The first, execution risk, has been well covered - I especially like this old post from Martin Tobias. In a nutshell, execution risk covers the risk involved with successfully doing those things that are under the company’s direct control. I think many entrepreneurs have a tendency to underestimate execution risk in the same way that drivers who correctly understand the population’s risk of having an auto accident underestimate their personal risk, but that will have to wait for another post.

The second category is what I think of as ‘assumption risk,’ the risk that the world does not actually end up working the way the entrepreneur believes it does. Market risk is one type of assumption risk, but it is by no means the only one.

Since I focus on ventures with a big technology component, I’ve noticed that technical assumption risk is often misclassified as execution risk. This may be due in part to the problem of modeling complex systems - many of the systems we work on are sufficiently complex that it is not feasible to prove the validity of a solution without some measure of real world trial.

The misclassification may also be due in part to the roles in a startup; often, the people thinking about risk in a business planning context are different from those thinking about technical assumptions. When senior management is relatively non-technical, there are a couple of ways to deal with technical risk:

1. Think about all technical risk in terms of “the chance that our technical team will pull this off.” Essentially, this is an intentional classification of all technical risk as execution risk.
2. Work with the technical team to distinguish execution and assumption risks, and plan accordingly

It likely goes without saying that well-run startups choose the 2nd approach.

When we first started building the TurnTide product, we faced plenty of technical risk from both categories. There were a couple of primary assumption risks: how effectively would the application of our traffic shaping techniques to email streams control spam? would good email get through even from nodes that also sent spam?

No amount of modeling or analysis could tell us with absolute certainty what would happen in the real world. The only way to deal with these assumption was to get a beta product deployed in a large, real-world mail stream and test.

There were also execution risks from every direction: would we build a stable network appliance? would the traffic shaping implementation work as designed? would the analysis system accurately determine how much of the network resources to allocate to each sending node?

Execution risk is certainly quite different, in that we know from the outset that a correct solution is possible. We knew that we could do all of these things successfully; in order to mitigate our execution risk we needed to figure out how to maximize the chances that we would do so.

In a sense, the elimination of assumption risk could be considered the creation of value, in the same way that the discovery of mineral deposits would be. The elimination of execution risk, then, is the realization of value - analogous to the process of extracting and refining the buried minerals. Both are clearly necessary, but the require different investments and deliver different returns.

The process for technical startups is nowhere near as linear as I’ve just implied, but since the amount of risk is inversely related to the valuation of a startup company during its various rounds of funding, it seems to be well worth thinking about these issues when planning funding rounds and the progress made between them.

As promised, here are the slides from today’s presentation. We talked so much about each point that I’m not sure how much value these have to folks who weren’t in the room… maybe we can get the organizers to do audio or video next time.

Whether building a new company from scratch, or keeping a big company competitive, emerging technologies have changed the rules. Open source software, web services, mashups, AJAX, Ruby, Rails, and RSS… only a few of a seemingly endless list of technologies that have redefined what is possible for small teams. This talk will focus on startup experiences, lessons learned about technology choices and tradeoffs, and on building, funding and running a technology startup in the new environment.

The Emerging Technologies for the Enterprise folks are also putting on a pre-conference breakfast for CxO’s and executives on the 15th. I’ll be the keynote speaker for that event, talking about Tiger Team Innovation:

Startup companies use small teams beginning with a blank slate and the latest emerging technologies to build incredible value overnight. This talk will focus on using the same techniques, with a few twists, to deliver measurable value to the enterprise in new ways.

The main conference is open to everyone and seems to be quite reasonably priced compared to most such events; registration is here. I believe the breakfast is by invitation, so if you’d like to attend leave a comment or send me email and I’ll ask the organizers to include you.

• Not valuable enough to the publisher to justify looking for advertisers or even signing up for a network
• Not valuable enough to advertisers or networks as inventory based on the low number of views

I think there are a number of things to talk about around advertising on the long tail, but I’m going to focus in this post on what happens when the tail wags. In other words, what happens when a chunk of content in the long, skinny part of the tail suddenly moves toward the fat part.

There are some interesting numbers in an analysis of the results of Digg and Reddit homepage hits for one long tail page. The author describes a site with less than 100 uniques per day growing to a total of 234,000 uniques over 5 days, and describes the process of adding AdSense ads to the site:

After being Dugg, our Adsense account was finally approved 36hrs later, and some adsense ads went on the front page. … Over the four days we used adsense, we made a total of 71.87 [dollars]. Our average click through rate was a dismal 0.24%, although the ads on our site seemed to be fairly highly targeted.

The first problem is the 36 hour delay in getting AdSense ads up on the site; this is a common issue with this type of content, as the ‘wag’ of the long tail content to high visibility can happen very quickly. I’m sure Google could optimize this somewhat, but the nature of long tail content is such that the publisher may not even know about the rush of traffic until days later. The only real solution is for the long tail content to somehow be enabled for advertising before the traffic comes.

The AdSense clickthrough rate of 0.24%, and thus the overall value to the publisher, is another problem: That site’s ad space was only worth a very low $0.70 on a CPM basis. This could be due to a lack of any historical data in AdSense to improve targeting, it could due to the differences in reader behavior for Digg/Reddit/etc referrals versus organic search referrals, or it could be something related to the specific content.

In all likelihood, there is an opportunity for publisher and ad network to get a lot more value from this content. Were Digg, for example, to create a new kind of ad network where signing up was just about as easy as putting a “Digg It” button on a page, they would have a good stab at signing up long tail publishers beforehand, and solving the problem that caused a 36 hour delay in monetization in the example above.

Digg, like similar sites, also has access to some great raw data about content. They know categorization, topics and tags, along with the momentum of individual content moving out of the long tail. Given an ad network, they would also be able to create multiple-impression campaigns across member sites, adding value for ad buyers. I’m betting the value of the space on each popular content page would be higher in the context of a common ad network across the Digg site and many of those linked pages. Consider the value to advertisers of being able to buy, in advance, ad space on the sites that will be on Digg’s front page in the future.

The real value may be the power of the social bookmarking sites’ community to, implicitly or explicitly, control the ads associated with each linked page. This could be as simple as doing what any smart ad network does, and showing the better performing ads more often on a given page. A more interesting approach might be to make the control explicit by giving users the ability to vote up or down the various ads associated with a page, or by showing how many previous users had clicked an ad.

I’m curious to see how directly the user generated content wave impacts advertising, and how sites like Digg evolve to increase the ad value of the long tail content that they rocket to prime time.

Looking at the technically innovative startup pitches I’ve heard from this perspective, I can break them down into two basic categories.

Horse before the cart: we have built this really incredible technology. Here’s what it is and how it works. Take a look at our demo, which proves that we have built this technology. Here’s a list of amazing things we can do better with this new technology.

Cart before the horse: we are doing this amazing thing. it wasn’t possible before, and doing this thing enables an awesome new business. Take a look at our demo, which proves that are doing this amazing thing. It works because of this really incredible technology we have built.

Yahoo, and other big companies, can afford to occasionally build and launch a new horse without putting a clear, focused application of the technology out in front. In fact, doing so can help them maintain their influence and claim leadership in new areas. Some successful startup companies have built technology first without a narrow application guiding the business, but I’d argue that these are the exception rather than the rule.

From the founder perspective, I think that focusing on a technology, however cool, can lead to some problems. When the technology comes first in my thinking, I can fall into a trap of not diligently evaluating even one of the many potential market opportunities. Focusing on one little application of the cool new technology means thinking about things like who, exactly, will buy it… what will they pay for it… how will they use it… how will it change their lives. Cool technology is really exciting to a few people, but the thing it does is what makes it exciting to lots of people.

Looking at companies from the outside, as an investor or otherwise, I find the technology/application focus question to be even more important. Founders may have a grand vision for an eventual application of the technology, but if they are focused on building a business around a focused, narrow application first then they will make better decisions about how much money to raise, how much to build before launching, and how to measure success. The focus on an initial application makes business decisions, external and internal, quite a bit easier. It also turns the technology into a key asset and competitive advantage, rather than the company’s raison d’être.

Funding or starting this type of company means that the initial, focused application stands on its own as a business that should be built. If that business is successful, the company can leverage its technology assets in tackling larger opportunities.

So, next time you are trying to convince a VC about the merits of your firm, show them how they can make 10x capital on a realistic exit scenario (not how to get a 40% IRR).

reminds me of the importance of the differences in risk model between VCs and entrepreneurs, something I’m going to explore a bit here. One example of a VC model is described in the post, so I’ll use those numbers for the comparison.

Bases loaded

The VC risk model is based on a portfolio of investments, and the VC should choose each investment on the basis that a 40% risk of negative return is justified by a 10% chance of 10x+ return and a 50% chance of a zero to 5x return. This model, along with its countless variations, is designed to accomodate the high risk of failure of startup companies. VC Confidential has this to say on the subject:

In the early stage world, if you target, say a 40% IRR, through assuming a number of 5x wins in a compressed period of time, you will likely be out of the business. Your 5x wins, while possibly generating high IRR’s, don’t return enough multiple to pay for the 4 tube shots and 2 break-even deals.

So, VC-funded startups fail at a high enough rate that the target outcome for each investment needs to be a 10x return in order to pay for the investments necessary to deliver that one return with confidence. In fact, even a high confidence investment in a company targeting a 5x return is a hedge: overall, it decreases the chances of a very low rate of return for the fund but also decreases the chances of an on-target rate of return.

To stick with my tenuous baseball analogy, as a VC you start with no outs and no strikes, so while each swing is unlikely to yield a home run, over all of the swings you will probably have at least one. Also, you only swing for the fences with runners on base, because the value of a successful home run needs to more than cover the outs and strikes used.

Two outs, two strikes

Unlike the VC, the entrepreneur runs one company at a time. This means that the entrepreneur can’t use the same risk model to achieve a high confidence level in a given rate of return. All other things being equal, the entrepreneur can’t target the 10x return desired by the VC risk model without having a lower confidence in the outcome.

From this, it seems like the entrepreneur is incented to target lower returns in order to achieve a higher confidence. Of course, entrepreneurs have a much larger range in their tolerance of risk than VCs. Some entrepreneurs will target a 100x return with a small chance of success, while those at the other end of the spectrum are happy to run lifestyle businesses.

Focusing on businesses that need institutional funding, how does the entrepreneur, with only one swing at the ball, manage risk and still target a return that works in the VC model? One approach is to look for businesses that have a real swing at the fences, but can be turned into triples or doubles along the way. Not every business can work this way: many startups are based on predictions about the future and won’t have exit opportunities until the core assumptions or predictions are validated; at the other end of the spectrum many are really trying for a single or double and could get very lucky with a triple. The ideal situation for this approach, which involves a real risk/reward decision between exit and growth at each stage of funding, doesn’t come along very often.

Wins vs. RBI’s

If a company has the opportunity to decide between exit and growth, the alignment of risk models comes to the center of attention. There has been a lot of discussion lately about founder liquidity as a means for aligning incentives between VCs and entrepreneurs, but in the context of this post, founder liquidity is a mechanism for aligning the risk models by hedging the founders’ risk at the expense of upside.

I think there is value in looking for companies that have a swing at the fences, but can be turned into triples or doubles. Hopefully, recognizing that as a target will help in choosing, and running, businesses that have a real addressable market from the start, can divide market risk over time while decreasing execution risk, and are able to validate their assumptions and predictions early. When it comes to decisions on exit vs. growth, the combination of discount/premium of the exit and alignment of risk models will come into play. Even if those decisions all favor growth, it seems to me that managing toward exit opportunities makes as much sense as managing every startup toward eventual IPO.