Innovation failure

Observing the Innovators Dilemma

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In 1997 Clayton Christensen published The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail.  The book (which we highly recommend) proposed an intriguing explanation as to why large companies with seemingly unlimited resources can fail to see their own demise in the emergence of disruptive technologies.  One oft cited example of this phenomenon is the demise of Kodak who not only failed to see the importance of digital photography on their core film business but in fact were the ones who invented digital photography in the first place.  The purpose of this post is not to discuss Christensen’s work however but instead to cast our eyes over some industries and see if we can spot companies who might well be in the midst of an innovators dilemma as we type.

In order to identify where an innovators dilemma might lie we need to quickly describe the required conditions for its occurrence.  A very common approach, and one used by Christensen, is to describe the situation using innovation S-curves as below.

A:  A new technology in its infancy.  Performance improvements are hard to generate as the innovation is becoming understood.  Generally, innovations at this point are only used by very early adopters and the value of the product offering may be limited.  B:  Rates of performance advances are peaking, rapidly catching up to incumbent technology. The technology becomes commonplace and even the industry standard.  New competitor technologies look hobbyist or misaligned.  C:  The technology matures, performance advances are harder to generate as the limitations of the technology are found.  Most people who might use the technology are doing so.  New competitor technologies seem to have higher potential and are gaining acceptance.  D:  The technology fades.  People stop using the technology and choose others.  A new technology becomes the industry standard.  The Dilemma Zone:  Technology A is well understood, the industry standard and an integral part of the business model of those employing it.  The profitability of the technology is peaking.  Technology B looks very promising even to the point where it is the odds on favorite to be the future of the industry; the only question is exactly when.

So, with this set of conditions in mind we will go hunting for some modern dilemmas in the businesses of today.  Kodak followed the red S curve to their well-publicized regret, who might be next?

Dilemma #1, HBO:  HBO are having a great run at the moment, their internally created content such as Game of Thrones and Boardwalk Empire have generated huge returns for their parent company Time Warner.  HBO is one of the most well known and entrenched premium cable channels in the world and its exclusive offerings are an important part of the business model of cable providers such as Verizon and DirecTV.  So where is the dilemma? Well, Game of Thrones Season II has been downloaded illegally about 25 million times over the year1 and HBO know why; there is no other way to get it apart from subscribing to a full cable service.  HBO could provide downloads through their own site or through iTunes or another vendor but (at least for season 2) chose to take the money i.e. maintained the high premiums from the cable providers at the expense of the pirated copies.  Financially this makes sense today but long term HBO may not always have such a gem as Game of Thrones with which to negotiate (or even define) the process of streaming its content on demand.

Dilemma #2, Big Pharma:  Big Pharma is REALLY big and is based primarily on a model that is around as old as your granny.  Two pillars, small molecule chemistry and blockbuster “one size fits all” treatments are what has driven the growth of this industry since the early 20th century but that is coming to an end.  Biotechnology in its many forms is most definitely the future of medicine in the 21st century.  A scan of where the breakthrough patents are being generated in the field and you can see the majority are coming out of small Biotechs and Universities not the massive health laboratories of the S&P 500.  The problem is that small molecule chemistry (what Big Pharma is great at) is not Biotechnology any more than plumbing is interpretive dance.  The initiative needed to transition the capabilities of say, a Pfizer (100,000+ employees2), to a new science is immense, perhaps too immense.  Coupled with this is a reality that Biotechnology tends to make very targeted drugs, limiting the opportunity for another “everyone gets a pill” Lipitor or Prosac, a model that Big Pharma now relies on.  So the dilemma is set, Big Pharma must re-skill, and possibly re-size, but to do it now or to hold on for just one more blockbuster?

Dilemma #3, Microsoft Office:  Microsoft itself is arguably in the middle of an innovators dilemma but I thought I would pose the case for one of its most profitable jewels, Office being very much in the middle of a technology revolution itself.  Office is everywhere, you can’t do business without the ability to open and edit Word, Excel and PowerPoint documents and this has ensured that the Office suite has remained the standard install for companies worldwide for many years.  The knock-on effect of Office being the choice of your company is that you are far more likely to install it on your home PC as well, and why learn two different systems?  So where is the dilemma?  Well, Microsoft knows that it won’t be long before the idea of having to boot up a desktop or notebook to balance the household budget or write your resume will be gone.  People will expect to run their households from their tablets and phones while sitting on their sofa not hiding away in the home office.  So, Office for tablets?  Where is it?  The problem is that fully functioning office products are complex, far more complex that we are used to dealing with on tablets and phones.  Microsoft’s choices seem to be a) cut back on the functionality (losing their technical advantage), b) teach us a new way of interacting (losing the synergy with the company office) or c) lose the home space all together.  You might be thinking that you would still be tied into the Office suite simply because even if you change your home tablet away from Office, other people will still send you Word documents. The simple fact however, is that file type is almost irrelevant these days. Download a free service like Open Office and you will see it is quite capable of opening Word docs and even saving them in Word format so on Monday morning your company PC will be compatible with your weekends endeavor.

1         http://www.forbes.com/sites/andygreenberg/2012/05/09/hbos-game-of-thrones-on-track-to-be-crowned-most-pirated-show-of-2012/

2         www.morningstar.com

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The Trap of the Knowledge Economy

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We take it for granted that we live in a world of data. Google and Facebook both rely on user data to drive their innovation and products while constantly creating new ways to collect and use that data. Facebook is a great example: “[it] has some 750 million users, half of whom log in every day. The average user has 130 friends and spends about 60 minutes a day tinkering on the network.”[1] Thanks to programs that allow them to monitor your every click the company is able to understand its consumer to an extent never before seen. In fact, it is now sometimes said that ‘Every second of every day, more data is being created than our minds can possibly process.’[2]

This new paradigm, what some term the ‘knowledge economy’, has spread into every industry and has quickly become a standard factor in internal product and innovation evaluations. We now have a wealth of information about our consumers that we can use for good . . . or bad. As well, consumers now know far more than they ever have before. For the Osmotic Innovator, it is important to understand how this happened and how it has impacted the way we should go about innovation.

In reviewing the impact of the knowledge economy on product development and innovation we don’t need to go to far into the past to find a time when data was not so easily obtained yet product development was undergoing a renaissance. At the turn of the century, after the industrial revolution had changed both the way consumers lived and companies grew, people like Ford and Edison made fortunes inventing new products that have since become iconic. But for every Ford and Edison success there were many failures – like Edisons’ automatic vote-tally system for Congress[3] – or a failed inventor whose radical ideas never were embraced in their time (think about Tesla). The number of failed innovations during this time period was enormous, just as some of the successes (the Model T and the Light bulb) were equally enormous. Without access to data, companies and inventors trusted their gut, valuing passion for a product as the driving force in creating and launching into the market.

Moving forward to the 1950’s & 1960’s one enters another golden age in product development in Western economies – the age of marketing. If you’ve ever watched an episode of Mad Men you know the stereotypes: bold marketers using data (and their intuition) to make new products successful by attempting to actually understand and meet new consumer needs. With a market already satiated by 50 years of new products and innovation, companies saw segmentation of markets and use of data to identify the best and safest product launches as one way to obtain a competitive advantage. Data was available and used, but it wasn’t the primary driving factor in innovation. Did this increase the number of product successes or the ratio of product success versus failure? We know that product launch success was actually higher in the 1950’s than now, but don’t have the data to understand how this stacks up against earlier periods.[4]

Fast forward 50 more years to present day – the era of the ‘knowledge economy’. Vast improvements in computing have allowed equivalent improvements in analysis and theory. No product is launched by a large company without extensive vetting through any number of market research toolkits and analytical systems. Consumers are split into segments and dissected in every way possible as companies in mature and developed industries look for any possible unmet need. Disruptive products that don’t fit the models are scrapped while incremental innovations are launched because they are able to fit into the segments that have been so carefully developed. In many ways, the innovator in a large firm is now crushed by the weight of the data available to them instead of empowered. With all of this data in hand guiding decisions for large firms we must have achieved a new high in new product launch success, right? Actually, current measures show that only 1/3 of NPD launches are ‘successful’[5] – and the consensus is that this number that has actually decreased since the 1950’s. Obviously, the exponential increases in data and advances in market research tools are not returning an equivalent value in NPD success.

One might argue that only companies ignoring the market research ‘facts’ are launching disruptive new products – the ones that actually capture real substantial value and create new markets. Take it from Steve Jobs, praised universally for being a disruptive thinker and innovator: “When asked what market research went into the iPad, Jobs replied: “None. It’s not the consumers’ job to know what they want…we figure out what we want. And I think we’re pretty good at having the right discipline think through whether a lot of other people are going to want it, too.”[6]

The figure below shows the growth of the ‘knowledge economy’, the corresponding drop in the capacity of a large company to launch disruptive or truly innovative products, and relative product launch success rates. Unless your company is willing to ignore conventional market research and analytical tools – which some might call wilfully ignoring your consumer – delivering true innovation to the market is nearly impossible. That is why disruptive innovation seems to come from smaller companies and start-ups; without a business to protect and with few resources, market research is an unnecessary luxury.

Looking at the discussion graphically – the question to ask is why are companies so reliant on data and market research when using it encourages incremental innovation without improving new product launch success? This is the trap of the knowledge economy: it promises to remove risk from new product development yet prevents the innovator from truly exploring disruptive innovation that could create new markets.

How can the Osmotic Innovator avoid falling into the trap of the knowledge economy?

  • don’t make every product fit the models currently used by your company
  • be willing to trust your organizations understanding of what the consumer will want (just like Steve Jobs)
  • be bold in launching risky products in the face of failure – most ‘safe’ products fail anyways

Are these prescription alone enough to solve the problem of low NPD launch success rates or change the reliance of mature companies on data? No. But they should give the Osmotic Innovator something to consider the next time you are working through the portfolio.

Appreciating ‘Constrained Innovation’

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When Fast Company ranked the 50 Most Innovative Companies for 2011 a glance at the top ten would seem to tell you one thing: the cutting edge of innovation is headquartered on the web and that, by-and-large, companies in mature industries need not apply. Does this mean that companies in mature industries need to begin raiding Silicon Valley for a new wave of entrepreneurs to replace their failing ones? I think not, as I will argue below many of the companies on this list benefit from an overly positive view of the virtues of ‘creative innovation’ compared to ‘constrained innovation’.

Most readers will be familiar with Everett Rogers theory of diffusion of innovations[i] – in his book Diffusion of Innovations Rogers’ argues that as technology is applied to a market, penetration phases from low to high along an S-curve while adoption follows a bell-curve with innovators and early adopters in the first phases and laggards at the end.

Technology Development S-curve

The S curve model is often also applied to technology development. Traditionally, the focus is on the creation of new technologies (embodying radical or disruptive innovations) that can create a new curve (curve B) and restart the adoption bell-curve.

Long term stagnation for a technology or market

However, firms in established markets often face a reality where no new technologies or disruptions are available to replace the current one and ‘incremental’ innovations become the only currency for retaining and gaining market share in the face of slow segment growth. It is within this space, where market penetration is near complete and technologies are mature that innovation becomes a real challenge (line C extension of Curve A). This is not to argue that radical and disruptive innovation is unimportant for corporate strategy, rather that it is essential to utilize incremental innovation to maintain market leadership in the absence (or during the development) of these types of innovation.

But why is it that incremental innovation is so often ignored in favor of innovation occurring at the leading edge of technology? Disruptive or radical innovation has the potential to capture the imagination not to mention the market, however, it is the incremental innovation that sustains a company while it seeks out success in these riskier areas. Companies themselves often discount their own incremental innovations – selling the innovators and themselves short in the process. This is especially frustrating to those in these environments because successful incremental innovation can be very difficult.

Constraints increase with technology development.

To understand the key differences between innovation on the leading edge of technology compared to the trailing edge the curve could be moved to a graph that shows ‘constraints’ increasing as a market or space matures. This reflects the fact that in mature markets development is constrained by higher expectations and limiting technical specifications. For sake of simplification, the curve could then be viewed in two halves around the inflection point: to the left, the ‘easy to innovate’ space, and to the right, the ‘hard to innovate’ space. (Figure 3) It is important to note – ‘easy to innovate’ is no guarantee of commercial success. For a specific example, Pets.com seemed like an easy win or innovation early in the development of the web however was a commercial failure.

Radical or disruptive innovations aren’t necessary early in a technology or market development because artistic creativity or marketing can be nearly enough to ensure continued growth, while later in development high levels of skill are necessary to continue innovation for growth when it is most important. If it were to be considered in scientific terms, it is as if in the initial stages of technology or market development innovation is free from constraint – few rules apply as companies develop the market. Take a look at the lists of ‘Most Innovative’ Companies for 2010 or earlier – it will be littered with failed start-ups and failing companies. The winners, or those that define the market, find themselves in an interesting position when the market or technology has matured. At this point, the survivors are forced to work within a falsifiable system – a system with highly developed rules and limits to pure creativity. Masters of innovation in these environments are people that can operate creatively within a highly restricted area, like Einstein developing the theory of relativity within the bounds of Physics.

Innovation in a developed market or technology field means meeting a very advanced set of customer expectations. Staying a market leading company in this case requires maintaining an advanced core technical skill set, absolute focus on the customer, and the ability to quickly transform ideas into products with limited time for redesign. This balance of skills is what we referred to earlier as ‘constrained innovation’ – the ability to create new products and excite the customer while operating within a strict set of conditions established by the market.

Can and should firms focus on creating new markets using radical or disruptive innovation? Yes. However, one cannot ignore the fact that without a strong ability to deliver ‘constrained innovation’ firms will not survive long enough to implement the more widely appreciated types of innovation. That, the ability to grow and survive regardless the market constraints, is why ‘constrained innovation’ should be recognized as an essential and critical function for any firm that should be more widely appreciated by the world at large. Imagine if we recognized the best innovations of 2012 as those that succeeded despite constraints – would we be talking about the innovators from Ragu, Dunlop, Haines and Colgate instead of Facebook, Twitter, and Google?

Innovation ownership and the transition

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Most product development processes within corporations rely on a regular transition of responsibility as an idea progresses from conceptual to final product.  Some of the most common examples are transitions between inventors and feasibility assessment teams, feasibility teams and development teams and even marketing and R&D operations.  Often these transitions are also where we see the product development process fail as the product idea never fully takes hold in the imagination of the new team and fades away.  One of my preferred ways of visualizing the issues that can arise during transition is through an analysis of idea “ownership”.

Ownership 

The chart in figure 1 describes the level of ownership that a team developing a particular innovation may experience over time.  As the team starts out the commitment level is relatively low, no one has spent much time on the initiative and there are many different paths to success still available to the team.  Over time the team feels more commitment towards the innovation path and the sunk cost of the current program of work increases, this increases the ownership of the program.

Figure 1 © OsmoticInnovation.com 2012

Complexity expectation

Within every product development project there is a level of complexity that the team is expecting to encounter.  This expectation can vary significantly (think landing a man on mars vs. developing a new flavour ice cream) but the important factor is that there is an expectation.  Deviations from this expectation are what we call “problems” (or occasionally “lucky breaks”).  Tasks that do not deviate from our expectation of complexity are what we call our day job.   The chart in figure 2 describes an example of the complexity curve for a project over time.

Figure 2 © OsmoticInnovation.com 2012

If we begin to think about how we could measure complexity in the above example it becomes obvious that the level of complexity of a given problem could be measured by the level of commitment and effort required to solve said problem.  In essence what we are saying is that every problem we encounter within the product development process will require a particular level of “ownership” in order to be overcome.  This allows us to overlay the two previous charts with the common y axis of the theoretical construct “ownership”, see figure 3.

Figure 3 © OsmoticInnovation.com 2012

Ownership and the transition

Turning our attention to the concept of ownership as it relates to idea transition between corporate groups we can imagine a (worst case?) scenario where the ownership level of the accepting group post transition is extremely low, Fig 4.  This low ownership scenario can arise for many reasons including a lack of understanding of the idea potential, a poor incentive structure (the fabled “not invented here”) or simple housekeeping such as stressed resources limiting the interest in yet another project.

Figure 4 Project transition between two groups occurs at point A. © OsmoticInnovation.com 2012

Fundamentally, a project in a low ownership situation is fragile.  Problems encountered during this state can cause the project to fail as those responsible for the next development stage see the hurdles as insurmountable, Fig 5.  Often these same hurdles are seen in a very different light by those who continue to experience high ownership for the project.

Figure 5 An insurmountable problem occurs at point B as real ownership drops below required ownership. © OsmoticInnovation.com 2012

Bridging the gap

How then to avoid issues arising from transition fragility?  Obviously one cannot control when an unexpected problem may arise during a project but it is possible to influence ownership in the lead up to, and directly after a responsibility transition.  Examples of how this can be achieved are numerous but a couple worth considering include shadowing programs which engage the second group in the decision processes of a project prior to transition, Fig 6.  Shadowing programs allow a new transition group to develop understanding and commitment before they take ultimate responsibility for the project.

Figure 6 Shadowing programs and ownership during transition. © OsmoticInnovation.com 2012

Another approach uses dovetailed leadership structures which create matrix teams during transition, Fig 7.  In this instance a temporary matrix team is formed during transition consisting of the (low ownership) core of the second team reporting into the (high ownership) management of the first team.  After a fixed period of time the matrix team is disbanded and reporting lines are reset.  This allows for a soft transition where high ownership individuals are always present in the responsible project team.

Figure 7 Matrix management and ownership during transition. © OsmoticInnovation.com 2012

The innovation transition process within a corporate innovation program can be a very complex procedure in which success is as much down to the personalities of the individuals as the rigor of the systems employed to control it.  Companies that focus on solely on the mechanical aspects (document control, handover procedures, technology debriefings etc) may be leaving themselves open to failure through no fault other than the realities of human psychology and the timing of a project hurdle.  Innovation programs that incorporate ownership management within their transition processes go a long way to reducing that risk of failure.