Category Archives: Bookings

What Are The Units On Your Lead SaaS Metric — And What Does That Say About Your Culture

Quick:

  • How big is the Acme deal?  $250K.
  • What’s Joe’s forecast for the quarter?  $500K
  • What’s the number this year?  Duh.  $7,500K.

Awesome.  By the way:  $250K what?  $500K what?  $7,500K what?  ARR, ACV, bookings, TCV, new ARR, net new ARR, committed ARR, contracted ARR, terminal ARR, or something else?

Defining those terms isn’t the point of this post, so see note [1] below if interested.

The point is that these ambiguous, unitless conversations happen all the time in enterprise software companies.  This isn’t a post about confusion; the vast majority of the time, everyone understands exactly what is being said.  What those implicit units really tell you about is culture.

Since there can be only one lead metric, every company, typically silently, decides what it is.  And what you pick says a lot about what you’re focused on.

  • New ARR means you’re focused on sales adding water to the SaaS leaky bucket — regardless of whether it’s from new or existing customers.
  • Net New ARR means you’re focused the change in water level in the SaaS leaky bucket — balancing new sales and churn — and presumably means you hold AEs accountable for both sales and renewals within their patch.
  • New Logo ARR means you’re focused on new ARR from new customers.  That is, you’re focused on “lands” [2].
  • Bookings means you’re focused on cash [3], bringing in dollars regardless of whether they’re from subscription or services, or potentially something else [4].
  • TCV, which became a four-letter word after management teams too often conflated it with ARR, is probably still best avoided in polite company.  Use RPO for a similar, if not identical, concept.
  • Committed ARR usually means somebody important is a fan of Bessemer metrics, and means the company is (as with Net New ARR) focused on new ARR net of actual and projected churn.
  • Terminal ARR means you’re focused on the final-year ARR of multi-year contracts, implying you sign contracts with built-in expansion, not a bad idea in an NDR-focused world, I might add.
  • Contracted ARR can be a synonym for either committed or terminal ARR, so I’d refer to the appropriate bullet above as the case may be.

While your choice of lead metric certainly affects the calculations of other metrics (a bookings CAC or a terminal-ARR CAC) that’s not today’s point, either.  Today’s point is simple.  What you pick says a lot about you and what you want your organization focused on.

  • What number do you celebrate at the all hands meeting?
  • What number do you tell employees is “the number” for the year?

For example, in my opinion:

  • A strong sales culture should focus on New ARR.  Yes, the CFO and CEO care about Ending ARR and thus Net New ARR, but the job of sales is to fill the bucket.  Someone else typically worries about what leaks out.
  • A shareholder value culture would focus on Ending ARR, and ergo Net New ARR.  After all, the company’s value is typically a linear function of its Ending ARR (with slope determined by growth).
  • A strong land-and-expand culture might focus on Terminal ARR, thinking, regardless of precisely when they come in, we have contracts that converge to a given total ARR value over time [5].
  • Conversely, a strong land and expand-through-usage culture might focus on New Logo ARR (i.e., “land”), especially if the downstream, usage-based expansion is seen as somewhat automatic [6].
  • A cash-focused culture (and I hope you’re bootstrapped) would focus on bookings.  Think:  we eat what we kill.

This isn’t about a right or wrong answer [7].  It’s about a choice for your organization, and one that likely changes as you scale.  It’s about mindfulness in making a subtle choice that actually makes a big statement about what you value.

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Notes
[1] For clarity’s sake, ARR is annual recurring revenue, the annual subscription value.  ACV is annual contract value which, while some treat as identical to ARR, others treat as first-year total contract value, i.e., first-year ARR plus year-one services.  Bookings is usually used as a proxy for cash and ergo would include any effects of multi-year prepayments, e.g., a two-year, prepaid, $100K/year ARR contract would be $200K in bookings.  TCV is total contract value which is typically the total (subscription) value of the contract, e.g., a 3-year deal with an ARR stream of $100K, $200K, $300K would have a $600K, regardless of when the cash payments occurred.  New ARR is new ARR from either new customers (often called New Logo ARR) or existing customers (often called Upsell ARR).  Net New ARR is new ARR minus churn ARR, e.g., if a regional manager starts with $10,000K in their region, adds $2,000K in new ARR and churns $500K, then net new ARR is $1,500K.  Committed ARR (as defined by Bessemer who defined the term) is “contracted, but not yet live ARR, plus live ARR netted against known projected ARR churn” (e.g., if a regional manager starts with $10,000K in their region, has signed contracts that start within an acceptable time period of $2,000K, takes $200K of expected churn in the period, and knows of $500K of new projected churn upcoming, then their ending committed is ARR is $11,500K.  (Why not $11,300K?  Because the $200K of expected churn was presumably already in the starting figure.)  Terminal ARR the ARR in the last year of the contract, e.g., say a contract has an ARR stream of $100K, $200K, $300K, the terminal ARR is $300K [1A].  Contracted ARR is for companies that have hybrid models (e.g., annual subscription plus usage fee) and includes only the contractually committed recurring revenues and not usage fees.

[1A] Note that it’s not yet clear to me how far Bessemer goes out with “contracted” ARR in their committed ARR definition, but I’m currently guessing they don’t mean three years.  Watch this space as I get clarification from them on this issue.

[2] In the sense of land-and-expand.

[3] On the assumptions that bookings is being used as a proxy for cash, which I recommend, but which is not always the case.

[4] e.g., non-recurring engineering; a bad thing to be focused on.

[5] Although if they all do so in different timeframes it becomes less meaningful.  Also unless the company has a track record of actually achieving the contractually committed growth figures, it becomes less credible.

[6] Which it never actually is in my experience, but it is a matter of degree.

[7] Though your investors will definitely like some of these choices better than others.

 

The Big Mistake You Might Be Making In Calculating Churn: Failing to Annualize Multi-Year ATR Churn Rates

Most of the thinking, definitions, and formulas regarding SaaS unit economics is based on assumptions that no longer reflect the reality of the enterprise SaaS environment.  For example, thinking in terms of MRR (monthly recurring revenue) is outdated because most enterprise SaaS companies run on annual contracts and thus we should think in terms of ARR (annual recurring revenue) instead.

Most enterprise SaaS companies today do a minimum one-year contract and many do either prepaid or non-prepaid multi-year contracts beyond that. In the case of prepaid multi-year contracts, metrics like the CAC payback period break (or at the very least, get difficult to interpret).  In the case of multi-year contracts, calculating churn correctly gets a lot more complicated – and most people aren’t even aware of the issue, let alone analyze it correctly.

If your company does multi-year contracts and you are not either sidestepping this issue (by using only ARR-pool-based rates) or correcting for it in your available-to-renew (ATR) churn calculations, keep reading.  You are possibly making a mistake and overstating your churn rate.

A Multi-Year Churn Example
Let’s demonstrate my point with an example where Company A does 100% one-year deals and Company B does 100% three-year deals.  For simplicity’s sake, we are going to ignore price increases and upsell [1].  We’re also not going to argue the merits of one- vs. three-year contracts; our focus is simply how to calculate churn in a world of them.

In the example below, you can see that Company A has an available-to-renew-based (ATR-based) [2] churn rate of 10%.  Company B has a 27% ATR-based churn rate.  So we can quickly conclude that Company A’s a winner, and Company B is a loser, right?

Capture

Not so fast.

At the start of year 4, a cohort of Company A customers is worth 72.9 units, the exact same as a cohort of Company B customers.  In fact, if you look at lifetime value (LTV), the Company B cohort is worth nearly 10% more than the Company A cohort [3].

my churn1

Wait a minute!  How can a company with 27% churn rate be “better” than a company with 10% churn rate?

It’s All About Exposure:  How Often are Deals Exposed to the Churn Rate?
One big benefit of multi-year deals is that they are exposed to the churn rate less frequently than one-year deals.  When you exclude the noise (e.g., upsell, discounts, and price increases), and look at churn solely as a decay function, you see that the N-year retention rate [4] is (1-churn rate)^N.  With 10% churn, your 2-year retention rate is (1-0.1)^2 = 0.9^2 = 0.81.  Your 3-year retention rate is (1-0.1)^3 = 0.9^3 = 0.729, or a retention rate of 73%, equivalent to a churn rate of 27%.

Simply put, churn compounds so exposing a contract to the churn rate less often is a good thing:  multi-year deals do this by excluding contracts from the ATR pool, typically for one or two years, before they come up for renewal [5].  This also means that you cannot validly compare churn rates on contracts with different duration.

This is huge.  As we have just shown, a 10% churn rate on one-year deals is equivalent to a 27% churn rate on three-year deals, but few people I know recognize this fact.

I can imagine two VCs talking:

“Yo, Trey.”

“Yes.”

“You’re not going to believe it, I saw a company today with a 27% churn rate.”

“No way.”

“Yep, and it crushed their LTV/CAC — it was only 1.6.”

“Melting ice cube.  Run away.”

“I did.”

Quite sad, in fact, because with a correct (annualized) churn rate of 10% and holding the other assumptions constant [6], the LTV/CAC jumps to healthy 4.4.  But any attempt to explain a 27% churn rate is as likely to be seen as a lame excuse for a bad number as it is to be seen as valid analysis.

Best Alternative Option:  Calculate Churn Rates off the Entire ARR Pool
I’m going to define the 27% figure as the nominal ATR-based churn rate.  It’s what you get when you take churn ARR / ATR in any given period.  I call it a nominal rate because it’s not annualized and it doesn’t reflect the varying distribution of 1Y, 2Y, and 3Y deals that are mixed in the ATR pool in any given quarter.  I call it nominal because you can’t validly compare it to anything [7].

Because correcting this to a more meaningful rate is going to involve a lot of brute force math, I’ll first advise you to do two things:

  • Banish any notion from your mind that ATR rates are somehow “more real” than churn rates calculated against the entire ARR pool [8].
  • Then use churn rates calculated against the entire ARR pool and sidestep the mess we’re about to enter in the next section [9] where we correct ATR-based churn rates.

In a world of mixed-duration contracts calculating churn rates off the entire ARR pool effectively auto-corrects for the inability of some contracts to churn.  I have always believed that if you were going to use the churn rate in a math function (e.g., as the discount rate in an NPV calculation) that you should only use churn rates calculated against the entire ARR pool because, in a mixed multi-year contract world, only some of the contracts come up for renewal in any given period.  In one sense you can think of some contracts as “excluded from the available-to-churn (ATC) pool.”  In another, you can think of them as auto-renewing.  Either way, it doesn’t make sense in a mixed pool to apply the churn rate of those contracts up for renewal against the entire pool which includes contracts that are not.

If you want to persist in using ATR-based churn rates, then we must correct for two problems:  we need to annualize the multi-year rates, and we then need to calculate ATR churn using an ATR-weighted average of the annualized churn rates by contract duration.

Turning Nominal ATR Churn into Effective, Annualized ATR Churn
Here’s how to turn nominal ATR churn into an effective, annualized ATR churn rate [10] [11]:

Step 1:  categorize your ATR and churn ARR by contract duration.  Calculate a 1Y churn rate and nominal 2Y and 3Y ATR churn rates.

Step 2:  annualize the nominal multi-year (N-year) churn rates by flipping to retention rates and taking the Nth root of the retention rate.  For example, our 27% 3-year churn rate is equivalent to a 73% 3-year retention rate, so take the cube root of 0.73 to get 0.9.  Then flip back to churn rates and get 10%.

Step 3:  do an ATR-weighted average of the 1Y and annualized 2Y and 3Y churn rates.  Say your ATR was 50% 1Y, 25% 2Y, and 25% 3Y contracts and your annualized churn rates were 10%, 12%, and 9%.  Then the weighted average would be (0.5*0.10) + (0.25*0.12) + (0.25*0.09) = 10.25%, as your annualized, effective ATR churn rate.

That’s it.  You’ve now produced an ATR churn rate that is comparable to a one in a company that does only 1-year contracts.

Conclusion
If nothing else, I hope I have convinced that you it is invalid to compare churn rates on contracts of different duration and ergo that is simpler to generally calculate churn rates off the entire ARR pool.  If, however, you still want to see ATR-based churn rates, then I hope I’ve convinced you that you must do the math and calculate ATR churn as a weighted average of annualized one-, two-, and three-year ATR churn rates.

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Notes
[1] In a world of zero upsell there is no difference between gross and net churn rates, thus I will simply say “churn rate” in this post.

[2] As soon as you start doing multi-year contracts then the entire ARR base is no longer up for renewal each year.  You therefore need a new concept, available to renew (ATR), which reflects only that ARR up for renewal in a given period.

[3] Thanks to its relatively flatter step-wise decay compared to Company A’s more linear decay.

[4] Retention rate = 1 – churn rate.

[5] If it helps, you can think of the ATR pool in a glass half-empty way as the available-to-churn pool.

[6] Assuming CAC ratio of 1.8 and subscription gross margins of 80%.

[7] Unless your company has a fixed distribution of deals by contract duration – e.g., a degenerate case being 100% 3Y deals.  For most companies the average contract duration in the inbound ATR pool is going to vary each quarter.  Ergo, you can’t even validly compare this rate to itself over time without factoring in the blending.

[8] Most people I meet seem to think ATR rates are more real than rates based on the entire ARR pool.  Sample conversation  — “what’s your churn rate?”  “6%.”  “Gross or net?  “Gross.”  “No, I mean your real churn rate – what gets churned divided only by what was up for renewal.”    The mistake here is in thinking that using ATR makes it comparable to a pure one-year churn rate – and it doesn’t.

[9] Gross churn = churn / starting period ARR.  Net churn = (gross churn – upsell) / starting period ARR.

[10] I thought about trying a less brute-force way using average contract duration (ACD) of the ATR pool, but decided against it because this method, while less elegant, is more systematic.

[11] Note that this method will still understate the LTV advantage of the more step-wise multi-year contract decay because it’s not integrating the area under the curve, but instead intersecting what’s left of the cohort after N years.  In our first example, the 1Y and 3Y cohorts both had 73 units of ARR, but because the multi-year cohort decayed more slowly it’s LTV to that point was about 10% higher.

Bookings vs. Billings in a SaaS Company

Financial analysts speak a lot about “billings” in a public SaaS companies, but in private VC-backed SaaS companies, you rarely hear discussion of this metric.  In this post, we’ll use a model of a private SaaS company (where we know all the internal metrics), to show how financial analysts use rules of thumb to estimate and/or impute internal SaaS metrics using external ones – and to see what can go wrong in that process.

For reference, here’s an example of sell-side financial analyst research on a public SaaS company that talks about billings [1].

saas1-zen

Let’s start with a quick model that builds up a SaaS company from scratch [1].  To simplify the model we assume all deals (both new and renewal) are for one year only and are booked on the last day of the quarter (so zero revenue is ever recognized in-quarter from a deal).  This also means next-quarter’s revenue is this-quarter’s ending annual recurring revenue (ARR) divided by 4.

saas13

Available to renew (ATR) is total subscription bookings (new and renewal) from four quarters prior.  Renewal bookings are ATR * (1 – churn rate).  The trickiest part of this model is the deferred revenue (DR) waterfall where we need to remember that the total deferred revenue balance is the sum of DR leftover from the current and the prior three quarters.

If you’re not convinced the model is working and/or want to play with it, you can download it, then see how things work by setting some drivers to boundary conditions (e.g., churn to 0%, QoQ sales growth to 0, or setting starting ARR to some fixed number [2]).

 The Fun Part:  Imputing Internal Metrics from External Ones

Now that we know what’s going on the inside, let’s look in from the outside [3]:

  • All public SaaS companies release subscription revenues [4]
  • All public SaaS companies release deferred revenues (i.e., on the balance sheet)
  • Few SaaS companies directly release ARR
  • Few SaaS companies release ATR churn rates, favoring cohort retention rates where upsell both masks and typically exceeds churn [5]

It wasn’t that long ago when a key reason for moving towards the SaaS business model was that SaaS smoothed revenues relative to the all-up-front, lumpy on-premises model.  If we could smooth out some of that volatility then we could present better software companies to Wall Street.  So the industry did [6], and the result?  Wall Street immediately sought a way to look through the smoothing and see what’s really going on in the inherently lumpy, backloaded world of enterprise software sales.

Enter billings, the best answer they could find to do this.  Billings is defined as revenue plus change in deferred revenue for a period.  Conceptually, when a SaaS order with a one-year prepayment term is signed, 100% of it goes to deferred revenue and is burned down 1/12th every month after that.  To make it simple, imagine a SaaS company sells nothing in a quarter:  revenue will burn down by 1/4th of starting deferred revenue [7] and the change in deferred revenue will equal revenue – thus revenue plus change in deferred revenue equals zero.  Now imagine the company took an order for $50K on the last day of the quarter.  Revenue from that order will be $0, change in deferred will be +$50K, implying new sales of $50K [8].

Eureka!  We can see inside the SaaS machine.  But we can’t.

Limitations of Billings as a SaaS Metric

If you want to know what investors really care about when it comes to SaaS metrics, ask the VC and PE folks who get to see everything and don’t have to impute outside-in.  They care about

Of those, public company investors only get a clear look at subscription gross margins and the customer acquisition cost (CAC) ratio.  So, looking outside-in, you can figure out how efficiency a company runs its SaaS service and how efficiently it acquires customers [9].

But you typically can’t get a handle on churn, so you can’t calculate LTV/CAC or CAC Payback Period.  Published cohort growth, however, can give you comfort around potential churn issues.

But you can’t get a precise handle on sales growth – and that’s a huge issue as sales growth is the number one driver of SaaS company valuation [10].  That’s where billings comes into play.  Billings isn’t perfect because it shows what I call “total subscription bookings” (new ARR bookings plus renewal bookings) [11], so we can’t tell the difference between a good sales and weak renewals quarter and a bad sales and a good renewals quarter.

Moreover, billings has two other key weaknesses as a metric:

  • Billings is dependent on prepaid contract duration
  • Companies can defer processing orders (e.g., during crunch time at quarter’s end, particularly if they are already at plan) thus making them invisible even from a billings perspective [12]

Let’s examine how billings depends on contract duration.  Imagine it’s the last day of new SaaS company’s first quarter.  The customer offers to pay the company:

  • 100 units for a prepaid one-year subscription
  • 200 units for a prepaid two-year subscription
  • 300 units for a prepaid three-year subscription

From an ARR perspective, each of the three possible structures represents 100 units of ARR [13].  However, from a deferred revenue perspective, they look like 100, 200, 300 units, respectively.  Worse yet, looking solely at deferred revenue at the end of the quarter, you can’t know if 300 units represents three 100-unit one-year prepay customers or a single 100-unit ARR customer who’s done a three-year prepay.

In fact, when I was at Salesforce we had the opposite thing happen.  Small and medium businesses were having a tough time in 2012 and many customers who’d historically renewed on one-year payment cycles started asking for bi-annual payments.  Lacking enough controls around a rarely-used payment option, a small wave of customers asked for and got these terms.  They were happy customers.  They were renewing their contracts, but from a deferred revenue perspective, suddenly someone who looked like 100 units of deferred revenue for an end-of-quarter renewal suddenly looked 50.  When Wall St. saw the resultant less-than-expected deferred revenue (and ergo less-than-expected billings), they assumed it meant slower new sales.  In fact, it meant easier payment terms on renewals – a misread on the business situation made possible by the limitations of the metric.

This issue only gets more complex when a company is enabling some varying mix of one through five year deals combined with partial up-front payments (e.g., a five-year contract with years 1-3 paid up front, but years 4 and 5 paid annually).  This starts to make it really hard to know what’s in deferred revenue and to try and use billings as a metric.

Let’s close with an excerpt from the Zuora S-1 on billings that echoes many of the points I’ve made above.

saas3

Notes

[1] Source:  William Blair, Inc., Zendesk Strong Start to 2018 by Bhavan Suri.

[2] Even though it’s not labelled as a driver and will break the renewals calculations, implicitly assuming all of it renews one year later (and is not spread over quarters in anyway).

[3] I’m not a financial analyst so I’m not the best person to declare which metrics are most typically released by public companies, so my data is somewhat anecdotal.  Since I do try to read interesting S-1s as they go by, I’m probably biased towards companies that have recently filed to go public.

[4] As distinct from services revenues.

[5] Sometimes, however, those rates are survivor biased.

[6] And it worked to the extent that from a valuation perspective, a dollar of SaaS revenue is equivalent to $2 to $4 of on-premises revenue.  Because it’s less volatile, SaaS revenue is more valuable than on-premises revenue.

[7] Provided no customers expire before the last day of the quarter

[8] Now imagine that order happens on some day other than the last day of the quarter.  Some piece, X, will be taken as revenue during the quarter and 50 – X will show up in deferred revenue.  So revenue plus change in deferred revenue = it’s baseline + X + 50 – X = baseline + 50.

[9] Though not with the same clarity VCs can see it — VCs can see composition of new ARR (upsell vs. new business) and sales customers (new customer acquisition vs. customer success) and thus can create more precise metrics.  For example, a company that has a solid overall CAC ratio may be revealed to have expensive new business acquisition costs offset by high, low-cost upsell.

[10] You can see subscription revenue growth, but that is smoothed/damped, and we want a faster way to get the equivalent of New ARR growth – what has sales done for us lately?

[11] It is useful from a cash forecasting perspective because all those subscription billings should be collectible within 30-60 days.

[12] Moving the deferred revenue impact of one or more orders from Q(n) to Q(n+1) in what we might have called “backlogging” back in the day.  While revenue is unaffected in the SaaS case, the DR picture looks different as a backlogged order won’t appear in DR until the end of Q(n+1) and then at 75, not 100, units.

[13] Normally, in real life, they would ask a small discount in return for the prepay, e.g., offer 190 for two years or 270 for three years.  I’ll ignore that for now to keep it simple.