Category Archives: Metrics

The Rule of 40 — Down, But Not Out!

Neeraj Agrawal and Logan Bartlett of Battery Ventures recently published the 2019 version of its outstanding annual software round-up report.  I highly recommend this report — it’s 78-pages chock full of great data about topics like:

  • Why Battery is long software overall
  • The four eras of software evolution
  • The five forces driving software’s accelerating growth
  • Key trends in 2018, including setting records in three areas:  (1) public company revenue multiples, (2) IPO volume (by over 2x), and (3) M&A volume (by over 2x).
  • Key trends from their 2017 report that are still alive, well, and driving software businesses.

But, most of all, it has some great charts on the Rule of 40 [1] that I want to present and discuss here.  Before doing that, I must note that I drank today’s morning coffee reading Alex Clayton’s CloudStrike IPO breakdown, a great post about a cloud security company with absolutely stunning growth at scale — 121% growth to $312M in Ending ARR in FY19.  And, despite my headline, well in compliance with the Rule of 40.  110% revenue growth + -26% free cashflow margin = 84%, one of the highest Rule of 40 scores that I’ve ever seen [2].  Keep an eye on this company, I expect it should have a strong IPO [3].

However, finding one superstar neither proves nor disproves the rule.  Let’s turn to the Battery data to do that.

When discussing the Rule of 40, most financial analysts make one of two plots.

  • They do a scatter plot with revenue growth on the X-axis and FCF margin on the Y-axis.  The Rule of 40 then becomes a line that separates the chart into two zones (compliant and non-compliant).  Note that a minority of public companies actually comply suggesting the rule of 40 is a pretty high bar [4].
  • Or, more interestingly, they do a linear regression of Rule of 40 score vs. enterprise-value/revenue (EV) multiple.  This puts focus on the question:  what’s the relationship between Rule of 40 score and company value? [5]

But that thing has always bugged me is that nobody does the linear regression against both the Rule of 40 score and revenue growth.  Nobody, until Battery.  Here’s what it shows.

First, let’s look at the classic Rule of 40 regression.  Recall that R-squared is a statistical measure that explains the dependence of the dependent variable (in this case, EV multiple) on the independent variable (Rule of 40 score).  Here you can see that about 58% of the variation in enterprise value multiple is explained by Rule of 40 score.  You can intuit that by looking at the dots relative to the line — while there is clearly some linear correlation between the data, it’s a long way from perfect (i.e., lots of dots are pretty far from the line).  [6]

rule of 40-4

Now, the fun part.  Let’s see the same regression against revenue growth alone.  R-squared here is 51%.  So the explanatory power of the Rule of 40 is only 7% higher than revenue growth alone.  Probably still worth looking at, but it sure gets a lot of PR for explaining only an incremental 7%.  It could be worse, I suppose.  Rule of 40 could have a lower R-squared than revenue growth alone — in fact, it did back in 2008 and in 2012.

rule of 40-3

In the vein, for some real fun let’s look at how this relationship has changed over time.  The first thing you’ll notice is that pre-2012 both last twelve month (LTM) revenue growth and the Rule of 40 had far weaker explanatory power, I suspect because profitability played a more important role in the equation.  In 2012, the explanatory power of both metrics doubled.  In 2015 and 2016 the Rule of 40 explained nearly 20% more than revenue growth alone.  In 2017 and 2018, however, that’s dropped to 7 to 8%.

rule of 40-2

I still think the Rule of 40 is a nice way to think about balancing growth vs. profit and Rule of 40 compliant companies still command a disproportionate share of market value.  But remember, its explanatory power has dropped in recent years and, if you’re running an early or mid-stage startup, there is very little comparative data available on the Rule of 40 scores of today’s giants when they were at early- or mid-stage scale.  That’s why I think early- and mid-stage startups need to think about the Rule of 40 in terms of glideslope planning.

Thanks to the folks at Battery for producing and sharing this great report. [7]

# # #

Notes
[1] Rule of 40 score = typically calculated as revenue growth + free cashflow (FCF) margin.  When FCF margin is not available, I typically use operating margin.   Using GAAP operating margin here would result in 110% + -55% = 55%, much lower, but still in rule of 40 compliance.

[2] If calculated using subscription revenue growth, it’s 137% + -26% = 111%, even more amazing.  One thing I don’t like about the fluidity of Rule of 40 calculations, as you can see here, is that depending what might seem small nuances in calculations, you can produce a very broad range of scores.   Here, from 55% to 137%.

[3] To me, this means ending day 1 with a strong valuation.  The degree to which that is up or down from the opening price is really about how the bankers priced the offer.  I am not a financial analyst and do not make buy or sell recommendations.  See my disclaimers, here.

[4] In fact, it’s actually a double bar — first you need to have been successful enough to go public, and second you need to clear the Rule of 40.  Despite a minority of public companies actually clearing this bar, financial analysts are quick to point out the minority who do command a disproportionate share of market cap.

[5] And via the resultant R-squared score, to what extent does the Rule of 40 score explain (or drive) the EV/R multiple?

[6] If R-squared were 1.0 all the dots would fall on the least-squares fit line.

[7] Which continues with further analysis, breaking the Rule of 40 into 4 zones.

Speaking at Host Perform 2019

hostperform

Just a quick post to plug the fact that the kind folks at Host Analytics have invited me to speak at Host Perform 2019 in Las Vegas on May 20-22nd, and I’ll be looking forward to seeing many old friends, colleagues, customers, and partners on my trip out.

I’ll be speaking on the “mega-track” on Wednesday, May 22nd at 9:00 AM on one of my favorite topics:  how EPM, planning, and metrics all look from the board and C-level perspectives.  My official session description follows:

session

The Perform 2019 conference website is here and the overall conference agenda is here.  If you’re interested in coming and you’ve not yet registered yet, it’s not too late!  You can do so here.

I look forward to another great Perform conference this year and should be both tweeting (hashtag #HostPerform) and blogging from the conference.  I look forward to seeing everyone there.  And attend my session if you want to get more insight into how boards and C-level executives view reporting, planning, EPM, KPIs, benchmarks, and metrics.

What’s the “Cause of Death” in Your Churn Reporting?

In looking at this issue across several companies, I’ve noticed a disturbing trend / missed opportunity in how many SaaS companies classify the reason for customer churn.  Roughly speaking, if companies were hospitals, they’d too frequently be reporting the cause of death as “stopped breathing.”

Yes, the patient who died stopped breathing; the question is why did they stop breathing.  In churn-speak, “yes, the customer who churned issued a churn notice and chose not to renew.”  The question is why did they choose not to renew?

Many people have written great posts on reasons customers churn and how to prevent them.  These reasons often look like hierarchies:

Uncontrollable:

  • Got acquired
  • Went bankrupt
  • Corporate edict
  • New sponsor

Controllable:

  • Failed implementation
  • Product functionality
  • Product ease of use
  • Oversold  / poor fit

These hierarchies aren’t a bad start, but they aren’t good enough, either.  A new sponsor isn’t an automatic death sentence for a SaaS product.  He or she might be, however, if the team using it had a rough implementation and was only half-satisfied with the product.  Similarly, a failed implementation will certainly reduce the odds of renewal, but sometimes people do have the will to start over — and why did the implementation fail in the first place?

Physicians have been in the “churn” business much longer than SaaS companies and I think they’ve arrived at a superior system.  Here’s an excerpt from the CDC’s Physicians’ Handbook on Medical Certification of Death — not a publication, I’d add, linked to by most SaaS bloggers:

chain of death

For example, when my dear father passed away from a stroke several years ago, I remember the form said:

example cod

(And, at the time, literally observing that it was a better way to classify churn.)

The rule above spells it out quite clearly  — “DO NOT enter terminal events such as respiratory arrest […] without showing the etiology.”  That is, “stopped breathing” by itself isn’t good enough.  Just like “sent churn notice” or “decided not to renew.”

I have not built out a full taxonomy here; classifying churn in this way remained a future item on my to-do list at the time we sold my last company.  Nevertheless, while I know it’s not easy, I believe that companies should start trying to find a way to richly encode churn reasons using this “chain” concept so as to not lose critical information in encoding their data.  Otherwise, we risk believing that all our customers churned because they sent us a churn notice (or some easily blamed “uncontrollable” event).

As one example:

  • Churned, due to
  • New sponsor, due to
  • Failed implementation, due to
  • Partner problem, due to
  • Partner training

Or, another:

  • Churned, due to
  • Corporate edict, due to
  • M&A, due to
  • Product dissatisfaction, due to
  • Oversold, due to
  • Sales training

These aren’t perfect, but I’m trying quickly demonstrate the real complexity behind why customers churn.  For example, happy customers might challenge a corporate edict issued after an acquisition — so you can’t just blame the edict.  You have to look more deeply.  If you knew that the customer fought the edict and failed, you might stop the chain there.  But if you knew they were never terribly happy with the system because they were overpromised capabilities at the start, then you should code that into the chain, too.

# # #

For more information on the warning signs and symptoms of a stroke, go here.

 

Are You Counting Payments as Renewals?

Enterprise SaaS has drifted to a model where many, if not most, companies do multi-year contracts on annual payment terms.  How did we get here?

  • Most enterprise SaaS products are high-consideration purchases. Buyers typically perform a thorough evaluation process before purchasing and are quite sure that the software will meet their needs when they deploy.  These are not try-and-buy or wing-it purchases.
  • Most SaaS vendors will jump at the opportunity to lock in a longer subscription term. For example, with an 85% gross retention rate you can offer a 5% discount for a two-year contract and end up mathematically ahead [1].  Moreover, with a default annual increase of 5 to 10% built into your standard contact, you can offer a “price lock” without any discount at all (i.e., the customer locks in the price for two years in exchange for a two-year commitment).

When you combine the vendor’s desire to lock in the longer term with the customer’s belief that the solution is going work, you find a fertile ground for doing two- or three-year contracts.  But these multi-year deals are almost always done on annual payment terms.

Most SaaS vendors don’t want to take the next step and ask for a multi-year prepayment.  The upside for the vendor would be to eliminate the need for collections in years 2 and 3, and eliminate the chance that the customer — even if unhappy — won’t make the out-year payments.  But most vendors refrain from this because:

  • It’s seen as an unusual practice that’s frowned upon by investors
  • Most investors believe you could better maximize ARR by simply raising more capital and sticking with annual payments
  • It can lead to lumpy renewals and cash flows that are both hard to manage and understand
  • It can lead to large long-term deferred revenues which can hinder certain M&A discussions.  (Think:  large balance of cashless revenue from suitor’s perspective.)
  • It complicates the calculation of SaaS metrics, sometimes confusing investors into believing that good metrics are bad ones. (I think I am literally the only person in Silicon Valley who is quick to point out that a 75% three-year retention rate is better than a 90% one-year one [2].)

Thus, we end up in a situation where the norm has become a two- or three-year contract with annual payments.  This begs a seemingly simple “if a tree falls in the forest and no one hears it, did it make any noise” kind of question:

Quick, what’s the difference between a one-year contract that’s renewing for the first time and a three-year contract that’s coming up for its first downstream annual payment?

I’ve often quipped that they’re both “renewals,” but in the former case they’re handled Customer Success and in the latter they’re handled by Legal. [3]

But let’s be clear, regardless of the process you use to manage them [4], they are not the same, and should not automatically be treated as such for the purposes of calculating SaaS metrics. One is the voluntary renewal of a subscription contract; the other is the payment of a contractual commitment.

If you don’t want to renew your subscription, there’s nothing I can do to force you.  If you don’t want to make a contractually committed payment I can sue you.

Let’s consider an example.  We have six customers, Alpha through Foxtrot.  The first three did one-year deals, the second three did three-years deals.  The simple question is:  what’s your gross dollar retention?  A merely acceptable 83% or a very healthy 95%?

payment renewal

If you calculate on an available-to-renew (ATR) basis, the rate is 83%.  There were 300 units up for renewal and you renewed 250 of them.  If you include the payments, the rate is 95%.  1,050 units were up for renewal or payment, and you invoiced 1,000.

This is a case that feels a little bit wrong both ways.  Including the payments uplifts the rate by mixing involuntary payments with voluntary renewals; to the extent you want to use the rate as a satisfaction indicator, it will be over-stated [5].  However, excluding the payments seems to fail to credit the company with the auto-renewing nature of multi-year deals.

One thing is clear:  payments certainly cannot be included in any ATR-based rate.  You cannot view making a contractually required payment as the same thing as voluntarily renewing a contract. 

Because of prepaid multi-year deals, I have always calculated retention rates two ways:  ATR-based and ARR-based.  The former is supposed to give you an idea of how often, given the chance, people want to renew their contacts.  The latter is supposed to show you, mathematically, what’s happening to your ARR pool [6].

I have an issue, which is highly subjective, when it comes to out-payments on non-prepaid, multi-year deals:

  • On one hand, I can argue they are contractual commitments that the vast majority of customers will honor and thus are effectively – save for a few rare cases – identical to prepaid multi-year deals. Think:  the money’s good as in the bank.
  • On the other hand, I can argue that a dissatisfied customer – particularly one who blames the vendor and/or the software for their failure – will not want to pay, even if the contract says they’re supposed to. Think:  it’s a toothless contract that the vendor will not likely not enforce against an angry customer.

Philosophically, I can argue that these out-year payments are either “good as in the bank” or I can argue that they’re “basically renewals that will ‘churn’ if the customer is not happy.”  The first argument says to treat them like prepaid multi-year deals and put them in ARR-based retention rates.  The second argument says they’re effectively voluntary renewals and should be counted as such.

In reality, you need to know what happens at your business.

I believe for the vast majority of businesses, customers honor the contracts and we should treat them like prepaid, multi-year deals in ARR-based rates — and you should always publish in parallel ATR-based rates, so people can see both.  However, if your company is an outlier and 10% of those payments are never collected, you’re going to need to look at them differently – perhaps like renewals because that’s how they’re behaving.  Or get better lawyers.  Or stop doing non-prepaid, multi-year deals because, for whatever reason, your customers are not honoring the commitment they made in exchange for you to give them a price lock.

# # #

Notes

[1] Over 2 years you get 190 units versus an expected 185.  (Not counting any expansion.)

[2] 0.75 > 0.9^3 = 0.73 – you need to compound annual rates to compare them to multi-year ones.

[3] Or, really, Accounts Receivable but that doesn’t sound as funny.

[4] I’d argue that when you define your customer success process that you should treat these two customers identically.  Whether it’s a payment or a renewal, in a good customer success process you should constantly monitor customer progress with the hope that the renewal (or the payment) is not some big decision, but merely incidental.  (“Yes, of course, we want to keep using the software – is it just a payment year or do we need to renew the contract?”)  This might increase your cost to renew a bit – because you’ll be paying CSMs or renewals reps to do collection work that could theoretically have been done by Accounts Receivable – but it’s still the right answer if you want to maximize ARR.

[5] While payment does not necessarily indicate satisfaction, it probably does indicate the absence of intense dissatisfaction.

[6] e.g., I’d use the the churn rate (1 minus the retention rate) as the discount rate in a present value calculation.

What It Takes to Make a Great SaaS Company

I’ve been making a few presentations lately, so I thought I’d share the slides to this deck which I presented earlier this week at the All Hands meeting of a high-growth SaaS company as part of their external speaker series.

This one’s kind of a romp — it starts with some background on Kellblog (in response to some specific up-front questions they had), takes a brief look back at the “good old days” of on-premises software, introduces my leaky bucket concept of a SaaS company, and then discusses why I need to know only two things to value your SaaS company:  the water level of your bucket and how fast it’s increasing.

It kind of runs backwards building into the conclusion that a great SaaS company needs four things.

  1. An efficient sales model.  SaaS companies effectively buy customers, so you need to figure out how to do it efficiently.
  2. A customer-centric culture.  Once you’ve acquired a customer your whole culture should be focused on keeping them.  (It’s usually far cheaper than finding a new one to back-fill.)
  3. A product that gets the job done.  I like Clayton Christensen’s notion that customers “hire products to do jobs for them.”  Do yours?  How can you do it better?
  4. A vision that leaves the competition one step behind.  Done correctly, the competition is chasing your current reality while you’re out marketing the next level of vision.

Here are the slides:

Rule of 40 Glideslope Planning

Enterprise SaaS companies need a lot of money to grow. The median company spends $1.32 to acquire $1.00 in annual recurring revenue (ARR) [1].  They need to make that investment for 14 years before getting to an IPO.  It all adds up to a median of $300M in capital raised prior to an IPO.

With such vast amounts of money in play, some say “it’s a growth at all costs” game.  But others hold to the Rule of 40 which attempts to balance growth and profitability with a simple rule:  grow as fast as you want as long as your revenue growth rate + your free cashflow margin >= 40%.

The Rule of 40 gets a lot of attention, but I think that companies are not asking the right question about it.  The right question is not “when should my growing startup be Rule of 40 compliant?” [2]

For more than half of all public SaaS companies, the answer to that question, by the way, is “not yet.”  Per multiple studies I’ve read the median Rule of 40 score for public SaaS companies is ~31%, meaning that more than half of public SaaS companies are not Rule of 40 compliant [3].

So, unless you’re an absolutely amazing company like Elastic (which had a Rule of 40 score of 87% at its IPO), you probably shouldn’t be unrealistically planning to become Rule of 40 compliant three years before your IPO [4].  If you do, especially if you’re well funded and don’t need additional expense constraints, you might well compromise growth with a premature focus on the Rule of 40, which could shoot off your corporate foot in terms of your eventual valuation.

If “when should we be Rule of 40 compliant” is the wrong question, then what’s the right one?

What should my company’s Rule of 40 glideslope be?

That is, over the next several years what is your eventual Rule of 40 score target and how do you want to evolve to it?  The big advantage of this question is that the answer isn’t “a year” and it doesn’t assume Rule of 40 compliance.  But it does get you to start thinking about and tracking your Rule of 40 score.

I built a little model to help do some what-if analysis around this question.  You can download it here.

r40-1

In our example, we’ve got a 5 year-old, $30M ARR SaaS company planning the next five years of its evolution, hopefully with an IPO in year 8 or 9.  The driver cells (orange) define how fast you want to grow and what you want your Rule of 40 glideslope to be.  Everything else is calculated.  At the bottom we have an overall efficiency analysis:  in each year how much more are we spending than the previous year, how much more revenue do we expect to get, and what’s the ratio between the two (i.e., which works like kind of an incremental revenue CAC).  As we improve the Rule of 40 score you can see that we need to improve efficiency by spending less for each incremental dollar of revenue.  You can use this as a sanity check on your results as we’ll see in a minute.

Let me demonstrate why I predict that 9 out 10 ten CFOs will love this modeling approach.  Let’s look at every CFO’s nightmare scenario.  Think:  “we can’t really control revenues but we can control expenses so my wake up in the middle of the night sweating outcome is that we build expenses per the plan and miss the revenues.”

r40-2

In the above (CFO nightmare) scenario, we hold expenses constant with the original plan and come in considerably lighter on revenue.  The drives us miles off our desired Rule of 40 glideslope (see red cells).  We end up needing to fund $42.4M more in operating losses than the original plan, all to generate a company that’s $30.5M smaller in revenue and generating much larger losses.  It’s no wonder why CFOs worry about this.  They should.

What would the CFO really like?  A Rule-of-40-driven autopilot.

As in, let’s agree to a Rule of 40 glideslope and then if revenues come up short, we have all pre-agreed to adjust expenses to fall in line with the new, reduced revenues and the desired Rule of 40 score.

r40-3

That’s what the third block shows above.  We hold to the reduced revenues of the middle scenario but reduce expenses to hold to the planned Rule of 40 glideslope.  Here’s the bad news:  in this scenario (and probably most real-life ones resembling it) you can’t actually do it — the required revenue-gathering efficiency more than doubles (see red cells).  You were spending $1.38 to get an incremental $1 of revenue and, to hold to the glideslope, you need to instantly jump to spending only $0.49.  That’s not going to happen.  While it’s probably impossible to hold to the original {-10%, 0%, 5%} glideslope, if you at least try (and, e.g., don’t build expenses fully to plan when other indicators don’t support it), then you will certainly do a lot better than the {-10%, -32%, -42%} glideslope in the second scenario.

In this post, we’ve talked about the Rule of 40 and why startups should think about it as a glideslope rather than a short- or mid-term destination.  We’ve provided you with a downloadable model where you can play with your Rule of 40 glideslope.  And we’ve shown why CFOs will inherently be drawn to the Rule of 40 as a long-term planning constraint, because in many ways it will help your company act like a self-righting ship.

# # #

Notes

[1] The 75th percentile spends $1.92.  And 25% spend more than that.  Per KeyBanc.

[2] Rule of 40 compliant means a company has an rule of 40 score >= 40%.  See next note.

[3] Rule of 40 score is generally defined as revenue growth rate + free cashflow (FCF) margin.  Sometimes operating margin or EBITDA margin is used instead because FCF margin can be somewhat harder to find.

[4] I’m trying to find data a good data set of Rule of 40 scores at IPO time but thus far haven’t found one.  Anecdotally, I can say that lots of successful high-growth SaaS IPOs (e.g., MongoDB, Anaplan, and Blackline) were not Rule of 40 compliant at IPO time — nor were they well after, e.g., as of Oct 2018 per JMP’s quarterly software review.  It seems that if growth is sufficiently there, that the profitability constraint can be somewhat deferred in the mind of the market.

An Update on the SaaS Rule of 40

Thanks to the folks at Piper Jaffray and their recently published 2018 Software Market Review, we can take a look at a recent chart that plots public software company enterprise value (EV) vs. Rule of 40 (R40) score = free cash-flow margin + revenue growth rate.

As a reminder, the Rule of 40 is an industry rule of thumb that says a high-growth SaaS company can burn as much cash as it likes in order to drive growth — as long as its growth rate is 40 percentage points higher than its free cashflow margin.  It’s an attempt at devising a simple rule to help software companies with the complex question of how to balance growth and profitability.

One past study showed that while Rule of 40 compliant software companies made up a little more than half of all public software companies that they captured more than 80% of all public market cap.

Let’s take a look at Piper’s chart which plots R40 score on the X axis and enterprise value (EV) divided by revenue on the Y axis.  It also plots a presumably least squares fit line through the data points.

newer rule of 40

Source: PJC Analysis and SAP Capital IQ as of 12/31/2018

Of note:

  • Less than half of all companies in this set are Rule of 40 compliant; the median R40 score was 31.7%.
  • The median multiple for companies in the set was 6.6x.
  • The slope of the line is 12, meaning that for each 10 percentage points of R40 score improvement, a company’s revenue multiple increases by 1.2x.
  • R^2 is 0.42 which, if I recall correctly, means that the R40 score explains 42% of the variability of the data.  So, while there’s lots it doesn’t explain, it’s still a useful indicator.

A few nerdier things of note:

  • Remember that the line is only valid in the data range presented; since no companies had a negative R40 score, it would be invalid extrapolation to simply continue the line down and to the left.
  • Early-stage startup executives often misapply these charts forgetting the selection bias within them. Every company on the chart did well enough at some point in terms of size and growth to become a public SaaS company.  Just because LivePerson (LPSN) has a 4x multiple with an R40 score of 10% doesn’t mean your $20M startup with the sames score is also worth 4x.   LPSN is a much bigger company (roughly $250M) and and already cleared many hurdles to get there.

The big question around the Rule of 40 is:  when should companies start to target it?   A superstar like Elastic had 76% growth and 8% FCF margin so a R40 score of 84% at its spectacular IPO.  However, Avalara had 26% growth and -28% FCF margin for an R40 score of -2% and its IPO went fine.  Ditto Anaplan.

I’ll be doing some work in the next few months to try and get better data on R40 trajectory into an IPO.  My instinct at this point is that many companies target R40 compliance too early, sacrifice growth in the process, and hurt their valuations because they fail to deliver high growth and don’t get the assumed customer acquisition cost efficiencies built in the financial models, which end up, as one friend called them, spreadsheet-induced hallucinations.