Category Archives: Uncategorized

The Independently Wealthy Salesperson

Technical founders and entrepreneurs can easily overlook the coin-operated nature of salespeople. Why? Because they aren’t salespeople, they’re product people and they’re just not wired the same way.

Founders might be motivated by changing the market, popularizing a product, or just proving they are right. Salespeople, almost all the time, are motivated by their compensation plans, so the compensation plan should be the de facto expression of what you want them to do and how they should spend their time.  Ergo, as mentioned in this post, you should get them done before kickoff. And put a lot of thought into them.

Why? Because a typical salesperson will spend the whole weekend after you give them their comp plan in Excel modeling how much money they will make under various scenarios. I’d also say to make them as simple as possible both to make it clear what you want salespeople to do and to avoid the inevitable unintended consequences that often accompany complexity.

One huge question is whether comp plans should be capped. Almost all salespeople would say no. Part of the reason they’re playing the game – particularly at a startup – is for the lottery ticket.  Think: while I know my on-target earnings are $250K, I want to have a shot at earning $1 or $2M – that’s what drives me to work those killer hours.  So while I recommend leaving comp plans uncapped, I also recommend that management model the full range of scenarios and, for example, accelerate rates in the 100-250% of plan range but to greatly decelerate them after that.

You can always hedge your bets in the compensation plan terms and conditions (e.g., plan can be changed at any time to correct for errors or unforeseen circumstances), but if you actually use that language the whole salesforce will know and you will quickly lose the lottery-ticket value in your comp plan. It is far better to put some more thought into the plan on the front-end. I know one guy at a startup who did a $10M deal off a $1M quota and received only a fraction of his stated comp. Because he didn’t want to burn bridges, he just quit. But in this scenario, everybody loses.

Outliers, however, can take several forms. I know one sales manager who groups salespeople into three buckets:

  • Those who clearly understand their comp plans. They sell what’s incented, when it’s incented, and make the most money per sales dollar.
  • Those who mostly understand their comp plans. Those who do a good job following the plan incentives, but not a perfect one.
  • The “independently wealthy” who seem to pay no regard to the incentives in the comp plan

I love bucketing reps in this way both because it’s funny and it immediately prompts an important question. Why are these reps not following the plan? Perhaps it’s just sloppiness or stupidity. Or perhaps there is more going on. My advice is to analyze reps in this way, show them that if they had sold different products at different times how their pay would have varied and then ask them why they didn’t. While some people invariably just miss the point, you might also discover “good reasons” why your people aren’t following your plan: maybe it’s too complicated and they don’t understand it, maybe they don’t think the higher incentive offsets the additional risk of selling a new, strategic product.

Or maybe they truly are independently wealthy and just doing sales for fun. But I doubt it.

The One Thing To Get Done Before Sales Kickoff

It’s that time of the year again.  With many tech companies now on a 1/31 fiscal year end, that makes February kickoff month.  So it’s a time to say “thanks” for a great last year, but also to move quickly on to launching the new year.  Every week lost costs you 1.9% of your FY12 selling days.  While that may not seem like a lot right now, trust me it will come end of the year.

Thus February is a month that requires great operational discipline.  I’ll tell you a story about my old friend Larry to explain why.  For the first few weeks of every year Larry would stroll into the office around 10 AM with a few different newspapers in his hands.  Then he’d sit in his office, with his feet on his desk, and quite visibly read the newspaper.  If you went into his office and said, “Larry, what the heck are you doing?” he’d reply:

“I’m reading the paper because I don’t know what to do.  I haven’t received my compensation plan yet.”

Larry was lucky not to get fired, but his management was lucky to have him around to make the point so dramatically.  I am a salesperson.  I am, by definition, coin-operated.  My compensation plan is supposed to be *the* definition of the behavior the company would like to incent in the new year.  So, if I haven’t received my comp plan, then I don’t know what to do.  QED.

I learned a simple trick from my old boss John Olsen to help your thinking when it comes to the timing of comp plans:

Your signed compensation plan is your admission ticket to the sales kickoff.

This is a fantastic rule for many reasons:

  • It forces management to reverse-engineer the timeline to get things done early.  Making comp plans (and dividing territories) is hard, iterative work that takes time.  Most salespeople want to negotiate certain terms, which adds time as well.  By putting this stake in the ground you are committing to starting early.
  • It creates a deadline.  Comp plans often linger for months into the new year and while most salespeople won’t overtly act like Larry, they may well be operating at reduced productivity until they understand what they’re supposed to do.
  • It lets kickoff be a real beginning.  Sales reps enter the room with your comp plan and territory.  They hear great things about last year.  And they hear what’s coming this year in terms of new products and new go-to-market strategies. So when reps get home, they can start selling. 

So have a great kickoff.  Fire up your salesreps about what a great year they’re going to have.  Send them home with new messaging and tools.  Whip them into a frenzy.  But, please don’t let them into the event unless they’ve signed their comp plan.  And make sure you’ve done work on your end, in advance, to make that a reasonable request.

The Three Faces of the Social Revolution: Media, Marketing, and Service

Thanks to MarkLogic‘s historical focus on the media industry, I had front-row seats to the social media revolution.  But the part of the stage visible from those seats was how social media disrupted traditional media.  How “weekly news magazine” became an oxymoron. How Craigslist wiped about classified advertising.  How Yelp clobbered Zagat.  How columnists became bloggers.  How YouTube impinged on TV.  How social sites gobbled up time from first traditional media and later alternative web-based media.  I could go on and on.

Perhaps the single best slide I produced was entitled Media’s Philosophical Coasts (presentation here) where I contrasted East vs. West Coast media mentality.

I first came at social media from a media perspective. Secondly, I came at social media from a marketing perspective.  After all, I’m a marketing guy by background and the more that people are saying in social media, the more you want to measure it the way we used measure traditional media.  How many column-inches did we get?  Was the tone positive negative?  But the explosion in content and The Long Tail effect meant that there was no way you could hire English majors with rulers to get that data.  You’d have to use technology.  Spidering and/or licensing content, text mining to find entities and sentiment, and analytics for summarizing and interactively analyzing the data.

Sometimes I kick myself for not founding a company like Radian6.  I was right in the middle of social media, unstructured information, text mining, and analytics.  I saw the opportunity — heck, I watched some early text miners like Attensity pivot to social media (aka “voice of the customer”) applications as strategic plan B’s.  But MarkLogic was perversely doing too well with its platform strategy to pivot to something else and I, at the time, didn’t have a practical way to so independently.  (Axiom:  burn your VCs and you never raise money again.  Corollary:  all departures must be organized and peaceful.)

If you’re not familiar with Radian6, check it out.  It is an amazing platform for social media monitoring and engagement.

When you come at social media from a marketing perspective, you tend to think listen / analyze / monitor.  What are people saying?  How are they reacting?  How can I slice-and-dice that information by location, by demographics, by target audience?

But when you come at social media from the third perspective — the customer service perspective — you can see another angle:  engagement.  If someone is Tweeting that they don’t like Comcast, you can do more than measure it.  You can respond.  See Comcast Cares.  If someone says that they can’t find a BofA ATM, you can help them out.  See @BofA_Help and their clever ^-based naming convention so you can see which person is helping you.

As the social revolution transforms us, it first hit media, then hit marketing, and is now hitting customer service.  Customer service is now the front lines.

And if for some reason, you’re not yet convinced on the enormity of the social revolution, look at this video from Socialnomics.

Social Customer Service at Comcast

About five years ago, Comcast was featured in a slew of home-made YouTube videos of field service representatives literally falling asleep while on-hold for back-line customer service.

What’s happened since then?  Well, the once-victim of social media is now a leader in how organizations can leverage social media to improve not only customer service, but their overall business.  In 2008 they launched the @ComcastCares initiative under the leadership of Director of Digital Care Frank Eliason, profiled in this BusinessWeek story.  In mid-2010, Eliason moved on to become SVP of Social Media at Citibank but @ComcastCares lives on and has grown under new leadership.

In this post, How Social Customer Service is Changing the Culture at Comcast, social media guru and author Brian Solis talks with Kip Wetzel, Senior Director of Social Media Servicing and Strategy at Comcast about the initiative today and its impact on the business.

Some key quotes and tidbits:

  • Most people feel that large companies “speak at” consumers.  We have the ability to “speak with” consumers and to let them know that their voice is heard.
  • Comcast does not use social media as a way to push customers into traditional channels.  You need both.  The situation should dictate which method is most appropriate and you need integration:  you can’t fix a line downed by a tree over Twitter.
  • Let people see one company; don’t show them silos or divisions.
  • You can scale your social media department to 100 people, but if you don’t fix the problems internally, you’ll never have enough.
  • We can use Twitter as well as proactive traps and alarms to quickly identify faults and problems.
  • There isn’t a social media team — it’s an extension of product, marketing, PR, care … we can evaluate what people are saying, take that feedback, see the impact on the different organizations at Comcast and adapt.
  • Social is a catalyst for internal conversation … how does a theme evolving about a product or service influence the different organizations, and how do we collectively evaluate those things and put our real change a day later, a week later.
  • Social is not just about reacting, listening, or responding, it’s about building better products, services, and processes that allow you to lead the customer experience.

Here is a direct link to the video interview.

Interview by SandHill.com on Big Data, Cloud Computing, and the Future of IT

[This is a re-post of a recent interview with me, authored by Darren Cunningham of Informatica.  The post originally appeared on SandHill.com where Darren writes a column on Cloud Computing.]

—-

The Cloud in Action

Big Data, Cloud Computing and Industry Perspectives with Dave Kellogg

BY Darren Cunningham

I had the pleasure of working with Dave Kellogg early in my marketing career and continue to learn from him as a regular subscriber to his popular blog, Kellblog. A seasoned Silicon Valley executive, Dave has been a board member (Aster Data), CEO (MarkLogic), CMO (Business Objects) and VP of Marketing (Versant and Ingres). I recently sat down with Dave to discuss industry trends. As always, he didn’t hold back.

Dave, you’ve written a lot about “Big Data” on your blog. Why is it such a hot topic in the world of data management?

First I think Big Data is a hot topic because it represents the first time in about 30 years that people are rethinking databases. Literally, since about 1980 people haven’t had to think much about databases. If you were an SMB, you went SQL server; if you were enterprise, you’d go Oracle or IBM depending on your enterprise preferences. But in terms of technology, to paraphrase Henry Ford: any color you want, as long it’s relational.

Overall, I think Big Data is hot for three reasons:

  • Major new innovation is finally happening with databases for the first time in three decades.
  • Hardware architectures have changed — people want to scale horizontally like Google.
  • We are experiencing a serious explosion in the amount of data people are analyzing and managing. Machine-generated data, the exhaust of the Web, is driving a lot of it.

I think Big Data is challenging on many fronts from the cool (e.g., analytics and query optimization), to the practical (e.g., horizontal scaling), to the mundane (e.g., backup and recovery).

What’s the intersection with Cloud Computing?

I think when people say cloud computing, they mean one of several things:

  • SaaS: The use of software applications or platforms as services.
  • Dynamic scaling: My favorite example of this is Britain’s Got Talent, which uses Cassandra. Most of the time they have nothing to do. Then one night half the country is trying to vote for their favorite contestants.
  • Service orientation: The ability to weave together applications by calling various cloud services — in effect using a series of cloud services as a platform on which to build applications.

I think Big Data intersects with cloud in several ways. First, the people running cloud services are dealing with Big Data problems. They are hosting thousands of customers’ databases and generating log records from hundreds of thousands of users. I also think Big Data analytics are very dynamic loads. One minute you want nothing, then suddenly you need to throw 100 servers at a complex problem for several hours.

How do you see these trends changing the role of IT?

I think corporate IT is constantly evolving because smart corporations want their internal resources focused on activities that they can’t buy elsewhere and that generate competitive advantage for the business.

IT used to buy and run computers. Then they used to build and run applications. Then they focused on weaving together packaged applications. Going forward, they will focus on tightly integrating cloud-based services. They will also continue to focus on company-proprietary analytics used to gain competitive advantage.

The other trend driving IT is consumerization. The Web sets expectations for functionality, user interface and quality that corporate IT must meet with internal systems. The bar has gone way up – people won’t tolerate old-school ERP-style interfaces at work when they’re used to Facebook or Yelp.

What does that mean for technology sales and marketing?

If Mr. McGuire in The Graduate were dishing out advice today, instead of saying “plastics,” he’d say “data science.” More and more companies will use data scientists to analyze their business and drive tactical operations. First you need to gather a whole bunch of data about your operations and customers. Then you need to throw world-class data analysts at it to get business value and to be sure you don’t draw false conclusions – e.g., mixing causality with correlation.

Today, most companies have their sales departments on salesforce.com. Leading marketing departments are on Marketo or Eloqua, but most marketers still don’t have much technology backing them. Going forward you will see a whole class of analytics applications vendors providing advanced analytics for Salesforce (e.g., Cloud9, Good Data) and the marketing automation vendors will move beyond lead incubation into providing overall marketing suites. I expect Marekto or Eloqua to try to do for the chief marketing officer what SuccessFactors did for the chief people officer – and if they don’t, then there’s a real opportunity for someone else.

Speaking of all things cloud, you often write about Silicon Valley trends. How would you characterize what’s going on in the market right now?

From my perception, the Silicon Valley innovation engine is running full out. Top VCs are raising new funds. I meet a few new startups every day. Of late, I’ve met fascinating companies in next-generation business intelligence, analytics, Big Data, social media monitoring and exploitation and Web application development. One of the more interesting things I’ve found is a VC fund dedicated to big data - IA Ventures (in New York). When I heard about them, I thought: oh, lots of Big Data infrastructure and platform technologies. Then I spent some time and realized that most of their portfolio is about exploiting new Big Data infrastructure technologies via vertical applications. That was really interesting.

People will debate whether we’re in a mini tech bubble or a social networking-specific bubble. Who knows? I just read an article in the The Wall Street Journal that argues $140B valuation for Facebook is realistic, and it was fairly convincing. So you can debate the bubble issue but you can’t debate that the IPO market has been closed for a long time. Now it is starting to open, and that’s a huge change in Silicon Valley.

Entrepreneurs have historically dreamed of creating $1B independent companies. I’d say for most of the last decade they’ve dreamed of getting bought for 5-10x revenues. Michael Arrington had a great quote a while back saying that “an entire generation of entrepreneurs [has been lost] building dipshit companies that sell to Google for $25M.” I think those days are over. When the IPO window opens, people dream of building stand-alone companies.

What advice do you have for both entrepreneurs and IT veterans?

Don’t build or run things that you can buy or rent. If you follow that mantra, you will follow market trends, and always stay at the right stack-layer to ensure that you are adding value as opposed to leveraging old skill sets. While you may know how to run a Big Data center, you can now rent time in one more cost-effectively. So either go work for a company that runs data centers (e.g., Equinix) if that’s your pleasure, or go leverage the people who do. Put differently, don’t be static. If you’re still using skills you learned 10 years ago, make sure that you’re not teeing yourself up to get left behind.

As always, great advice, Dave! Thank you.

Darren Cunningham is VP of Marketing for Informatica Cloud.

[Notes:  Minor changes made from the SandHill post.  I added emphasis via bolding and I corrected the attribution of the famous lines “plastics” from The Graduate.  It was not Mr. Robinson, but Mr. McGuire, who said it.]

Some Fun Analysis with Indeed.com Job Trend Mapping

Indeed.com, an up-and-coming job search website, has an interesting trends search feature.  Because you easily argue that the rational side of Silicon Valley hype waves is driven by jobs/employment, one great way to analyze these technology waves is to analyze job postings.  Let’s have some fun.

First, let’s look at some web application development technologies:

Note how HTML5 (in yellow) is flying up the rankings. Now, let’s take a look in database-land.

Now, let’s take a look in NoSQL land.

I’m not surprised to see MongoDB and Hadoop shooting up the charts.  Frankly, I thought Cassandra had lost some momentum, but I guess not.  Finally, let’s take a look in semantic web-land.

I don’t actually view XQuery as a semantic web technology — I just threw it in for fun — and was surprised to see that XQuery generally correlates better with semantic web than actual semantic web technologies like RDF or OWL!

Finally, note that the scales are not the same.  The first two charts are in percent of job postings, the third is in tenths of a percent, and the fourth is in hundredths of a percent.  So the sobering example is to take the top trend from each of our four charts and graph them together.

Interest Misalignments in Silicon Valley Startups

Everyone’s aligned in a Silicon Valley startup, right?  Give everyone some options so everyone has skin in the game and then everyone wants what’s best for the share price:  one for all and all for one!

Not so fast.

In this post, inspired by a chat with longtime serial entrepreneur Ken Ross, I’ll delve into what I see as the common alignment issues in Silicon Valley startups.  While I am a big believer in broad employee share ownership, one shouldn’t make the mistake of believing that simply because everyone has shares that they are automatically and permanently aligned.

In my estimation, there are four drivers of potential misalignment.

  • Portfolio theory
  • Shareholdings and net worth
  • The “exit” concept
  • Irrational considerations

Portfolio Theory

The most common cause of misalignment is driven by portfolio theory.  VCs typically invest in 10-15 companies and work in partnerships of 5-10 partners.  Thus a VC might get “carry” (i.e., a slice of the investment profits) on 50-80 companies.  A friend once calculated that a VC gets the equivalent of a VP-level (or better) equity stake in each of the portfolio’s companies.

Entrepreneurs and executives, however, have but one life to give and must work at one company at a time.

Divergence can result when VCs want to take more risk than founders and executives because they have placed 80 bets while the executives have placed one.  This can manifest itself in pushing for overly aggressive operating plans or declining “base hit” acquisition offers in favor of “swinging for the fences” each time.   Time can compound this divergence as accumulated sweat equity tends to make the founders and executives more conservative over time.  (Think:  “I have 8 years of my life in this thing, we can’t take that risk.”)

In addition, VC is increasingly a “hits business” – i.e., a fund that delivered an IRR of 35% might deliver only 15% excluding its top two investments.  Thus, VCs are generally more afraid of selling too early than too late.  While founders often tell tales of VCs declining early acquisition offers that could have earned them a quick $20M, VCs might tell the tale of VMware, which sold for $625M in 2004 and is now worth $41B.

Portfolio theory has other effects that are more subtle.  You might think of a given venture-backed company as in one portfolio.  In reality, the company is in numerous “portfolios” at different levels:

  • The fund level.  The expectations for a company become a function of the performance of the other companies in the fund.  If they are performing poorly, pressure may increase to deliver a big result.  Alternatively, if the fund is old, has lackluster performance, and the VC firm has subsequently launched several high-performing funds, a lack of interest may develop.
  • The partnership level.  Different VC firms set have different investment objectives and reputations.  Some want to quietly deliver great returns.  Some favor operating guys as partners, other favor financial types.  Some like seeing their name in the press; some don’t.    As a general rule, the more early-stage and the more big-name the partnership, the more they will want portfolio companies to swing for the fences across the entire portfolio.
  • The partner level.  Each partner in a fund has his own set of companies.  VC partners track each other’s performance closely and a partner’s fate over time is, in large part, determined by his investment performance.  In addition, since most VC firms are fairly stove-piped, expectations for a given company are probably more shaped by its partner’s portfolio than any other.  Factors that influence the partner’s motivations include the performance his portfolio, his existing status in the firm (e.g., venture partner looking for a big-hit to make general partner, or established leader in the firm, or in-trouble and need of a big-hit to stay in the game), and his future plans (e.g., retirement).
  • The partnership-partnership level.  Suppose early-stage VC firm 1 does a lot of business with late-stage VC firm 3, as is often the case.  You can then think of your company in the “intersection” portfolio between these two partnerships.   Why does this matter?  To the extent that VC3 is dependent on VC1, they may make decisions that optimize the VC1/VC3 relationship over those that they might think best for a given portfolio company.  (Think:  “if Bob ever wants to work with us again, he’d better go along with us on this decision.”)

Shareholdings and Net Worth

The size of someone’s position, particularly relative to net worth, can cause a divergence of interests.  Consider a hypothetical company with 25M shares:

  • The founder owns 5M shares.
  • The total employee option pool is 5M shares.  (Of which Joe Engineer has 20K shares.)
  • VC1 owns 10M shares, having paid an average of $1.60/share across two rounds.
  • VC2 owns 5M shares, having paid $3.00/share in leading the second round.

Let’s consider a proposed $6.00/share offer for this company, for a total exit of $150M.

  • The founder would make $30M and be set for life.  He votes yes.
  • VC1 would receive $60M which does not move the needle relative to the size of his $600M fund.  On a return basis, he makes 3.75x, a poor result for an early-stage VC.  He votes no.
  • VC2 would receive $30M which moves his needle even less.  He makes a 2.0x return, low for a late-stage investor.  He votes no.
  • Neither VC partner will gain any bragging rights because the exit is small in an absolute sense.   This confirms their no votes.
  • Joe Engineer would get $120K pretax or about $60K post-tax.  He can buy nice car, but he still can’t touch a Silicon Valley house.  Joe doesn’t get a vote, but if he did, he’d vote no, too.

The interesting thing here is that Joe Engineer is much more aligned with Winston the VC than he is with the company’s founders and executives.  Joe would vote no for two reasons:  first, $60K after tax doesn’t move the needle for him and odds are (since he chose to work at the startup), Joe is a true believer in the technology and thus thinks of this deal as sell-out.  Amazingly, Winston votes no for the same reason:  $60M doesn’t do much for his fund and he also sees the deal as a sell-out.

Now, the founder would have made $30M and, using typical ratios, the CEO would have made $7.5M, and the key VPs somewhere between $1.5M and $3.0M.   In most cases, they would all vote yes.  (But in reality only the founder and CEO are on the board and actually get a vote.)

The scenario changes dramatically if the founder is already rich.  Imagine the founder made $100M on his previous startup.  Now, a $30M exit is uninteresting because it results in neither a lifestyle upgrade nor a status change.  Now, the founder aligns with Winston and Joe in voting against the deal.  You can analyze the CEO’s vote in a similar fashion.

The “Exit” Concept

Managers want to build great companies; VCs want great exits.

Unfortunately, building a great company is neither a necessary nor sufficient condition to enable a great exit.  At only 3 years old, Bebo sold to AOL for $850M.  A spectacular exit, no doubt, but a little more than 2 years later AOL sold it for less than $10M.  A great company?  Certainly not.  YouTube, while infinitely more ubiquitous, barely makes money but was sold to Google at 18 months old for $1.65B.  A great exit?  Yes — goosebumps quality even.  A great company?  Not.

The best managers tend not to focus on great exits.  They focus on building great companies.  In fact, the “IPO as exit” is almost purely a VC notion.  In reality, an IPO is almost certainly not an exit for the CEO; he or she is de facto bound to the company for at least the next several years and his/her ability to sell shares is highly restricted.

I have always believed that IPOs are like high-school graduations – they are a beginning, not an end.  Godfrey Sullivan, CEO of the red-hot company Splunk, seems to feel similarly, saying “we consider an IPO the 3rd mile of a marathon. The IPO is an early milepost, not the destination.”

In the best-case scenarios, building a great company will indeed lead to an IPO which will be yet another milestone in a long journey of success.  But this is not always the case.  I’ve seen companies (e.g., Versant back in the day) twist into pretzels to make it through the IPO window and provide a reasonable exit for the investors only to end up living-dead zombies thereafter.

Now, I have not found the particular VCs with whom I have worked over the past 20 years particularly exit-focused.  Most are surprisingly patient and indeed want to focus on building great companies.  But, you cannot ignore the possibility of divergence when some of the passengers can exit the bus reasonably quickly post-IPO while others cannot.

The terminology “exit” reflects this pretty clearly.  For employees, customers, staff, and executives, the IPO is not an exit.  Nor, for that matter, are most acquisitions.  Founders, key executives, and key staff are often locked in (through various mechanisms) for 1-3 years after a deal closes.

Irrational Considerations

As humans, we must recognize that we do not always act rationally.  Behavioral economics reminds us that we are subject to a bevy of rules and heuristics that can cause us to make sub-optimal decisions.

Some decisions that appear irrational are rationally motivated  – but by either an unknown personal or non-shared goal.  Others actually are just plain irrational.  For example:

  • Anchoring:  I need to make $50M.  (Because I decided that I need to make $50M.)
  • Benchmarking:  I need to make $50M.  (Because my roommate at Stanford made $50M and I’m smarter than he is.)
  • Fame-seeking:   I need to be famous and will take increasingly risky bets in order to achieve that.  (Arguably this is a rational decision derived from a non-shared goal, but if you are on the board of a company you have a duty to its shareholders so I’d argue it’s irrational from that perspective.)
  • Dreaming:   This technology is going to change the world, despite much evidence to support that contention.  (Because I made it and it’s really cool.)
  • One-more go:  I will take increasingly risky bets because I’m retiring soon and this is my last chance to get one more for my legacy.  Shoot the moon.

The trick here is most founders are, by definition, a little crazy.  The confidence and zeal it takes to quit one’s job, develop a product idea, start a company, and raise venture capital is well beyond that of the average “reasonable” person.  Thus, it can be hard for founders to know when to stop pressing bets.  The same traits that enabled them to be successful as founders present a risk they overplay their hands, and destroy shareholder value in the process, in the long term.

Conclusion

In this post, I’ve tried to highlight some of the common sources of potential misalignment between the various shareholders of a startup enterprise:  founders, venture capitalists, CEOs, executives and rank-and-file staff.  Hopefully, I’ve demonstrated that things aren’t as simple as they might appear and that just because everyone might own shares, doesn’t mean they have aligned goals and motivations.

If you think I’ve missed any good examples, please let me know.

The Open Source Software Paradox

As a marketer, I’m a fan of open source software.   After all, if you can’t dislodge Microsoft from mid-range server operating systems, Microsoft Office from desktop productivity suites, or Oracle from relational databases — and doing so through traditional means is a virtual impossibility —  then blowing up the whole business model isn’t a bad start.  It’s creative and it cuts right to the core of the problem.

But as a business-person I am not.  When you play the role of market spoiler it’s much easier to be famous than rich.  For example, when MySQL was acquired by Sun in 2008 for $1.2B, MySQL was doing only about $65M in annual revenues.  While the revenue multiple on the exit was spectacular, their capture rate was not:  MySQL disrupted literally billions in “big three” (i.e., Oracle, DB2, SQL Server) database revenues.  But if your value proposition is rooted in “almost free relative to leading commercial alternatives,” then you won’t succeed at 50% of their cost; you’ll need to be more like 2-5%.

I refer to open source as both a development model —  i.e., a way of building software — and a business model.  While the former is more well defined than the latter, the typical way to make money in open source is through selling subscriptions or licenses to certified and more-quickly-patched releases as well as selling technical support and/or consulting services to go with them.

While a spectacular exit multiple may occasionally pay off big time for shareholders (e.g., JBoss, MySQL), my theory is that in general it’s very hard to make money with the open source business model.  Red Hat is the obvious exception, and we’ll talk about them in a minute.

The basic paradox of open source is this:

  • The smaller the community the worse the software quality and the more people need certified releases and support.
  • The bigger the community the higher quality the software and the less people need certified releases and support (i.e., the community version will do).

So you can have a large community who doesn’t need to buy from you or a small community who does.

Two other drivers complete the picture:

  • The nature of the software and to what extent it truly requires an almost-daily stream of patches and updates and …
  • The monetization rate which is a function of the commercial market structure.  For example, the lower-level the software (e.g., operating systems) the more the market tends towards natural monopoly as customers want to minimize entropy at the bottom of the stack.  This should drive high pricing/margins on the commercial side of the market, and a parallel opportunity for someone to establish clear leadership on the open source side.

This is why Red Hat does so well when most others end up stagnating in the tens-of-millions of revenues range. The market is huge.  The software is low-level and thus the market “wants” a clear leader (think:  increasing returns) who can provide a hardware-independent, low-cost, supported product as an alternative to the proprietary Unix-es of days past.

Put differently, the bigger the commercial market and the more monopolistic its structure, the better the open source opportunity.  Conversely, the smaller the commercial market and the more fragmented leadership is within it (e.g., enterprise search, document management, and to some extent BI), the worse the open source opportunity.

Social Software for Business Performance Report by Deloitte

I wanted to do a quick post to highlight an excellent report by Deloitte, entitled Social Software for Business Performance (PDF, 32 pg).  The report argues that while today enterprises primarily measure the “success” of social software by user adoption (on the assumption that if people use it, then it helps business performance), that going forward enterprises will seek to find a more concrete way to measure their return on investment — e.g., by tracking the use of social software to improvement on a key performance metric.

The report further argues that social software has five unique capabilities that can help a business:

  • Expertise identification.
  • Facilitating cross-boundary communication
  • Preserving institutional memory
  • Harnessing distributed knowledge
  • Opportunity discovery

One of the better graphics in the report is this heatmap, which shows which capabilities are delivered by which type of social software.

If you’re interested in social software (e.g. Jive, Yammer, Socialtext, wikis in general) and its impact on the enterprise, then this report is a must-read.

An Amazing Story about Twitter and the Japan Earthquake

Every once in a while, I have an “aha” moment where I’m blown away by an unsuspected use or combination of technologies.

Prior to yesterday, the last such moment was when I heard my son shouting in French while playing alone in his room on a new game console:  “Cache-toi derriere le rocher … tire, tire, tire!”  (“Hide beind the rock, shoot, shoot, shoot”).  Had he gone crazy, I thought?  Then it clicked.  I knew the console was Internet connected.  I knew it had a bluetooth headset.  I knew it supported multi-player games.  And I knew he spoke French.  It had just never occurred to me that it would all come together such that he’d end up playing videogames with kids in France and talking to them while so doing.

Yesterday, I had a similar moment while I was talking to a friend with family in Japan.  We discussed the recent earthquake and she told me the following story.

We were on Twitter that night and suddenly the Japanese Twittersphere lit up with tweets about the earthquake.  So we called our family and got through to them while the earthquake was still in progress.  As it got stronger the line got cut, but were nevertheless really happy that we spoke as, after that, we couldn’t get through on the phone lines for at least 12 hours.”

This blew me away.  Think about that.  Someone can tweet about an earthquake as it hits, you can get the tweet 5000 miles away and call your friend while the earthquake’s still happening.  In fact, once I really started to think about it, I realized that you can actually call your friend before the earthquake arrives if he is far enough from the epicenter.

Seismic waves travel at 4 km/second plus or minus.  I don’t know what Twitter’s latency is, but let’s assume it’s 5 seconds.  Recall that an earthquake’s duration is related to its size (i.e., big earthquakes last longer) and that a major earthquake might last 60 to 90 seconds.  Consider this scenario:

  • You are working at your computer in San Diego
  • An earthquake strikes epicentered in San Diego and you recognize that in 5 seconds
  • You tweet it
  • 5 seconds later that tweet gets to a friend in New York City, some 3000 miles away
  • Your friend calls your brother in San Luis Obispo and warns him of the earthquake, figure that takes another 10 seconds
  • At this point the waves have traveled 80 km.  They have another 180 km to go before they hit San Luis Obispo
  • You have given your friend 45 seconds advance notice of the earthquake

Recall, I’m earthquake geek since I majored in geophysics  and worked during school at the Center for Computational Seismology at Lawrence Berkeley Lab (LBL).  At LBL, one of the grad students I supported was working on a related question — could you, given the first few seconds of waves, tell if an earthquake was going to be big or little?   Was there something different about big earthquakes that you could quickly detect and then potentially alert critical facilities?  Sadly, for my friend’s dissertation, the answer was basically no.

But I think with Twitter, we’re darn close.  After every earthquake I race to Twitter to be the first to tweet it –  and I never win.  So I believe that Twitter is a near instantaneous earthquake detection system and with geocoded tweets I am certain that you can easily locate an earthquake and its size / scariness (i.e., intensity).  Think:  sentiment analysis on “OMG that was huge #EQ in SF. #scary.”

I picked San Luis Obispo in my example above for a reason.  There’s a nuclear reactor there.  Hopefully, some grad student is trying to pick up where my friend left off and instead of analyzing the first few seconds of p-waves, they’re analyzing twitter feeds instead.

[Revised:  rewrote introductory aside again for brevity and clarity]