Dispatches from the Frontier

Accelerating the Perception of Difference

The widespread adoption of a new product can be agonizingly slow for at least a couple of reasons:

• Users tend to be biased toward the status quo, which means that a prospective innovation typically must offer a compelling relative advantage.
• The perception of advantage is a function of familiarity.

Innovation is a hard game, in part because it tends to require insight and superior product design and manufacturing execution and effective marketing.

My friend and colleague, John Funk, is living that multi-dimensional challenge.  Recently, he formed Long Tail Pet Products to launch the DogPause bowl, the healthy dog bowl for dogs that eat too fast (based on an invention developed by, and licensed from, our firm, Evergreen Innovation Partners).

The 9X Effect

In Eager Sellers and Stony Buyers, John Gourville asserts:

There's a fundamental problem for companies that want consumers to embrace innovations: While developers are already sold on their products and see them as essential, consumers are reluctant to part with what they have.  This conflict results in a mismatch of nine to one between what innovators believe consumers want and what consumers truly desire..

In part, that's because prospective consumers are reasonably satisfied with the status quo and, consequently, fail to see the need for the new product.

Noticeable Difference

The 9X effect seems to hold true even when the new product really does offer a dramatic difference in performance.  That is, of course, because the difference has to be perceived before it can motivate a change in the behavior of consumers.  Because most consumers won't be intimately familiar with the context for which the new product is designed, they are less likely to notice an actionable difference.

Bill Buxton of Microsoft Research explains:

I've decided to name a new law "GGR," or the law of gradual granularity refinement...JND significance ~ 1 / familiarity.  The term JND (just noticeable difference)...asks, "What is the smallest level of differentiation that you can perceive as being significant?"  The tilde character (~) means "varies with."

Hence, the law says that the granularity at which we distinguish meaningful differences gets finer the more our familiarity with a subject grows.  Conversely, it also says the less familiar we are with something, the coarser the granularity will be before we can distinguish differences as being significant.

So, we aspiring innovators face a dilemma:  In order to increase the odds of breakthrough success, it helps if we identify and develop products that are really new and different.  However, because of the their newness, prospective buyers won't be familiar with such products and are, consequently, less likely to notice an actionable degree of difference on their own.  That means we have to find creative ways to accelerate familiarity.

Reasons to Believe

Doug Hall has deconstructed the elements of effective marketing communication into a framework he calls marketing physics.  Assuming a clearly articulated benefit (e.g., "The Healthy Dog Bowl"), it is the marketer's responsibility to provide real reasons to believe the claim.  After all, what makes the DogPause bowl healthy?  Does the bowl offer enough of a difference to my dog's health to matter to me enough to buy the product?

Big companies have an advantage in that they have the capacity to use mass advertising to build consumer awareness of a new product's difference.  On the other hand, as Amar Bhide has shown, that capacity has a high opportunity cost, and opportunity cost translates into risk, which tends to inhibit big company's willingness to launch really new and different products.

Small companies (like Long Tail Pet Products) are more likely to launch new and different products, but they don't have the resources to launch carpet-bombing marketing campaigns.  Guerrilla marketing is more their style, but it can take longer to achieve results.

Check out the DogPause web site to see how John is using the elements of kitchen logic, personal experience, pedigree, testimonials, and a guarantee to provide reasons to believe in order to accelerate consumer's understanding of why the DogPause bowl deserves to be purchased.  Here is a sample:

David Eckoff Blog

I've been reading the David Eckoff Blog.  David is VP-New Product Development & Innovation at a big media company.  As readers of this blog know, my day job involves figuring out how to bridge profitable connections among individual inventors (sources of prospective innovations) and "BigCos" (organizations that have established distribution, brand, and access to manufacturing).  It is encouraging to know that there are thoughtful folks such as David working on removing obstacles to innovation from the BigCo end of the bridge.

In Search of Inexperience

In a recent post, Guy Kawasaki tackles the assumption that serial entrepreneurs represent better bets than first-time entrepreneurs.  He notes:

Serial entrepreneurs cannot distinguish between causation and correlation. The root cause of earlier success may have simply been blind, dumb luck, but few people realize this and even fewer will admit. Thus, they have the hollow arrogance of people who just got lucky instead of people who have been truly tested, and arrogance is a bad thing in entrepreneurs.

Kawasaki's observations remind me of Art De Vany's careful analysis of the "curse of the superstar" in Hollywood Economics:

Surprisingly, a good part of [superstars'] success and productivity can be attributed to luck rather than talent...there is evidence of statistical self-similarity on the creative side of the business...Extreme events and risk participate in all aspects of an artist's career...The differences between the talents of kurtocrats and others may be small, but...nonlinearity can magnify small differences in talent (or luck) into extreme differences in outcomes...Once that top star status is earned, it doesn't go away easily because it is hard to disentangle performance from movie.  Causality is hard to nail down in a complex system, so when a movie is a big hit the most readily identified element, the artist, gets much of the credit.

Fundamental attribution error is pervasive.  As a general rule, we tend toward attributions based on dispositional rather than systematic characteristics.  So, one might expect it be relatively rare to find successful actors and entrepreneurs who forcefully and publicly attribute their success to luck.  However, those who invest in new ventures (artistic or otherwise) are well served to consider the curse of the superstar.

Breakthroughs and the Long Tail of Innovation

Lee Fleming, a researcher at Harvard Business School, has written an interesting article titled Breakthroughs and the "Long Tail" of Innovation in the Fall 2007 issue of MIT Sloan Management Review.  In it, Fleming analyzes the impact of the following variables (the "levers of invention") given his assumptions that "invention is essentially a process of recombinant search" and "Almost all inventions are useless; a few are of moderate value; and only a very, very few are breakthroughs."  (Click on image for larger view of Fleming's summary table.)

The Presence of Lone Inventors

My fieldwork consistently indicates that innovators working by themselves can be the source of more failures as well as more breakthroughs...[My analysis of a sample of all patented U.S. inventors since 1975 indicates] that lone inventors generate fewer novel combinations and that they combinations they create are less likely to be used, on average, by future inventors...But the future use of their work is also much more variable, such that they are more likely to be the source of a highly skewed outlier, thus bolstering the argument that they are indeed more likely to be the sources of radical innovations...

A less rigorous selection process for loners...means that they will have a greater variance of output [relative to collaborative inventors].  Because they are less constrained by convention and skeptical groupthink, lone inventors are more likely to invent (and not immediately dismiss) the radical breakthrough.  Thus, lone inventors are less creative on average and yet are also more likely to come up with a breakthrough.  In other words, lone inventors make fewer shots on average and tend to score both very low and extremely high.  They are on average both less successful—and more likely to be the source of breakthroughs.

This is consistent with our experience at EIP.  Over the last two years, we have screened over 2,000 inventions submitted by individual inventors and have made collaboration proposals on less than 1%.  On the other hand, using the Merwyn Concept Accelerator tool as our measuring stick, we have found that the best concepts submitted by individual inventors score at least a standard deviation higher than the average of concepts developed by collaborative, corporate teams.

Brokered Collaboration

Collaborations among inventors can be either brokered or cohesive.  Brokerage occurs when a single individual is the hub through which others interact.  In a cohesive collaboration, people develop separate and independent relationships with one another that do not include a central individual...

By brokering others, hub inventors gain first access to and control of information, enabling them to generate a greater number of new combinations.  Yet this same social networking structure also makes in inherently more difficult for others to understand a local inventor's idea in order to critique, transfer and evolve it.  Hence, brokers tend to generate more new combinations, but those innovations are less likely to be picked up and developed by others...

Neither brokered nor cohesive collaboration is inherently superior to the other; much depends on the organizational culture and the specific environment of the inventors.  For example, although brokerage is better for the generation of new combinations, cohesion confers strong marginal benefits in collaborations that lack trust or involve fresh information.

This rings true, as well.  A number of large companies that have embraced the idea of Open Innovation have designated certain people to broker connections among external and internal networks of inventors and experts.  Their organizational objective is to increase access to prospective breakthrough innovations.  However, in a number of circumstances, we've found that the person charged with being the "hub inventor" behaves in manner that preserves her proprietary control over the flow of information.  Because "lack of trust or...fresh information" is a common characteristic in this context, we've found that an effective implementation of Open Innovation requires the transformation of brokered collaborations into cohesive collaborations.

Diversity of Teams

Diversity helps generate more shots on goal although, on average, those shots are less successful.  But diversity also gives rise to new and unexplored combinations that increase the probability of a highly skewed breakthrough...

My own research studying more than 17,000 patents has found that the greater the divergence between collaborators' fields of expertise, the lower the overall quality of their output.  But multi-disciplinary collaboration increases the variance of the outcome, such that failures as well as breakthroughs are more likely.

This, too, makes sense.  Collaboration requires the cultivation of shared meaning.  People from diverse backgrounds view the world differently, use different language, and use the same language in different ways.  The development of effective boundary objects that effectively facilitate shared meaning is hard, subtle work.

Application of Science

My fieldwork, research and experience suggest that the scientific method and knowledge help provide a useful map of the technology landscape...

In effect, scientific methods make the process of invention less random, enabling inventors to make fewer but better shots on goal.

Or, as my partner Vandy Van Wagener would say, "Run the damned experiment."  The iterative resolution of uncertainty through prototyping, valid market research, and in-market tests are well-known techniques to the product development world, but surprisingly underutilized.  In previous posts, I've speculated as to why that might be.

On balance, I'm singing from Fleming's hymnal.  But, I would quibble with his use of the term "average score."  Although Fleming posits the existence of a "long tail" or power law distribution of market outcomes for inventions, he speaks of the "mean value" of outcomes.  As Art De Vany and others have observed, the mean of a power law distribution has little, if any, meaning.  Nevertheless, if one were to substitute "median" for "mean" in his article, I believe Fleming's analysis is apt.

One Entrepreneur's Take on VCs

ax - i - om n. A self-evident or universally recognized truth.

This morning, I came across a list of axioms regarding venture capitalists posted by an entrepreneur who calls himself "jetskier."  The following are my favorites.  Substitute "product capitalist" or "big company" for "VC" as you see fit.

• If you show a VC a potential $1B market, they won't believe you.  If you show a VC a potential $100M market, they will consider the market to be too small.
• Patents are important as a barrier to entry only if you don't have any.  Otherwise, they are considered unimportant.
• Investments outside the comfort zone will not be considered, even if potentially lucrative.  Investments inside the comfort zone will not be considered, as there is too much competitive pressure.
• Entrepreneurs are a dime-a-dozen; VCs are a rare breed.
• Any idea worthy of investment must be fatally flawed in some respect.
• No means "no," and yes means "maybe."

Critiques of the Red Queen Model

One of the immensely valuable aspects of a weblog is the opportunity it provides to test ideas.  On occasion, I've forwarded my version of a Red Queen model (7:13 video).  Polymath Benoit Mandelbrot might call it a "cartoon":

I use the term in the sense of the Renaissance fresco painters and tapestry designers: a preliminary sketch in which the artist tries out a few ideas, and which if successful becomes a pattern for the full ouvre to come.

The basic idea of my cartoon is pretty simple:  If the fundamental pace of an industry—its clockspeed—increases by, say, 10% per year, the compounded effect is a kind of innovation deficit.  Taken literally, one would have to "run" twice as fast today just to keep still.  I chose to define innovation as launching new products.  I've also inferred that the model supports a trend toward Open Innovation.  In this case, Open Innovation would be the leveraging of external capacity to launch new products.

Fortunately, the idea has prompted critical feedback.  For instance, John Hagel wrote:

One quibble I would make is with Dave's definition of innovation as "the adoption of products by customers".  This is a product-centric view of innovation and ignores the impact of process innovation (which also includes innovations in work practices).  In fact, process innovations are ultimately much more powerful in terms of generating business value because, if done right, they can generate a compounding effect of their own - they keep on giving, in contrast to most product innovations where the tyranny of product life cycles limits the potential value creation.  Rapid incremental process innovation combined with aggressive leveraging of third party resources may in fact hold the key to diminishing, if not overcoming, the Red Queen effect.

That's fair enough: innovation is more than launching new products.  On the other hand, I think that even my cartoon model accommodates John's point.  Companies generate revenues by selling products and services to users.  As the pace of change forces the "retirement" of products and enabling technologies, new products must be launched for the company to sustain itself and grow.  Although I didn't expand upon it in my cartoon, process innovations (as I understand the term) increase the capacity to launch new products by increasing capability.  (The way I use the terms is that capacity is the product of resources times capability, a kind of productivity rate.)  In other words, an increase in capability decreases the average cost to launch a successful new product.  So, absolutely, process innovations can increase capacity and, hence, mitigate the Red Queen effect.  Furthermore, because capability building is often subject to S-shaped learning curves, an investment in process innovations can yield attractive returns versus simply throwing more resources at the problem.

Kathleen Fasanella commented:

I found the Red Queen presentation problematic; the conclusions don't necessarily dictate the adoption of open innovation (read: confirmation bias).

Quite right.  Conceptually, there are considerable potential advantages of a closed (i.e., internally focused) model of innovation.  For instance, combining capacities within a single organization can make hand-offs between functions easier.  That's the essence of the classic transaction cost theory of the firm.  Nevertheless, I think John Hagel and his collaborator John Seely Brown are on to something:

Specialization requires connectivity and effective methods of coordination.  If enterprises cannot depend on other specialized entities to complement their own activities, they will avoid specialization themselves and suffer productivity penalties as a consequence...By connecting with other specialized institutions, we create an opportunity for leveraged capability building—getting better faster by working with others.

To the extent the Red Queen effect is real, it doesn't dictate the adoption of Open Innovation, I just think it's a good idea (but not the only good idea).

Although Richard Veryard's original critique of my Red Queen cartoon echoed John Hagel's, I find Richard's skepticism regarding the fundamental assumption of accelerating industry clockspeed the most intriguing:

I have always been wary of the common belief that technological change is accelerating.  I think this belief derives from a combination of proximity, selectivity and distorted perception.  I think we can sometimes be disproportionately impressed by the glamour of recent technology, and misled by the commercially-driven measures of intellectual property (such as volumes of patent activity and product releases).

I think there is evidence of accelerating clockspeed.  Nevertheless, Richard's skepticism raises a really interesting point: Is such acceleration constant?  In other words, might the evolution of innovation be punctuated?  As I understand it, Mandelbrot has hypothesized a kind of variable market pacing he's called "trading time" and "multifractal time".  He acknowledges that the concept "remains mostly speculation.  But it already permits some extraordinarily faithful reproductions of a financial market."

So, here's where I think this exploration is taking me:

• It's conceivable that different industries and markets feel alternatively fast and slow over time.
• An accelerating market fuels the need for innovation—of all kinds.
• An Open Innovation model offers the prospect of value in two guises.  First of all, it is a way to source compelling new products and services.  Secondly, collaboration among specialist organizations offers the potential of accelerated capability building.
• The degree of emphasis on Open Innovation models may wax and wane as a function of perceived changes in industry clockspeed.

Thus, the cartoon evolves.

Thanks for the feedback!

For more: The Only Sustainable Edge, The (Mis)Behavior of Markets, Open Innovation: Researching a New Paradigm

Innovation is Harder than We Think

I believe that "growing inventions into innovation" is an inherently worthwhile activity, and it can be lucrative.  I love working with people who are passionate about practicing "the art of the new."  In the realm of innovation, optimism is healthy and necessary.  However, the landscape is littered with those organizations that were blindly optimistic, and most of us are too optimistic.

Consider this assertion by Art De Vany:

Pervasive optimistic bias is based on (1) unrealistically positive self-evaluations; (2) unrealistic optimism about future events and plans; (3) an illusion of control.  People exaggerate their control over events and the importance of their own actions in ensuring desirable outcomes.

Certainly, there is craft in doing innovation, and some people are more expert than others.  Even so, such expertise almost always takes longer to develop than we acknowledge.  The good news is that expertise is within our reach, if we do the work.  As neuroscientist Daniel Levetin observes:

The emerging picture...is that ten thousand hours of practice is required to achieve the level of mastery associated with being a world-class expert—in anything...Learning requires the assimilation and consolidation of information in neural tissue.  The more experiences we have with something, the stronger the memory/learning trace for that experience becomes...Memory strength is also a function of how much we care about the experience...It is impossible to overestimate the importance of these factors; caring leads to attention, and together they lead to measurable neurochemical changes.

Unfortunately, most of aren't really that good at learning.  As experimental psychologist Cordelia Fine notes:

Our first problem is that we are, at root, very poor scientists.  All sorts of biases slip in unnoticed as we form and test our beliefs, and these tendencies lead us astray to a surprising degree...[E]ven when we genuinely seek the truth, our careless data collection and appraisal can leave us in woeful error about ourselves, other people, and the world.

Even if we successfully apply diligence, passion, and healthy skepticism to the cultivation of our craft, the accumulation of skill is insufficient.  As I use the term, innovation means the adoption of a new way of doing something by users, and the interdependent social dynamics of users are impossible to predict or control.  That matters, because users play a major role in determining the success of a prospective innovation.  As De Vany concludes:

The craft of filmmaking can be learned, but there is no learnable craft when it comes to predicting how a movie will play with audiences.

I can't help but agree with Andy Hargadon, when he writes:

The pursuit of innovation requires patiently and humbly building a new network of people, ideas, and objects around the original [idea].

Patience and humility: repeat that 10,000 times, Dave.

For more reading: How Breakthroughs Happen, Hollywood Economics, A Mind of its Own, and This is Your Brain on Music

Managing Innovation

Today's Wall Street Journal includes an article¹ describing a discussion about cultivating a culture of innovation among senior executives from Cisco Systems, Packet Design, Google, and IDEO.  The conversation spans some topics that I've touched upon recently in this blog, including time span of discretion:

WSJ: In a big company, how do you get people to think beyond 18 months if the whole company is focused on 18 months?

MR. SOLOMON [IDEO]: I think it's very difficult to do in most big companies, and very few big companies have been able to do it.  There are a number of different models that have been tried...But in big companies, they have the resources, but they don't always have the thought processes and the skills to really think outside their current business, nor the permission to really do it.

Judith Estrin, chief executive of Packet Design, notes that part of the problem is the lack of spare capacity:

MS. ESTRIN: One of the challenges I think about is that all of the things that companies have done for quality and efficiency are essentially enemies of innovation.  They are the things that have made us so efficient and so productive, we've taken out all of the slop and all of the room that you need for innovation.

A relatively short time span of discretion combined with a lack of spare capacity is at odds with the observation by Marthin De Beer, senior vice president of the emerging-markets technology group at Cisco:

MR. DE BEER: It probably takes about four years at a minimum to get from an idea to a successful business.

The perception of risk and the cost of failure also come into play:

MR. MERRILL [Google]: Every company in the world says, "It's OK to fail."  And for 99% of them, it's probably not true.

MS. ESTRIN: If you're very successful in one business, it tends to take all of the oxygen of the company.  New ideas--everybody says they're too small.  In order to start an idea, you have to go in front of a committee, you have to show a [return on investment] that's going to meet a certain return over a period of time.  Those types of processes that are put there to vet ideas that stop people from trying are just the type of things that will kill the surprises that are going to end up being big.

  Ms. Estrin draws upon a familiar analogy in order to suggest a way to mitigate the problem:

MS. ESTRIN: In thinking about large companies, think of them as farms.  And what you're trying to do is grow rows of corn.  You don't want surprises, you want it to work well, you apply incremental innovation to be as productive as you can.  And then when you're thinking about start-ups or disruptive innovation, think about that as either a greenhouse or maybe a small garden plot, where surprises are fun...you can decide to develop greenhouses and small garden plots on the farm.  But you have to keep them separate, and then the trick is transplanting...[H]ow are the best ways to transplant...How much do you let the business grow before you transplant it, how do you prepare the soil?

Cisco's industry is characterized by a highly developed venture capital industry that serves as a greenhouse for prospective innovations:

MR. DE BEER: We've acquired 120 companies, most of them small.

The Open Innovation model is more developed in the high technology sector than it is elsewhere, which is not surprising given the sector's relative clockspeed.  The consumer products sector, however, appears to be following on a similar evolutionary trajectory.  That said, there is little venture capital infrastructure that connects big consumer products companies with incubating innovations.  Consequently, a reliance on acquisitions can be expensive.  So, I lean toward Ms. Estrin's perspective:

A systematic approach to innovation mitigates large companies' disadvantages while leveraging its advantages.   The idea would be to incubate products in a small company environment and then facilitate the rapid, and relatively inexpensive, transplant of validated growth opportunities prior to the investment in redundant capacities.  That prescription may be obvious; the trick is in the design of collaborative protocols.

Here's a video excerpt on the topic of "enemies of innovation":

¹Managing Innovation: How to get the most out of your company's big ideas; September 24, 2007; Page R6.

A High Rate of Product Innovation Pays

A couple of months ago, I made the following observation about the game of professional basketball:

While crunching the numbers on 3-pointers, I noticed something kind of funny about Kobe [Bryant]: he's the highest producing scorer in the NBA, but his shooting accuracy doesn't stand out - he's pretty average, in fact.  (Granted, the average NBA player can shoot the ball very well, but in a competitive world, it's relative, not absolute, performance that matters.)  So, how can an average shooter be the league's leading scorer?  Simple: he takes more shots per minute than his peers.  Kobe scores a lot of points because he's willing to miss shots at a higher rate than his competitors.

It turns out that my analogy to business and product innovation wasn't a stretch.  According to PRTM, a global consulting firm that helps large companies innovative faster and more effectively:

The [PRTM Global Product Innovation Benchmark] study...revealed a remarkable finding about development productivity–that is, delivering more products for the same R&D investment or less.  Companies that experience the greatest revenue growth launch up to 45 percent more products for the same development budget as compared with low-growth companies.  Source: Cashing In on Innovation: Lessons on managing product development for greater profitability and growth (registration required)

In other words, the highest scoring companies take more shots on goal per development dollar.  Furthermore, they take faster shots:

PRTM found that the fastest companies could launch a new product in 40 weeks and pay back research and development outlay in 25.  Source: CNBC European Business: The Innovation Issue

Let's be clear: these high performing companies aren't throwing spaghetti on the wall to see what sticks.  Their foundational skills in product development, supply chain management, and marketing are competitive.  However, the league-leading scorers–like Kobe–are willing to take more shots faster than their competitors.  They are able to embrace the counter-intuitive notion that exposing oneself to a higher incidence of failure can lead to a higher rate of relative performance.

Large Companies' Systematic Aversion to Failure

Fail fast and cheap.

It is a key to successful innovation in an uncertain world.  However, large companies have a consistent and strong aversion to ambiguity and the prospect of failure.

Why?

It can be tempting to make dispositional attributions regarding the people who comprise large companies.  "If we could just learn how to become more creative, our company could become more innovative and successful," goes this line of thought.  There is little doubt that becoming more creative, cultivating collaborative capacity, and being more open to Open Innovation can help.  Nevertheless, there is good reason to believe that large companies' aversion to failure is systematic and, thus, resistant to purely dispositional remediation.

What is an acceptable rate of failure?  10%?  90%?  Nassim Taleb provides the answer:

The frequency or probability of [a] loss, in and by itself, is totally irrelevant; it needs to be judged in connection with the magnitude of the outcome.

In other words, for a given magnitude of anticipated success, a high cost of failure means that you cannot fail very often.  Furthermore, the perceived cost of failure at a large company is inherently high:

• Critically, the opportunity cost of a large company is high.  Large companies, by definition, have more to lose than small companies.  For starters, established brands represent an accumulation of trust, and trust is easier to destroy than to build.
• Decision-making cycles are slow at large companies.  Consequently, the commitment of resources tends to occur in relatively large chunks.  Expenditures can mount before somebody can hit the kill switch.
• Hierarchy exacerbates the bias toward commitment escalation.  As a consequence, large companies are more likely to put good money after bad than are small companies.
• Individual benefits and losses may be asymmetric.  Individual decision-makers in a large company can typically assume that she'll have to share the credit for success, but may bear a disproportionate burden of a failure.  Risk aversion is an emotional response that is likely to increase the perceived cost of failure.

Given the high cost of failure, large companies' growth strategies tend to cluster around two scenarios:

• Very high revenue impact—In this scenario, the "size of prize" measured in incremental revenue is relatively large.  Because new, large businesses typically don't spring full-grown from the ground, this scenario typically manifests itself  as an acquisition.
• Low risk of failure—This approach take the form of incremental improvements to, and extensions of, existing products and brands.

Acquisitions are expensive and encourage the development (and subsequent destruction) of redundant manufacturing, marketing, and distribution capacities.  Brand extensions and incremental innovations can dilute brand equity and expose the company to disruptive innovation.  Nevertheless, these corporate behaviors are entirely and systematically consistent with a high cost of failure.

Systematic solutions must be part of large companies' innovation strategy.  Small companies are better suited to exploring the fitness landscape than large companies, because small companies have a lower cost of failure.  They are advantaged in their ability to fail fast and cheap.  On the other hand, small companies are challenged by success: scaling a consumer product company fast enough to hit the market window of opportunity is a daunting challenge.  Large companies have the advantage there.  A systematic approach to innovation mitigates large companies' disadvantages while leveraging its advantages.   The idea would be to incubate products in a small company environment and then facilitate the rapid, and relatively inexpensive, transplant of validated growth opportunities prior to the investment in redundant capacities.  That prescription may be obvious; the trick is in the design of collaborative protocols.