Category Archives: Metrics

How to Fix a Broken Go-To-Market Motion Using a Steady-State Funnel

In my consulting and advising work, I’ve worked with a number of enterprise SaaS companies that get stuck with a broken go-to-market (GTM) motion.  What do I mean by broken?

  • Chronic plan misses, and not by 5-10%, but by 30-50% [1]
  • Weak sales productivity, measured either relative to the company’s model or industry averages (median $675K) [2]
  • Scarce quota attainment, measured by percentage of reps hitting quota. Instead of 80% at 80%, they’re more like 80% at 40% [3]
  • High sales turnover. Good sellers quit when they’re not making money and they perceive themselves in a no-win situation.
  • Poor pipeline conversion, closing perhaps 10-20% of early-period pipeline instead of 30% to 40% [4]
  • Poor close rates, eventually winning only 5-10% of your deals as opposed to 20-30% [5]

In such situations, it’s easy to conclude “that dog don’t hunt” when examining the company’s go-to-market.  It’s harder to know what to do about it.  Typical reactions include:

  • Fire everyone, a popular response which is sometimes correct, but risks wasting an additional year due to chaos if the people were, in fact, not the problem.
  • Pivot the company, making a major change in strategy or sales model. Let’s go product-led growth (PLG).  Let’s sell our platform instead of our application.  Let’s do only enterprise accounts and account-based marketing (ABM).  While these pivots may make sense, many companies should get called for strategic “traveling” because they pivot too often [6].
  • Hope it will get better. If I only had a dollar for every time that I heard a CRO say,” all the changes are on track, the only thing I need is time for them to work.”  Maybe they will, maybe they won’t.  But what are the tell-tales will let us know before we miss three more quarters and execute plan-A, above?

It’s an utterly soul-sucking exercise to watch sales, marketing, and finance talk about these issues when the players are not all quantitative by nature, using the same metrics definitions, using the same models, all aware of the differences between averages and distributions, and all having a good understanding of ramping and phase lags [7].  That is, well, the vast majority of the time.

So, if you’re in this situation, what should you do about it?  Three things:

  • Agree on the problem, which is often shockingly more difficult than it appears
  • Build a steady-state funnel, which among other things focuses everyone on the present
  • Ensure your leadership team is part of the solution, not part of the problem

Agree on the Problem
You can’t make a coherent plan to fix something unless you have a clear, shared, data-driven understanding of what’s causing it.  To get that, you need to block a series of meetings with a single topic:  why are we missing plan?

You want a series of meetings because you will likely need to iterate on data collection and analysis.  Someone will assert something (e.g., saying that pipeline coverage is weak) and – unless your metrics are already in perfect shape — you’ll want to look at the data you have, clean it up, get historical data for trend analysis, and then reconvene.  It’s more effective to have a series of meetings like this than it is to have one mega-meeting where you’re committed to leaving the room with a plan, but you’re simply debating opinions.  As Jim Barksdale used to say, “if we have data, let’s look at the data; if all we have is opinions, let’s go with mine.”  So, get the data.

There will invariably be some blame games in this process.  Focus on the assertions, not who made them, and focus on the data you’d need to see to back them up.


CMO: “I think conversion rates are the problem.”
CEO: “Based on what data are you arriving at conclusion?”
CMO: “Overall pipeline is up, but the results are flat.”
CEO: “Please put up the slides from the last QBR on pipeline conversion.”
CEO:  “OK, this only shows one quarter so we can’t analyze historical trends, and it’s looking at rolling four-quarter pipeline so we can’t tell if actual current-quarter pipeline is sufficient.  Salesops, how can you help?”
Salesops: “I can make a trailing-five-quarter count- and dollar-based, week 3 pipeline conversion chart and make a pipeline progression chart that shows a better view of how the pipeline is evolving.” [8]
CEO: “Great, do that, and let’s reconvene on Friday to see what it says.”

Finally, ensure that you keep the conversion moving by forcing people to answer questions.  Call out people who “Swiss village” their answers [9].  Ask people who are being defensive to focus on the go-forward.  Interrupt people when they’re waxing poetic.  Time is of the essence and you can’t waste it.

Build and Focus on a Steady-state Funnel
To make things simple, concrete, and focused on the immediate future, I think the best thing you can do is build a steady-state funnel model.

If you’re missing plan consistently and significantly, there’s no need to have in-depth future hiring, ramping, and capacity conversations, phase-lagging lead generation to opportunity creation and then opportunities to deals.  That’s all besides the point.  The point is your model isn’t working and you need to get back on track.

Here are the magic words that change the conversation: “what if we just wanted to add $1M in ARR per quarter?”  No ramps, no phase lags, no ramp resets, none of that planning for future scaling that actually doesn’t matter when you’re presently, chronically missing plan [10].  None of the complexity that turns conversations into rabbit holes, all for invalid analytical reasons.

Think:  how about before we start planning for sequential quarterly growth, we start to consistently add ARR that closely resembles the plan number from two quarters ago that we never came close to hitting?  Got it?

Here’s what that steady-state funnel model looks like:

Let’s be clear, you can build much more complex funnel models, and I’ve written about how.  But now is not the time to use them.  The purpose here is simple.  Think: “team, if we want to add $1M in ARR per quarter …”

  • Can we get (usually down) to 7 sellers?
  • Can we get the deal size to $50K
  • Can each seller close 4 deals per quarter?
  • Can we generate 112 oppties per quarter?
  • Can we close 25% of early-period oppties?
  • Can we generate oppties for $3.5K?

For each assumption, you need to look at historical actuals, have a debate, and decide if the proposed steady-state model is realistic.  Not, “does finance think the math works,” but “can the GTM team sign up to execute it?” If you’re trying to move the needle on a metric (e.g., taking deal size from $30K to $50K) there has to a clear and credible reason why.

If you can’t convince yourself that you can deliver against the model, then maybe it’s time to let the company find someone who does.  It’s far better to part ways with integrity than to “fake commit” to a model you don’t believe in and then unsurprisingly fail to execute.  Or, if the whole team can’t commit to the model, or you can’t find a model to which they would commit that produces an investable CAC ratio, then maybe it is time to pivot the company.  These are hard questions.  There are few easy answers.

Ensure Leadership is Part of the Solution   
As you move forward, you need to ensure that your leadership team is part of the solution and not part of the problem.  This is always a difficult question, not only for relationship reasons, but for more practical ones as well.

  • If you replace an exec, what are the odds their successor will be better? If you have a solid, competent person in place, odds are the next person (who will be knowingly joining a company that’s off-rails) will be no better.  But who’s to decide if someone’s solid and competent?  Board members, your peer network, and advisors can certainly help (but beware halo effects in their assessments).  So-called “calibration meetings” can help you make your own assessment, by simply meeting – not in a recruiting context – other CXOs at similar and next-level companies.
  • If you replace an exec, how long will the resultant turmoil last? Four quarters is not uncommon because the new person will frequently rebuild the organization over their first two quarters and then you’ll need at least two additional quarters to see if it worked.  A failed replacement hire can easily cost you (another) year.  It’s criminal to incur that cost only to replace reasonably-good person X with reasonably-good person Y.

Other questions you should consider in assessing if you want to weather the storm with your current team:

  • Do they really believe in the plan? Execs can’t just be going through the motions.  You need leaders on your team who can enlist their teams in the effort.
  • Are they truly collaborating?  Some execs don’t internalize the Three Musketeers attitude that’s required in these situations.  You need leaders on your team who want to see their peers succeed.  One for all and all for one.
  • Are they still in the fight? Sometimes execs decide the situation is hopeless, but lack the nerve to quit.  They’ll pay lip service to the plan, but not give their best effort.  You need leaders on the team who are still in the fight and giving their best each day.

If you’re going through a rough situation, my advice is stay strong, stay data-driven, leverage the resources around you, and demand the best of your team.  Focus on first diagnosing the problem and then on building and attaining a steady-state funnel model to get things back on track.

It may feel like you’re going through hell, but remember, as Winston Churchill famously said, “if you’re going through hell, keep going.”

# # #


[1] Plan meaning New ARR bookings and not Ending ARR balance.  The latter can mask problems with the former.  If we’re trying to measure sales performance, we should look the amount of ARR sales pours into the SaaS leaky bucket and not what happens to its overall level.

[2] New ARR per seller per year.  Remember this is a median across all SaaS companies and my guess is enterprise is more $800K to $1200K and SMB is more $400-500K.  Introducing ramping to this discussion is always a superb way to burn a few hours of your life.  The pragmatic will just look at ramped rep productivity, excluding momentarily the effects of ramping reps.  Pros will use ramped req equivalents and then look at ARR/RRE.

[3] See prior point.  The pragmatic will look only at ramped rep attainment.  Pros will look at attainment relative to ramped quota.

[4] For companies on quarterly cadence:  new ARR booked / week 3 new ARR pipeline.

[5] Don’t confuse early-period pipeline conversion with opportunity close rate.  The former looks within one period.  The latter measures what closes in the fullness of time.   Example:  you can have a week 3 pipeline conversion rate of 33% (which suggests the need for 3x starting pipeline coverage) and an opportunity win rate of 20%.  See my post on time-based close rates for more.

[6] In the basketball sense that a player is called for a traveling violation when they pivot off more than one foot.

[7] Phase lags here meaning the time between generating a lead and it becoming an opportunity or generating an opportunity and it becoming a deal.

[8] This begs the question why those charts aren’t in the QBR template.  Hopefully, going forward, they’ll ensure they are.  Odds are, however, that they don’t exist so hopefully a good debate and a Google search on Kellblog pipeline will help people find the analytical tools they need.

[9] The expression is based on this quip: “When you ask them the time, some people tell you how to build a watch.  Some tell you how to build a Swiss village.”

[10] To state the obvious, for your company that magic number might be $2M, $5M or $10M – but the same principle applies. Let’s pick a steady-state, per-quarter, net-new ARR number and keep focusing on it until we start to achieve it.

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.

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.

# # #

[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.

You Can’t Fix a CAC Payback Period: The Operator vs. Investor View of SaaS Metrics

Just a quick post to share the slides for the presention I gave today at SaaS Metrics Palooza, entitled You Can’t Fix a CAC Payback Period: The Operator vs. Investor View of SaaS Metrics.  (For those with Slideshare issues, Google Drive share is here.)

The presentation discusses:

  • The ways VCs can use metrics in discussions with founders and CEOs.
  • A deep dive into CAC payback period (CPP) itself, how it’s defined, what it measures, and how its often “corrected.”
  • How investors like compound metrics (e.g., CPP, Rule of 40) whereas operators are best focused on atomic metrics — e.g., you should set accountability and OKRs around atomic metrics.
  • How some metrics are stealthly more compound that you might think — e.g., CAC based on net-new ARR or gross profit (or both).
  • Why I like to say, “you can’t fix a CAC payback period.”  It’s a compound metric which can be driven by at least 5 different factors.
  • How to apply my observations to everyday SaaS life.

The slides are below.  Thanks to Ray Rike for inviting me to the palooza!