Tag Archives: ARR

New ARR and CAC in Price-Ramped vs. Auto-Expanding Deals

In this post we’re going to look at the management accounting side of multi-year SaaS deals that grow in value over time.  I’ve been asked about this a few times lately, less because people value my accounting knowledge [1] but rather because people are curious about the CAC impact of such deals and how to compensate sales on them.

Say you sign a three-year deal with a customer that ramps in payment structure:  year 1 costs $1M, year 2 costs $2M, and year 3 costs $3M.  Let’s say in this example the customer is getting the exact same value in all 3 years (e.g., the right for 1,000 people to use a SaaS service) – so the payment structure is purely financial in nature and not related to customer value.

Equal Value:  The Price-Ramped Deal
The question on my mind is how do I look at this from a new ARR bookings, ending ARR, CAC, and sales compensation perspective?

GAAP rules define precisely how to take this from a GAAP revenue perspective – and with the adoption of ASC 606 even those rules are changing.  Let’s take an example from this KPMG data sheet on ASC 606 and SaaS.

(Price-Ramped) Year 1 Year 2 Year 3
Payment structure $1M $2M $3M
GAAP revenue $1M $2M $3M
GAAP unbilled deferred revenue $5M $3M $0M
ASC 606 revenue $2M $2M $2M
ASC 606 unbilled accounts receivable $1M $1M $0M
ASC 606 revenue backlog $4M $2M $0M

When I look at this is I see:

  • GAAP is being conservative and saying “no cash, no revenue.” For an early stage startup with no history of actually making these deals come true, that is not a bad position.  I like the concept of GAAP unbilled deferred revenue, but I don’t actually know anyone who tracks it, let alone discloses it.  Folks might release backlog in some sort of unbilled total contract value (TCV) metric which I suspect is similar [2].
  • ASC 606 is being aggressive and mathematical – “hey, if it’s a 3-year, $6M deal, then that’s $2M/year, let’s just smooth it all out [3]”. While “unbilled A/R” strikes me as (another) oxymoron I see why they need it and I do like the idea of ASC 606 revenue backlog [4].  I think the ASC 606 approach makes a lot of sense for more mature companies, which have a history of making these deals work [5].

Now, from an internal, management accounting perspective, what do you want to do with this deal in terms of new ARR bookings, ending ARR balance, CAC ratio, and sales comp?  We could say:

  • It’s $2M in new ARR today
  • Ergo calculate this quarter’s CAC with it counted as $2M
  • Add $2M in ending ARR
  • Pay the salesrep on a $2M ARR deal – and let our intelligently designed compensation plan protect us in terms of the delayed cash collections [6] [6A]

And I’d be OK with that treatment.  Moreover, it jibes with my definition of ARR which is:

End-of-quarter ARR / 4 = next-quarter subscription revenue, if nothing changes [7]

That’s because ASC 606 also flattens out the uneven cash flows into a flat revenue stream.

Now, personally, I don’t want to be financing my customers when I’m at a high-burn startup, so I’m going to try and avoid deals like this.  But if I have to do one, and we’re a mature enough business to be quite sure that years 2 and 3 are really coming, then I’m OK to treat it this way.  If I’m not sure we’ll get paid in years 2 and 3 – say it’s for a brand-new product that has never been used at this scale – then I might revert to the more GAAP-oriented, 1-2-3 approach, effectively treating the deal not as a price ramp, but as an auto-expander.

Increasing Value:  The Auto-Expanding Deal
Let’s say we have a different use-case.  We sell a SaaS platform and year 1 will be exclusively focused on developing a custom SaaS app, we will roll it to 500 users day 1 of year 2, and we will roll it to 500 more users on day 1 of year 3.  Further assume that the customer gets the same value from each of these phases and each phase continues until the end of the contract [8].  Also assume the customer expects that going forward, they will be paying $3M/year plus annual inflation adjustments.

Oy veh.  Now it’s much harder.  The ramped shape of the curve is not about financing at all.  It’s about the value received by the customer and the ramped shape of the payments perfectly reflects the ramped shape of the value received.  Moreover, not all application development projects succeed and if they fall behind on building the customized application they will likely delay the planned roll-outs and try to delay the payments along with them.  Moreover, since we’re an early-stage startup we don’t have enough history to know if they’ll succeed at all.

This needs to be seen as an auto-expanding deal:  $1M of new-business ARR in year 1, $1M of pre-sold upsell ARR in year 2, and another $1M of pre-sold upsell ARR in year 3.

When you celebrate it at the company kickoff you can say the customer has made a $6M commitment (total contract value, or TCV [9]) to the company and when you tier your customers for customer support/success purposes you might do so by TCV as opposed to ARR [10].  When you talk to investors you can say that $1M of next year’s and $1M of the subsequent year’s upsell is already under contract, ergo increasing your confidence in your three-year plan.  Or you could roll it all together into a statement about backlog or RPO [11].  That part’s relatively easy.

The hard part is figuring out sales compensation and CAC.  While your rep will surely argue this is a $2M ARR deal (if not a $3M ARR deal) and that he/she should be paid accordingly, hopefully you have an ARR-driven (and not a total bookings-driven) compensation plan and we’ve already established that we can’t see this as $2M or $3M ARR deal.  Not yet, at least.

This deal is a layer cake:  it’s a three-year $1M ARR deal [12] that has a one-year-delayed, two-year $1M ARR deal layered atop it, and a two-year-delayed, one-year $1M ARR deal atop that.  And that, in my opinion, is how you should pay it out [13].  Think:  “hey, if you wanted to get paid on a three-year $3M ARR deal, then you should have brought me one of those [14].”

Finally, what to do about the CAC?  One might argue that the full cost of sale for the eventual $3M in ARR was born up-front.  Another might argue that, no, plenty of account management will be required to ensure we actually get the pre-sold upsell.  The easiest and most consistent thing to do is to treat the ARR as we mentioned (1+1+1) and calculate the CAC, as you normally would, using the ARR that we put in the pool.

If you do a lot of these deals, then you would see a high new-business CAC ratio that is easily explained by stellar net-dollar expansion rates (173% if these were all you did).  Think:  “yes, we spend a lot up-front to get a customer, but after we hook them, they triple by year three.”

Personally, I think any investor would quickly understand (and fall in love with) those numbers.  If you disagree, then you could always calculate some supplemental CAC ratio designed to better amortize the cost of sale across the total ARR [14].  Since you can’t have your cake and eat it too, this will make the initial CAC look better but your upsell CAC and net-dollar expansion rates worse.

As always, I think the right answer is to stick with the fundamental metrics and let them tell the story, rather than invent new metrics or worse yet, new definitions for standard metrics, which can sow the seeds of complexity and potential distrust.

# # #

Notes

For more information on ASC 606 adoption, I suggest this podcast and this web page which outlines the five core principles.

[1] I am not an accountant.  I’m a former CEO and strategic marketer who’s pretty good at finance.

[2] And which I like better as “unbilled deferred revenue” is somewhat oxymoronical to me.  (Deferred revenue is revenue that you’ve billed, but you have not yet earned.)

[3] I know in some cases, e.g., prepaid, flat multi-year deals, ASC 606 can actually decide there is a material financing event and kind of separate that from the core deal.  While pure in spirit, it strikes me as complex and the last time I looked closely at it, it actually inflated revenue as opposed to deflating it.

[4] Which I define as all the future revenue over time if every contract played out until its end.

[5] Ergo, you have high empirical confidence that you are going to get all the revenue in the contract over time.

[6] Good comp plans pay only a portion of large commissions on receipt of the order and defer the balance until the collection of cash.  If you call this a $2M ARR deal, you do the comp math as if it’s $2M, but pay out the cash as dictated by the terms in your comp plan.  (That is, make it equivalent to a $2M ARR deal with crazy-delayed payment terms.)  You also retire $2M of quota, in terms of triggering accelerators and qualifying for club.

[6A] This then begs the question of how to comp the $1M in pre-sold upsell in Year 3.  As with any of the cases of pre-sold upsell in this post, my inclination is to pay the rep on it when we get the cash but not on the terms/rates of the Year 1 comp plan, but to “build it in” into their comp plan in year 3, either directly into the structure (which I don’t like because I want reps primarily focused on new ARR) or as a bonus on top of a normal OTE.  You get a reward for pre-sold upsell, but you need to stay here to get it and you don’t year 1 comp plan rates.

[7] That is, if all your contracts are signed on the last day of the quarter, and you don’t sign any new ones, or churn any existing ones until the last day of the quarter, and no one does a mid-quarter expansion, and you don’t have to worry about any effects due to delayed start dates, then the ARR balance on the last day of the quarter / 4 = next quarter’s subscription revenue.

[8] Development is not “over” and that value released – assume they continue to fully exploit all the development environments as they continue to build out their app.

[9] Note that TCV can be seen as an “evil” metric in SaaS and rightfully so when you try to pretend that TCV is ARR (e.g., calling a three-year $100K deal “a $300K deal,” kind of implying the $300K is ARR when it’s not).  In this usage, where you’re trying to express total commitment made to the company to emphasize the importance of the customer, I think it’s fine to talk about TCV – particularly because it also indirectly highlights the built-in upsell yet to come.

[10] Or perhaps some intelligent mix thereof.  In this case, I’d want to weight towards TCV because if they are not successful in year 1, then I fail to collect 5/6th of the deal.  While I’d never tell an investor this was a $6M ARR deal (because it’s not true), I’d happily tell my Customer Success team that this a $6M TCV customer who we better take care of.  (And yes, you should probably give equal care to a $2M ARR customer who buys on one-year contracts – in reality, either way, they’d both end up “Tier 1” and that should be all that matters.)

[11] Or you could of the ASC 606 revenue backlog and/or Remaining Performance Obligation (RPO) – and frankly, I’d have trouble distinguishing between the two at this point.  I think RPO includes deferred revenue whereas ASC 606 revenue backlog doesn’t.

[12] In the event your compensation plan offers a kicker for multi-year contracts.

[13] And while you should factor in the pre-committed upsell in setting the reps targets in years 2 and 3, you shouldn’t go so far as to give them a normal upsell target with the committed upsell atop it.  There is surely middle ground to be had.  My inclination is to give the rep a “normal” comp plan and build in collecting the $1M as a bonus on top — but, not of course at regular new ARR rates.  The alternative is to build (all or some of) it into the quota which will possibly demotivate the rep by raising targets and reducing rates, especially if you just pile $1M on top of a $1M quota.

[14] This ain’t one – e.g., it has $6M of TCV as opposed to $9M.

Important Subtleties in Calculating Quarterly, Annual, and ATR-based Churn Rates

This post won’t save your life, or your company.  But it might save you a few precious hours at 2:00 AM if you’re working on your company’s SaaS metrics and can’t foot your quarterly and annual churn rates while preparing a board or investor deck.

The generic issue is a lot of SaaS metrics gurus define metrics in a generic way using “periods” without paying attention to some subtleties that can arise in calculating these metrics for a quarter vs. a year.  The specific issue is, if you do what many people do, that your quarterly and annual churn rates won’t foot — i.e., the sum of your quarterly churn rates won’t equal your annual churn rate.

Here’s an example to show why.

saas churn subtle

If I asked you to calculate the annual churn rate in the above example, virtually everyone would get it correct.  You’d look at the rightmost column, see that 2018 started with 10,000 in ARR, see that there were 1,250 dollars of churn on the year, divide 1,250 by 10,000 and get 12.5%.  Simple, huh?

However, if I hid the last column, and then asked you to calculate quarterly churn rates, you might come up with churn rate 1, thinking churn rate = period churn / starting period ARR.  You might then multiply by 4 to annualize the quarterly rates and make them more meaningful.  Then, if I asked you to add an annual column, you’d sum the quarterly (non-annualized) rates for the annual churn and either average the annualized quarterly rates or simply gray-out the box as I did because it’s redundant [1].

You’d then pause, swear, and double-check the sheet for errors because the sum of your quarterly rates (10.2%) doesn’t equal your annual rate (12.5%).

What’s going on?  The trap is thinking churn rate = period churn / starting period ARR.

That works in a world of one-year contracts when you look at churn on an annual basis (every contract in the starting ARR base of 10,000 faces renewal at some point during the year), but it breaks on a quarterly basis.  Why?  Because starting ARR is increasing every quarter due to new sales that aren’t in the renewal base for the year.  This depresses your churn rates relative to churn rate 2, which defines quarterly churn as churn in the quarter divided by starting-year ARR.  When you use churn rate 2, the sum of the quarterly rates equals the annual rate, so you can mail out that board deck and go back to bed [2].

Available to Renew (ATR-based) Churn Rates

While we’re warmed up, let’s have some more fun.  If you’ve worked in enterprise software for more than a year, you’ll know that the 10,000 dollars of starting ARR is most certainly not distributed evenly across quarters:  enterprise software sales are almost always backloaded, ergo enterprise software renewals follow the same pattern.

So if we want more accurate [3] quarterly churn rates, shouldn’t we do the extra work, figure out how much ARR we have available to renew (ATR) in each quarter, and then measure churn rates on an ATR basis?  Why not!

Let’s first look at an example, that shows available to renew (ATR) split in a realistic, backloaded way across quarters [4].

ATR churn 1

In some sense, ATR churn rates are cleaner because you’re making fewer implicit assumptions:  here’s what was up for renewal and here’s what we got (or lost).  While ATR rates get complicated fast in a world of multi-year deals, for today, we’ll stay in a world of purely one-year contracts.

Even in that world, however, a potential footing issue emerges.  If I calculate annual ATR churn by looking at annual churn vs. starting ARR, I get the correct answer of 12.5%.  However, if I try to average my quarterly rates, I get a different answer of 13.7%, which I put in red because it’s incorrect.

Quiz:  what’s going on?

Hint:  let me show the ATR distributed in a crazy way to demonstrate the problem more clearly.

atr churn 2

The issue is you can’t get the annual rate by averaging the quarterly ATR rates because the ATR is not evenly distributed.  By using the crazy distribution above, you can see this more clearly because the (unweighted) average of the four quarterly rates is 53.6%, pulled way up by the two quarters with 100% churn rates.  The correct way to foot this is to instead use a weighted average, weighting on an ATR basis.  When you do that (supporting calculations in grey), the average then foots to the correct annual number.

# # #

Notes:

[1] The sum of the quarterly rates (A, B, C, D) will always equal the average of the annualized quarterly rates because (4A+4B+4C+4D)/4 = A+B+C+D.

[2] I won’t go so far as to say that churn rate 1 is “incorrect” while churn rate 2 is “correct.”  Churn rate 1 is simple and gives you what you asked for “period churn / starting period ARR.”  (You just need to realize that the your quarterly rates will only sum to your annual rate if you have zero new sales and ergo you should calculate the annual rate off the yearly churn and starting ARR.)  Churn rate 2 is somewhat more complicated.  If you live in a world of purely one-year contracts, I’d recommend churn rate 2.  But in a world of mixed one- and multi-year contracts, then lots of contracts are in starting period ARR aren’t in the renewal base for the year, so why would I exclude only some of them (i.e,. those signed in the year) as opposed to others.

[3] Dividing by the whole ARR base basically assumes that the base renews evenly across quarters.  Showing churn rates based on available-to-renew (ATR) is more accurate but becomes complicated quickly in a world of mixed, multi-year contracts of different duration (where you will need to annualize the rates on multi-year contracts and then blend the average to get a single, meaningful, annualized rate).  In this post, we’ll assume a world of exclusively one-year contracts, which sidesteps that issue.

[4] ATR is normally backloaded because enterprise sales are normally backloaded.  Here the linearity is 15%, 17.5%, 25%, 42.5% or a 32.5/67.5 split across the first vs. second half of the year (which is pretty backloaded even for enterprise software).

[5] The spreadsheet I used is available here if you want to play with it.

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.  Renew 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.

Theoretical TCV: A Necessary New SaaS Metric?

The more I hear about SaaS companies talking up big total contract value (TCV) figures, the more I worry about The Tightening, and the more I think we should create a new SaaS metric:  TTCV = theoretical total contract value.

TTCV = PCV + NPCV

Prepaid contract value (PCV) is the prepaid portion and NPCV is the non-prepaid portion of the subscription in multi-year SaaS agreements.  We could then calculate your corporate hype ratio (CHR) with TTCV/ARR, the amount by which you overstate ARR by talking about TTCV.

I make the suggestion tongue-in-cheek, but do so to make real point.

I am not against multi-year SaaS contracts.  I am not against prepaid SaaS contracts.  In high-consideration enterprise SaaS categories (e.g., EPM), buyers have spent months in thorough evaluations validating that the software can do the job.  Thus, it can make good sense for both buyer and seller to enter into a multi-year agreement.  The seller can shield contracts from annual churn risk and the buyer can get a modest discount for the contractual commitment to renew (e.g., shielding from annual prices increases) or a bigger discount for that plus a prepayment.

But it’s all about degree.  A three-year  prepaid contract often makes sense.  But, for example, an eight-year agreement with two-years prepaid (8/2) often doesn’t.  Particularly if the seller is a startup and not well established.  Why?

Let’s pretend the 8/2 deal was written by an established leader like Salesforce.  In that case:

  • There is a very high likelihood the software will work.
  • If there are problems, Salesforce has major resources to put behind making it work.
  • If the customer is nevertheless unhappy, Salesforce will presumably not be a legal lightweight and enforce the payment provisions of the contract.

Now, let’s pretend that 8/2 deal was written by a wannacorn, a SaaS vendor who raised a lot of money, made big promises in so doing, and is way out over its skis in terms of commitments.

  • There is a lower likelihood the software will work, particularly if working means building a custom application, as opposed to simply customizing an off-the-shelf app.
  • If there are problems, the wannacorn has far fewer available resources to help drive success — particularly if they are spread thin already.
  • If the customer is unhappy they are much less likely to pay because they will be far more willing to say “sue me” to a high-burn startup than to an established leader.

So while that 8/2 deal might be a reasonable piece of business for an established leader, it looks quite different from the perspective of the startup:  three-fourths of its value may well end up noncollectable — and ergo theoretical.  That’s why startups should neither make those deals (because they are offering something for an effectively fictitious commitment) nor talk them up (because large portions of the value may never be realized).

Yet many do.  And somehow — at least before The Tightening — some investors seem to buy the hype.  Remember the corporate hype ratio:  TTCV / ARR.