based formula.

Finally, Hashicorp, a company known for both land-and-expand and usage-based pricing, defines NRR as follows, which includes their definition of ARR (which is roughly annualized spend):

So, basically, in a monthly or usage-based SaaS world where ARR doesn’t really exist, you can either look at trailing spend or annualizing periods.  And, as we’ve seen, there really aren’t any standards here so caveat emptor when comparing the NRR reported by different companies.  Personally, in the absence of actual ARR, I prefer tracking actual spend as it reduces the risk associated with annualizing seasonally strong (or weak) periods and getting an over- or under-stated result [19].

What is Implied ARR?
All public SaaS companies report revenue.  Few report ARR.  Thus, long ago public investors and financial analysts created new SaaS metrics to try and approximate the SaaS leaky bucket:

  • Implied ARR, which estimates the size of the ARR pool and is calculated by multiplying last-quarter’s (subscription) revenue by 4 [20].
  • Billings.  Revenue plus change in deferred revenue, which is designed to estimate bookings (i.e., new sales).  If payment terms and contract lengths are constant, this one works pretty well, but can break when they’re not.

You might wonder, in a monthly or usage-based SaaS world, if you couldn’t just use implied ARR and then calculate SaaS metrics like the CAC ratio off that.  Sometimes the answer is yes:  CAC ratio (and magic number) and CAC Payback Period are often calculated off changes in implied ARR.  Sometimes the answer is no:  you can’t do NRR because you can’t get the cohorts, and you can’t do churn rates because you don’t see the offsets between new-logo, expansion, and churn ARR.  But the real reason is that these tend to be investor metrics, not calculated by public companies but calculated for (or about) them by financial analysts.  Internally, since they have all the non-disclosed ingredients, I think they look at the real thing.

Well, this turned out to be a lot bigger than I’d thought when I came up with the Martini bar analogy. Hopefully (particularly if you were raised in the annual SaaS world) you’ve appreciated this walk over the long and rickety bridge that connects traditional SaaS metrics to the world of monthly and usage-based SaaS.  I think I’ve answered all the questions I posed at the top, though admittedly in a somewhat unstructured way.  If you think I missed one, or this post has prompted another, please let me know.

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[1] Let’s pretend every month has four Thursdays to keep MRR simple here.  (Later we’ll use 52 weeks per year.)

[2] Arguably deserves the MRR moniker more than the month-to-month SaaS service, where the customer might must just be trying it out.  In this example, the customer has stated this will be their Thursday night Martini spot.  He’s ours to lose.

[3] I am somewhat fanatical that ARR isn’t just MRR multiplied by twelve.  Why?  Because if al your contracts are month-to-month, I think it’s misleading to talk about ARR.  Conversely, if all (or the majority) of your contracts are annual, I think it’s silly to talk about MRR.  Yes, math wise, one is 12x the other, but the choice of unit does make an implication about the nature of the contracts.

[4] At least for now.  The downturn may well reveal the Achilles’ Heel of usage-based models — it’s great when usage is always going up.  When it’s not, well, not so much (and those annual commitments start to look a whole lot better).

[5] Also known as consumption-based pricing.  I tend to use the terms interchangeably.

[6] While I’m not sure people think about this way, in reality, ARR is a forward-looking metric.  It’s about what people are promising to pay you in the future.

[7] In the annual subscription world, NDR is also forward-looking.  You’re looking at what customers are promising to pay you in the coming year vs. what they promised to pay you in the then-coming year, one year ago.  

[8] There’s ostensibly considerable irony in the word “perpetual” meaning one-shot, but remember perpetual was describing the duration of the license, not the nature of the revenue.

[9] This varying period made it hard to interpret some SaaS metrics.  Should a company that does exclusively two-year contracts calculate churn rates based on the entire ARR pool or on an available-to-renew (ATR) basis?  It’s a factor of 2 difference with a company that does purely annual contracts, yet people will often unknowingly compare them.  See Churn is Dead, Long Live NDR.

[10] Salesforce started out with months as the contract and billing period, but quickly moved to years to avoid the hassle of monthly invoicing for enterprises, who generally preferred the simplicity of annual contracts, and to avoid running out of cash by billing a year up-front.

[11] That’s just revenue recognition.

[12] Which is why some perpetual license companies first moved to term licensing (e.g., selling three-year term licenses) as a discounting alternative and, while not widely recognized at the time, pretty strongly resembled SaaS companies, with the major exception that they didn’t run the software.

[13] I’m not sure how many companies allow rollover, but I think it’s not that common, though Snowflake is an example of someone who does, provided your next-year commitment is bigger than this year’s.

[14] Or, as Snowflake calls it, “product revenue.”

[15] The standard definition of churn compares ARR/MRR at this year’s renewal to last year’s initial contract or renewal.  Not last quarter’s.

[16] If you say NRR is 108%, it’d sure be helpful to know if that’s classic year-over-year (in which case it’s just OK) or lazy quarterly, which compounds to 136% year-over-year (in which case it’s amazing).

[17] Note the subtlety here that we’ve quietly switched the units of churn to simply dollars (for a period) as opposed to MRR or ARR dollars.  In the rates, the units cancel out.

[18] Except for Implied ARR, which we’ll discuss in a minute.  But I’m not in love with using a calculated or implied metric as an input to a formula.

[19] As a dramatic example, if you annualized December bookings at most software companies, you might get 2-3x the actual annual result as a typical enterprise software company might get 20% of its annual bookings in the last month of the year.  Tracking trailing twelve months of any metric that shows annual (or shorter) seasonality will tend to damp that out.

[20] This works pretty well in enterprise SaaS where new bookings are generally quite backloaded.  Thus, last quarter’s ending ARR is the heavy-majority source of this quarter’s subscription revenue.  (Few contracts stop before quarter’s end because they were backloaded when signed, and few new contracts get signed before quarter’s end.)  This, however does mean that implied ARR is effectively one quarter phase lagged.