August 2007
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Lijit Search

I've made the move to TypePad.  The new URL is http://swni.typepad.com/dispatches/.  The change should not effect many, because the RSS feed has been re-directed to the new URL, and subscriptions should be automatically updated.

I published my blog via Radio Userland between August 2002 and June 2007.  Radio was a cool tool with which to learn about blogging.  However, I have been particularly interested in TypePad since Michael Sippey joined SixApart some years ago.  Since that time, I have had the chance to use TypePad for other blogs, and I have been impressed.  In addition, I have grown concerned about the relative lack of development over at Radio Userland.

I think I was able to successfully copy the entire archive of my posts to TypePad.  There are a couple of dozen non-chronological "stories" that I may copy, as well.  In any case, I'll maintain the Radio site as an archive.

4:23:58 PM  

Since last September, I've been making a series of posts that describes innovation through the lens of the evolutionary algorithm.  I've struggled to reach some definitive conclusions regarding ways in which companies might build their adaptive capacity.  I suspect that I will continue to struggle.  Nevertheless, my research and experience points toward a consistent set of ideas that offer promising avenues of exploration:

• Uncertainty is the dominant characteristic of the social and, hence, business world.  Consequently, business strategy must necesssarily be contingent.  We should work hard on imagining a full range of possible futures in order to anticipate, reduce the shock of surprise, and accelerate our appropriate response to new information.
• In order to increase flexibility, it makes sense to organize our capabilities in a modular fashion.  That is really hard, because it forces us to acknowledge and understand interdependencies (in order to reduce them) and define and manage interfaces between modules (in order to get them to work together).  The upside is that modularity facilitates reconfigurability, which means that we can do new and different things by recombining existing capacities in new ways.
• Core and contingent capabilities should be complementary.  Our ability to do something depends capacities accumulated over time.  Consequently, it makes sense to think about how to leverage existing capacities than to default to having to build new capacities from scratch (or acquire others' capacities at a premium).
• The future never arrives, so anticipating the future is a dynamic and iterative process.  Furthermore, pacing and industry clockspeed are probably even more important than you think.  The Red Queen is a demanding taskmistress.

Others have written far more eloquently and persuasively on these topics than I'm capable of doing.  So, I've appended this post with a bibliography of some of the books to which I find myself referring on a regular basis.

I began this series of posts by quoting Orgel's Second Rule: Evolution is cleverer than you are.  I continue to be humbled by how the process of innovation is much, much cleverer than me.  That has pointed me toward the following set of personal objectives:

• Innovate, innovate, innovate.  As Paul Oremerod concludes, "It is the best strategy for individual survival, and it is a strategy from which we all, as consumers and citizens, have benefited immensely."
• Adopt a strategy of humility.  Prediction is a fool's game; we don't know as much as we think we know.
• Aspire to wisdom.  As Jeffrey Pfeffer and Bob Sutton suggest, "Be confident enough to act on the best knowledge you have now, humble enough to doubt what you know, and wise enough to face the hard facts when new - and better - evidence comes along."
• Practice your own norms of adaptive behavior.

For further reading, I recommend the following (in no particular order):

The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
The Only Sustainable Edge: Why Business Strategy Depends on Productive Friction and Dynamic Specialization
The Strategy Paradox: Why Committing to Success Leads to Failure (and What to Do About It)
Why Most Things Fail: Evolution, Extinction & Economics
20|20 Foresight: Crafting Strategies in an Uncertain World
Adaptive Enterprise: Creating and Leading Sense-and-Respond Organizations
How Breakthroughs Happen: The Surprising Truth About How Companies Innovate
Six Degrees: The Science of a Connected Age
The Modern Firm: Organizational Design for Performance and Growth

1:34:25 PM  

The other day, my EIP partners and I had a beer with an experienced member of the corporate development group of a consumer products company.  With typical candor, this person noted:

Show me any idea, and I'll show you at least one fairly senior person in our company who is interested.  It's pretty easy to get someone here interested; it's a lot harder to convert interest into action.

I'm sure many of you who are inventors can relate.  Getting stuck in the "we're interested but not willing to commit right now" do-loop is akin to inventor purgatory.  Consequently, I can imagine many an inventor nodding their heads in agreement at the following line from Men in Black:

A person is smart.  People are dumb, panicky, dangerous animals, and you know it.

How is it that an organization full of smart people (i.e. people who are interested in our great ideas) can be so dumb (i.e. fail to commit to act upon those same ideas)?

Sociologists such as Duncan Watts might say that I'm asking the wrong question, because the question is based upon the fallacy of composition (the whole behaves like the part) or division (the part behaves like the whole).  Specifically, my question assumes a representative agent.  Think about how many times you use phrases like "the market believes" or "the customer thinks" or "the company decided" as if you were talking about an individual.  How many times have you assumed that if you can persuade individuals, in isolation, you can persuade the group?  The assumption that the collection can be described in terms of a representative agent holds up if individuals' behaviors are independent of one another.  However, in business, that's rarely the case. 

Consider the following from Robert Cialdini's book, Influence: The Psychology of Persuasion:

In general, when we are unsure of ourselves, when the situation is unclear or ambiguous, when uncertainty reigns, we are most likely to look to and accept the actions of others as correct.

In other words, when we are uncertain either because we don't have access to sufficient information or we don't have the individual capacity to understand all of the information that is available, we'll turn to our colleagues for guidance.  It should come as no surprise, then, that when faced with the uncertainties inevitably surrounding the decision whether or not to commit substantial resources to a prospective innovation, the beliefs of individuals within the organization would be highly interdependent.

As Watts notes in a recent essay titled "The Collective Dynamics of Belief,"

Under fairly broad conditions...it is possible to show that when individuals make decisions in response to the decisions of other people, the relationship between individual preferences and collective decision breaks down.

and

When individual beliefs are formed interdependently, the collective outcome is fundamentally ambiguous, in the sense that it is not determined in any obvious way by the characteristics of the individuals, considered in isolation of each other.

We experienced this truth quite recently.  The individual members of the management team of a prospective licensee of one of our products seemed quite positively inclined to move forward toward an agreement.  However, in the span of just a week, their collective decision was to pass.  Why the apparent reversal?  I'm not sure that we'll ever know for sure, notwithstanding our respective ability to construct plausible explanations.

So, on the one hand, it is important for inventors to understand that group decisions are fundamentally unpredictable in the strict "if X, then Y" case of linear causality.  A great product concept pitched well to a group of rational decision-makers can fail simply due to the complex dynamics of collective decision-making.  On the other hand, that's not the same thing as saying that anything is possible - weak concepts pitched poorly aren't going to win the approval of a licensee.

As I think about the varied challenges of influencing the probability of adoption, I'll share my thoughts in subsequent posts.  Even if we can't predict the future, we can influence the probability distribution of possible futures.

12:42:19 PM  

Ashton Udall over at the Product Global blog makes a couple of straightforward, but frequently ignored, observations:

• Companies have constrained capacities.  That is, they cannot do everything at once.  Furthermore, commitment to a strategy - by definition - consumes a great deal of capacity.  Often, little capacity is reserved for the cultivation of additional strategic and growth options.
• A portfolio of prospective innovations need not be created in one shot; they can be created dynamically over time.

As Ashton points out, it is extraordinary unlikely that a company will get it right with its first shot - even if they use a shotgun.  It sure helps to be aimed in the correct direction, but rapid, iterative learning is still the norm.

7:48:07 AM  

In the June 2007 issue of Harvard Business Review, Satish Nambisan and Mohan Sawhney present a framework for understanding the differing roles of Invention Capital, Innovation Capital, and Venture Capital firms in the context of Open Innovation.  In their article, A Buyer's Guide to the Innovation Bazaar, they observe, "Companies can shop for innovation in various stages of development - from raw ideas to market-ready products - with the help of intermediaries."  Along this continuum, there are some key trade-offs:

• Risk and Reach - Licensing agents, patent brokers, electronic R&D marketplaces, idea scouts, and Invention Capitalists can help companies access a broad range of potential innovations, but the uncertainty related to such raw ideas is high.  At the other end of the spectrum, business incubators and Venture Capital firms can provide access to market-ready products in which there is higher confidence regarding market potential.  However, there are fewer such offerings to choose from.
• Speed and Cost - Raw ideas are inexpensive, but can take a relatively long time before they are ready for the market.  Market-ready products, as the name suggests, are ready to go (or are already in the market on a limited basis), but are expensive to acquire.  (Click here for the SpinBrush example.)

Per Satish and Mohan, the middle ground is the domain of Innovation Capitalists such as my firm, Evergreen IP.  As I interpret the authors' hypothesis, the unique characteristics of the different intermediaries along the spectrum from raw ideas to market-ready products include the following:

• Invention Capitalists, licensing agents, idea scouts, and electronic marketplaces offer access to the Long Tail of the distribution of prospective innovations.  They create options in an uncertain world.
• Venture Capitalists and incubators, on the other hand, are focused on the inflection point between the Long Tail and the Short Head of the innovation spectrum.  The ability to commit increasing amounts of capital in order to progressively resolve uncertainty distinguishes the Venture Capitalists from other innovation intermediaries.
• Innovation Capitalists sit at the precarious intersection between the creation and execution of innovation options.

I say precarious, because I suspect that Michael Raynor is probably on to something with his theory of Requisite Uncertainty, in which Raynor concludes that the management of uncertainty (the creation of options) should be separated from the making of commitments (the execution of options).  We at EIP constantly live with the tension between searching, screening, and refining ideas (creating options) and testing the marketability of such ideas by making increasing commitments of time, money, and relationships (preliminary execution of options).  Nevertheless, despite the challenge, we believe that there is value to be generated and earned in the management of a portfolio of real options, including facilitating the selection of options for execution.

Furthermore, Satish and Mohan observe that the relationship between acquiring companies and Invention and Venture Capitalists is fundamentally transactional.  On the other hand, they posit that the relationship between Innovation Capitalists and companies must be deeper:

Companies seeking innovation at the two ends of the continuum focus primarily on the type of innovation they want to buy, whereas in the middle they need to focus on the intermediary.  That is, because of the nature of the innovation capitalist's offering, large client companies need to build and nurture long-term and trusting relationships with selected IC firms.

Satish and Mohan's work is suggestive in a number of ways:

• As an "intermediate intermediary" Innovation Capitalists such as EIP may stand to benefit from cultivating relationships with its counterparts on either side of the development continuum.  We've certainly been doing just that in regard to upstream collaborators.  And, we're in the process of exploring co-development relationships and other downstream collaborations in order to run rapid go-to-market experiments.
• An auction may be consistent with the transactional nature of the sale of raw ideas and market-ready products to an innovation buyer.  On the other hand, full-on auctions may be counterproductive to the development of the kind of trusting relationships that Satish and Mohan believe are required for Innovation Capitalists.  Again, we at EIP find ourselves walking a tightrope.

Who said it was easy?  After all, Satish and Mohan call it the "innovation bazaar" for good reason:

Like a traditional bazaar, it can be chaotic and bewildering...Just contemplating a plunge into the hurly-burly of this space can be daunting.

Indeed.

10:01:17 AM  

Ask the chairman of P&G - the $70 billion consumer goods giant - who is more important to his company's success: Wal-Mart or housewives.  Sure, it's a tough choice, but I suspect that he'd answer "housewives."  In other words, although the Long Tail distribution of market outcomes in the highly competitive world of consumer products is undoubtedly the result of the interaction of supply and demand, it seems likely to me that the dynamics of demand provide the foundation for the emergence of the winner-takes-most characteristic of consumer markets.  Furthermore, the fundamental uncertainty of such dynamics are critically important for an individual inventor (and others in the innovation chain) to understand and appreciate when making investment decisions.

Skewed Demand

Robert Cialdini wrote Influence: The Psychology of Persuasion, one of the best selling books of its kind over the last decade or so.  In it, he notes the following:

In general, when we are unsure of ourselves, when the situation is unclear or ambiguous, when uncertainty reigns, we are most likely to look to and accept the actions of others as correct.

In the context of buying decisions, we tend to look to others to the extent that we're uncertain about what movie to watch, book to buy, or music to listen to.  It's a reasonably rational strategy in an uncertain world.  More generally, our buying behaviors are more likely to be socially contingent to the extent that the promised value is qualitative, experiential, and emotional.  That's important, because the dynamics of "social contagion" or "information cascades" are characterized by threshold rules or tipping points that are the signatures of "power law", "scale free", or "Long Tail" distributions.

As Chris Anderson has explored in a book, his weblog, and a downloadable manifesto, many consumer markets can be described in terms of a "short head" composed of a handful of hits and a "long tail" composed of everybody else.  This is popularly known as the 80/20 rule, in which 80% of the revenue is generated by 20% of the products.  Although directionally correct, the relative proportions aren't fixed.  For example, data from the book business shows that something like 87% of book sales are attributable to just under 8% of the 1.2 million books published in a recent year.  In other words, the book publishing industry reflects something like a 87/8 rule, which might look something like this:

(Distribution generated by demonstration tool developed by Fiona Maclachlan.)

Movies, similarly, seem to be distributed according to something like an 80/6 "rule".  Clearly, books and movies fit the criteria of qualitative, experiential, and emotional.  But, what about more tangible and objectively functional consumer products?  Well, even sporting goods, for instance, are well described by something along the lines of a 60/10 rule:

In any case, the improbable (i.e. hits) dominate the distribution, a point that Nassim Nicholas Taleb makes more generally in Fooled by Randomness and The Black Swan.  The implication for inventors of consumer products is sobering: Given a market composed of 100 distinct offerings, a 10% market share implies a #2 market ranking.  If your product's sales rank falls outside of the top 10%, your market share is likely to fall below 1%.  That is why, in my previous post, that I suggest that even in a success case, the range of revenue for a new, compelling product in a $400 million annual market might range between $4 million and $40 million and his most likely to be at the low end of the range.

Many times when we use words such as likely or expected, we are referring to the average or the mean of a distribution.  That makes a lot of sense if the distribution is the familiar bell-shaped curve, or normal distribution.  But, the average has no meaning (i.e. there is no representative scale) in the case of highly skewed Long Tail distributions.  In such cases (which, ironically, may be the more normal when it comes to the demand for consumer products), the median or mode of the distribution are better indicators of "likely" outcomes.

Democratizing Supply Effects

Although I think it is appropriate to focus on demand dynamics, Chris Anderson has some interesting hypotheses regarding changes on the supply side.  More specifically, he sees three key trends:

• The tools of production are becoming less expensive and more widely available.
• The cost of inventory and distribution is falling.
• The cost of search - the matching of supply and demand - is falling.

While most pronounced in the digital world of entertainment products such as movies and music, these trends extend to the physical world, as well.  P&G has explicitly acknowledged the democratization of invention - the fuzzy front end of production - in its embrace of Open Innovation.  As I wrote about here, the emergence of readily accessible and relatively inexpensive CAD software and expertise, rapid prototyping, rapid manufacturing, just-in-time inventory management, and various direct marketing techniques may mean that the distinction between digital products and physical products is more blurry than we commonly assume.

Anderson believes that these trends will shift the relative proportion of demand away from the short head toward the long tail.  But, Anderson acknowledges the continuing importance of hits, while offering hope for the masses:

I've described the Long Tail as the death of the 80/20 Rule, even though it's actually nothing of the sort.  The real 80/20 Rule is just the acknowledgment that a Pareto distribution is at work, and some things will sell a lot better than others...What the Long Tail offers, however, is the encouragement to not be dominated by the Rule.  Even if 20 percent of the products account for 80 percent of the revenue, that's no reason not to carry the other 80 percent of the products.  In Long Tail markets, where the carrying costs of inventory are low, the incentive is there to carry everything, regardless of the volume of its sales.  Who knows - with good search and recommendations, a bottom 80 percent product could turn into a top 20 percent product.

Much of Anderson's writing describes the emergence of Long Tail aggregators such as Rhapsody, iTunes, and NetFlix.  More broadly, Google, eBay, and Amazon.com are aggregators in that they provide a platform for reducing the cost of distribution and search across a broad range of products - digital and tangible, alike.  Guthy-Renker, HSN, and EIP are variants on what might be called Long Tail prospectors - firms that search the "Long Tail of things" in order to connect the next hit with the capacities that can take advantage of economies of scale and scope, which remain important in the tangible world.  (In a very real sense, EIP represents a search innovation.)

Implications for Inventors

• Having a great product helps, but there is no linear, causal relationship between the quality of your invention and market success.  (See the suggested reading list below for more.) 
• Look at the potential market from the top-down and the bottom-up.  Try to be realistic.  Express your conclusions in terms of a range of revenue.  Divide the result by 10 to give you an idea of the "home run" potential of your product.  Divide that number by 10 to give you a "likely success case" revenue estimate.  Think in terms of probabilities; avoid point forecasts.
• Look for ways to run an in-market experiment as quickly and inexpensively as possible.  In highly information-based markets, Anderson notes, "It's more expensive to evaluate than to release.  Just do it!."  (As I noted here, some venture capitalists are beginning to favor market experimentation over analysis, as well.)
• Invest assuming the likelihood of modest returns, but recognize that there is at least a small chance that your product will trigger a global cascade of demand.  That is, it could really be a hit.  Consequently, give early thought about how you might sell or license your product to a company that has the operational capacity to take advantage of the hit when it happens, because the window of opportunity is likely to slam shut very quickly.
• Respect the unavoidable uncertainty that surrounds the innovation process.  Acknowledge the risk that others - including licensees and customers - are taking in adopting your product as theirs.  On the other hand, don't fear uncertainty - it is the source of disproportionate opportunity.

More Reading

Feedback-driven, complex, non-linear systems are hard to understand and, quite often, counter-intuitive.  In addition to the resources mentioned above, you may find the following useful in your exploration of the Long Tail:

• Critical Mass by Philip Ball - This book goes deeper than The Tipping Point and brings together some of the ideas presented in books such as Emergence, Ubiquity, Nexus, and Linked.
• Six Degrees by Duncan Watts - Includes a nice description of the dynamics of information cascades as well as to the limits of our ability to engineer social epidemics.
• Hollywood Economics by Arthur De Vany - This is for more advanced readers, but is a terrific resource.

9:33:36 AM  

The likely sales of a compelling product in a large consumer market is smaller than you think.  On the other hand, there is small - but meaningful - chance that your product could be a runaway hit.  Many, maybe most, individual inventors tend to fixate on the latter and ignore the former.  The results can be disasterous.

The trap is easy to fall into.  Just the other day, an inventor sent me some research that suggests that the total U.S. retail market for products like his may be as large as $400 million per year.  To be honest, my initial reaction was, "That's pretty big.  If this product achieved a mere 10% market share, it could generate $40 million of retail revenue per year."

Maybe, but highly unlikely.

A better range for planning purposes would be to assume that in a success case the revenue for this product may be between $4 million and $40 million.  Furthermore, the most likely outcome will be $4 million or less.  In contrast, a typical inventor might assume that $40 million represents a success case, while, say, half of that - $20 million in revenue - might represent a conservative case.  The difference in expectations is very likely to drive quite different investing behavior, for starters.  In other words, an inventor who is convinced that his product can generate retail revenues of from $20 million to $40 million is likely to behave differently than an inventor that believes that his product could generate revenues as high as $40 million, but is most likely to generate revenues of $4 million or less.  The distinction could be the difference between a strained marriage and divorce; a smaller savings account and personal bankruptcy.

Consequently, I strongly recommend that inventors devote some time toward understanding the implications of the Long Tail distribution.  To that end, I'm going to summarize my understanding as succinctly as possible in my next post.  I hope it helps.

8:45:10 AM  

John Cook, a venture capital reporter in Seattle, quotes a recent study that names Montana as the most entrepreneurial state in the U.S.  Heck, nobody is going to hire us, so we have to create our own work.

6:50:52 PM