Category Archives: SaaS

Lazy NRR is Not NRR. Accept No Imitations or Subtitutes.

Posted on October 5, 2022 | 2 comments

The other day I was looking at an ARR bridge [1] with a young finance ace.  He made a few comments and concluded with, “and net revenue retention (NRR) is thus 112%, not bad.”

I thought, “Wait, stop!  You can’t calculate NRR from an ARR bridge [2].”  It’s a cohort-based measure.  You need to look at the year-ago cohort of customers, get their year-ago ARR, get that same group’s current ARR, and then divide the current ARR by the year-ago.

“Yes, you can,” he said.  “Just take starting ARR, add net expansion, and divide by starting ARR.  Voila.”

Expecto patronum!  Protect me from this dark magic.  I don’t know what that is, I thought, but that’s not NRR.

Then I stewed on it for a bit.  In some ways, we were both right.

  • Under the right circumstances, I think you can calculate NRR using an ARR bridge [3]. But the whole beauty of the metric is to float over that definitional swamp and just divide two numbers — so I inherently don’t want to.
  • My friend’s definition, one I suspect is common in finance whiz circles, was indeed one shortcut too short. But, under the right circumstances, you can improve it to work better in certain cases.

The Trouble with Churn Rates
For a long time, I’ve been skeptical of calculations related to churn rates.  While my primary problems with churn rates were in the denominator [4], there are also potential problems with the numerator [5].  Worse yet, once churn rates get polluted, all downstream metrics get polluted along with them – e.g., customer lifetime (LT), lifetime value (LTV), and ergo LTV/CAC.  Those are key metrics to measure the value of the installed base — but they rely on churn rates which are easily gamed and polluted.

What if there were a better way to measure the value of the installed base?

There is.  That’s why my SaaStr 2019 session title was Churn is Dead, Long Live Net Dollar Retention [6].  The beauty of NRR is that it tells you want you want to know – once you acquire customers, what happens to them? – and you don’t have to care which of four churn rates were used.  Or how churn ARR itself was defined.  Or if mistakes were made in tracking flows.

You just need to know two things:  ARR-now and ARR-then for “then” cohort of customers [7].

A Traditional ARR Bridge
To make our point, let’s review a traditional ARR bridge.

Nothing fancy here.  Starting ARR plus new ARR of two types:  new logo customers (aka, new logo ARR) and existing customers (aka, expansion ARR).  We could have broken churn ARR into two types as well (shrinkage and lost), but we didn’t need that breakout for this exercise.

Now, let’s add my four favorite rows to put beneath an ARR bridge [8]:

Here’s a description:

  • Net new ARR = New ARR – churn ARR. How much the water level increased in the SaaS leaky bucket of ARR.  Here in 1Q21, imagine we spent $2,250K in S&M in the prior quarter.  Our CAC ratio would be a healthy 1.0 on a new ARR basis, but a far less healthy 2.1 on a net new ARR basis.  That’s due to our quarterly churn of 8%, which when annualized to 32%, flies off the charts.
  • Expansion as a percent of new ARR = expansion ARR / new ARR. My sense is 30% is a magic number for an established growth-phase startup.  If you’re only at 10%, you’re likely missing the chance to expand your customers (which will also show up in NRR).  If you’re at 50%, I wonder why you can’t sell more new logo customers.  Has something changed in the market or the salesforce?
  • Net expansion = expansion ARR – churn ARR. Shows the net expansion or contraction of the customer base during the quarter.  How much of the bucket increase was due to existing (as opposed to new) customers?
  • Churn rate, quarterly. I included this primarily because it raises a point we’ll hit when discussing lazy NRR.  Many people calculate this as = churn ARR / starting ARR (quarter).  That’s what I call “simple quarterly,” and you’ll note that it’s always lower than just “quarterly,” which I define as = churn ARR / starting ARR (year) [9].  The trace-precedents arrows below show the difference.

Lazy NRR vs. Cohort-Based NRR
With that as a rather extensive warm-up, let’s discuss what I call lazy NRR.

Lazy NRR is calculated as described above = (starting ARR + net expansion) / starting ARR.  Lazy NRR is a quarterly expansion metric.

Let’s look at a detailed example to see what’s really being measured.

This example shows the difference between cohort-based NRR and Lazy NRR:

  • Cohort-based NRR, a year-over-year metric that shows expansion of the two year-ago customers (customers 1 and 2).  This is, in my book, “real NRR.”
  • Lazy NRR, simple quarterly, which compares net expansion within the current quarter to starting ARR for that quarter.

The point of the trace-precendents arrows shows you that while the result coincidentally might be similiar (and in this case it is not), that they are measuring two completely different things.

Let’s talk about the last row, lazy NRR, cohort-based approximation, which takes starting ARR from year-ago customers, then adds (all) net expansion over the year and divides by the year-ago starting ARR. The problem?  Customer 3.  They are not in the year-ago cohort, but contribute expansion to the numerator because, with only an ARR bridge, you can’t separate year-ago cohort net expansion from new-customer net expansion.  To do that, you’d need to have ARR by customer [10].

Lazy NRR is not NRR.  NRR is defined as snapshot- and cohort-based.  Accept no substitutes or imitations.  Always calculate NRR using snapshots and cohorts and you’ll never go wrong.

Layer Cakes Tell No Lies
While I’m usually quite comfortable with tables of numbers and generally prefer receiving them in board reports, this is one area where I love charts, such as this layer cake that stacks annual cohorts atop each other.  I like these layer cakes for several reasons:

  • They’re visual and show you what’s happening with annual cohorts.
  • Like snapshot- and cohort-based NRR, they leave little to no room for gaming.  (They’re even harder to survivor bias as you’d have to omit the prior-period ARR.)
  • Given my now-distant geophysics background, they sometimes remind me of sedimentary rock.  (Hopefully yours don’t look like that, as unmetamorphized, sedimentary rock represents an NRR of only 100%!)

The spreadsheet for this post is available here.

(The post was revised a few times after initial publication to fix mistakes and clarify points related to the cohort-based approximation.  In the end, the resultant confusion only convinced me more to only and always calcuate NRR using cohorts and snapshots.)

# # #

Notes
Edited 10/8/22 to replace screenshots and fix spreadsheet bug in prior version.

[1] Starting ARR + new ARR (from new logo and expansion) – churn ARR (from shrinkage and lost) = ending ARR

[2] I probably should have said “shouldn’t.”  Turns out, I think you can, but I know you shouldn’t.  We’ll elaborate on both in this post.

[3] Those conditions include a world where customers expand or contract only on an annual basis (as you are unable to exclude expansion or contraction from customers signed during the year since they’re not sepearated in an ARR bridge) and, of course, a clear and consistent definition of churn, playing fairly with no gaming designed understate churn or overstate expansion, and avoidance of mistakes in calculations.

[4] Churn rates based off the whole ARR pool can halve (or more than halve) those based on the available to renew (ATR) pool, for example if a company’s mean contract duration is 2 or 3 years.  ARR churn rates are probably better for financial calculations, but ATR churn rates are a better indicator of customer satisfaction

[5] Examples of potential problems, not all strictly related to calculation of churn ARR, but presented for convenience.

  • Expansion along the way. Consider a customer who buys 100-unit contract, expands to 140 the next quarter (without signing a new one-year agreement that extends the contract), and then at the annual renewal renews for 130.  The VP of CS wants to penalize the account’s CSM for 10 units of churn whereas the CFO wants to tell investors its 30 units of expansion.  Which is it?  Best answer IMHO is 40 units of expansion in the second quarter and 10 units of churn at the renewal, but I’ve seen people/systems that don’t do it that way.   NRR sees 130% rate regardless of how you count expansion and churn.
  • Potential offsets and the definition of customer – division 1 has 100 units and shrinks to 80 at renewal while a small 40-unit new project starts at division 2. Is that two customers, one with 20 units of churn and one new 40-unit customer or is it one customer with 20 units of expansion?  NRR sees either 80% rate or 120% rate as function of customer definition, but I’d hope the NRR framing would make you challenge yourself to ask:  was division 2 really a customer and ergo belong in the year-ago cohort?
  • Potential offsets and the definition of product – a customer has 100 units of product A, is unhappy, and shrinks to A to 60 units while buying your new product B for 40. Did any churn happen?  In most systems, the answer is no because churn is calculated at the account level.  Unless you’re also tracking product-level churn, you might have trouble seeing that your new product is simply being given away to placate customers unhappy with your first one.  NRR is inherently account-level and doesn’t solve this problem – unless you decide to calculate product-level NRR, to see which products are expanding and which are shrinking.
  • Adjustments.  International companies need to adjust ARR for fluctuations in exchange rates.  Some companies adjust ARR for bad debt or non-standard contracts.  Any and all of these adjustments complicate the calculation of churn ARR and churn rates.
  • Gaming.  Counting trials as new customers and new ARR, but excluding customers <$5K from churn ARR calculations (things won’t foot but few people check).  Renewing would-be churning customers at $1 for two years to delay count-based churn reporting (ARR churn rates and NRR will see through this).  Survivor biasing calculations by excluding discontinuing customers.  Deferring ARR churn by renewing would-be churning customers with net 360 payables and a handshake (e.g., side letter) to not collect unless thing XYZ can be addressed (NRR won’t see through this, but cash and revenue won’t align).

[6] Since I now work frequently with Europe as part of my EIR job with Balderton Capital, I increasingly say “NRR” instead of “NDR” (net dollar retention), because for many of the companies I work with it’s actually net Euro retention.  The intent of “dollar” was never to indicate a currency, but instead to say:  “ARR-based, not count-based.”  NRR accomplishes that.

[7] Some companies survivor bias their NRR calculation by using the now-value and then-value of the now cohort, eliminating discontinuing customers from the calculation.   Think:  of the mutual funds we didn’t shut down, the average annual return was 12%.

[8] If you download the spreadsheet and expand the data groups you can see some other interesting rows as well.

[9] The flaw in “simple quarterly” churn is that, in a world that assumes pure annual contracts, you’re including people who were not customers at the start of the year and ergo cannot possibly churn in the calculations.  While you use the same numerator in each case, you’re using an increasing denominator and with no valid reason for doing so.  See here for more.

[10] In which case you might as well calculate NRR as defined, using the current and year-ago snapshots.

 

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Posted in Churn, Customer Success, Metrics, NDR, SaaS, Startups

Slides from my SaaStock Workshop on US Expansion for European Startups

Posted on September 9, 2022 | Leave a comment

Just a quick post to share the slides I used at a recent SaaStock member workshop on Rising to the Challenges of US Expansion.  Thanks to those who attended — everyone had great questions and feedback.  My favorite was roughly:  “listen to Dave, I literally have made every one of these mistakes!”  (From someone who happily got it right in the end and now gets 40% of ARR from the US market.)

This material is based on the series of posts I wrote for Balderton on US expansion, the first post of which is linked to here.

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Posted in Management, SaaS, Sales, Startups, Strategy

The Decomposition of Marketing

Posted on August 31, 2022 | 1 comment

To adapt Julius Caesar’s famous opening line of the Gallic Wars:  marketing as a whole is divided in three parts.

  • Product marketing (prodmkt), responsible in a word, for the message and how well it resonates with customers in the market [1].
  • Demand generation (demandgen), responsible in a word, for generating opportunities (oppties) to feed sales [2].
  • Corporate communications (corpcomm), responsible in a word, for communications, including branding, public relations, and corporate-level messaging [3].

Two important notes:

  • There is an optional fourth part, sales development, i.e., managing the team of sales development reps (SDRs) who convert MQLs into stage-1 oppties.  Whether this team should report into sales or marketing is a separate debate.
  • The lines between these parts are not black and white.  Social media advertising is demandgen, but posting is either comms or prodmkt.  Prodmkt helps provide content for demandgen campaigns.  Content marketing is a form of light prodmkt.

Marketing leaders grow up [4] in one of those parts and thus take one of the three basic flavors. But just as few CFOs grew up in legal, few CMOs grew up in corpcomm.  So it really comes down to two:  most CMOs either grew up in product marketing or demandgen (just as most CFOs either grew up in either finance or accounting).  The point being that virtually no one grew up in both.

It was this realization that led me to create “pillar profiles” for marketers — a score from 1-5 on each of the three (plus one) pillars of marketing: prodmkt, demandgen, corpcomm, and sales development.  The trick is you only get a maximum of 15 points to assign [4a].  While I’ve never blogged on pillar profiles, I did cover them in my SaaStr 2021 talk, A CEO’s Guide to Marketing.

If you’re searching for a CMO, the first thing you should do is identify your target pillar profile [5].  Remember that you get a maximum of 15 points and, harder yet, you’ll find that {5, 4, x, x} and {4, 5, x, x} candidates are very hard to come by.  Usually you’ll find {5, 3, x, x} and {3, 5, x, x} candidates.

In the past, this forced startup CEOs to choose between a prodmkt- and a demandgen-oriented head of marketing.  But, with marketing ever more accountable for building pipeline [6], it was a Hobson’s choice:  most picked demandgen-oriented leaders because without pipeline, well, everything stops.  It’s the physiological needs level of marketing.

Increasingly though, I see startup CEOs interested in changing the rules so they have both strong prodmkt and demandgen leadership.  They’re doing this by decomposing marketing and reconstituting it in various ways:

  • Generally hiring demandgen-oriented CMOs [7].
  • Moving product marketing to report into either the chief product officer (CPO) or the CEO directly.
  • Occaisionally moving demandgen under the CRO, leaving behind either a prodmkt-oriented CMO or additionally putting prodmkt under product, leaving a corpcomm-oriented CMO.

I think all of these options can work in different situations, so let’s review the pro/cons of four different marketing org structures [8].

New, Traditional Marketing Structure
I believe the new traditional structure [9] is to hire a demandgen-oriented CMO and put prodmkt under that CMO.

The new, traditional marketing structure. Demandgen-oriented CMO with prodmkt reporting in.

The strength is that you get to hire a strong demandgen leader in the CMO slot and they will likely do well at filling the pipeline.  It may, however, be difficult to attract the level of prodmkt leader that you want because they may feel like “they understand the business bettter than their boss,” even if they lack the demandgen skills required for into the CMO role.

Prodmkt Under Product Structure
In this structure, your hire a demandgen-oriented CMO and move prodmkt under product.

Prodmkt under Product marketing structure. Demandgen-oriented CMO who also runs comms.

The strength here is that you get to hire a strong demandgen leader as CMO and — if you happen to have the right CPO — then prodmkt can work quite effectively under product.  This works best when the CPO is a great outbound communicator who has both a genuine interest and prior experience in prodmkt.  Never, ever force-fit this.  Some product leaders just want to manage the backlog [10].  I would note that large-company general manager (GM) positions, e.g., the one I held at Salesforce, are effectively “product management on steroids” and those steroids include taking over a lot of product marketing duties.  In such organizations, product marketing also exists outside the business units, but it is staffed at a lower ratio, and more product-line and campaigns-support in nature.

Prodmkt Under CEO Structure
Here again you hire a demandgen-oriented CMO but move prodmkt directly under the CEO, instead of under the CPO.

Prodmkt reporting to CEO structure. A demandgen-oriented CMO runs DG and comms.

I like this structure for early-stage startups because it lets the CEO have their cake and eat it, too — i.e., they can attract strong demandgen and prodmkt leaders.  This structure also keeps the CEO close to the action during the early days when the company is still evolving its message frequently.  It gives the CMO a chance to keep their job while still subtly giving the VP of Prodmkt the chance to earn the CMO job if they work well with the CEO, crush the prodmkt role, and demonstrate significant understanding of demandgen [11].  A little internal competition keeps everyone on their toes.

Fully Decomposed Structure
Fewer people contemplate this structure, but I have seen it once or twice.  Here you move demandgen under sales, prodmkt under product, and hire a corpcomm-oriented head of marketing.

Fully decomposed with DG under sales, prodmkt under product, and a corpcomm-oriented CMO

I’m generally not a fan of this structure because I don’t like marketing reporting into sales [12].  The strength is theoretically alignment:  by putting demandgen under sales, the CEO can effectively delegate responsibility for aligning demandgen to the CRO.  This structure may work for product-oriented founders who have little interest in go-to-market (GTM) functions [13].  The weakness here will be potential difficultly in finding good people to staff both the head of demandgen and the head of prodmkt roles.  Additionally, I’d guess that only 1 in 5 CPOs are good fits to run prodmkt.

Conclusion
In this post, we covered several topics:

  • The idea that marketing fundamentally has three parts — product, demandgen, and corpcomm — and that sometimes there’s an optional fourth, sales development.
  • That we can and should make pillar profiles to identify, at the start of a CMO search, which pillar profile we are looking for.
  • That startup CEOs are increasingly exploring alternative organizational structures to have their cake and eat it, too, when it comes to hiring marketing talent.
  • We examined four different structures and quickly discussed the strengths and weaknesses of each.

# # #

Notes
[1] A more detailed list:  positioning, messaging, high-value content (e.g., collateral), sales tools and training, support for public relations (PR) and analyst relations (AR).  Because the latter is fairly product-focused and time-intensive (e.g., extensive RFPs, briefings, and demos), you increasingly see AR split from PR and often reporting into product marketing.

[2] I contemplated, but deliberately did not pick “pipeline,” because sales must be responsible for the pipeline.  I could have said “generating pipeline,” but that’s two words and marketing is only one of four sources for so doing.

[3] As companies get larger and have multiple products, the need emerges for a corporate-level capstone message that transcends product line messages.  Note that I contemplated but deliberately avoided chosing “brand” as the single word, because it’s highfalutin for an early-stage startup.  See my post, practical thoughts on branding.

[4] Meaning specifically, had their formative career experiences working in and thus both have a deep knowledge of and the embedded point of view of that given department.  Finance people generally look at the company differently than accounting people.  Ditto for product marketers and demandgen people.

[4A] On the rough theory that, “the universe doesn’t make those,” so if you’re actually going to hire someone you need to realize that even 15 points is a pretty good score.

[5] As a board member, nothing is a bigger red flag on a marketing search than when the three finalists bear no resemblance to each other, e.g., a {5, 3, 3, 2}, a {2, 5, 3, 4}, and a {2, 3, 5, 3}.  If you need a {2, 5, 3, 4} then all finalists should be pretty close to that pillar profile.  You’ve effectively deferred deciding what you need until picking, which wastes time and changes your final decision from, “of the three people who look like what we need, which one is best for us,” to “what type of person do we need again?”

[6] It may be hard to believe but 25 years ago, before the widespread adoption of CRM, marketing had largely tactical and poorly measured responsibilities on lead generation.  Here’s my take on the evolution of software marketing.

[7] While I know early-stage startups don’t often have CXO-style titles, please consider CXO here to be a compact notation for saying “VP of <function>” or “Head of <function>”.

[8] I’ll leave the SDR question to the side as I view it as orthogonal and addressed in this post.

[9] I suppose I could just say contemporary, but “new, traditional” strikes me as more precise.  It’s now the “traditional” way, but yes, it’s still fairly “new” from where I sit.  Neotraditional doesn’t work as that means a new adaptation of a traditional thing.

[10] Becoming an incrementalist is the occupational hazard of a career in product management.

[11] In which case the CMO would have no functional job change but get put under the prodmkt leader. If this is a possiblity far better to call one VP of Prodmkt and the other VP of Marketing, so when that occurs it’s a promotion to CMO for one of them, but not a demotion for the other.

[12] There is no doubt some religion in my dislike, having been CMO of three companies for over a decade.  I think the rational argument is that you don’t need to put marketing under sales to align marketing with sales, and such, doing so is rather a brute-force approach that will result in a smaller candidate pool.  Moreover, most CROs know little about marketing and are not able to add much value when they manage it.

[13] Of course my general advice is to develop that interest.  The typical SaaS company spends twice on S&M what it spends on R&D.  Thus, while you may think you founded a product company, you actually founded a distribution business.  So go figure it out!  (And it’s fun.)

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Posted in Leadership, Management, Marketing, People, SaaS, Startups

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

Posted on August 27, 2022 | 7 comments

“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.

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Posted in Management, Marketing, SaaS, Startups, Strategy, VC

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

Posted on August 21, 2022 | 1 comment

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.

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