Innovation Blog

Blockbuster Busted Part 1: An S-Curve Analysis

March 23, 2015

While there are exceptions, the evolution of most technologies and companies can be depicted with the help of an S-curve. Similar to the birth, growth and maturity phases of living organisms, the S-curve analogy shows the rise and fall of companies and industries in terms of growth. Blockbuster is a classic case of the S-curve blues, hitting a stall point, and failing to adapt to the changing environment.

The argument could be made that Blockbuster was disrupted by other more innovative and motivated companies, but the fact still remains that Blockbuster’s management team made strategic decisions that caused more pain than their actual competitors did. In this post, we explore how Blockbuster was busted by taking a historical look at their S-curve and considering—from a strategic perspective—what Blockbuster missed.

The Blockbuster Timeline

The first Blockbuster store opened October 1985 in Dallas; it started as a small neighborhood business. By the mid-1980s, Blockbuster was acquired by Wayne Huizenga and John Melk. In a relatively short time, Blockbuster grew from 133 stores in 1987 to 3,400 in 1993; in 1994 it was acquired by Viacom, the telecom conglomerate, for $8.4 billion. From 1994 to the early 2000s, Blockbuster was on blockbuster growth, peaking at 9,100 stores in 2004 with revenues upwards of $6 billion. Blockbuster started down a fast slippery slope in 2004 however, from which it would never recover.

Blockbuster’s main growth strategy was based on acquiring independent video stores across the U.S. and internationally. Blockbuster was experiencing massive growth and significant gross margins and growing profits; so, why would Blockbuster even consider new, untested, technology? Why would a company like Netflix, or any online streaming, even be worth a look? Netflix was founded in 1998 by Reed Hastings because, coincidentally, he lost Blockbuster’s Apollo 13 video, and when he returned it was hit with a $40 late fee. As Hastings told The New York Times:

“I got the idea for Netflix after my company was acquired. I had a big late fee for ‘Apollo 13.’ It was six weeks late and I owed the video store $40. I had misplaced the cassette. It was all my fault. I didn’t want to tell my wife about it. And I said to myself, ‘I’m going to compromise the integrity of my marriage over a late fee?’ Later, on my way to the gym, I realized they had a much better business model. You could pay $30 or $40 a month and work out as little or as much as you wanted.”

To make Blockbuster feel even more secure, by 2000, Netflix was losing money—a lot of it; Reed Hastings proceeded to exhaust all options, one of which was to set up a meeting with Blockbuster’s then CEO, John Antioco. Blockbuster was given a chance to buy out Netflix for $50 million, just drops in the bucket for a company that had just raised over $5 billion in an IPO a year earlier. Blockbuster laughed them out of the room, literally, and at the time, Blockbuster was right to do so. Would customers be willing to order DVDs online and wait for their arrival by mail several days later when they can walk into a store and pick up their favorite movie? Netflix did not charge for late fees, something that was a key source of income and profit for Blockbuster. The greatest change was that broadband didn’t exist, only dial up. Did Blockbuster consider the crossroads of broadband and Internet streaming, or how the Internet adoption rate would change or what broadband technology would do in the near future? Blockbuster was right at that time, but not considering the scenario of next-day delivered DVDs with complementary streaming proved to be a critical mistake.

It wasn’t until 2003 when Netflix finally posted a profit, and market competition only became hotter in 2002 with a new rival, Redbox; Redbox started to make waves based on a revolutionary concept of using automated rental kiosks and partnerships with grocery stores and gas stations. In the meantime, by 2004, Blockbuster hit its peak with 9,100 stores, four years after Netflix and two after Redbox. Blockbuster was flying high. In 2004, Blockbuster split from Viacom and introduced Blockbuster.com, the online competitor to Netflix. By the time Blockbuster finally got into the mail DVD market in 2004, Netflix had already grown to $270 million in revenue and 1 million subscribers.

With great success comes challenges, and 2007, little did Blockbuster know, would become its stall point. Revenue had dropped to just over $5 billion and 5,100 stores, compared to 9,100 three years before, though Blockbuster did still have a commanding market share of the video industry with over 50 million members worldwide. Meanwhile, Netflix quietly grew to $1.25 billion with 7.5 million subscribers; Reed Hastings tried again to convince Blockbuster to acquire the struggling start-up DVD rental company, but still was denied. Fierce market competition was taking a toll on Netflix; subscribers were down, it was paying the highest per user acquisition cost to date, just over $48 per user, and the rental market was becoming more saturated. Netflix took the bull by the horns and put in place a complementary business model innovation by adding streaming content in conjunction with the mail order rental market. Both strategies from Netflix and Redbox, online and kiosk, Blockbuster ignored.

The last straw—a CEO shakeup—broke Blockbuster’s back. The new CEO, Jim Keyes, decided to remove Blockbuster from the digital space and focus on the brick-and-mortar model. As quoted from this article, “The new man didn’t understand what business Blockbuster was really in. He started changing the game plan, including pulling out of their Internet efforts. Within 18 months, he had lost 85% of the capital value of the company.”

By 2009, Blockbuster started to feel the consequences of previous strategic decisions. Revenue had dropped to $4.1 billion and 4,500 stores. With a last pitch effort, Blockbuster Express was introduced to compete with Redbox. Blockbuster was now distributing through retail, rental kiosks, and DVD rental. Blockbuster finally busted in 2010 filing for bankruptcy protection in September. Revenue was down to $3 billion and 3,000 stores, with the final nail in the coffin of just under $1 billion in debt. Blockbuster, once a $6 billion company, now was only valued at $24 million.

In 2011, Blockbuster finally jumped into the streaming space, but that market was fierce with Hulu, Amazon and the previous laugh of the town, Netflix. The main strategic advantage Blockbuster saw was the business model ecosystem of online, kiosk and brick-and-mortar. Customers could rent from any of the three platforms, and then return to the local store, hoping to capture a seemingly instant gratification competitive advantage.

By the end of 2011, revenue was down below $1 billion, 1,500 stores, and declining. By 2013, revenue was only $120 million by Q2 and 50 stores and the online mail-service was shut down in December. In April 2011, Blockbuster was acquired in bankruptcy court for $320 million.

Currently, Blockbuster is keeping content live with Blockbuster On-Demand streaming, a part of the Dish Network service package. Non-subscribers are also able to access movie content on any device for $2.99 per movie.

Lessons Learned and the Blockbuster S-Curve

Blockbuster’s rise and fall lends itself to a classic S-Curve depiction. After the initial start-up, acquisitions and positioning, Blockbuster was off and running, growing exponentially from 1987 to 1993 with a compound annual growth rate (CAGR) of 58 percent. From 1993 to 2004, however, growth slowed to a CAGR of just over 8 percent. The long painful decline started in 2004, producing a CAGR of -13 percent from 2004 to 2009 and -59 percent growth from 2009 to 2013.

Looking back, hindsight is always 20/20, but what was it from a strategic perspective that Blockbuster just didn’t get right?

There are three strategic points worth pointing out that had critical roles in Blockbuster’s fate:

The obvious: not buying Netflix; of course, if Blockbuster did acquire Netflix, it may have died with the rest of the company in 2011.

Not only changing CEOs, but also shedding their total access strategy in favor of a brick-and-mortar retail model focused more on selling add-on items, such as candy and popcorn.

The underlying root cause: a company that would never adapt, pivot or modify its existing mental models and biases of what the movie industry was or could be.

Finally, a few questions to ask when contemplating not just what Blockbuster did, but why:

Did Blockbuster pay attention to the changes in the external environment or scenarios of how the world could be different in the movie rental business?

Did Blockbuster understand the true customer need and the problem to be solved?

Did Blockbuster focus any attention on the shifting marketplace trends?

Did Blockbuster consider any of the competition seriously?

Did Blockbuster not focus on a core business by diversifying itself in too many places?

Did Blockbuster employ the best business model to compete in the changing market space?

History has shown that no company should take its position for granted. Every S-curve gets replaced at some point in time. In fact, the only guarantee for success is to re-invent the business to keep relevant within an ever changing market. If you don’t, someone else will.

While growth is the No. 1 priority for most CEOs, roughly 90 percent of companies fail in sustaining growth over the long-term. Growth can be approached organically—by employing new and improved products and services to enhance revenue—or via mergers and acquisitions. Neither approach has a very impressive track record.

For example, a research study published by Businessweek in conjunction with Boston Consulting Group concluded that 61 percent of the M&As included in the study actually eroded shareholder value. And on the organic front, research has shown that approximately 75 percent of new products introduced by established companies fail.

Although it’s more difficult to quantify, many companies today are also struggling with the concept of innovation—what it means to them and how exactly it contributes to their growth. For those responsible for delivering results to shareholders, growth through innovation has generated a sense of risk and uncertainty.

At BMGI, we believe that the primary reason for this is that many organizations do not understand the key variables that affect innovation, growth and the creation of new market space. Fortunately, experiential data is now available to draw viable conclusions about how to accelerate growth through innovation, as well as how to mitigate the risk associated with unstructured innovation. Following are five key variables for successful innovation.

Key #1: Develop a Balanced Innovation Portfolio

Innovation-elite firms understand that achieving uncommon industry growth rates means going beyond the traditional research and development focus. Companies that seek growth through innovation benefit from developing a balanced, comprehensive portfolio that spans many areas—products and services, processes, strategy and even the organization's core business model. These companies also vary the required degree of innovation, from incremental to significant to breakthrough levels.

Organizations that execute innovation projects in this way almost always generate higher return on investment than companies that limit innovation to new products. Also, companies that innovate simultaneously in multiple areas reap more rewards than those that innovate in a single area.

For example, Apple has experienced tremendous success with the iPod, a product innovation. However, the success of the iPod is largely due to the introduction of iTunes, a business model innovation. Through this combination of product and business model innovation, Apple created $70 billion in shareholder value in just three years. As Mark Stein of the Kaiser Associates research firm notes, "What is especially impressive about Apple's feat is that [Apple] did it with an R&D spend that is one-tenth the size of Microsoft's annual spend, and that they did it during a period of otherwise flat industry performance. This clearly demonstrates the approach, the techniques, the strategy, and the focus matter far more than how much [you spend]."

Companies can identify and manage their own balanced innovation project portfolio by using a set of growth and innovation opportunity assessment techniques. In addition to project prioritization and scope, these tools help organizations identify unarticulated, latent and underserved customer expectations that may indicate an unoccupied market space—and a potential direction for growth.

Key #2: Build Effective Teams for Collaboration

The second key to innovation success is to assemble innovation teams that are capable of flawless and speedy execution, and then manage these teams for high performance and collaboration. This is easier said than done. To begin with, the best teams will be composed of people with diverse problem-solving styles.

In addition to a well-managed balance of problem-solving styles, effective teams must have a cognitive level (i.e., knowledge) and motivation level appropriate to the problem they are trying to solve. Companies can use a set of assessments, inventories and management approaches to assemble effective and collaborative teams for specific growth projects.

Key #3: Deploy a Systematic Process for Innovation Projects

The third key to success is to make innovation repeatable, predictable and scalable. This means making it systematic using a consistent process that is applied by all teams (as DMAIC is applied by Six Sigma teams, for example). The process must also be robust enough to accommodate multiple innovation pathways; while some growth projects require “thinking outside of the box,” others require more structure within existing paradigms.

D4 is BMGI’s methodology for enabling consistent, results-oriented innovation projects. This flexible methodology consists of four project phases (Define, Discover, Develop and Demonstrate). D4 was specially designed to accommodate the natural flow of the innovation thought process. Thus, it encourages participants to agree upon a specific problem, and then depart enough from the current way of thinking that novel solutions can be discovered.

Key #4: Apply Proven Techniques and Tools

The D4 innovation methodology provides a consistent approach to innovation. D4 practitioners must also understand how to apply a variety of tools and techniques that enable success in each project phase. For example, the main objective of the Define phase is to identify unmet customer expectations. Techniques such as ethnography, archetype research and heuristic redefinition all help capture the unarticulated needs of customers.

The Discover phase of D4 features tools designed to generate new innovative ideas you can use to meet the unmet needs of your customers. These tools range from random entry techniques to provocation and movement techniques to technical and physical contradictions.

The most promising ideas generated in the Discover phase of D4 are further investigated during the Develop phase using techniques and tools that enable the analysis of data and the subsequent design process. Techniques such as axiomatic design, function structure, conjoint analysis, design of experiments and Lean design enable smooth execution through this phase.

Finally, successful solutions are implemented in the Demonstrate phase using techniques and tools such as piloting, rapid prototyping and mistake proofing.

Key #5: Establish a Climate for Innovation

One way to mitigate the challenges of innovation is by establishing a climate that is best suited for innovation; in other words, an organizational culture that promotes calculated risk-taking, collaboration and trust. Such a climate enables people to learn from their mistakes (instead of being punished for them). It also supports quicker execution of ideas and a more agile organizational structure, all of which minimize exposure from innovation risk.

Two years ago Massive Open Online Courses (MOOCs) were all the rage. Elite universities were lining up to partner with MOOC providers, such as Coursera, and they were free to boot. MOOCs had something new, cool, and disruptive to how larger institutions provided curriculum. But they also had one major flaw: Few people took them seriously, including me.

I would sign up for dozens of Coursera MOOCs, download some material, watch a few videos and call it good. Very few did I actually complete and even then I just watched the videos and took some notes, never engaging in any of the forums, discussions, or the exams. There was little true incentive to stay 100 percent engaged in the course. In fact, most people don’t. According to recent analysis of MOOCs, only 10 percent of registered students actually complete the course.

Still, with enrollment reaching hundreds of thousands, MOOCs are providing access to higher education to the masses—just maybe not the masses they originally expected to reach. As The New York Times article “Demystifying the MOOC” shows, most people enrolled in MOOCs already have a degree.

So with high numbers of enrollment from educated users, but low completion rates, what now? Until recently, no one had cracked that code. Now Coursera is making a run at it with Specializations—a series of classes strung together to provide an elite university certificate for a minimal fee, especially compared to if you took the same series at a local institution. For example, the Data Scientist Specialization through Johns Hopkins costs $470 or Data Mining through Duke University is $294, a bargain compared to other certificate programs or universities that might charge 10 times that much. This lends itself to an important question: Are MOOCs, for the first time, developing a model that could disrupt education as we know it?

If MOOC specialization skills become as valuable as the skills obtained from a regular university, this model will have the ability to disrupt what higher education has to offer, especially the ones positioned as continuing education. MOOC specializations are also competing against other traditional online universities such as University of Phoenix and Walden University. These MOOCs offer similar advantages compared to other major online degree granting universities while keeping costs down even more. The MOOC platform started out by offering individual courses, but now is extending into specializations. If this platform can be scaled to provide online degrees, it will be able to disrupt both campus-based and online universities.

You might be thinking, “True, but how will these specializations hold up in the real world, especially compared to a Data Scientist degree from say the University of California Berkley?” And you would be right to have that question. But remember the true reason you educate yourself in institutions and universities—to make yourself more marketable in the real world, so businesses find you valuable. And remember who makes that decision; it is the HR reps, CEO, your manager, etc. These days, where and how you get your education is becoming less relevant as a differentiator than it used to be. The business decides whether you fit the bill. Clearly, Coursera is recognizing this trend with its users—that they are taking targeted courses as part of their professional development to build their value to the business. Udacity is also shifting its focus to concentrate on fee-based corporate and vocational training.

So here is the big question: When will a business treat someone with a Data Science degree from Berkley the same as one with a specialization in Data Science and Data Mining from Coursera? I understand there are other classes and activities from a university that comprise an education, but will the business care? Or do they just want the best person with practical experience, application, drive, and relentless passion and curiosity? Then ask this: When will the $50,000 degree matter compared to the $766 certificate of Coursera specialization?

In my experience, it is as much about the person as it is the education, which leads me to my last point about the business model for universities and educational institutions: It is broken. This isn’t a model that should be fixed, it should be buried, and a new model created around the value proposition for universities and institutions. Universities are trying to compete based on the education they offer instead of focusing on the real value they can provide.

I always said my degree in engineering, even though I never used it professionally after school, was a great investment for me. But it wasn’t the degree that mattered; it was the life lessons it taught me along the way—time management, stress management, focus, and attention to detail, among others. The truth is universities and institutions are not in trouble just because they are failing to reinvigorate their business models, but because they lost focus on a value proposition MOOCs simply cannot, and will never be able to, give you—an education in life itself.

Innovation appears prominently as part of almost any company’s strategy. Why then is it so hard to make it repeatable, scalable and lasting success? Scholars name key elements that bring innovation in sync, such as leadership, strategy and governance. Often, though, it’s not what organizations aren’t doing that causes a problem, but what they are doing—they’re tripping themselves up.

While there are many ways to trip, see if you recognize one of these three common ways in your organization. Fixing them can turn into a fast win and create the momentum necessary to get all the other pieces in sync.

We Don’t Have problems; We Have Challenges

“I don’t want to hear about problems, show me solutions.” Sound familiar? There are multiple reasons why different corporate cultures come up with different terms to beat around the fact that problems exist. Some cultures use “challenges,” “hiccups” or “issues,” for example. I’m sure you can think of others. Language both reflects and shapes thinking and behavior. What does this do to the overall culture?

Let me introduce you to Alexej. He has been hired from a startup-gone-bust into product development for a large German corporation. His first weekly report is greeted frostily. He has identified a problem, but merely naming in a report is considered unethical finger-pointing because of a silent consensus on whose fault it was. This bright young man learns this lesson fast. His reports turn into a list of “last week’s accomplishments.” He hides from others the challenges he is working on and stays away from sharing the opportunities for improvement he comes across. This already siloed organization not only loses the creativity and enthusiasm of a highly skilled individual, but also foregoes the enormous potential residing in an all-one-team approach to tackling problems.

Organizations should acknowledge: Human life is problem solving. For people, any level on Maslow’s hierarchy of needs can quickly turn into a problem. Processes and entire departments are there to solve problems: “I don’t know next quarter’s financial results.” Industries solve problems, too: “I can’t communicate with a far-away person.”

The Russian innovation thinker Genrich Altshuller, inventor of the Theory of Inventive Problem Solving (TRIZ), observed: What sets the inventor apart is his or her ability to spot problems where the rest of us have grown accustomed to living with the hassle. Indeed, at times we don’t even notice that hassle anymore, that is, until someone comes up with the solution. Did anyone have a problem before the wheel was invented?

So firstly, from an organizational perspective, learn to recognize and acknowledge problems at face value, and to value the individuals who spot and communicate these problems.

The other two common ways organizations trip when it comes to innovation are adhering to the equation “innovative = creative = good” and overdoing the “let’s form a team approach.”

It seems lately that everyone is talking about increasing access to data, data and more data. In fact, no longer is there an issue of sourcing data, but instead a problem of what to look at, when and how to interpret it.

All well and good, you say—data is the lifeblood of intelligence and innovation! Well, not exactly. In order to convert data into knowledge, you need to process it. You need to connect the dots and turn information into meaning. Then you need to act on that meaning. As technology progresses, we are becoming better, not just at collecting data, but also at assimilating it. The challenge is that very few of us are good at or qualified to turn this assimilated data into decisions and strategy.

We see this in our personal life, in business and also in society. This article on the investment in “sci-fi tech” for the UK police is one such example. At first glance such equipment—including a range of wearable computers—seems like a great innovation designed to protect our forces and give them more live information for policing on the streets. But dig a little deeper and you will quickly find problems with utilization. The recent BlackBerry investment has returned much less than the expected ROI and I expect the wearable glasses and smart watch to come up against the same problem.

Compare this technology on a personal level with sports wearable tech. How many of you have running watches or devices that collate everything from heart rate to pace and even elevation but, at the end of the day, simply plug this into a social platform, turning the data into little more than a boast or at best a GPS track of your activity? How many of you read and assess the data in order to truly improve your performance?

Reflecting the same challenge back to policing, are we seriously saying it is productive for police to spend their time monitoring social feeds through a watch, rather than talking to individuals and using their eyes in order to know what is happening in their area? And if they were to do this, what would they be looking for? How would they respond? Would it not be better to have an intelligence team in headquarters dedicated to this?

In a business context, I recently had a conversation with a big data executive leading a company that acts as a “curator” for the wealth of data streaming into businesses today. They sort through the data, connect the dots and provide this back in a more usable and value-add form. This concept of curation is essential to turning the mass of data we continue to have at our fingers into knowledge, decision and actions. Even in business this is a relatively new concept that leaders are still getting to grips with.

Sadly it seems that many of the innovations in data technology will remain untapped and perhaps even counterproductive until the skills and systems for curating this new world of data are advanced and widely used. Personally I’d like to see more innovation in this field. Until then, I’m afraid, technology and data will only be as good as its wearer.

Have you seen this equation: innovative = creative? Novelty always comes from “outside the box,” right? It’s a land of confusion to many, who then conclude they are just not the creative type. As a result, organizations lose out because being innovative is but one of a myriad of ways to being creative. All people can be creative—in their own way.

An organization’s ability to bring to best use the individual, team and collective creativity of its people is an important differentiator. That being widely acknowledged, organizations strive for diversity: diversity in gender, age, education, culture and so forth. The argument here is that they can’t overlook another type of diversity: that of being creative.

The under-appreciated obvious

There is nothing new about the call for creative diversity. In December 2007, Coyne, Clifford and Dye published an article in Harvard Business Review entitled “Breakthrough Thinking from Inside the Box.” They stated the obvious that nobody seemed to have noticed: People who like to explore “inside the box” should not be under-appreciated.

This point is backed up by robust research in cognitive psychology. Since the 1970s, Dr. Michael Kirton has been exploring how people solve problems. Seen in the light of cognitive science, solving problems and being creative is one and the same thing. As Genrikh Saulovich Altshuller, inventor himself of the Theory of Inventive Problem Solving (TRIZ), observed: Inventors are able to see problems where the rest of us have grown used to living with the hassle.

Acknowledging these key elements, Dr. Kirton investigated how people actually solve problems. Complex problems are solved in teams, which comes at a price. If you want to solve a technical “Problem A” with your team, then you have to face the additional “Problem B” of managing that same team: finding a place and time to meet, going through the stages of team development and tackling the team’s diversity.

In his research, Dr. Kirton found that people tend to confuse two things: level and style of creativity. If someone’s style of being creative is different from yours, then you might conclude that their level of creativity doesn’t match yours. Examples abound: For some time, Tesla worked for Edison. Both are recognized to have been highly creative people, but creative with very different styles (see this study for more). To solve difficult problems, we need creative diversity in our problem solving teams, but then we struggle to deal with it.

Insights gained with the Kirton Adaption-Innovation Theory help individuals understand their own preferred problem solving style, appreciate their colleagues’ styles and manage diverse teams so that complex problems can be solved.