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 Every Startup Needs an Inverted Demand Generation Funnel, Part II

In the previous post, I introduced the idea of an inverted demand generation (demandgen) funnel which we can use to calculate a marketing demandgen budget based given a sales target, an average sales price (ASP), and a set of conversion rates along the funnel. This is a handy tool, isn’t hard to make, and will force you into the very good habit of measuring (and presumably improving) a set of conversion rates along your demand funnel.

In the previous post, as a simplifying assumption, we assumed a steady-state situation where a company had a $2M new ARR target every quarter. The steady-state assumption allowed us to ignore two very real factors that we are going to address today:

  • Time. There are two phase-lags along the funnel. MQLs might take a quarter to turn into SALs and SALs might take two quarters to turn into closed deals. So any MQL we generate now won’t likely become a closed deal until 3 quarters from now.
  • Growth. No SaaS company wants to operate at steady state; sales targets go up every year. Thus if we generate only enough MQLs to hit this-quarter’s target we will invariably come up short because those MQLs are working to support a (presumably larger) target 3 quarters in the future.

In order to solve these problems we will start with the inverted funnel model from the previous post and do three things:

  • Quarter-ize it. Instead of just showing one steady-state quarter (or a single year), we are going to stretch the model out across quarters.
  • Phase shift it. If SALs take two quarters to close and MQLs take 1 quarter to become SALS we will reflect this in the model, by saying 4Q20 deals need come from SALs generated in 2Q20 which in turn come from MQLs generated in 1Q20.
  • Extend it. Because of the three-quarter phase shift, the vast majority of the MQLs we’ll be generating 2020 are actually to support 2021 business, so we need to extend the model in 2021 (with a growth assumption) in order to determine how big of a business we need to support.

Here’s what the model looks like when you do this:

You can see that this model generates a varying demandgen budget based on the future sales targets and if you play with the drivers, you can see the impact of growth. At 50% new ARR growth, we need a $1.47M demandgen budget in 2020, at 0% we’d need $1.09M, and at 100% we’d need $1.85M.

Rather than walk through the phase-shifting with words, let me activate Excel’s trace-precedents feature so you can see how things flow:

With these corrections, we have transformed the inverted funnel into a pretty realistic tool for modeling MQL requirements of the company’s future growth plan.

Other Considerations

In reality, your business may consist of multiple funnels with different assumption sets.

  • Partner-sourced deals are likely to have smaller deal sizes (due to margin given to the channel) but faster conversion timeframes and higher conversion rates. (Because we will learn about deals later in the cycle, hear only about the good ones, and the partner may expedite the evaluation process.)
  • Upsell business will almost certainly have smaller deal sizes, faster conversion timeframes, and much higher conversion rates than business to entirely new customers.
  • Corporate (or inside) sales is likely to have a materially different funnel from enterprise sales. Using a single funnel that averages the two might work, provided your mix isn’t changing, but it is likely to leave corporate sales starving for opportunities (since they do much smaller deals, they need many more opportunities).

How many of these funnels you need is up to you. Because the model is particularly sensitive to deal size (given a constant set of conversion rates) I would say that if a certain type of business has a very different ASP from the main business, then it likely needs its own funnel. So instead of building one funnel that averages everything across your company, you might be three — e.g.,

  • A new business funnel
  • An upsell funnel
  • A channel funnel

In part III of this series, we’ll discuss how to combine the idea of the inverted funnel with time-based close rates to create an even more accurate model of your demand funnel.

The spreadsheet I made for this series of posts is available here.

Why Every Startup Needs an Inverted Demand Generation Funnel, Part I

Does my company spend too much on marketing? Too little? How I do know? What is the right level of marketing spend at an enterprise software startup? I get asked these questions all the time by startup CEOs, CMOs, marketing VPs, and marketing directors.

You can turn to financial benchmarks, like the KeyBanc Annual SaaS Survey for some great high-level answers. You can subscribe to SiriusDecisions for best practices and survey data. Or you can buy detailed benchmark data [1] from OPEXEngine. These are all great sources and I recommend them heartily to anyone who can afford them.

But, in addition to sometimes being too high-level [2], there is one key problem with all these forms of benchmark data: they’re not about you. They’re not based on your operating history. While I certainly recommend that executives know their relevant financial benchmarks, there’s a difference between knowing what’s typical for the industry and what’s typical for you.

So, if you want to know if your company is spending enough on marketing [3], the first thing you should do is to make an inverted demand generation (aka, demandgen) funnel to figure out if you’re spending enough on demandgen. It’s quite simple and I’m frankly surprised how few folks take the time to do it.

Here’s an inverted demandgen funnel in its simplest form:

Inverted demandgen funnel

Let’s walk through the model. Note that all orange cells are drivers (inputs) and the white cells are calculations (outputs). This model assumes a steady-state situation [4] where the company’s new ARR target is $2,000,000 each quarter. From there, we simply walk up the funnel using historical deal sizes and conversion rates [5].

  • With an average sales price (ASP) of $75,000, the company needs to close 27 opportunities each quarter.
  • With a 20% sales qualified lead (SQL) to close rate we will need 133 SQLs per quarter.
  • If marketing is responsible for generating 80% of the sales pipeline, then marketing will need to generate 107 of those SQLs.
  • If our sales development representatives (SDRs) can output 2.5 opportunities per week then we will need 5 SDRs (rounding up).
  • With an 80% SAL to SQL conversion rate we will need 133 SALs per quarter.
  • With a 10% MQL to SAL conversion rate we will need 1,333 MQLs per quarter.
  • With a cost of $250 per MQL, we will need a demandgen budget [6] of $333,333 per quarter.

The world’s simplest way to calculate the overall marketing budget at this point would be to annualize demandgen to $1.3M and then double it, assuming the traditional 50/50 people/programs ratio [7].

Not accounting for phase lag or growth (which will be the subjects of part II and part III of this post), let’s improve our inverted funnel by adding benchmark and historical data.

Let’s look at what’s changed. I’ve added two columns, one with 2019 actuals and one with benchmark data from our favorite source. I’ve left the $2M target in both columns because I want to compare funnels to see what it would take to generate $2M using either last year’s or our benchmark’s conversion rates. Because I didn’t want to change the orange indicators (of driver cells) in the left column, when we have deviations from the benchmark I color-coded the benchmark column instead. While our projected 20% SQL-to-close rate is an improvement from the 18% rate in 2019, we are still well below the benchmark figure of 25% — hence I coded the benchmark red to indicate a problem in this row. Our 10% MQL-to-SQL conversion rate in the 2020 budget is a little below the benchmark figure of 12%, so I coded it yellow. Our $250 cost/MQL is well below the benchmark figure of $325 so I coded it green.

Finally, I added a row to show the relative efficiency improvement of the proposed 2020 budget compared to last year’s actuals and the benchmark. This is critical — this is the proof that marketing is raising the bar on itself and committed to efficiency improvement in the coming year. While our proposed funnel is overall 13% more efficient than the 2019 funnel, we still have work to do over the next few years because we are 23% less efficient than we would be if we were at the benchmark on all rates.

However, because we can’t count on fixing everything at once, we are taking a conservative approach where we show material improvement over last year’s actuals, but not overnight convergence to the benchmark — which could take us from kaizen-land to fantasy-land and result in a critical pipeline shortage downstream.

Moreover because this approach shows not only a 13% overall efficiency improvement but precisely where you expect it to come from, the CEO can challenge sales and marketing leadership:

  • Why are we expecting to increase our ASP by $5K to $75K?
  • Why do you think we can improve the SQL-to-close rate from 18% to 20% — and what you are doing to drive that improvement? [8]
  • What are we doing to improve the MQL-to-SAL conversion rate?
  • How are we going to improve our already excellent cost per MQL by $25?

In part II and part III of this post, we’ll discuss two ways of modeling phase-lag, modeling growth, and the separation of the new business and upsell funnels.

You can download my spreadsheet for this post, here.

Notes

[1] For marketing or virtually anything else.

[2] i.e., looking at either S&M aggregated or even marketing overall.

[3] The other two pillars of marketing are product marketing and communications. The high-level benchmarks can help you analyze spend on these two areas by subtracting your calculated demandgen budget from the total marketing budget suggested by a benchmark to see “what’s left” for the other two pillars. Caution: sometimes that result is negative!

[4] The astute reader will instantly see two problems: (a) phase-lag introduced by both the lead maturation (name to MQL) and sales (SQL to close) cycles and (b) growth. That is, in a normal high-growth startup, you need enough leads not to generate this quarter’s new ARR target but the target 3-4 quarters out, which is likely to be significantly larger. Assuming a steady-state situation gets rid of both these problems and simplifies the model. See part II and part III of this post for how I like to manage that added real-world complexity.

[5] Hint: if you’re not tracking these rates, the first good thing about this model is that it will force you to do so.

[6] When I say demandgen budget, I mean money spent on generating leads through marketing campaigns. Sometimes that very directly (e.g., adwords). Other times it’s a bit indirectly (e.g., an SEO program). I do not include demandgen staff because I am trying to calculate the marginal cost of generating an extra MQL. That is, I’m not trying to calculate what the company spends, in total, on demandgen activities (which would include salary, benefits, stock-based comp, etc. for demandgen staff) but instead the marketing programs cost to generate a lead (e.g., in case we need to figure out how much to budget to generate 200 more of them).

[7] In an increasingly tech-heavy world where marketing needs to invest a lot in infrastructure as well, I have adapted the traditional 50/50 people/programs rule to a more modern 45/45/10 people/programs/infrastructure rule, or even an infrastructure-heavy split of 40/40/20.

[8] Better closing tools, an ROI calculator, or a new sales training program could all be valid explanations for assuming an improved close rate.

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.

# # #

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.”