Category Archives: VC

The Balderton Founder’s Guide to B2B Sales

Working in my capacity at as an EIR at Balderton Capital, I have recently written a new publication, The Balderton Founder’s Guide to B2B Sales, with the able support of Balderton Principal Michael Lavner and the entire Balderton Capital team.  This guide is effectively a new edition, and a new take, on the prior, excellent B2B Sales Playbook.

The guide, which is published as a microsite, is also available in PDF format for downloading.

I’ll put the opening quote here that the editors omitted because it’s nearly unparseable:

“I have learned everything I need to know about sales.  Sales is saying ‘yes’ in response to every question.  So, now, when a customer asks if the product has a capability that it currently lacks, I say, ‘yes, the product can’t do that.'”

— Anonymous CS PhD founder who didn’t quite learn everything they needed to know about sales.

In short, this guide’s written for you, i.e., the product-oriented founder who thought they founded a technology business only to discover that SaaS companies, on average, spend twice as much on S&M as they do on R&D, and ergo are actually running a distribution business.

The guide has seven parts:

  • Selling: what founders need to know about sales
  • Building: how to build a sales organization
  • Managing: how to manage a sales organization
  • Renewing/expanding: teaming sales and customer success
  • Marketing: using marketing to build sales pipeline
  • Partnering: how to use partners to improve reach and win rate
  • Planning: planning and the role of key metrics and benchmarks

While there are numerous good SaaS benchmarking resources out there, the guide includes some benchmark figures from the Balderton universe (i.e., European, top-tier startups) and — hint, hint — we expect to release those benchmarks more fully and in a more interactive tool in the not-too-distant future.

The guide is also chock full of links which I will attempt to maintain as sources change over time.  But I’ve written it with both in-line links (often to Kellblog) and end-of-section links that generally point to third-party resources.

I’ve packed 30 years of enterprise software experience into this.  I come at sales from an analytical viewpoint which I think should be relatable for most product-oriented founders who, like me, get turned off by claims that sales has to be artisanal magic instead of industrial process.

I hope you enjoy the guide.  Feel free to leave comments here, DM me on Twitter, or reach me at the contact information in my FAQ.

Key Takeaways from the 2022 KeyBanc SaaS Metrics Survey

KeyBanc Capital Markets (KBCM) recently published their 13th annual private SaaS company survey.  This post has three purposes:  to let you know it’s out, to provide you with a link so you can get it, and to offer some quick takeaways on skimming through the results.

The first thing to remember about this survey is that it’s private SaaS companies.  Unlike Meritech Public Comps, where you can see metrics for the best [1], public SaaS companies, this private company data is somewhat harder to come by (the only other source that springs to mind is RevOps Squared) and, for most of us, it provides much more realistic comparables than Meritech [2].

The second thing to remember is that there are a lot of smaller companies in the sample:  about 20% of respondents are less than $5M in ARR and about 40% are less than $10M.   (The overall median is $13MM.)  Depending on who you want to compare to, this may be a good or a bad thing.  In addition, for most of the metrics they exclude companies <$5M in ARR from the calculations, which brings up the overall median for that set to $17.6M.

Net:  this is not VC-backed SaaS companies (62% are), this is not IPO-track SaaS companies (presumably some small subset of that 62%).  This is all private SaaS companies, including 22% PE-backed and 13% boostrapped.

One of my new benchmarking themes is that people need to pay more attention to matching their benchmarks with their aspirations. If your aspirations are to raise money from top VCs at a good valuation, my guess is you should be thinking 75th precentile of this data set; if they’re to IPO, you should be thinking 90th.

That said, let’s meet the Joneses, who have median:

  • ARR growth of 31%, lower than I’d hope.
  • Forecast 2022 ARR growth of 36%, so they’re planning to accelerate.  Everyone’s an optimist.
  • Expansion ARR of 46%, higher than I’d hope.
  • Net dollar retention (NDR) of 109%.
  • Customer acquisition cost (CAC) ratio of 1.2 blended, 1.8 new, and 0.6 expansion, in line with my expectations.
  • Gross churn of 14%, in line, perhaps a tad high, relative to my guess.
  • Available to renew (ATR) gross churn of 10%, but it’s hard to understand how ATR rate can be lower than gross churn rate [3].
  • Margin profile of 77% subscription, 73% blended.  In line.
  • Sales and marketing (S&M) expense of 40% of revenues.  They’re frugal, but they’re not growing that fast, either.
  • Free cashflow (FCF) margin of -5%.
  • New ARR per seller of $673K, which I if I understand, is what I’d call sales productivity.
  • Contract length and billing frequency of one year.
  • ARR/FTE of $143K, lower than I’d guess (for public companies it’s nearly double that).
  • Valuation of 6.1x ARR at their most recent round (in 2021 or later).

Since I don’t want to lift too many of their slides, I’ll extract just two.  The first shows S&M spend as a function of growth rate.

If there’s one area where you really need to look at metrics as a function of growth rate, it’s customer acquistion cost and, by extension S&M spend, on the theory that in enterprise SaaS you need to invest up front to grow.  Therefore a high-growth company is theoretically carrying the cost of as-yet-unproductive capacity where as a steady-state one is not.  You can see this pretty clearly here where the sub-20% growth companies spend 27% on S&M, which surprisingly drops to 17% at the 30-40% bucket, but then begins a steady upward march to 59% for those growing faster than 80%.

The second discusses a concept I’ve called The Rule of 56789

Here, KeyBanc is saying roughly what I say, which is [4]:

  • 5 years to $10M (5.6 years, per KCBM)
  • 6 years to $20M (7.1 years, but to $25M)
  • 7 years to $50M (7.6 years)
  • 8 years to $75M (they have no threshold here)
  • 9 years to $100M (9.3 years)

I’m glad they’re now tracking this, along with net burn rate (aka, cash conversion score) though I’d say their implied cash conversion scores are more efficient than I’d guess based on my experience and Bessemer’s data.

Overall, this is a seminal report for SaaS companies.  Every private SaaS company should read it.  Grab yours here.

Notes

[1]  In the sense that even a “bad” public SaaS company (dare I suggest Domo or C3 as two of my favorites to scrutinize) was still good enough to get public in the first place and ergo creme de la creme when viewed more broadly.

[2]  As I said in a recent speech, it’s the difference between benchmark off all SAT test takers and Ivy League applicants.  See slide 13 of this presentation.

[3]  KBCM calls this non-renewal rate, but I think it’s 1 – ATR churn.  The reason it’s hard to believe it’s lower is that it should be the same numerator over a smaller denominator.

[4]  I was looking at European 75th percentiles and they are looking at worldwide (but US-weighted) medians

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

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

The presentation discusses:

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

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

Playing Bigger vs. Playing To Win: How Shall We Play the Marketing Strategy Game?

“I’m an CMO and it’s 2018.  Of course I’ve read Play Bigger.  Duh.  Do you think I live under a rock?” — Anonymous repeat CMO

Play Bigger hit the Sand Hill Road scene in a big way after its publication in 2016.  Like Geoffrey Moore’s Crossing the Chasm some 25 years earlier, VCs fell in love with the book, and then pushed it down to the CEOs and CMOs of their portfolio companies.  “Sell high” is the old sales rule, and the business of Silicon Valley marketing strategy books is no exception.

Why did VCs like the book?  Because it’s ultimately about value creation which is, after all, exactly what VCs do.  In extreme distillation, Play Bigger argues:

  • Category kings (companies who typically define and then own categories in the minds of buyers) are worth a whole lot more than runner-ups.
  • Therefore you should be a category king.
  • You do that not by simply creating a category (which is kind of yesterday’s obsession), but by designing a great product, a great company, and a great category all the same time.
  • So, off you go.  Do that.  See you at the next board meeting.

I find the book a tad simplistic and pop marketing-y (in the Ries & Trout sense) and more than a tad revisionist in telling stories I know first-hand which feel rather twisted to map to the narrative.  Nevertheless, much as I’ve read a bunch of Ries & Trout books, I have read Play Bigger, twice, both because it’s a good marketing book, and because it’s de rigeur in Silicon Valley.  If you’ve not read it, you should.  You’ll be more interesting at cocktail parties.

As with any marketing book, there is no shortage of metaphors.  Geoffrey Moore  had D-Day, bowling alleys, and tornados.  These guys run the whole something old, something new, something borrowed, and something blue gamut with lightning strikes (old, fka blitzkreigs), pirates (new, to me if not Steve Jobs), flywheels (borrowed, from Jim Collins), and gravity (blue, in sense of a relentless negative force as described in several cautionary tales).

While I consider Play Bigger a good book on category creation, even a modernized version of Inside the Tornado if I’m feeling generous, I must admit there’s one would-be major distinction that I just don’t get:  category creation vs. category design, the latter somehow being not just about creating and dominating a category, but “designing” it — and not just a category, but a product, category, and company simultaneously.  It strikes me as much ado about little (you need to build a company and a product to create and lead a category) and, skeptically, a seeming pretense for introducing the fashionable word, “design.”

After 30 years playing a part in creating, I mean designing, new categories — both ones that succeeded (e.g., relational database, business intelligence, cloud EPM, customer success management, data intelligence) and ones that didn’t (e.g., XML database, object database) — I firmly believe two things:

  • The best way to create a category is to go sell some software.  Early-stage startups excessively focused on category creation are trying to win the game by staring at the scoreboard.
  • The best way to be a category king is to be the most aggressive company during the growth phase of the market.  Do that by executing what I call the market leader play, the rough equivalent of Geoffrey Moore’s “just ship” during the tornado.  Second prize really is a set of steak knives.

I have some secondary beliefs on category creation as well:

  • Market forces create categories, not vendors.  Vendors are simply in the right place (or pivot to it) at the right time which gives them the opportunity to become the category king.  It’s more about exploiting opportunities than creating markets.  Much as I love GainSight, for example, I believe their key accomplishment was not creating the customer success category, but outexecuting everyone else in exploiting the opportunity created by the emergence of the VP of Customer Success role.  GainSight didn’t create the VP of Customer Success; they built the app to serve them and then aggressively dominated that market.
  • Analysts name categories, not vendors.  A lot of startups spend way too much time navel gazing about the name for their new category.  Instead of trying to sell software to solve customer problems, they sit in conference rooms wordsmithing.  Don’t do this.  Get a good-enough name to answer the question “what is it?” and then go sell some.  In the end, as a wise, old man once told me, analysts name categories, not vendors.
  • Category names don’t matter that much.  Lots of great companies were built on pretty terrible category names (e.g., ERP, HCM, EPM, BTO, NoSQL).  I have trouble even telling you what category red-hot tech companies like Hashicorp and Confluent even compete in.  Don’t obsess over the name.  Yes, a bad name can hurt you (e.g., multi-dimensional database which set off IT threat radar vs. OLAP server, which didn’t).  But it’s not really about the name.  It’s about what you sell to whom to solve which problem.  Again, think “good enough,” and then let a Gartner or IDC analyst decide the official category name later.

To hear an interesting conversation on category creation,  listen to Thomas Otter, Stephanie McReynolds, and me discuss the topic for 60 minutes.  Stephanie ran marketing at Alation, which successfully created (or should I say seized on the market-created opportunity to define and dominate) the data catalog category.  (It’s all the more interesting because that category itself is now morphing into data intelligence.)

Since we’re talking about the marketing strategy game, I want to introduce another book, less popular in Silicon Valley but one that nevertheless deserves your attention: Playing to Win.  This book was written not by Silicon Valley denizens turned consultants, but by the CEO of Proctor & Gamble and his presumably favorite strategy advisor.  It’s a very different book that comes from a very different place, but it’s right up there with Blue Ocean Strategy, Inside the Tornado, and Good Strategy, Bad Strategy on my list of top strategy books.

Why?

  • Consumer packaged goods (CPG) is the major league of marketing.   If they can differentiate rice, yogurt, or face cream, then we should be able to differentiate our significantly more complex and inherently differentiated products.  We have lots to learn from them.
  • I love the emphasis on winning.  In reality, we’re not trying to create a category.  We’re trying to win one, whether we happened to create it or not.  Strategy should inherently be about winning.  Strategy, as Roger Burgelman says, is the plan to win.  Let’s not dance around that.
  • I love the Olay story, which opens the book and alone is worth the price of the book.  Take an aging asset with the wrong product at the wrong price point in the wrong channel and, instead of just throwing it away, build something amazing from it.  I love it.  Goosebumps.
  • It’s practical and applied.  Instead of smothering you in metaphors, it asks you to answer five simple questions.  No pirates, no oceans, no tornados, no thunderstorms, no gorillas, no kings, no beaches.

Those five questions:

  • What is your winning aspiration? The purpose of your enterprise, its motivating aspiration.
  • Where will you play? A playing field where you can achieve that aspiration.
  • How will you win? The way you will win on the chosen playing field.
  • What capabilities must be in place? The set and configuration of capabilities required to win in the chosen way.
  • What management systems are required? The systems and measures that enable the capabilities and support

Much as I love metaphors, I’d bury them all in the backyard in exchange for good answers to those five questions.  Strategy is not complex, but it is hard.  You need to make clear choices, which business people generally resist.  It’s far easier to fence sit, see both sides of the issue, and keep options open (which my old friend Larry used to call the MBA credo).  That’s why most strategy isn’t.

Strategy is about answering those questions in a way that is self-consistent, consistent with the goals of the parent organization (if you’re a brand or general manager in a multi-product company), and with the core capabilities of the overall organization.

In our view, Olay succeeded because it had an integrated set of five strategic choices that fit beautifully with the choices of the corporate parent. Because the choices were well integrated and reinforced category-, sector-, and company-level choices, succeeding at the Olay brand level actually helped deliver on the strategies above it.

I won’t summarize the entire book, but just cherrypick several points from it:

  • As with Burgelman, playing to win requires you to define winning for your organization in your context.  How can we make the plan to win if we don’t agree on what winning is?  (How many startups desperately need to have the “what is winning” conversation?)
  • Playing to win vs. playing to play.  Which are you doing?  A lot of people are doing the latter.
  • Do think about competition.  Silicon Valley today is overloaded with revisionist history:  “all we ever focused on was our customers” or “we always focused only on our vision, our north star.”  Ignoring competition is the luxury of retired executives on Montana ranches.  Winning definitionally means beating the competition.  You shouldn’t be obsessed with the competition, but you can’t ignore them either.
  • While they don’t quite say it, deciding where you play is arguably even more important than deciding what you sell.  Most startups spend most of their energy on what (i.e., product), not where (i.e., segment).  “Choosing where to play is also about choosing where not to play,” which for many is a far more difficult decision.
  • The story of Impress, a great technology, a product that consumers loved, but where P&G found no way to win in the market (and ultimately created a successful joint venture with Clorox instead), should be required reading for all tech marketers.  A great product isn’t enough.  You need to find a way to win the market, too.
  • The P&G baby diapers saga sounds similar to what would have happened had Oracle backed XQuery or when IBM originally backed SQL — self-imposed disruptions that allowed competitors entry to the market.  IBM accidentally created Oracle in the process.  Oracle was too smart to repeat the mistake.  Tech strategic choices often have their consumer analogs and they’re sometimes easier to analyze in that more distant light.
  • The stories of consumer research reveal a depth of desired customer understanding that we generally lack in tech.  We need to spend more time in customers’ houses, watching them shave, before we build them a razor.  Asking them about shaving is not enough.
  • I want to hug the person who described the P&G strategy process as, “corporate theater at its best.”  Too much strategy is exactly that.

Overall, it’s a well-written, well-structured book.  Almost all of it applies directly to tech, with the exception of the brand/parent-company intersection discussions which only start to become applicable when you launch your second product, usually in the $100M to $300M ARR range.  If you don’t have time for the whole book, the do’s and don’ts at the end of each chapter work as great summaries.

To wrap this up, I’d recommend both books.  When thinking about category creation, I’d try to Play Bigger.  But I’d always, always be Playing to Win.

How Quickly Should You Grow to Key ARR Milestones? The Rule of 56789

Question:  what do you call a 10-year old startup with $10M in ARR?
Answer:  a small business [1].

When you make a list of key SaaS metrics, you’ll rarely find age listed among them.  That’s correct in the sense that age by itself tells you little, but when size is measured against age, you get a rough measure of velocity.

It’s a lot like people.  Tell me you can play Mozart’s Piano Concerto No. 23 and I’ll be impressed [2].  Tell me you can play it at age 12, and I’ll think you’re an absolute prodigy.  Tell me you have $10M in ARR after 10 years and I’ll be impressed [3].  Tell me you have it after 3 and I’ll run for my checkbook.

All this begs the question of growth velocity:  at what age is a given size impressive?  Towards that end, and working with my friends at Balderton Capital, I’ve come up with what I’m calling the Rule of 56789.

  • 5 years to break $10M
  • 6 years to break $20M
  • 7 years to break $50M
  • 8 years to break $75M
  • 9 years to break $100M

Concretely put, if you walk through the doors to Balderton’s London offices with $54M in ARR after 7 years, you’ll be in the top quartile of those who have walked before you.

Commentary

  • I’m effectively defining “impressive” as top quartile in the Balderton universe of companies [4].
  • Remembering 56789 is easy, but remembering the milestones is harder.  Once you commit the series {10, 20, 50, 75, 100} to memory, it seems to stick [5].
  • Remember that these are milestones to pass, not ending ARR targets, so this is not equivalent to saying grow 100% from $10M to $20M, 150% from $20 to $50M, and so on.  See note [6] before concluding {100%, 150%, 50%, 33%} is an odd growth trajectory.
  • For example, this is a 56789-compliant growth trajectory that has no whipsawing in growth rates.

Three Situtions That Break The Rule
Rules are made to be broken, so let’s talk about three common situations which confound the Rule of 56789.

  • Bootstraps, which are capital constrained and grow more slowly.  Bootstraps should largely ignore the rule (unless they plan on changing their financing strategy) because they are definitionally not trying to impress venture capitalists [7].
  • Platforms, that require years of time and millions of dollars before they can go to market, effectively resetting the starting clock from company inception to beta product release [8].
  • Pivots, where a company pursues strategy A for a few years, abandons it, and takes some salvage value over to a new strategy B. This effectively resets the starting clock from inception to pivot [9].

Alternative Growth Velocity Rules
Let’s compare the trajectory we showed above to similar one generated using a slightly different rule, which I’ll call the 85% Growth Retention Rule, which says to be “impressive” (as defined above), you should:

  • Pass $1M in ARR at a high growth rate (e.g., above ~180%)
  • Subsequently retain 85% of that growth rate every year

I view these as roughly equivalent rules, or more precisely, alternate expressions of nearly the same underlying rule.  I prefer 56789 because it’s more concrete (i.e., do X by Y), but I think 85% growth retention is somewhat more general because it says no matter where you are and how you got there, try to retain 85% (or more) of your growth rate every year.  That said, I think it stops working at 8-10 years because the asymptote on great company growth is somewhere around 40% [10] and some would argue 60% [11].  It also fails in situations where you need to reaccelerate growth.

There’s one well-known growth velocity rule to which we should also compare.  The triple/triple/double/double/double (T2D3) rule, which says that once you hit $2M in ARR, you should triple to $6M, triple again to $18M, then double three times to $36M, $72M, and $144M.

Let’s compare the 56789 and the 85% Growth Retention rules to the T2D3 rule:

Clearly T2D3 is more aggressive and sets a higher bar.  My beef is that it fails to recognize the law of large numbers (by failing to back off on the growth rates as a function of size across considerable scale), so as an operator I’m more intuitively drawn to the 85% Growth Retention rule.  That said, if you want to be top 5% to 10% (vs. top 25%), then go for T2D3 if you can do it [12].  You’ll clearly be creating a lot more value.

I like all of these rules because they help give you a sense for how quickly you should be getting to a certain size.  Growth conversations (e.g., trying to get a CRO to sign up for a number) are never easy.  Rules like these help by providing you with data not about what the average companies are doing, but what the great ones are.  The ones you presumably aspire to be like.

The limitation, of course, is that none of these rules consider the cost of growth.  There’s a big difference between a company that gets to $100M in 9 years on $100M in capital vs. one that does so on $400M in capital.  But that’s why we have other metrics like cash conversion score.  Different metrics measure different things and these ones are focused solely on size/growth vs. age.

A big tip of the hat to Michael Lavner at Balderton Capital for working with me on this post.

# # #

Notes

[1] See the definition of small business, which is somewhat broader than I’d have guessed.

[2] Even though it’s only classified as “less difficult” on this rather amazing scale from less difficult to difficult, very difficult, extremely difficult, ridiculously difficult, and extraordinarily difficult.  (Perhaps CEO’s can use that scale to classify board members.)

[3] It’s not as if just anybody can do either.  Founding a company and building it to $10M is impressive, regardless of the timeframe.

[4] Balderton universe = European SaaS startups who wanted to raise venture capital, who were sufficiently confident to speak with (what’s generally seen as) a top-tier European firm, and who got far enough into the process to submit performance data.

[5] I remember it by thinking that since it’s still pretty early days, jumping from $10M+ to $20M+ seems more reasonable than from $10M to $25M+.

[6] Don’t equate this rule with a growth vector of {100%, 150%, 50%, 33%} in years 5 through 9.  For example, years in which companies break $10M often don’t conclude with $10.1M in ARR, but more like $15M, after having doubled from a prior year of $7 to $8M.

[7] The rule would probably be more useful in projecting the future of VC-backed competitor.  (I think sometimes bootstrapped companies tend to underestimate the aggressiveness of their VC-backed competition.)  This could help you say, “Well, in N years, BadCo is likely to be a $50M business, and is almost certainly trying to be.  How should that affect our strategy?”

[8] That said, be sure you’re really building a mininum viable product and not overengineering either because it’s fun or it allows you to delay the scary of moment of truth when you try to sell it.

[9] Financings after a pivot sometimes require a recapitalization, in which case the company’s entire lifeclock, from strategy to product to cap table, are all effectively reset.

[10] Current median growth in Meritech Public Comps is 32% at median scale $657M in ARR.

[11] 0.85^10 = 0.2 meaning you’ll cut the starting growth rate by 80% after ten years.  So if you start at 200% growth, you’ll be down to 40% after 10 years with 85% growth retention.

[12] I’ll need to take a homework assignment to figure out where in the distribution T2D3 puts you in my data set.