Category Archives: SaaS

Aligned to Achieve: A B2B Marketing Classic

Tracy Eiler and Andrea Austin’s Aligned to Achieve came out today and it’s a great book on an important and all too often overlooked topic:  how to align sales and marketing.

I’m adding it to my modern SaaS executive must-read book list, which is now:

So, what do I like about Aligned to Achieve?

The book puts a dead moose issue squarely on the table:  sales and marketing are not aligned in too many organizations.  The book does a great job of showing some examples of what misalignment looks like.  My favorites were the one where the sales VP wouldn’t shake the new CMO’s hand (“you’ll be gone soon, no need to get to know you”) and the one where sales waived off marketing from touching any opportunities once they got in the pipeline.  Ouch.  #TrustFail.

Aligned to Achieve makes great statements like this one:  “We believe that pipeline is absolutely the most important metric for sales and marketing alignment, and that’s a major cultural shift for most companies.”  Boom, nothing more to say about that.

The book includes fun charts like the one below.  I’ve always loved tension-surveys where you ask two sides for a view on the same issue and show the gap – and this gap’s a doozy.

sm gap

Aligned to Achieve includes the word “transparency” twenty times.  Transparency is required in the culture, in collaboration, in definitions, in planning, in the reasons for plans, in process and metrics, in data, in assessing results, in engaging customers, and in objectives and performance against them.  Communication is the lubricant in the sales/marketing relationship and transparency the key ingredient.

The book includes a nice chapter on the leadership traits required to work in the aligned environment:  collaborative, transparent, analytical, tech savvy, customer focused, and inspirational.  Having been a CMO fifteen years ago, I’d say that transparent, analytical, and tech savvy and now more important than ever before.

Aligned to Achieve includes a derivative of my favorite mantra (marketing exists to make sales easier) in the form of:

Sales can’t do it alone and marketing exists to make sales easier

The back half of that mantra (which I borrowed from CTP co-founder Chris Greendale) served me well in my combined 12 years as a CMO.  I love the insertion of the front half, which is now more true than ever:  sales has never been more codependent with marketing.

The book includes a fun, practical suggestion to have a bi-monthly “smarketing” meeting which brings sales and marketing together to discuss:

  • The rolling six-week marketing campaign calendar
  • Detailed review of the most recently completed campaigns
  • Update on immediately pending campaigns
  • Bigger picture items (e.g., upcoming events that impact sales and/or marketing)
  • Open discussion and brainstorming to cover challenges and process hiccups

Such meetings are a great idea.

Back in the day when Tracy and I worked together at Business Objects, I always loved Tracy’s habit of “crashing” meetings.  She was so committed to sales and marketing alignment – even back then – that if sales were having an important meeting, invited or not, she’d just show up.  (It always reminded me of the Woody Allen quote, 80% of success is showing up.)  In her aligned organization today, the CEO makes sure she doesn’t have to do that, but by hook or by crook the sales/marketing discussion must happen.

Aligned to Achieve has a nice discussion of the good old sales velocity model which, like my Four Levers of SaaS, is a good way to think about and simplify a business and the levers that drive it.

Unsurprisingly, for a book co-authored by the CMO of a company that sells market data and insights, Aligned to Achieve includes a healthy chapter on the importance of data, including a marketing-adapted version of the DIKW pyramid featuring data, insights, and connections as the three layers.  The nice part is that the chapter remains objective and factual – it doesn’t devolve into an infomercial by any means.

The book moves on to discuss the CIO’s role in a sales/marketing-aligned organization and provides a chapter reviewing the results of a survey of 1000 sales and marketing professionals on alignment, uncovering common sources of misalignment and some of the practices used by sales/marketing alignment leaders.

Aligned to Achieve ends with a series of 7 alignment-related predictions which I won’t scoop here.  I will say that #4 (“academia catches up”) and #6 (“account-based everything is a top priority”) are my two favorites.

Congratulations to my long-time friend and colleague Tracy Eiler on co-authoring the book and to her colleague Andrea Austin.

The Four Levers of SaaS

There are a lot of SaaS posts out there with some pretty fancy math in them.  I’m a math guy, so I like to geek on SaaS metrics myself.  But, in the heat of battle running a SaaS company, sometimes you just need to keep it simple.

Here’s the picture I keep on my wall to help me do that.

It reminds me that new ARR in any given period is the product of four levers.

  • The MQL to stage 2 opportunity conversion rate (MTS2CR), the rate at which MQLs convert to stage 2, or sales-accepted, opportunities.  Typically they pass through a stage 1 phase first when a sales development rep (SDR) believes there is a real opportunity, but a salesperson has not yet agreed.
  • The stage 2 to close rate (S2TCR), the rate at which stage 2 opportunities close into deals, and avoid being lost to a competitor or derailed (e.g., having the evaluation project cancelled).
  • The annual recurring revenue average sales price (ARR ASP), the average deal size, expressed in ARR.

That’s it.  Those four levers will predict your quarterly new ARR every time.

Aside:  before diving into each of the four levers, let me note that sales velocity is omitted from this model.  That keeps it simple, but it does overlook a potentially important lever.  So if you think you have a sales velocity (i.e., sales cycle length) problem, go look at a different model that includes this lever and suggests ways to decrease it.

So now that we have identified the four levers, let’s focus on what we can do about them in order to increase our quarterly new ARR.

Marketing Qualified Leads (MQLs)

Getting MQLs is the domain of marketing, which should be constantly measuring the cost effectiveness of various marketing programs in terms of generating MQLs (cost/MQL).  This isn’t easy because most leads will require numerous touches over time in order to graduate to MQL status, but marketing needs to stay atop that complexity (e.g., by assigning credits to various programs as MQL-threshold points accumulate).

The best marketers understand the demand is variable and have designed their programs mix so they can scale spending quickly in response to increased needs.  Nothing is worse than an MQL shortage and a marketing department that’s not ready to spend incremental money to address it.

The general rule is to constantly A/B test your programs and nurture streams and do more of what’s working and less of what isn’t.

MQL to Stage 2 Opportunity Conversion Rate

Increasing the MQL to stage 2 opportunity conversion rate (MTS2CR) requires either generating better MQLs or doing a better job handling them so that they convert into stage 2 opportunities.

Generating better MQLs can be accomplished by analyzing past programs to determine which generated the best-converting MQLs and increasing them, putting a higher gate on what you pass over to sales (using predictive or behavioral scoring), or using buyer personas to optimize what you say to buyers, when, and through which channels.

Do a better job handling your existing MQLs comes down ensuring your operational processes work and you don’t let leads fall between the cracks.  Basic activity and aging reports are a start.  Establishing a formal service-level agreement between sales and marketing is a common next step.

Moving up a level and checking that your whole process fits well with the customer’s buying journey is also key.  While each step of your process might individually make sense, when assembled the process may not — e.g., are you irritating customers by triple-qualifying them with an SDR, a salesrep, and a solution consultant each doing basic discovery?

The Stage 2 to Close Rate

Once created, one of three things can happen to a stage 2 opportunity:  you can win it, you can lose it, or it can derail (i.e., anything else, such as project cancellation or “slips” to the distant future).

Increasing your win rate can be accomplished through better product positioning, sales tools, and sales training, improved competitive intelligence, improved buzz/aura, improved case studies and customer references, and better pricing and discounting strategy.  That’s not to mention more strategic approaches via improved sales methodology and process or product improvements, in terms of functionality, non-functional requirements, and product design.

Decreasing your loss rate can be accomplished through better up-front sales qualification, better sales tools and training, improved competitive strategy and tactics, and better pricing and discounting.  Improved sales management can also play a key role in catching in-trouble deals early and escalating to get the necessary resources deployed to win.

Reducing your derail rate is hard because project slips or cancellations seem mostly out of your control.  What’s the best way to reduce your derail rate?  Focus on velocity — take deals off the table before the company has a chance to prioritize another project, do a reorganization, or hire a new executive that kills it.  The longer a deal hangs around, the more likely something bad happens to it.  As the adage goes, time kills all deals.


The easiest way to increase ARR ASP is to not shrink it through last-minute discounting.  Adopt a formal discount policy with approvals so that, in the words of one famous sales leader, “your rep is more afraid of his/her sales manager than the customer” when it comes to speaking about discounts.

Selling value and product differentiation are two other discount reduction strategies.  The more customers see real value and a concrete return for their business the less they will focus on price.  Additionally, the more they see your offering as unique, the less price pressure you will face from the competition.  Conversely, the more they see your product as a cost and your company as one of several suppliers from whom they can buy the same capabilities, the more discount pressure you will face.

Up-selling to a higher edition or cross selling (“fries with your burger?”) are both ways to increase your ASP as well.  Just be careful to avoid customers feeling nickled and dimed in the process.

For SaaS businesses, remember that multi-year deals typically do not help your ARR ASP (though, if prepaid, they do help with year-one cash).  In fact, it’s usually the opposite — a small ARR discount is typically traded for the multi-year commitment.  My general rule of thumb is to offer a multi-year discount that’s less than your churn rate and everybody wins.


Hopefully this framework will make it easier for you to diagnose and act upon the problems that can impede achieving your company’s new ARR goals.  Always remember that any new ARR problem can be broken down into some combination of an MQL problem, an MQL to stage 2 conversion rate problem, a stage 2 to close rate problem, or an average sales price problem.  By focusing on these four levers, you should be able to optimize the productivity of your SaaS sales model.



CAC Payback Period:  The Most Misunderstood SaaS Metric

The single most misunderstood software-as-a-service (SaaS) metric I’ve encountered is the CAC Payback Period (CPP), a compound metric that is generally defined as the months of contribution margin to pay back the cost of acquiring a customer.   Bessemer defines the CPP as:

bess cac

I quibble with some of the Bessemerisms in the definition.  For example, (1) most enterprise SaaS companies should use annual recurring revenue (ARR), not monthly recurring revenue (MRR), because most enterprise companies are doing annual, not monthly, contracts, (2) the “committed” MRR concept is an overreach because it includes “anticipated” churn which is basically impossible to measure and often unknown, and (3) I don’t know why they use the prior period for both S&M costs and new ARR – almost everybody else uses prior-period S&M divided by current-period ARR in customer acquisition cost (CAC) calculations on the theory that last quarter’s S&M generated this quarter’s new ARR.

Switching to ARR nomenclature, and with a quick sleight of mathematical hand for simplification, I define the CAC Payback Period (CPP) as follows:

kell cac

Let’s run some numbers.

  • If your company has a CAC ratio of 1.5 and subscription gross margins of 75%, then your CPP = 24 months.
  • If your company has a CAC ratio of 1.2 and subscription gross margins of 80%, then your CPP = 18 months.
  • If you company has a CAC ratio of 0.8 and subscription gross margins of 80%, then your CPP = 12 months.

All seems pretty simple, right?  Not so fast.  There are two things that constantly confound people when looking at CAC Payback Period (CPP).

  • They forget payback metrics are risk metrics, not return metrics
  • They fail to correctly interpret the impact of annual or multi-year contracts

Payback Metrics are for Risk, Not Return

Quick, basic MBA question:  you have two projects, both require an investment of 100 units, and you have only 100 units to invest.  Which do you pick?

  • Project A: which has a payback period of 12 months
  • Project B: which has a payback period of 6 months

Quick, which do you pick?  Well, project B.  Duh.  But wait — now I tell you this:

  • Project A has a net present value (NPV) of 500 units
  • Project B has an NPV of 110 units

Well, don’t you feel silly for picking project B?

Payback is all about how long your money is committed (so it can’t be used for other projects) and at risk (meaning you might not get it back).  Payback doesn’t tell you anything about return.  In capital budgeting, NPV tells you about return.  In a SaaS business, customer lifetime value (LTV) tells you about return.

There are situations where it makes a lot of sense to look at CPP.  For example, if you’re running a monthly SaaS service with a high churn rate then you need to look closely how long you’re putting your money at risk because there is a very real chance you won’t recoup your CAC investment, let alone get any return on it.  Consider a monthly SaaS company with a $3500 customer acquisition cost, subscription gross margin of 70%, a monthly fee of $150, and 3% monthly churn.  I’ll calculate the ratios and examine the CAC recovery of a 100 customer cohort.

saas fail

While the CPP formula outputs a long 33.3 month CAC Payback Period, reality is far, far worse.  One problem with the CPP formula is that it does not factor in churn and how exposed a cohort is to it — the more chances customers have to not renew during the payback period, the more you need to consider the possibility of non-renewal in your math [1].  In this example, when you properly account for churn, you still have $6 worth of CAC to recover after 30 years!  You literally never get back your CAC.

Soapbox:  this is another case where using a model is infinitely preferable to back-of-the-envelope (BOTE) analysis using SaaS metrics.  If you want to understand the financials of a SaaS company, then build a driver-based model and vary the drivers.  In this case and many others, BOTE analysis fails due to subtle complexity, whereas a well-built model will always produce correct answers, even if they are counter-intuitive.

Such cases aside, the real problem with being too focused on CAC Payback Period is that CPP is a risk metric that tells you nothing about returns.  Companies are in business to get returns, not simply to minimize risk, so to properly analyze a SaaS business we need to look at both.

The Impact of Annual and Multi-Year Prepaid Contracts on CAC Payback Period

The CPP formula outputs a payback period in months, but most enterprise SaaS businesses today run on an annual rhythm.  Despite pricing that is sometimes still stated per-user, per-month, SaaS companies realized years ago that enterprise customers preferred annual contracts and actually disliked monthly invoicing.  Just as MRR is a bit of a relic from the old SaaS days, so is a CAC Payback Period stated in months.

In a one-hundred-percent annual prepaid contract world, the CPP formula should output in multiples of 12, rounding up for all values greater than 12.  For example, if a company’s CAC Payback Period is notionally 13 months, in reality it is 24 months because the leftover 1/13 of the cost isn’t collected until the a customer’s second payment at month 24.  (And that’s only if the customer chooses to renew — see above discussion of churn.)

In an annual prepaid world, if your CAC Payback Period is less than or equal to 12 months, then it should be rounded down to one day because you are invoicing the entire year up-front and at-once.  Even if the formula says the CPP is notionally 12.0 months, in an annual prepaid world your CAC investment money is at risk for just one day.

So, wait a minute.  What is the actual CAC Payback Period in this case?  12.0 months or 1 day?  It’s 1 day.

Anyone who argues 12.0 months is forgetting the point of the metric.  Payback periods are risk metrics and measured by the amount of time it takes to get your investment back [2].  If you want to look at S&M efficiency, look at the CAC ratio.  If you want to know about the efficiency of running the SaaS service, look at subscription gross margins.  If you want to talk about lifetime value, then look at LTV/CAC.  CAC Payback Period is a risk metric that measures how long your CAC investment is “on the table” before getting paid back.  In this instance the 12 months generated by the standard formula is incorrect because the formula misses the prepayment and the correct answer is 1 day.

A lot of very smart people get stuck here.  They say, “yes, sure, it’s 1 day – but really, it’s not.  It’s 12 months.”  No.  It’s 1 day.

If you want to look at something other than payback, then pick another metric.  But the CPP is 1 day.  You asked how long it takes for the company to recoup the money it spends to acquire a customer.  For CPPs less than or equal to 12 in a one-hundred percent annual prepaid world, the answer is one day.

It gets harder.  Imagine a company that sells in a sticky category (e.g., where typical lifetimes may be 10 years) and thus is a high-consideration purchase where prospective customers do deep evaluations before making a decision (e.g., ERP).  As a result of all that homework, customers are happy to sign long contracts and thus the company does only 3-year prepaid contracts.  Now, let’s look at CAC Payback Period.  Adapting our rules above, any output from the formula greater than 36 months should be rounded up in multiples of 36 months and, similarly, any output less than or equal to 36 months should be rounded down to 1 day.

Here we go again.  Say the CAC Payback Period formula outputs 33 months.  Is the real CPP 33 months or 1 day?  Same argument.  It’s 1 day.  But the formula outputs 33 months.  Yes, but the CAC recovery time is 1 day.  If you want to look at something else, then pick another metric.

It gets even harder.  Now imagine a company that does half 1-year deals and half 3-year deals (on an ARR-weighted basis).  Let’s assume it has a CAC ratio of 1.5, 75% subscription gross margins, and thus a notional CAC Payback Period of 24 months.  Let’s see what really happens using a model:


Using this model, you can see that the actual CAC Payback Period is 1 day. Why?  We need to recoup $1.5M in CAC.  On day 1 we invoice $2.0M, resulting in $1.5M in contribution margin, and thus leaving $0 in CAC that needs to be recovered.

While I have not yet devised general rounding rules for this situation, the model again demonstrates the key point – that the mix of 1-year and 3-year payment structure confounds the CPP formula resulting in a notional CPP of 24 months, when in reality it is again 1 day.  If you want to make rounding rules beware the temptation to treat the average contract duration (ACD) as a rounding multiple because it’s incorrect — while the ACD is 2 years in the above example, not a single customer is paying you at two-year intervals:  half are paying you every year while half are paying you every three.  That complexity, combined with the reality that the mix is pretty unlikely to be 50/50, suggests it’s just easier to use a model than devise a generalized rounding formula.

But pulling back up, let’s make sure we drive the key point home.  The CAC Payback Period is the single most often misunderstood SaaS metric because people forget that payback metrics are about risk, not return, and because the basic formulas – like those for many SaaS metrics – assume a monthly model that simply does not apply in today’s enterprise SaaS world, and fail to handle common cases like annual or multi-year prepaid contracts.

# # #


[1] This is a huge omission for a metric that was defined in terms of MRR and which thus assumes a monthly business model.  As the example shows, the formula (which fails to account for churn) outputs a CAC payback of 33 months, but in reality it’s never.  Quite a difference!

[2] If I wanted to be even more rigorous, I would argue that you should not include subscription gross margin in the calculation of CAC Payback Period.  If your CAC ratio is 1.0 and you do annual prepaid contracts, then you immediately recoup 100% of your CAC investment on day 1.  Yes, a new customer comes with a future liability attached (you need to bear the costs of running the service for them for one year), but if you’re looking at a payback metric that shouldn’t matter.  You got your money back.  Yes, going forward, you need to spend about 30% (a typical subscription COGS figure) of that money over the next year to pay for operating the service, but you got your money back in one day.  Payback is 1 day, not 1/0.7 = 17 months as the formula calculates.

Book Review:  From Impossible to Inevitable

This post reviews Aaron Ross and Jason Lemkin’s new book, From Impossible to Inevitable, which is being launched at the SaaStr Conference this week.  The book is a sequel of sorts to Ross’s first book, Predictable Revenue, published in 2011, and which was loaded with great ideas about how to build out your sales machine.

From Impossible to Inevitable is built around what they call The Seven Ingredients of Hypergrowth:

  1. Nail a niche, which is about defining your focus and ensuring you are ready to grow. (Or, as some say “nail it, then scale it.)  Far too many companies try to scale it without first nailing it, and that typically results in frustration and wasted capital.
  2. Create predictable pipeline, which about “seeds” (using existing successful customers), “nets” (classical inbound marketing programs), and “spears” (targeted outbound prospecting) campaigns to create the opportunities sales needs to drive growth.
  3. Make sales scalable, which argues convincingly that specialization is the key to scalable sales. Separate these four functions into discrete jobs:  inbound lead handing, outbound prospecting, selling (i.e., closing new business), and post-sales roles (e.g., customer success manager).  In this section they include a nice headcount analysis of a typical 100-person SaaS company.
  4. Double your deal size, which discusses your customer mix and how to build a balanced business built off a run-rate business of average deals topped up with a lumpier enterprise business of larger deals, along with specific tactics for increasing deal sizes.
  5. Do the time, which provides a nice reality check on just how long it takes to create a $100M ARR SaaS company (e.g., in a great case, 8 years, and often longer), along with the wise expectations management that somewhere along the way you’ll encounter a “Year of Hell.”
  6. Embrace employee ownership, which reminds founders and executives that employees are “renting, not owning, their jobs” and how to treat them accordingly so they can act more like owners than renters.
  7. Define your destiny, which concludes the book with thoughts for employees on how to take responsibility for managing their careers and maximizing the opportunities in front of them.

The book is chock full of practice advice and real-world stories.  What it’s not is theoretical.  If Crossing the Chasm offered a new way of thinking about product lifecycle strategy that earned it a place on the top shelf of the strategy bookcase, From Impossible to Inevitable is a cookbook that you keep in the middle of the kitchen prep table, with Post-It’s sticking out the pages and oil stains on the cover.  This is not a book that offers one big idea with a handful of chapters on how to apply it.  It’s a book full of recipes and tactics for how to improve each piece of your go-to-market machine.

This book — like Predictable Revenue, The Lean Startup, Zero to One, and SalesHood — belongs on your startup executive’s bookshelf.  Read it!  And keep up with Jason’s and Aaron’s great tweetstreams and the awesome SaaStr blog.

The SaaSacre Part II: Time for the Rebound?

In response to my post, SaaS Stocks:  How Much Punishment is in Store, a few of my banker friends have sent me over some charts and data which shine more light on the points I was trying to make about SaaS forward twelve month (FTM) enterprise value (EV) revenue multiples, normal trading ranges, and the apparent “floor” value for this metric.

This chart comes from the folks at Pacific Crest:

paccrest saas multiples

In English, it says that SaaS stocks are trading at an EV/FTM revenue multiple of 3.2, 35% below the average since 2005, and down 66% since the peak in Jan 2014.  It also shows the apparent floor at around 2.0x, which they dipped below only once in the past decade during the crisis of 2008.

This is not to say that Wall Street doesn’t over-correct, that a new floor value could not be established, or that cuts in revenue forecasts due to macroeconomics couldn’t cause significant valuation drops at a constant, in-range EV/FTM ratio.

It is to say that, given historical norms, if you believe in reversion to the mean and that FTM revenue forecasts will not be materially reduced, that we are in “buying opportunity” territory.   The question is then which sentiment will win out in the market.

  • Fear of a potential 30% drop before hitting the floor value, breaking through the floor value, or cuts in FTM revenue forecasts.
  • Greed and the opportunity to get a nearly 50% return in a simple reversion to the mean.

My quick guess is more fear short-term, followed by some healthy greed winning out after that.

Might we see a temporary dead cat bounce before a further sell-off?  Maybe.  Should we remember the Wall Street maxim about catching falling knives?  Yes.

But at the same time remember that mixed in among the inflated, private, unicorn wreckage, that we have some high-quality, public, recurring-revenue companies trading at what’s starting to approach decades-low multiples.  At some point, that will become a real opportunity.

See my FAQ for disclaimers and more background information.  I am not a financial analyst and I do not make stock recommendations.  I am simply a CEO sharing his experience and opinion which, as my wife will happily attest, is often incorrect.

SaaS Stocks: How Much Punishment is in Store?

The stock market feels like Nordstrom Rack these days:

  • Salesforce at $56/share
  • Tableau at $38/share
  • ServiceNow at $47/share
  • Zendesk at $15/share
  • Workday at $49/share
  • NetSuite at $54/share

Redpoint’s Tomasz Tunguz points out that SaaS forward revenue multiples have been more than cut in half, dropping from 7.7 in January 2014 to 3.3 today.

So where’s it all going to end?  Much as the P/E  of the S&P 500 tends to converge to around 15 over time, I have always felt that quality on-premises enterprise software companies converged to a valuation of 2.0 to 3.0x revenues and there was a floor around 1.0x revenues.  That’s not to say that Wall St doesn’t over-correct and you’d never see on-premises valuations less than 1.0x revenues — but that should be rare and anything less 2.0x could indicate a good buying opportunity and anything near 1.0x — for a healthy company — could mean a real bottom-fishing opportunity.

The question is what are the equivalent numbers for SaaS companies?  I think the norm range is 3.0 to 5.0x revenues and I think the floor is around 2.0x.  That would suggest that in a bad case — despite all the recent carnage — there’s still 30% downside potential in SaaS stocks.  And that’s not including the case where you think we’re in a macroeconomic situation such that the forward four-quarter revenue estimates drop, which would mean more downside potential on top of that.

But I do think at 3.3x, we are now near the bottom-end of the norm range so the question is which sentiment is going to win out in the market:

  • Fear of the 30%+ remaining downside potential in SaaS stocks
  • Greed to capture the potential 50%+ return of a SaaS bounce back to the mid/high-end of the range.

I’d speculate on more fear in the short-term followed by some nice greed in the mid-term.

See my subsequent post, the SaaSacre part II for more in this vein.

# # #

See my disclaimers:  I am not a financial analyst and I do not make recommendations on specific stocks.  The purpose of this post was to share my non-scientific rule of thumb for SaaS trading ranges and do some analysis based on that.

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.


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.