Category Archives: Startups

Kellblog's 10 Predictions for 2020

As I’ve been doing every year since 2014, I thought I’d take some time to write some predictions for 2020, but not without first doing a review of my predictions for 2019.  Lest you take any of these too seriously, I suggest you look at my batting average and disclaimers.

Kellblog 2019 Predictions Review

1.  Fred Wilson is right, Trump will not be president at the end of 2019.  PARTIAL.  He did get impeached after all, but that’s a long way from removed or resigned. 

2.  The Democratic Party will continue to bungle the playing of its relatively simple hand.  HIT.  This is obviously subjective and while I think they got some things right (e.g., delaying impeachment), they got others quite wrong (e.g., Mueller Report messaging), and continue to play more left than center which I believe is a mistake.

3.  2019 will be a rough year for the financial markets.  MISS.  The Dow was up 22% and the NASDAQ was up 35%.  Financially, maybe the only thing that didn’t work in 2019 were over-hyped IPOs.  Note to self:  avoid quantitative predictions if you don’t want to risk ending up very wrong.  I am a big believer in regression to the mean, but nailing timing is the critical (and virtually impossible) part.  Nevertheless, I do use tables like these to try and eyeball situations where it seems a correction is needed.  Take your own crack at it.

4.  VC tightens.  MISS.  Instead of tightening, VC financing hit a new record.  The interesting question here is whether mean reversion is relevant.  I’d argue it’s not – the markets have changed structurally such that companies are staying private far longer and thus living off venture capital (and/or growth-stage private equity) in ways not previously seen.  Mark Suster did a great presentation on this, Is VC Still a Thing, where he explains these and other changes in VC.  A must read.

5. Social media companies get regulated.  PARTIAL.  While “history may tell us the social media regulation is inevitable,” it didn’t happen in 2019.  However, the movement continued to gather steam with many Democratic presidential candidates calling for reform and, more notably, none other than Facebook investor Roger McNamee launching his attack on social media via his book Zucked: Waking Up To The Facebook Catastrophe.  As McNamee says, “it’s an issue of ‘right vs. wrong,’ not ‘right vs. left.’”

 

6. Ethics make a comeback.  HIT.  Ethics have certainly been more discussed than ever and related to the two reasons I cited:  the current administration and artificial intelligence.  The former forces ethics into the spotlight on a daily basis; the later provokes a slew of interesting questions, from questions of accidental bias to the trolley car problem.  Business schools continue to increase emphasis on ethics.  Mark Benioff has led a personal crusade calling for what he calls a new capitalism.

7.  Blockchain, as an enterprise technology, fades away.  HIT.  While I hate to my find myself on the other side of Ray Wang, I’m personally not seeing much traction for blockchain in the enterprise.  Maybe I’m running with the wrong crowd.  I have always felt that blockchain was designed for one purpose (to support cybercurrency), hijacked to another, and ergo became a vendor-led technology in search of a business problem.  McKinsey has a written a sort of pre-obituary, Blockchain’s Occam Problem, which was McKinsey Quarterly’s second most-read article of the year.  The 2019 Blockchain Opportunity Summit’s theme was Is Blockchain Dead?  No. Industry Experts Join Together to Share How We Might Not be Using it Right which also seems to support my argument. 

8.  Oracle enters decline phase and is increasingly seen as a legacy vendor.  HIT.  Again, this is highly subjective and some people probably concluded it years ago.  My favorite support point comes from a recent financial analyst note:  “we believe Oracle can sustain ~2% constant currency revenue growth, but we are dubious that Oracle can improve revenue growth rates.”  That pretty much says it all.

9.  ServiceNow and/or Splunk get acquired.  MISS.  While they’re both great businesses and attractive targets, they are both so expensive only a few could make the move – and no one did.  Today, Splunk is worth $24B and ServiceNow a whopping $55B.

10.  Workday succeeds with its Adaptive Insights agenda.  HIT.  Changing general ledgers is a heart transplant while changing planning systems is a knee replacement.  By acquiring Adaptive, Workday gave itself another option – and a far easier entry point – to get into corporate finance departments.  While most everyone I knew scratched their head at the enterprise-focused Workday acquiring a more SMB-focused Adaptive, Workday has done a good job simultaneously leaving Adaptive alone-enough to not disturb its core business while working to get the technology more enterprise-ready for its customers.  Whether that continues I don’t know, but for the first 18 months at least, they haven’t blown it.  This remains high visibility to Workday as evidenced by the Adaptive former CEO (and now Workday EVP of Planning) Tom Bogan’s continued attendance on Workday’s quarterly earnings calls.

With the dubious distinction of having charitably self-scored a 6.0 on my 2019 predictions, let’s fearlessly roll out some new predictions for 2020.

Kellblog 2020 Predictions

1.  Ongoing social unrest. The increasingly likely trial in the Senate will be highly contentious, only to be followed by an election that will be highly contentious as well.  Beyond that, one can’t help but wonder if a defeated Trump would even concede, which could lead to a Constitutional Crisis of the next level. Add to all that the possibility of a war with Iran.  Frankly, I am amazed that the Washington, DC continuous distraction machine hasn’t yet materially damaged the economy.  Like many in Silicon Valley, I’d like Washington to quietly go do its job and let the rest of us get back to doing ours.  The reality TV show in Washington is getting old and, happily, I think many folks are starting to lose interest and want to change the channel.

2.  A desire for re-unification.  I remain fundamentally optimistic that your average American – Republican, Democrat, or the completely under-discussed 38% who are Independents — wants to feel part of a unified, not a divided, America.  While politicians often try to leverage the most divisive issues to turn people into single-issue voters, the reality is that far more things unite us as Americans than divide us.  Per this recent Economist/YouGov wide-ranging poll, your average American looks a lot more balanced and reasonable than our political party leaders.  I believe the country is tired of division, wants unification, and will therefore elect someone who will be seen as able to bring people together.  We are stronger together.

3.  Climate change becomes the new moonshot.  NASA’s space missions didn’t just get us to the moon; they produced over 2,000 spin-off technologies that improve our lives every day – from emergency “space” blankets to scratch-resistant lenses to Teflon-coated fabrics.  Instead of seeing climate change as a hopeless threat, I believe in 2020 we will start to reframe it as the great opportunity it presents.  When we mobilize our best and brightest against a problem, we will not only solve it, but we will create scores to hundreds of spin-off technologies that will benefit our everyday lives in the process.  See this article for information on 10 startups fighting climate change, this infographic for an overview of the kinds of technologies that could alleviate it, or this article for a less sanguine view on the commitment required and extent to which we actually can de-carbonize the air. Or check out this startup which makes “trees” that consume the pollution of 275 regular trees.

4.  The strategic chief data officer (CDO).  I’m not a huge believer in throwing an “O” at every problem that comes along, but the CDO role is steadily becoming mainstream – in 2012 just 12% of F1000 companies reported having a CDO; in 2018 that’s up to 68%.  While some of that growth was driven by defensive motivations (e.g., compliance), increasingly I believe that organizations will define the CDO more strategically, more broadly, and holistically as someone who focuses on data, its cleanliness, where to find it, where it came from, its compliance with regulations as to its usage, its value, and how to leverage it for operational and strategic advantage.   These issues are thorny, technical, and often detail-oriented and the CIO is simply too busy with broader concerns (e.g., digital transformation, security, disruption).  Ergo, we need a new generation of chief data officers who want to play both offense and defense, focused not just tactically on compliance and documentation, but strategically on analytics and the creation of business value for the enterprise. This is not a role for the meek; only half of CDOs succeed and their average tenure is 2.4 years.  A recent Gartner CDO study suggests that those who are successful take a more strategic orientation, invest in a more hands-on model of supporting data and analytics, and measure the business value of their work.

5.  The ongoing rise of DevOps.   Just as agile broke down barriers between product management and development so has DevOps broken down walls between development and operations.  The cloud has driven DevOps to become one of the hottest areas of software in recent years with big public company successes (e.g., Atlassian, Splunk), major M&A (e.g., Microsoft acquiring GitHub), and private high-flyers (e.g., HashiCorp, Puppet, CloudBees).  A plethora of tools, from configuration management to testing to automation to integration to deployment to multi-cloud to performance monitoring are required to do DevOps well.  All this should make for a $24B DevOps TAM by 2023 per a recent Cowen & Company report.  Ironically though, each step forward in deployment is often a step backward in developer experience, why is one reason why I decided to work with Kelda in 2019.

6. Database proliferation slows.  While 2014 Turning Award winner Mike Stonebraker was right over a decade ago when he argued in favor of database specialization (One Size Fits All:  An Idea Whose Time Has Come and Gone), I think we may now too much of a good thing.   DB Engines now lists 350 different database systems of 14 different types (e.g., relational, graph, time series, key-value). Crunchbase lists 274 database (and database-related) startups.  I believe the database market is headed for consolidation.  One of the first big indicators of a resurgence in database sanity was the failure of the (Hadoop-based) data lake, which happened in 2018-2019 and was the closest thing I’ve seen to déjà vu in my professional career – it was as if we learned nothing from the Field of Dreams enterprise data warehouse of the 1990s (“build it and they will come”).  Moreover, after a decade of developer-led database selection, developers and now re-realizing what database people knew along – that a lot of the early NoSQL movement was akin to throwing out the ACID transaction baby with the tabular schema bathwater.

7.  A new, data-layer approach to data loss prevention (DLP).  I always thought DLP was a great idea, especially the P for prevention.  After all, who wants tools that can help with forensics after a breach if you could prevent one from happening at all — or at least limit one in progress?  But DLP doesn’t seem to work:  why is it that data breaches always seem to be measured not in rows, but in millions of rows?  For example, Equifax was 143M and Marriott was 500M.  DLP has many known limitations.  It’s perimeter-oriented in a hybrid cloud world of dissolving perimeters and it’s generally offline, scanning file systems and database logs to find “misplaced data.”  Wouldn’t a better approach be to have real-time security monitored and enforced at the data layer, just the same way as it works at the network and application layer?  Then you could use machine learning to understand normal behavior, detect anomalous behavior, and either report it — or stop it — in real time.  I think we’ll see such approaches come to market in 2020, especially as cloud services like Snowflake, RDS, and BigQuery become increasingly critical components of the data layer.

8. AI/ML continue to see success in highly focused applications.  I remain skeptical of vendors with broad claims around “enterprise AI” and remain highly supportive of vendors applying AI/ML to specific problems (e.g., Moveworks and Astound who both provide AI/ML-based trouble-ticket resolution).  In the end, AI and ML are features, not apps, and while both technologies can be used to build smart applications, they are not applications unto themselves.  In terms of specificity, the No Free Lunch Theorem reminds us that any two optimization techniques perform equivalently when averaged across all possible problems – meaning that no one modeling technique can solve everything and thus that AI/ML is going to be about lots of companies applying different techniques to different problems.   Think of AI/ML more as a toolbox than a platform.  There will not be one big winner in enterprise AI as there was in enterprise applications or databases.  Instead, there will be lots of winners each tackling specific problems.  The more interesting battles will those between systems of intelligence (e.g., Moveworks) and systems of record (e.g., ServiceNow) with the systems-of-intelligence vendors running Trojan Horse strategies against systems-of-record vendors (first complementing but eventually replacing them) while the system-of-record vendors try to either build or acquire systems of intelligence alongside their current offerings. 

9.  Series A rounds remain hard.  I think many founders are surprised by the difficulty of raising A rounds these days.  Here’s the problem in a nutshell:

  • Seed capital is readily available via pre-seed and seed-stage investments from angel investors, traditional early-stage VCs, and increasingly, seed funds.  Simply put, it’s not that hard to raise seed money.
  • Companies are staying in the seed stage longer (a median of 1.6 years), increasingly extending seed rounds, and ergo raising more money during seed stage (e.g., $2M to $4M).
  • Such that, companies are now expected to really have achieved something in order to raise a Series A.  After all, if you have been working for 2 years and spent $3M you better have an MVP product, a handful of early customers, and some ARR to show for it – not just a slide deck talking about a great opportunity.

Moreover, you should be making progress roughly in line with what you said at the outset and, if you took seed capital from a traditional VC, then they better be prepared to lead your round otherwise you will face signaling risk that could imperil your Series A.

Simply put, Series A is the new chokepoint.  Or, as Suster likes to say, the Series A and B funnel hasn’t really changed – we’ve just inserted a new seed funnel atop it that is 3 times larger than it used to be.

10.  Autonomy’s former CEO gets extradited.  Silicon Valley is generally not a place of long memories, but I saw the unusual news last month that the US government is trying to extradite Autonomy founder and former CEO Mike Lynch from the UK to face charges.  You might recall that HP, in the brief era under Leo Apotheker, acquired enterprise search vendor Autonomy in August, 2011 for a whopping $11B only to write off about $8.8B under subsequent CEO Meg Whitman a little more than a year later in November, 2012.  Computerworld provides a timeline of the saga here, including a subsequent PR war, US Department of Justice probe, UK Serious Fraud Office investigation (later dropped), shareholder lawsuits, proposed settlements, more lawsuits including Lynch’s suing HP for $150M for reputation damages, and HP’s spinning-off the Autonomy assets.  Subsequent to Computerworld’s timeline, this past May Autonomy’s former CFO was sentenced to five years in prison.  This past March, the US added criminal charges of securities fraud, wire fraud, and conspiracy against Lynch.  Lynch continues to deny all wrongdoing, blames the failed acquisition on HP, and even maintains a website to present his point of view on the issues.  I don’t have any special legal knowledge or specific knowledge of this case, but I do believe that if the US government is still fighting this case, still adding charges, and now seeking extradition, that they aren’t going to give up lightly, so my hunch is that Lynch does come to the US and face these charges. 

More broadly, regardless of how this particular case works out, in a place so prone to excess, where so much money can be made so quickly, frauds will periodically happen and it’s probably the most under-reported class of story in Silicon Valley.  Even this potentially huge headline case – the proposed extradition of a British billionaire tech mogul —  never seems to make page one news.  Hey, let’s talk about something positive like Loft’s $175M Series C instead.

To finish this up, I’ll add a bonus prediction:  Dave doesn’t get a traditional job in 2020.  While I continue to look at VC-backed startup and/or PE-backed CEO opportunities, I am quite enjoying my work doing a mix of boards, advisory relationships, and consulting gigs.  While I remain interested in looking at great CEO opportunities, I am also interested in adding a few more boards to my roster, working on stimulating consulting projects, and a few more advisory relationships as well.

I wish everyone a happy, healthy, and above-plan 2020.

Why I'm Advising Kelda

A few months ago I signed up to be an advisor to Kelda, and I thought I’d do a quick post to talk about the company and why I decided to sign up.

What is Kelda?

Kelda provides developer sandboxes in a customer’s cloud within their Kubernetes cluster. Why does this matter?

  • The world is moving to cloud computing at a rapid place.
  • Cloud computing is moving away from virtual machines as the unit of abstraction and towards containers, microservices, and serverless architectures.
  • The exact technologies that make microservices powerful in production environments have made the development experience worse.

In short, nobody was thinking much about developers when they started migrating to these new architectures.

Think for a minute about being a developer building a microservices-based application. Then think about testing it. Your code has dependencies on scores or hundreds of microservices which in turn have dependencies on other microservices. Any or all of these microservices are themselves changing over time. How you are you supposed to find a stable test-bed on which to test your code?

Unlike production environments, run by DevOps teams with a sophisticated CI/CD platform, development environments are often primitive by comparison. Tools for collecting dependencies are not robust. Developers often have to test on their own laptops, running all the required microservices locally, which elongates test cycles because of slow performance. Moreover, debugging is potentially complicated by non-deterministic interactions among microservices.

Kelda solves all that by effectively spinning up a private, stable, server-based Kubernetes cluster where developers can test their code. If that sounds pretty practical, well it is. If that sounds pedestrian, remember that one of VMware’s top early use-case was … stable test environments for QA teams across different version of operating systems, middleware, and databases. Pragmatic solutions often generalize way beyond their initial landing point.

For more technical information on Kelda, here’s a link where you can download their white paper. And here’s an excerpt that sums things up quite nicely:

Why Did I Sign Up to Advise Kelda?

There are always many reasons behind such a decision, so in no particular order:

  • The awesome founder, Ethan Jackson, who put his Berkeley computer science PhD on the back burner in order create the company. I like that this isn’t his first corporate rodeo (he worked at Nicira –> VMware) for 5 years. I also like the burn-the-ships level of commitment.
  • The practical logic behind the product idea. Remember the famous William Gibson quote: “the future is already here — it’s just not very evenly distributed.” When you’re working at the cutting edge, the next step looks kind of obvious. So while this looks very high-tech to me, it looks pretty obvious to Ethan and, in my humble opinion, a lot of people have been very successful doing the next pretty-obvious thing (e.g., from PeopleSoft building apps atop Oracle to NetSuite taking financials to the cloud to Palo Alto Networks doing application-based firewalls).
  • The trends driving the company. Kelda is dead center of the movement to containers and microservices-based architectures in the cloud. The technology elite can use all these technologies today. Kelda makes them more accessible to the typical corporate development shop.

Should SDRs Report to Sales or Marketing?

Slowly and steadily, over the past decade, the industry has evolved from a mentality of “all salesreps must do everything” – including some percent of their time prospecting — to one of specialization.  We, with the help of books like Predictable Revenue, have collectively decided that in-bound lead processing is different from outbound lead prospecting is different from low-end, velocity sales is different from high-end, enterprise sales.

Despite the old-school, almost-character-building emphasis on prospecting, we have collectively realized that having our top hunters dialing for dollars and digging through inbound leads isn’t, well, the best use of their time.

Industrialization typically involves specialization and the industrialization of once purely artisanal software sales has been no exception.  As part of this specialization the sales development representative (SDR) role has risen to prominence.  In this post, we’ll do a quick review of what SDRs typically do and discuss the relative merits of having them report into sales vs. marketing.

“Everyone under 25 in San Francisco is an SDR.” – Anonymous startup CEO

SDRs Bridge the Two Departments

SDRs typically form the bridge between sales and marketing.  A typical SDR job is take inbound leads from marketing, perform some basic BANT-style [1] qualification on them, and then pass them to sales if indicated. While SDRs typically have activity quotas (e.g., 50 calls/day) they should be primarily measured on the number of opportunities they create per week. In enterprise software, typically that quota is 2-3 oppties/week. 

As companies get bigger they tend to separate SDRs into two groups:

  • Inbound SDRs, those who only process in-bound leads, and
  • Outbound SDRs, those who primarily do targeted outreach over the phone or email

Being an SDR is a hard job.  Typical SDR challenges include:

  • Adhering to service-level agreements for all leads (i.e., touches with timeframes)
  • Contacting prospects in an increasingly spam-hostile, call-hostile environment
  • Figuring out which leads to work on the hardest (e.g., which merit homework to customize the message and which don’t)
  • Remembering that their job is to sell meetings and not product [2]
  • Supporting multiple salespeople with often conflicting priorities [3]
  • Managing the conflict between supporting salespeople and executing the process
  • Getting salespeople to show-up at the hand-off meeting [4]
  • Avoiding burnout in a high-pressure environment

To Which Department Should SDRs Report:  Sales or Marketing?

Historically, SDRs reported to sales.  That’s probably because sales first decided to fund SDR teams as a way getting inbound lead management out of the hands of salespeople [5].  Doing so would:

  • Enable the company to consistently respond in a timely manner to all inquiries
  • Free up sales to spend more time on selling
  • Avoid the problem of individual reps not processing new leads once they are “full up” on opportunities [6]

The problem is that most enterprise software sales VPs are not particularly process-oriented [7], because they grew up in a pre-industrialized era of sales [8].  In fact, nothing drives me crazier than an old-school, artisanal, deal-person CRO insisting on owning the SDR organization despite the total inability to manage it.  They rationalize:  “Oh, I can hire someone process-oriented to manage it.”  And I think:  “but what can that person learn from you [9] about how to manage it?”  And the answer is nothing.  Your desire to own it is either pure ego or simply a ploy to enrich your resume.

I’ll say again because it drives me crazy:  do not be the VP of Sales who insists on owning the SDR organization in the annual planning meeting but then shows zero interest in it for the rest of the year.  You’re not helping anyone!

As mentioned in a footnote in a prior post, I greatly prefer SDRs reporting to marketing versus sales.  Why?

  • Marketing leadgen and nurture people are metrics- and process-oriented animals, naturally suited to manage a process-oriented department.
  • It provides a simple, clear conceptual model:  marketing is the opportunity creation factory and sales is the opportunity closing machine.

In short, marketing’s job is to make opportunities.  Sales’ job is to close them.

# # #

Notes

[1] BANT = budget, authority, need, time-frame.

[2] Most early- and mid-stage startups put SDRs in their regular sales training sessions which I think does them a disservice.  Normal sales training is about selling products/solutions.  SDRs “sell” meetings.  They should not attempt to build business value or differentiation. Training them to do so tempts them to do – even when it is not their job.

[3] A typical QCR:SDR ratio is 3-4:1, though I’ve seen as low as 1:1 and as high as 6:1

[4] Believe it or not, this sometimes happens (typically when your reps are already carrying a lot of oppties).  Few things reflect worse on the company than a last-minute rescheduling of the meet-your-salesperson call. You don’t get a second chance to make a firm impression.

[5] Although most early models had wide bypass rules  – e.g.,  “leads with VP title at this list of key accounts will get passed directly to reps for qualification” – reflecting a lack of trust in marketing beyond dropping leaflets from airplanes.

[6] That problem could still exist at hand-off (i.e., opportunity creation) time but at least we have combed through the leads to find the good ones, and reports can easily identify overloaded reps.

[7] While they may be process-oriented when it comes to the sales process for a deal moving across stages during a quarter, that is not quite the same thing as a velocity mentality driven by daily or weekly goals with tracking metrics.  If you will, there’s process-oriented and Process-Oriented.

[8] One simple test:  if your sales org doesn’t have monthly cadence (e.g., goals, forecasts) then your sales VP is probably not capital P process-oriented.

[9] On the theory you should always build organizations where people can learn from their managers.

A Historical Perspective on Why SAL and SQL Appear to be Defined Backwards

Most startups today use some variation on the now fairly standard terms SAL (sales accepted lead) and SQL (sales qualified lead).  Below see the classic [1] lead funnel model from marketing bellwether Sirius Decisions that defines this.

One great thing about working as an independent board member and consultant is that you get to work with lots of companies. In doing this, I’ve noticed that while virtually everyone uses the terminology SQL and SAL, that some people define SQL before SAL and others define SAL before SQL.

Why’s that?  I think the terminology was poorly chosen and is confusing.  After all, what sounds like it comes first:  sales accepting a lead or sales qualifying a lead?  A lot of folks would say, “well you need to accept it before you can qualify it.”  But others would say “you need to qualify it before you can accept it.”  And therein lies the problem.

The correct answer, as seen above, is that SAL comes before SQL.  I have a simple way of remembering this:  A comes before Q in the alphabet, and SAL comes before SQL in the funnel. Until I came up with that I was perpetually confused.

More importantly, I think I also have a way of explaining it.  Start by remembering two things:

  • This model was defined at a time when sales development reps (SDRs) generally reported to sales, not marketing [2].
  • This model was defined from the point of view of marketing.

Thus, sales accepting the lead didn’t mean a quota-carrying rep (QCR) accepted the lead – it meant an SDR, who works in the sales department, accepted the lead.  So it’s sales accepting the lead in the sense that the sales department accepted it.  Think: we, marketing, passed it to sales.

After the SDR worked on the lead, if they decided to pass it to a QCR, the QCR would do an initial qualification call, and then the QCR would decide whether to accept it.  So it’s a sales qualified lead, in the sense that a salesperson has qualified it and decided to accept it as an opportunity.

Think: accepted by an SDR, qualified by a salesrep.

Personally, I prefer avoid the semantic swamp and just say “stage 1 opportunity” and “stage 2 opportunity” in order to keep things simple and clear.

# # #

Notes

[1] This model has since been replaced with a newer demand unit waterfall model that nevertheless still uses the term SQL but seems to abandon SAL.

[2] I greatly prefer SDRs reporting to marketing for two reasons:  [a] unless you are running a pure velocity sales model, your sales leadership is more likely to deal-people than process-people – and running the SDRs is a process-oriented job and [b] it eliminates a potential crack in the funnel by passing leads to sales “too early”.  When SDRs report to marketing, you have a clean conceptual model:  marketing is the opportunity creation factory and sales is the opportunity closing factory.

The Red Badge of Courage: Helping Overachievers to Manage and Process Failure

When I lived in France for five years I was often asked to compare it to Silicon Valley in an attempt to explain why — in the land of Descartes, Fourier, and Laplace, in a country where the nation’s top university (École Polytechnique) is a military engineering school that wraps together MIT and West Point, in a place that naturally reveres engineers and scientists, why was there not a stronger tech startup ecosystem?

My decade-plus-old answer is here: Is Silicon Valley Reproducible? [1]

My answer to the question was “no” and the very first reason I listed was “cultural attitudes towards failure.” In France (at least at that time) failure was a death sentence. In Silicon Valley, I wrote, failure was a red badge of courage, a medal of valor on one’s resume for service in the startup wars, and a reference to the eponymous classic written by Stephen Crane.

In this post, I want to explore two different aspects of the red badge of courage. First, from a career development perspective, how one should manage the presence of such badges on your resume. And second, from an emotional perspective, how thinking of startup failure as a red badge of courage can help startup founders and employers process what was happened.

Managing Failure: Avoiding Too Many Consecutive Red Badges

In Silicon Valley you’ll often hear adages like “failure is a better teacher than success,” but don’t be too quick to believe everything you hear. While failure is certainly not a scarlet letter in Silicon Valley, companies nevertheless hire for a track record of success. In the scores of C-level position specifications that I’ve read and collected over the years, I cannot recall a single one that ever listed any sort of failure as required experience.

We talk as if we love all-weather sailors, but when it comes to actually hiring people — which often requires building consensus around one candidate in a pool [2] — we seem to prefer the fair-weather ones. Back in the day, we’d all love a candidate who went from Stanford to Oracle to Siebel to Salesforce [3].

But, switching metaphors, I sometimes think Silicon Valley is like a diving competition that forgot the degree of difficulty rating. Hand a CEO $100M, 70% growth company — and the right to burn $10M to $15M per quarter — and it will likely go public in a few years, scoring the company a perfect 10 — for executing a swan dive, degree of difficulty 1.2.

Now, as an investor, I’ll put money into such swan dives whenever I can. But, as an operator, remember that the charmed life of riding in (or even driving) such a bus doesn’t necessarily prepare you for the shocks of the regular world.

Consider ServiceMax who, roughly speaking, was left at the altar by Salesforce with a product built on the Salesforce platform and business plan most thought predicated on an acquisition by Salesforce. That team survived that devastating shock and later sold the company for $900M. That’s a reverse 4½ somersault in pike position, degree of difficulty 4.8. Those folks are my heroes.

So, in my estimation, if Silicon Valley believes that failure is a better teacher than success, I’d say that it wants you to have been educated long ago — and certainly not in your most recent job. That means we need to look at startup failure as a branding issue and the simple rule is don’t get too many red badges in a row on your LinkedIn or CV.

Using Grateful Dead concert notation, if your CV looks like Berkeley –> Salesforce –> failure –> Looker, then you’re fine. You’ve got one red badge of courage that you can successful argue was a character-building experience. However, if it looks like Berkeley –> Salesforce –> failure –> failure –> failure, then you’ve got a major positioning problem. You’ve accidentally re-positioned yourself from being the “Berkeley, Salesforce” person to the “failed startup person.” [4]

How many consecutive red badges is too many? I’d say three for sure, maybe even two. A lot of it depends on timing [5].

Practically, it means that after one failed startup, you should reduce your risk tolerance by upping the quality bar on your next gig. After two failed startups, you should probably go cleanse and re-brand yourself via duty at a large successful vendor. After a year or two, you’ll be re-positioned as a Brand-X person and in a much better position to again take some career risk in the startup world [6].

Processing Failure: Internalizing the Red Badge Metaphor

This second part of this post deals with the emotional side of startup failure, which I’m going to define quite broadly as materially failing to obtain your goals in creating or working at a startup. Failure can range from laying off the entire staff and selling the furniture to getting an exit that doesn’t clear the preference stack [7] to simply getting a highly disappointing result after putting 10 years into building your company [8]. Failure, like success, takes many forms.

But failures also have several common elements:

  • Shock and disappointment. Despite knowing that 90% of startups fail, people are invariably shocked when it happens to them. Remember, startup founders and employees are often overachievers who’ve never experienced a material setback before [9].
  • Anger and conflict. In failed startups there are often core conflicts about which products to build, markets to target, when to take financing, and whether to accept buy-out offers.
  • Economic loss. Sometimes personal savings are lost along with the seed and early-round investors’ money. With companies that fail-slow (as opposed to failing-fast), opportunity cost becomes a significant woe [10].

For the people involved in one — particular the founders and C-level executives — a failed startup feels Janis Joplin singing:

Come on. Come on. Come on. Come on. And take it! Take another little piece of my heart now, baby! Oh, oh, break it! Break another little bit of my heart now Darling yeah, yeah, yeah, yeah.

I was reminded of this the other day when I had a coffee with a founder who, after more than four years, had just laid of his entire team and sold the furniture the week before.

During the meeting I realized that there are three things people fresh from failed startups should focus on when pursuing their next opportunity:

  • You need to convince yourself that it was positive learning experience that earned you a red badge of courage. If you don’t believe it, no one else will — and that’s going to make pursuing a new opportunity more difficult. People will try to figure out if you’re “broken” from the experience. Convincing them you’re not broken starts out with convincing you. (Don’t be, by the way. Startups are hard. Cut yourself some slack.)

  • You need to suppress your natural desire to tell the story. I’m sure it’s a great story, full of drama and conflict, but does telling it help you one iota in pursuing a new opportunity? No. After leaving MarkLogic — which was a strong operational success but without an investor exit — I was so bad at this that one time a VC stopped me during a CEO interview and said, “wow, this is an amazing story, let me get two of my partners to hear it and can you start over?” While I’m sure they enjoyed the colorful tale, I can assure you that the process didn’t result in a dynamite CEO offer. Tell your story this way: “I [founded | worked at] a startup for [X] years and [shut it | sold it] when [thing happened] and we realized it wasn’t going to work. It was a great experience and I learned a lot.” And then you move on. The longer you talk about it, the worse it’s going to go.

  • You need to convince prospective employers that, despite the experience, you can still fit in a round hole. If you were VP of product management (PM) before starting your company, was a founder/CEO for two years, and are now pursuing a VP of PM role, the company is going to wonder about two things: (1) as per the above, are you broken as a result of the experience and (2) can you successfully go back into a VP of PM role. You’ll need to convince them that PM has always been your passion, that you can easily go back and do it again, and in fact, that you’re quite looking forward to it. Only once that’s been accomplished, you can try to convince them that you can do PM even better than before as a result of the experience. While your natural tendency will probably be to make this argument, remember that it is wholly irrelevant if the company doesn’t believe you can return to the role. So make sure you’ve won the first argument before even entertaining the second.

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Notes

[1] A lot has presumably changed since then and while I sit on the board of a French startup (Nuxeo), I no longer feel qualified, nor is the purpose of this essay, to explore the state of tech entrepreneurship in France.

[2] And ergo presumably reduces risk-taking in the process.

[3] And not without good reason. They’ve probably learned a lot of best practices, a lot about scaling, and have built out a strong network of talented coworkers.

[4] Think of how people at a prospective employer might describe you in discussing the candidates. (“Did you prefer the Stanford/Tableau woman; the CMU/Salesforce man; or the poor dude who did all those failed startups?”)

[5] Ten years of impressive growth at Salesforce followed by two one-year failures looks quite different than three years at Salesforce followed by two three-year failures. One common question about failures is: why did you stay so long?

[6] And see higher quality opportunities as a result.

[7] Meaning investors get back all or part of what they are entitled to, but there is nothing leftover for founders and employees.

[8] And, by extrapolation, expected that they never world.

[9] For example, selling the company for $30M, and getting a small payout via an executive staff carve-out.

[10] Think: “with my PhD in AI/ML, I could have worked at Facebook for $1M per year for the past six years, so in addition to the money I’ve lost this thing has cost me $6M in foregone opportunity.”

The Most Important Chart for Managing the Pipeline: The Opportunity Histogram

In my last post, I made the case that the simplest, most intuitive metric for understanding whether you have too much, too little, or just the right amount of pipeline is opportunities/salesrep, calculated for both the current-quarter and the all-quarters pipeline.

This post builds upon the prior one by examining potential (and usually inevitable) problems with pipeline distribution.  If the problem uncovered by the first post was that “ARR hides weak opportunity count,” the problem uncovered by this post is that “averages hide uneven distributions.”

In reality, the pipeline is almost never evenly distributed:

  • Despite the salesops team’s best effort to create equal territories at the start of the year, opportunities invariably end up unevenly distributed across them.
  • If you view marketing as dropping leads from airplanes, the odds that those leads fall evenly over your territories is zero.  In some cases, marketing can control where leads land (e.g., a local CFO event in Chicago), but in most cases they cannot.
  • Tenured salesreps (who have had more time to develop their territories) usually have more opportunities than junior ones.
  • Warm territories tend to have more opportunities than cold ones [1].
  • High-activity salesreps [2] tend to have more opportunities than their more average-activity counterparts.

The result is that even my favorite pipeline metric, opportunities/salesrep, can be misleading because it’s a mathematical average and a single average can be produced by very different distributions.  So, much as I generally prefer tables of numbers to charts, here’s a case where we’re going to need a chart to get a look at the distribution.

Here’s an example:

oppty histo

Let’s say this company thinks its salesreps need 7 this-quarter and 16 all-quarters opportunities in order to be successful.  The averages here, shown by the blue and orange dotted lines respectively, say they’re in great shape — the average this-quarter opportunities/salesrep is 7.1 and the average all-quarters is 16.6.

But behind that lies a terrible distribution:  only 4 salesreps (reps 2, 7, 10, and 13) have more than 7 opportunities in the current quarter.  The other 11 are all starving to various degrees with 5 reps having 4 or fewer opportunities.

The all-quarters pipeline is somewhat healthier.  There are 8 reps above the target of 16, but nevertheless, certain reps are starving on both a this-quarter and all-quarters basis (reps 4, 11, 12, and 14) and have little chance at either short- or mid-term success.

Now that we can use this chart to highlight this problem, let’s examine the three ways to solve it.

  • Generate more opportunities, ideally in a super-targeted way to help the starving reps without further burying the loaded reps.  Sales loves to ask for this solution.  In practice, it’s hard to execute and inherently phase-lagged.
  • Reduce the number of reps.  If reps 4, 11, and 12 have been at the company for a long time and continuously struggled to hit their numbers, we can “Lord of the Flies” them, and reassign their opportunities to some of the surviving reps.  The problem here is that you’re reducing sales quota capacity — it’s a potentially good short-term fix that hurts long-term growth [3].
  • Reallocate opportunities from loaded reps to starving reps.  Sales management usually loathes this “Robin Hood” approach because there are few things more difficult than taking an opportunity from a sales rep.  (Think:  you can pry it from my cold dead fingers.)  This is a real problem because it is the best solution to the problem [4] — there is no way that reps 7 and 13 can actively service all their opportunities and the company is likely to be losing deals it could have won because of it [5].

You can download the spreadsheet for this post, here.

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Notes

[1] The distinction here is whether the territory has been continuously and actively covered (warm) vs. either totally uncovered or partially covered by another rep who did not actively manage it (cold).

[2] Yes, David C., if you’re reading this while doing a demo from the back seat of your car that someone else is driving on the NJ Turnpike, you are the archtype!

[3] It’s also a bad solution if they are proven salesreps simply caught in a pipeline crunch, perhaps after having had a blow-out result in the prior quarter.

[4] Other solutions include negotiating with the reps — e.g., “if you hand off these four opportunities I’ll uplift the commissions twenty percent and you’ll split it with salesrep I assign them to — 60% of something is a lot more than 100% of zero, which is what you’ll get if you can’t put enough time into the deal.”

[5] Better yet, in anticipation of the inevitable opportunity distribution problem, sales management can and should leave fallow (i.e., unmapped) territories, so they can do dynamic rebalancing as opportunities are created without enduring the painful “taking” of an opportunity from a salesrep who thinks they own it.

Do We Have Enough Pipeline? The One Simple Metric Many Folks Forget.

Pipeline is a frequently scrutinized SaaS company metric because it’s one of relatively few leading indicators in a SaaS business — i.e., indicators that don’t just tell us about the past but that help inform us about the future, providing important clues to our anticipated performance this quarter, next quarter, and the one after that.

Thus, pipeline gets examined a lot.  Boards and investors love to look at:

  • Aggregate pipeline for the year, and how it’s changing [1]
  • Pipeline coverage for the quarter and whether a company has the magical 3x coverage ratio that most require [2]
  • Pipeline with and without the high funnel (i.e., pipeline excluding stage 1 and stage 2 opportunities) [3]
  • Pipeline scrubbing and the process a company uses to keep its pipeline from getting inflated full of junk including, among other things, rolling hairballs.
  • Expected values of the pipeline that create triangulation forecasts, such as stage-weighted expected value or forecast-category-weighted expected value.

But how much pipeline is enough?

“I’ve got too much pipeline, I wish the company would stop sending so many opportunities my way”  — Things I Have Never Heard a Salesperson Say.

Some try to focus on building an annual pipeline.  I think that’s misguided.  Don’t focus on the long-term and hope the short-term takes care of itself; focus consistently on the short-term and long-term will automatically take care of itself.  I made this somewhat “surprised that it’s seen as contrarian” argument in I’ve Got a Crazy Idea:  How About We Focus on Next-Quarter’s Pipeline?

But somehow, amidst all the frenzy a very simple concept gets lost.  How many opportunities can a salesperson realistically handle at one time? 

Clearly, we want to avoid under-utilizing salespeople — the case when they are carrying too few opportunities.  But we also want to avoid them carrying too many — opportunities will fall through the cracks, prospect voice mails will go unreturned, and presentations and demos will either be hastily assembled or the team will request extensions to deadlines [4].

So what’s the magic metric to inform you if you have too little, too much, or just the right amount of pipeline?  Opportunities/salesrep — measured both this-quarter and for all-quarters.

What numbers define an acceptable range?

My first answer is to ask salesreps and sales managers before they know what you’re up to.  “Hey Sarah, out of curiosity, how many current-quarter opportunities do you think a salesrep can actually handle?”  Poll a bunch of your team and see what you get.

Next, here are some rough ranges that I’ve seen [5]:

  • Enterprise reps:  6 to 8 this-quarter and 12 to 15 all-quarters opportunities
  • Corporate reps:  10 to 12 this-quarter and 15 to 20 all-quarters opportunities

I’ve been in meetings where the CRO says “we have enough pipeline” only to discover that they are carrying only 2.5 current-quarter opportunities per salesrep [6].  I then ask two questions:  (1) what’s your close rate and (2) what’s your average sales price (ASP)?  If the CRO says 40% and $125K, I then conclude the average salesrep will win one (0.4 * 2.5 = 1), $125K deal in the quarter, about half a typical quota.  I then ask:  what do the salesreps carrying 2.5 current-quarter opportunities actually do all day?  You told me they could carry 8 opportunities and they’re carrying about a quarter of that?  Silence usually follows.

Conversely, I’ve been in meetings where the average enterprise salesrep is carrying close to 30 large, complex opportunities.  I think:  there’s no way the salesreps are adequately servicing all those deals.  In such situations, I have had SDRs crying in my office saying a prospect they handed off to sales weeks ago called them back, furious about the poor service they were getting [7].  I’ve had customers call me saying their salesrep canceled a live demo on five minutes’ notice via a chickenshit voicemail to their desk line after they’d assembled a room full of VIPs to see it [8].  Bad things happen when your salesreps are carrying too many opportunities.

If you’re in this situation, hire more reps.  Give deals to partners.  Move deals from enterprise to corporate sales.  But don’t let opportunities that cost the company between $2,000 and $8,000 to create just rot on the table.  As I reminded salesreps when I was a CEO:  they’re not your opportunities, they’re my opportunities — I paid for them.

Hopefully, I’ve made the case that going forward, while you should keep tracking pipeline on an ARR basis and looking at ARR conversion rates, you should add opportunity count and opportunity count / salesrep to your reports on the current-quarter and the all-quarters pipeline.  It’s the easiest and most intuitive way to understand the amount of your pipeline relative to your ability to process it.

# # #

Notes

[1] With an eye to two rules of thumb:  [a] that annual starting pipeline often approximate’s this year’s annual sales and [b] that the YoY growth rate in the size of the pipeline predicts YoY growth rate in sales.

[2] Pipeline coverage = pipeline / plan.  So if you have 300 units of pipeline and a new ARR plan of 100 units, then you have 3.0x pipeline coverage.

[3] Though there’s a better way to solve this problem — rather than excluding early-stage opportunities that have been created with a placeholder value, simply create new opportunities with value of $0.  That way, there’s nothing to exclude and it creates a best-practice (at most companies) that sales can’t change that $0 to a value without socializing the value with the customer first.

[4] The High Crime of a company slowing down its own sales cycles!  Never forget the sales adage:  “time kills all deals.”

[5] You can do a rough check on these numbers using close rates and ASPs.  If your enterprise quota is $300K/quarter, your ASP $100K, and your close rate 33%, a salesrep will need 9 current-quarter opportunities to make their number.

[6] The anemic pipeline hidden, on an ARR basis, by (unrealistically) large deal sizes.

[7] And they actually first went to HR seeking advice about what to do, because they didn’t want “rat out” the offending salesrep.

[8] Invoking my foundational training in customer support, I listened actively, empathized, and offered to assign a new salesrep — the top rep in the company — to the account, if they’d give us one more chance.  That salesrep turned a deal that the soon-to-be-former salesrep was too busy to work on, into the deal of the quarter.