Category Archives: Venture Capital

Highlights from the Fenwick & West 3Q18 Venture Capital Survey

It’s been a few years since I wrote about this survey, which I post about less to communicate recent highlights and more to generate awareness of its existence.  The Fenwick & West Venture Capital Survey is must-read material for any entrepreneur or startup CEO because it not only makes you aware of trends in financing, but also provides an excellent overview of venture capital terminology as well as answering the important question of “what’s normal” in today’s venture funding environment (also known as “what’s market.”)

So if you’re not yet subscribed to it, you can sign up here.

3Q18 F&W Venture Capital Survey Highlights

  • 215 VC financing rounds were closed by companies with headquarters in Silicon Valley
  • Up rounds beat down rounds 78% to 9%
  • The average price increase (from the prior round) was 71%
  • 24% of rounds had a senior liquidation preference
  • 8% of rounds had multiple liquidation preferences
  • 11% of rounds has participation
  • 6% of rounds had cumulative dividends
  • 98% of rounds had weighted-average anti-dilution provisions
  • 2% of rounds had pay-to-play provisions
  • 6% of rounds had redemption rights

In summary, terms remained pretty friendly and valuations high.  Below is Fenwick’s venture capital barometer which focuses on price changes from the prior financing round.  It’s a little tricky to interpret because the amount of time between rounds varies by company, but it does show in any given quarter what the price difference is, on average, across all the financings closed in that quarter.  In 3Q18, it was 71%, slightly down from the prior quarters, but well above the average of 57%.

vc barometer

 

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Lost and Founder: A Painful Yet Valuable Read

Some books are almost too honest.  Some books give you too much information (TMI).  Some books can be hard to read at times.  Lost and Founder is all three.  But it’s one of the best books I’ve seen when it comes to giving the reader a realistic look at the inside of Silicon Valley startups.NeueHouse_Programming_LostandFound

In an industry obsessed with the 1 in 10,000 decacorns and the stories of high-flying startups and their larger-than-life founders, Lost and Founder takes a real look at what it’s like to found, fund, work at, and build a quite successful but not media- and Sand-Hill-Road-worshiped startup.

Rand Fishkin, the founder of Moz, tells the story of his company from its founding as a mother/son website consultancy in 2001 until his handing over the reins, in the midst of battling depression, to a new CEO in February 2014.  But you don’t read Lost and Founder to learn about Moz.  You read it to learn about Rand and the lessons he learned along the way.

Excerpt:

In 2001, I started working with my mom, Gillian, designing websites for small businesses in the shadow of Microsoft’s suburban Seattle-area campus. […] The dot-com bust and my sorely lacking business acumen meant we struggled for years, but eventually, after trial and error, missteps and heartache, tragedy and triumph, I found myself CEO of a burgeoning software company, complete with investors, employees, customers, and write-ups in TechCrunch.

By 2017, my company, Moz, was a $45 million/ year venture-backed B2B software provider, creating products for professionals who help their clients or teams with search engine optimization (SEO). In layman’s terms, we make software for marketers. They use our tools to help websites rank well in Google’s search engine, and as Google became one of the world’s richest, most influential companies, our software rose to high demand.

Moz is neither an overnight, billion-dollar success story nor a tragic tale of failure. The technology and business press tend to cover companies on one side or the other of this pendulum, but it’s my belief that, for the majority of entrepreneurs and teams, there’s a great deal to be learned from the highs and lows of a more middle-of-the-road startup life cycle.

Fishkin’s style is transparent and humble.  While the book tells a personal tale, it is laden with important lessons.  In particular, I love his views on:

  • Pivots (chapter 4).  While it’s a hip word, the reality is that pivoting — while sometimes required and which sometimes results in an amazing second efforts — means that you have failed at your primary strategy.  While I’m a big believer in emergent strategy, few people discuss pivots as honestly as Fishkin.
  • Fund-raising (chapters 6 and 7).  He does a great job explaining venture capital from the VC perspective which then makes his conclusions both logical and clear.  His advice here is invaluable.  Every founder who’s unfamiliar with VC 101 should read this section.
  • Making money (chapter 8) and the economics of founding or working at a startup.
  • His somewhat contrarian thoughts on the Minimum Viable Product (MVP) concept (chapter 12).  I think in brand new markets MVPs are fine — if you’ve never seen a car then you’re not going to look for windows, leather seats, or cup-holders.  But in more established markets, Fishkin has a point — the Exceptional Viable Product (EVP) is probably a better concept.
  • His very honest thoughts on when to sell a startup (chapter 13) which reveal the inherent interest conflicts between founders, VCs, and employees.
  • His cheat codes for next itme (Afterword).

Finally, in a Silicon Valley where failure is supposedly a red badge of courage, but one only worn after your next big success, Fishkin has an unique take on vulnerability (chapter 15) and his battles with depression, detailed in this long, painful blog post which he wrote the night before this story from the book about a Foundry CEO summit:

Near the start of the session, Brad asked all the CEOs in the room to raise their hand if they had experienced severe anxiety, depression, or other emotional or mental disorders during their tenure as CEO. Every hand in the room went up, save two. At that moment, a sense of relief washed over me, so powerful I almost cried in my chair. I thought I was alone, a frail, former CEO who’d lost his job because he couldn’t handle the stress and pressure and caved in to depression. But those hands in the air made me realize I was far from alone— I was, in fact, part of an overwhelming majority, at least among this group. That mental transition from loneliness and shame to a peer among equals forever changed the way I thought about depression and the stigma around mental disorders.

Overall, in a world of business books that are often pretty much the same, Lost and Founder is both quite different and worth reading.  TMI?  At times, yes.  TLDR?  No way.

Thanks, Rand, for sharing.

Video of my SaaStr 2018 Presentation: Ten Non-Obvious Things About Scaling SaaS

While I’ve blogged about this presentation before, I only recently stumbled into this full-length video of this very popular session — a 30-minute blaze through some subtle SaaS basics.  Enjoy!

I look forward to seeing everyone again at SaaStr Annual 2019.

The Big Mistake You Might Be Making In Calculating Churn: Failing to Annualize Multi-Year ATR Churn Rates

Most of the thinking, definitions, and formulas regarding SaaS unit economics is based on assumptions that no longer reflect the reality of the enterprise SaaS environment.  For example, thinking in terms of MRR (monthly recurring revenue) is outdated because most enterprise SaaS companies run on annual contracts and thus we should think in terms of ARR (annual recurring revenue) instead.

Most enterprise SaaS companies today do a minimum one-year contract and many do either prepaid or non-prepaid multi-year contracts beyond that. In the case of prepaid multi-year contracts, metrics like the CAC payback period break (or at the very least, get difficult to interpret).  In the case of multi-year contacts, calculating churn correctly gets a lot more complicated – and most people aren’t even aware of the issue, let alone analyze it correctly.

If your company does multi-year contracts and you are not either sidestepping this issue (by using only ARR-pool-based rates) or correcting for it in your available-to-renew (ATR) churn calculations, keep reading.  You are possibly making a mistake and overstating your churn rate.

A Multi-Year Churn Example
Let’s demonstrate my point with an example where Company A does 100% one-year deals and Company B does 100% three-year deals.  For simplicity’s sake, we are going to ignore price increases and upsell [1].  We’re also not going to argue the merits of one- vs. three-year contracts; our focus is simply how to calculate churn in a world of them.

In the example below, you can see that Company A has an available-to-renew-based (ATR-based) [2] churn rate of 10%.  Company B has a 27% ATR-based churn rate.  So we can quickly conclude that Company A’s a winner, and Company B is a loser, right?

Capture

Not so fast.

At the start of year 4, a cohort of Company A customers is worth 72.9 units, the exact same as a cohort of Company B customers.  In fact, if you look at lifetime value (LTV), the Company B cohort is worth nearly 10% more than the Company A cohort [3].

my churn1

Wait a minute!  How can a company with 27% churn rate be “better” than a company with 10% churn rate?

It’s All About Exposure:  How Often are Deals Exposed to the Churn Rate?
One big benefit of multi-year deals is that they are exposed to the churn rate less frequently than one-year deals.  When you exclude the noise (e.g., upsell, discounts, and price increases), and look at churn solely as a decay function, you see that the N-year retention rate [4] is (1-churn rate)^N.  With 10% churn, your 2-year retention rate is (1-0.1)^2 = 0.9^2 = 0.81.  Your 3-year retention rate is (1-0.1)^3 = 0.9^3 = 0.729, or a retention rate of 73%, equivalent to a churn rate of 27%.

Simply put, churn compounds so exposing a contract to the churn rate less often is a good thing:  multi-year deals do this by excluding contracts from the ATR pool, typically for one or two years, before they come up for renewal [5].  This also means that you cannot validly compare churn rates on contracts with different duration.

This is huge.  As we have just shown, a 10% churn rate on one-year deals is equivalent to a 27% churn rate on three-year deals, but few people I know recognize this fact.

I can imagine two VCs talking:

“Yo, Trey.”

“Yes.”

“You’re not going to believe it, I saw a company today with a 27% churn rate.”

“No way.”

“Yep, and it crushed their LTV/CAC — it was only 1.6.”

“Melting ice cube.  Run away.”

“I did.”

Quite sad, in fact, because with a correct (annualized) churn rate of 10% and holding the other assumptions constant [6], the LTV/CAC jumps to healthy 4.4.  But any attempt to explain a 27% churn rate is as likely to be seen as a lame excuse for a bad number as it is to be seen as valid analysis.

Best Alternative Option:  Calculate Churn Rates off the Entire ARR Pool
I’m going to define the 27% figure as the nominal ATR-based churn rate.  It’s what you get when you take churn ARR / ATR in any given period.  I call it a nominal rate because it’s not annualized and it doesn’t reflect the varying distribution of 1Y, 2Y, and 3Y deals that are mixed in the ATR pool in any given quarter.  I call it nominal because you can’t validly compare it to anything [7].

Because correcting this to a more meaningful rate is going to involve a lot of brute force math, I’ll first advise you to do two things:

  • Banish any notion from your mind that ATR rates are somehow “more real” than churn rates calculated against the entire ARR pool [8].
  • Then use churn rates calculated against the entire ARR pool and sidestep the mess we’re about to enter in the next section [9] where we correct ATR-based churn rates.

In a world of mixed-duration contracts calculating churn rates off the entire ARR pool effectively auto-corrects for the inability of some contracts to churn.  I have always believed that if you were going to use the churn rate in a math function (e.g., as the discount rate in an NPV calculation) that you should only use churn rates calculated against the entire ARR pool because, in a mixed multi-year contract world, only some of the contracts come up for renewal in any given period.  In one sense you can think of some contracts as “excluded from the available-to-churn (ATC) pool.”  In another, you can think of them as auto-renewing.  Either way, it doesn’t make sense in a mixed pool to apply the churn rate of those contracts up for renewal against the entire pool which includes contracts that are not.

If you want to persist in using ATR-based churn rates, then we must correct for two problems:  we need to annualize the multi-year rates, and we then need to calculate ATR churn using an ATR-weighted average of the annualized churn rates by contract duration.

Turning Nominal ATR Churn into Effective, Annualized ATR Churn
Here’s how to turn nominal ATR churn into an effective, annualized ATR churn rate [10] [11]:

Step 1:  categorize your ATR and churn ARR by contract duration.  Calculate a 1Y churn rate and nominal 2Y and 3Y ATR churn rates.

Step 2:  annualize the nominal multi-year (N-year) churn rates by flipping to retention rates and taking the Nth root of the retention rate.  For example, our 27% 3-year churn rate is equivalent to a 73% 3-year retention rate, so take the cube root of 0.73 to get 0.9.  Then flip back to churn rates and get 10%.

Step 3:  do an ATR-weighted average of the 1Y and annualized 2Y and 3Y churn rates.  Say your ATR was 50% 1Y, 25% 2Y, and 25% 3Y contracts and your annualized churn rates were 10%, 12%, and 9%.  Then the weighted average would be (0.5*0.10) + (0.25*0.12) + (0.25*0.09) = 10.25%, as your annualized, effective ATR churn rate.

That’s it.  You’ve now produced an ATR churn rate that is comparable to a one in a company that does only 1-year contracts.

Conclusion
If nothing else, I hope I have convinced that you it is invalid to compare churn rates on contracts of different duration and ergo that is simpler to generally calculate churn rates off the entire ARR pool.  If, however, you still want to see ATR-based churn rates, then I hope I’ve convinced you that you must do the math and calculate ATR churn as a weighted average of annualized one-, two-, and three-year ATR churn rates.

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Notes
[1] In a world of zero upsell there is no difference between gross and net churn rates, thus I will simply say “churn rate” in this post.

[2] As soon as you start doing multi-year contracts then the entire ARR base is no longer up for renewal each year.  You therefore need a new concept, available to renew (ATR), which reflects only that ARR up for renewal in a given period.

[3] Thanks to its relatively flatter step-wise decay compared to Company A’s more linear decay.

[4] Retention rate = 1 – churn rate.

[5] If it helps, you can think of the ATR pool in a glass half-empty way as the available-to-churn pool.

[6] Assuming CAC ratio of 1.8 and subscription gross margins of 80%.

[7] Unless your company has a fixed distribution of deals by contract duration – e.g., a degenerate case being 100% 3Y deals.  For most companies the average contract duration in the inbound ATR pool is going to vary each quarter.  Ergo, you can’t even validly compare this rate to itself over time without factoring in the blending.

[8] Most people I meet seem to think ATR rates are more real than rates based on the entire ARR pool.  Sample conversation  — “what’s your churn rate?”  “6%.”  “Gross or net?  “Gross.”  “No, I mean your real churn rate – what gets churned divided only by what was up for renewal.”    The mistake here is in thinking that using ATR makes it comparable to a pure one-year churn rate – and it doesn’t.

[9] Gross churn = churn / starting period ARR.  Net churn = (gross churn – upsell) / starting period ARR.

[10] I thought about trying a less brute-force way using average contract duration (ACD) of the ATR pool, but decided against it because this method, while less elegant, is more systematic.

[11] Note that this method will still understate the LTV advantage of the more step-wise multi-year contract decay because it’s not integrating the area under the curve, but instead intersecting what’s left of the cohort after N years.  In our first example, the 1Y and 3Y cohorts both had 73 units of ARR, but because the multi-year cohort decayed more slowly it’s LTV to that point was about 10% higher.

Bookings vs. Billings in a SaaS Company

Financial analysts speak a lot about “billings” in a public SaaS companies, but in private VC-backed SaaS companies, you rarely hear discussion of this metric.  In this post, we’ll use a model of a private SaaS company (where we know all the internal metrics), to show how financial analysts use rules of thumb to estimate and/or impute internal SaaS metrics using external ones – and to see what can go wrong in that process.

For reference, here’s an example of sell-side financial analyst research on a public SaaS company that talks about billings [1].

saas1-zen

Let’s start with a quick model that builds up a SaaS company from scratch [1].  To simplify the model we assume all deals (both new and renewal) are for one year only and are booked on the last day of the quarter (so zero revenue is ever recognized in-quarter from a deal).  This also means next-quarter’s revenue is this-quarter’s ending annual recurring revenue (ARR) divided by 4.

saas13

Available to renew (ATR) is total subscription bookings (new and renewal) from four quarters prior.  Renew bookings are ATR * (1 – churn rate).  The trickiest part of this model is the deferred revenue (DR) waterfall where we need to remember that the total deferred revenue balance is the sum of DR leftover from the current and the prior three quarters.

If you’re not convinced the model is working and/or want to play with it, you can download it, then see how things work by setting some drivers to boundary conditions (e.g., churn to 0%, QoQ sales growth to 0, or setting starting ARR to some fixed number [2]).

 The Fun Part:  Imputing Internal Metrics from External Ones

Now that we know what’s going on the inside, let’s look in from the outside [3]:

  • All public SaaS companies release subscription revenues [4]
  • All public SaaS companies release deferred revenues (i.e., on the balance sheet)
  • Few SaaS companies directly release ARR
  • Few SaaS companies release ATR churn rates, favoring cohort retention rates where upsell both masks and typically exceeds churn [5]

It wasn’t that long ago when a key reason for moving towards the SaaS business model was that SaaS smoothed revenues relative to the all-up-front, lumpy on-premises model.  If we could smooth out some of that volatility then we could present better software companies to Wall Street.  So the industry did [6], and the result?  Wall Street immediately sought a way to look through the smoothing and see what’s really going on in the inherently lumpy, backloaded world of enterprise software sales.

Enter billings, the best answer they could find to do this.  Billings is defined as revenue plus change in deferred revenue for a period.  Conceptually, when a SaaS order with a one-year prepayment term is signed, 100% of it goes to deferred revenue and is burned down 1/12th every month after that.  To make it simple, imagine a SaaS company sells nothing in a quarter:  revenue will burn down by 1/4th of starting deferred revenue [7] and the change in deferred revenue will equal revenue – thus revenue plus change in deferred revenue equals zero.  Now imagine the company took an order for $50K on the last day of the quarter.  Revenue from that order will be $0, change in deferred will be +$50K, implying new sales of $50K [8].

Eureka!  We can see inside the SaaS machine.  But we can’t.

Limitations of Billings as a SaaS Metric

If you want to know what investors really care about when it comes to SaaS metrics, ask the VC and PE folks who get to see everything and don’t have to impute outside-in.  They care about

Of those, public company investors only get a clear look at subscription gross margins and the customer acquisition cost (CAC) ratio.  So, looking outside-in, you can figure out how efficiency a company runs its SaaS service and how efficiently it acquires customers [9].

But you typically can’t get a handle on churn, so you can’t calculate LTV/CAC or CAC Payback Period.  Published cohort growth, however, can give you comfort around potential churn issues.

But you can’t get a precise handle on sales growth – and that’s a huge issue as sales growth is the number one driver of SaaS company valuation [10].  That’s where billings comes into play.  Billings isn’t perfect because it shows what I call “total subscription bookings” (new ARR bookings plus renewal bookings) [11], so we can’t tell the difference between a good sales and weak renewals quarter and a bad sales and a good renewals quarter.

Moreover, billings has two other key weaknesses as a metric:

  • Billings is dependent on prepaid contract duration
  • Companies can defer processing orders (e.g., during crunch time at quarter’s end, particularly if they are already at plan) thus making them invisible even from a billings perspective [12]

Let’s examine how billings depends on contract duration.  Imagine it’s the last day of new SaaS company’s first quarter.  The customer offers to pay the company:

  • 100 units for a prepaid one-year subscription
  • 200 units for a prepaid two-year subscription
  • 300 units for a prepaid three-year subscription

From an ARR perspective, each of the three possible structures represents 100 units of ARR [13].  However, from a deferred revenue perspective, they look like 100, 200, 300 units, respectively.  Worse yet, looking solely at deferred revenue at the end of the quarter, you can’t know if 300 units represents three 100-unit one-year prepay customers or a single 100-unit ARR customer who’s done a three-year prepay.

In fact, when I was at Salesforce we had the opposite thing happen.  Small and medium businesses were having a tough time in 2012 and many customers who’d historically renewed on one-year payment cycles started asking for bi-annual payments.  Lacking enough controls around a rarely-used payment option, a small wave of customers asked for and got these terms.  They were happy customers.  They were renewing their contracts, but from a deferred revenue perspective, suddenly someone who looked like 100 units of deferred revenue for an end-of-quarter renewal suddenly looked 50.  When Wall St. saw the resultant less-than-expected deferred revenue (and ergo less-than-expected billings), they assumed it meant slower new sales.  In fact, it meant easier payment terms on renewals – a misread on the business situation made possible by the limitations of the metric.

This issue only gets more complex when a company is enabling some varying mix of one through five year deals combined with partial up-front payments (e.g., a five-year contract with years 1-3 paid up front, but years 4 and 5 paid annually).  This starts to make it really hard to know what’s in deferred revenue and to try and use billings as a metric.

Let’s close with an excerpt from the Zuora S-1 on billings that echoes many of the points I’ve made above.

saas3

Notes

[1] Source:  William Blair, Inc., Zendesk Strong Start to 2018 by Bhavan Suri.

[2] Even though it’s not labelled as a driver and will break the renewals calculations, implicitly assuming all of it renews one year later (and is not spread over quarters in anyway).

[3] I’m not a financial analyst so I’m not the best person to declare which metrics are most typically released by public companies, so my data is somewhat anecdotal.  Since I do try to read interesting S-1s as they go by, I’m probably biased towards companies that have recently filed to go public.

[4] As distinct from services revenues.

[5] Sometimes, however, those rates are survivor biased.

[6] And it worked to the extent that from a valuation perspective, a dollar of SaaS revenue is equivalent to $2 to $4 of on-premises revenue.  Because it’s less volatile, SaaS revenue is more valuable than on-premises revenue.

[7] Provided no customers expire before the last day of the quarter

[8] Now imagine that order happens on some day other than the last day of the quarter.  Some piece, X, will be taken as revenue during the quarter and 50 – X will show up in deferred revenue.  So revenue plus change in deferred revenue = it’s baseline + X + 50 – X = baseline + 50.

[9] Though not with the same clarity VCs can see it — VCs can see composition of new ARR (upsell vs. new business) and sales customers (new customer acquisition vs. customer success) and thus can create more precise metrics.  For example, a company that has a solid overall CAC ratio may be revealed to have expensive new business acquisition costs offset by high, low-cost upsell.

[10] You can see subscription revenue growth, but that is smoothed/damped, and we want a faster way to get the equivalent of New ARR growth – what has sales done for us lately?

[11] It is useful from a cash forecasting perspective because all those subscription billings should be collectible within 30-60 days.

[12] Moving the deferred revenue impact of one or more orders from Q(n) to Q(n+1) in what we might have called “backlogging” back in the day.  While revenue is unaffected in the SaaS case, the DR picture looks different as a backlogged order won’t appear in DR until the end of Q(n+1) and then at 75, not 100, units.

[13] Normally, in real life, they would ask a small discount in return for the prepay, e.g., offer 190 for two years or 270 for three years.  I’ll ignore that for now to keep it simple.

The Question that CEOs Too Often Don’t Discuss with the Board

Startup boards are complex.  While all board members own stock in the company their interests are not necessarily aligned.

  • Founders may be motivated by a vision to change the world, to hit a certain net worth target, to see their name in an S-1, to make the Forbes 500, or — and I’ve seen crazier things — to make more than their Stanford roommate.  First-time founders with little net worth can be open to selling at relatively low prices.  Conversely, serial successful founders may need a large exit simply to move the needle on their net worth.  Founders can also be religious zealots and take positions like “I wouldn’t sell to Microsoft or Oracle at any price.”
  • Independent board members typically have significant net worth (i.e., they’ve been successful at something which is why want them on your board) and relatively small stakes which, by default, financially incents them to seek large exits.  While they notionally represent the common stock, they are often aligned with either the founders or one of the investors in the company — they got on the board for a reason, often existing relationships —  and thus their views may be shaped by the real or perceived interest of those parties.  Or, they can simply drive an agenda that they believe is best for the company — whatever they happen to think “best” means.
  • Venture capitalists (VCs) are motivated by generating returns for their funds.  Simple, right?  Not so fast.  VC is increasingly a “hits business” where a few large outcomes can mean the difference between at 10% and 35% IRR over a fund’s ten-year life.  Thus, VCs have a general tendency to seek huge exits (“better to sell too late than too early”), but they are also motivated by other factors such as the expectations they set when they raised their fund, the performance of other investments in the fund (e.g., do they need a big hit to bail out a few bad bets), and their relationships with members of other funds represented on the company’s board.

In this light, it’s clearly simplistic to say that everyone is aligned around a single goal:  to maximize the value of the stock.  Yes, surely that is true at one level.  But it gets a bit more complicated than that.

That’s why it’s so important that CEOs ask the board one question that, somewhat amazingly, they all too often don’t:  what does success look like?  And it doesn’t hurt to re-ask it every few years as any given board member’s position may change over time.

I’m always shocked how the simplest of questions can generate the most debate.

Aside:  back in the day at Business Objects (~1998), I suggested bringing in the Chasm Group to help us with a three-day, strategic planning offsite.  I figured we’d spend a morning reviewing the key concepts in Crossing the Chasm, at most one afternoon generating consensus on where we sat on their technology adoption lifecycle curve, and then two days working on strategic goals and operational plans after that.

Tech-Adoption-Lifecycle-01

With about 12 people who had worked together closely for years, after three full days we never agreed where we sat on the curve.  We spent literally the entire time arguing, often intensely, and never even got to the rest of the agenda.  Fortunately, that didn’t end up impeding our success, but it was a big lesson for me.  End aside.

So be ready for that simple question to generate a long answer.  Most probably, several long answers.  In fact, in order to get the best answer, I’d suggest asking board members about it first individually (to avoid any group decision-making biases) and then discuss it as a group.

But before examining the answers you can expect to this question, let’s take a minute to consider why this conversation doesn’t occur more often and more naturally.  I think there are three generic reasons:

  • Conflict aversion.  Perhaps sensing real misalignment, like in a bad marriage the CEO and board tacitly agree to not discuss the problem until they must.  You may hear or make excuses like “let’s cross that bridge when we come to it,”  “let’s execute this year’s plan and then discuss that,” or “if there’s no offer on the table then there’s nothing to discuss.”  Or, in a more Machiavellian situation, a board member may be thinking, “let’s ride Joe like a rented mule to $5M and then shoot him,” continuallying defer the conversation on that logic.  Pleasant or unpleasant, it’s usually better to address conflicts early rather than letting them fester.
  • Rationalization of unrealistic expectations.  If some board members constantly refrain “this can be a billion-dollar company,” perhaps the CEO rationalizes it, thinking “they don’t really believe that; they’re just saying it because they think they’re supposed to.”  But what if they do believe it?
  • The gauche factor.  Some people seem to think it’s a gauche topic of conversation.  “Hey, our company vision statement says we’re making the world a better place through elegant hierarchies for maximum code reuse and extensibility, we shouldn’t be focusing on something so crass as the exit, we should be talking about making the world better.”  VCs invest money for a reason, they measure results by the IRR, and they can typically cite their IRRs (and those of their partners) from memory.  It’s not gauche to discuss expectations and exits.

When you ask your board members what success looks like these are the kinds of things you might hear:

  • Disrupting the leader in a given market.
  • Building a $1B revenue company.
  • Becoming a unicorn ($1B valuation).
  • Changing the way people work.
  • Getting a 10x in 5-7 years for an early stage fund, or getting a 3x in 3-5 years for a later stage fund.
  • Showing my Mother my name in an S-1 (a sub-case of “going public”).
  • Getting our software into the hands of over 1M people.
  • Realizing the potential of the company.
  • Selling the company for more than I think it’s worth.
  • Getting acquired by Google or Cisco for a price above a given threshold.
  • Building a true market leader.
  • Creating a Silicon Valley icon, a household name.
  • Selling the company for {a base-hit, double, triple, home-run, or grand-slam} outcome.

Given the possibility of a list as heterogeneous as this, doesn’t it make sense to get this question on the table as opposed to in the closet?

I learned my favorite definition of strategy from a Stanford professor who defined strategy as “the plan to win.”  The beauty of this definition is that it instantly begs the question “what is winning?”  Just as that conversation can be long, contentious, and colorful, so is the answer to the other, even more critical question:  what does success look like?

Don’t Let Product Management Turn Into “The Roadmap Guys”

At many enterprise software companies product management (PM) ends up defaulting into a role that I can’t stand:  The Roadmap Guys*.

Like a restaurant with one item on the menu, the company defaults into ordering one thing from product management:  a roadmap pitch.

  • “The VP of PM is in Boston and Providence this week, can she visit some customers and do a few roadmap presentations?”
  • “Hey, there’s a local user group in NY this week; can PM do a roadmap pitch?”
  • “There’s a big customer in the executive briefing center today; can the PM do a roadmap?”
  • “As part of our sales cycle with prospect X, we’d love to get PM in to discuss the roadmap.”
  • “We’ve got a SAS day with Gartner next week, can PM come in a present the roadmap?”

You hear it all the time.  And I hate it.  Why?

From a sales perspective, roadmap presentations are the anti-sales pitch:  a well organized presentation of all the things your products don’t do.  Great, let’s spend lots of time talking about that.

From a competitive perspective, you’re broadcasting your plans.  If you’re presenting roadmap to every prospect who comes through the briefing center and at every local user group meeting, your competition is going to learn your roadmap, and fast.  Then they can copy it and/or blunt it.

But what irks me the most is what happens from a product management perspective:  you turn PM into “the talking guys” instead of “the listening guys.”  Given enough time, PM starts to view itself as the folks who show up and pitch roadmaps.

But that’s not their job.

PM should be the listening folks, not the talking folks.  Just like sales, PM should remember the adage:  we have two ears and one mouth; use them in proportion.

Wouldn’t the world be a better place if we changed the five previous bullets as follows?

  • “The VP of PM is in Boston and Providence this week, can she visit some customers and observe how people actually use the product?”
  • “Hey, there’s a local user group in NY this week; can PM break off a small focus group to ask customers about how they use the product?”
  • “There’s a big customer in the executive briefing center today; can PM come in and interview them about their impressions on evaluating the product?”
  • “As part of our sales cycle with prospect X, we’d love to get PM in to discuss what specifically they are trying to accomplish and how the product can do that?”
  • “We’ve got a SAS day with Gartner next week, can PM come in and hear from Gartner about what they’re seeing in the market and in their interactions with customers?”

So every time you hear the word “roadmap” in the same sentence as “product management,” stop, pause, and think of a better way to use the PM team.  Sure, there are certainly times when a roadmap presentation is in order.  But don’t default to it.  Keep your PM team listening instead of talking.

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* I’m using “guys” here in a gender-neutral sense like “folks.”