Netflixed: 3 Analytics Age Principles That Could Save Your Business – Stratalytica™

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In the last Stratalytica issue, we examined, on an anecdotal basis, some of the causal factors that might have played a role in the David vs. Goliath battle between Blockbuster and Netflix (not that we have an axe to grind over all those late fees from back in the day). Over the past several years, a number of Fortune 1000 companies have been “netflixed,” a term which comes from the title of a book by Gina Keating.  We felt that examining the failure of a company like Blockbuster from an Analytics Age perspective might just provide insight on the reason behind the failure of such companies.  As we’ve seen, size is no protection; number of years in operation is no protection; and power or influence is no protection from being netflixed.  Any data scientist worth his or her salt will tell you that causation requires far more data than can be sussed out through casual internet research.  Yet, it is clear that even though Blockbuster seemed to possess every possible advantage over the brash upstart that was Netflix in the early years, there were certain competitive assets that Netflix had and Blockbuster did not.

When Netflix set up shop in 1997, with 8 employees and a $2.7 million investment from entrepreneur Reed Hastings – who would later become the CEO – the small company faced daunting odds in an extremely competitive and fragmented marketplace. Blockbuster was the undisputed leader in the movie rental market with approximately 20 million active users. At the height of the company’s popularity in 1989, a new Blockbuster opened every 17 hours.  No one, possibly not even Netflix, believed that the Scotts Valley, California company would do much more than provide an alternative business model to the Blockbuster brand.  No one realized that the startup, which began the same year that Blockbuster hired John Antioco as CEO, marked a significant shift in the delivery and consumption of home entertainment.

Originally focused on a business model of delivering DVD’s through mail order, which they offered through online sign-up, Netflix finally settled on the subscription model.  By 2000, the company had turned a profit of approximately $5 million. In that same year, Netflix co-founder, Marc Randolph; Netflix co-founder, Reed Hastings; and Netflix CFO, Barry McCarthy, met with Blockbuster CEO John Antioco to offer Blockbuster a $50 million partnership stake in Netflix.  Given the broad disparity in earnings between the two companies, Blockbuster’s response to the offer wasn’t unreasonable; they laughed.  At the time, Blockbuster couldn’t see the appeal of the Netflix model.  Instead, Blockbuster held tightly to the idea that consumers liked what the company called the “Blockbuster experience,” whereby a customer could browse movies, and if desired, get expert advice from Blockbuster’s movie buff employees. Still, Antioco was no fool.  Despite the laughter, Blockbuster was very aware that the company faced threats from numerous competitors, including Hollywood Video, Walmart, and yes, Netflix.

“In the Analytics Age, customer behavior patterns, not traditional sales metrics, drive performance, innovation, and growth.”
Bryant Avey, Chief Geek, internuntius
Two years after the Netflix meeting, industry data began projecting extreme weakness in the bricks and mortar channel.  It is at this point that Blockbuster finally realized that the internet was not a fad.  To remain competitive, they would have to go online. In 2002, attempting to correct course, the company purchased online movie rental company, Film Caddy. No longer laughing, Blockbuster executives, under the direction of Antioco, made extensive investments in building an online presence. It was finally acknowledged by the company that the rules had changed.  If Blockbuster wanted to continue to be a viable business, they had to play by a new set of rules. Moreover, in this new arena of competition, Blockbuster was no longer the market leader, that honor belonged to Netflix. In contrast, Netflix spent the years following the failed partnership meeting with Blockbuster, building a strong online business model. As Blockbuster was coming to the realization of the need for an online presence, Netflix had already grown their subscriber base to 3.6 million. Other changes had taken place as well.  Netflix, now had the full attention of Reed Hastings, a mathematician and programmer, as full-time CEO. In addition, they also had Cinematch, a mathematical algorithm focused on identifying the wants and desires of its online subscribers.  Still, Blockbuster was determined to regain the lost momentum from strategic mistakes.  Intent on making up for lost time, they entered the online arena with a bang.  Blockbuster dropped late fees, negotiated favorable deals with Hollywood studios and, created a new online product called Total Access. To many, it seemed there was no way they could fail. They had the appearance of permanence and stability with their network of stores. They had video games and retail.  And in 2004, they finally had an online presence. Blockbuster had both “bricks and clicks.” So, what happened?

While hindsight is 20/20, it is possible that the Blockbuster failure represents more than just a failure in competing business models. Blockbuster was a digital era company, meaning they used computers to run their enterprise and to collect data. Yet, such enterprises rarely used data as a part of the context of defining their organizations and service offerings. This is not the case for Analytics Age businesses. In the Analytics Age, data is not collected simply for the sake of running reports to assess the health of the enterprise. Netflix used the data they were able to collect in a completely different manner from Blockbuster. Netflix used data to better understand and engage their customers.  From this viewpoint, the conflict between Blockbuster and Netflix could be considered a signal event reflecting the start of the Analytics Age.

In establishing a foundation for competing at the beginning of what has now become the Analytics Age, a term often associated with journalist, Malcolm Gladwell, Blockbuster did not have a data infrastructure that would allow them to collect and analyze data. They lacked an ability to model the existing business landscape from a contextual framework. Such modeling, increasingly commonplace in present-day conference rooms, might have given Blockbuster executives the data they needed to make a more informed decision regarding the Netflix opportunity, or to begin restructuring their business model to better understand customer behavior and ensure customer loyalty.

With data serving as the linchpin of the Analytics Age, it is no surprise that the first Analytics Age challenge that Blockbuster could not overcome was one of insufficient data. Contrary to appearances, Blockbuster executives were savvy, intelligent leaders.  However, it took them 2 years, following the meeting with Netflix to act on the changes taking place in the movie rental industry. Although still in the early days during the Blockbuster/Netflix battle, online analytics made it possible to gather a wealth of important information about customers from online browsing behaviors. In waiting so long to establish an online presence, Blockbuster ceded a significant data advantage to Netflix, allowing them to collect important customer data, unavailable in a traditional bricks and mortar environment. Information such as: In browsing through the movies, which movies did the customer consider? If the customer had a queue, which movies populated the queue? Which movies are popular by region?  In contrast, Blockbuster, was essentially a marketing arm for the movie industry, as opposed to an advocate for movie lovers. In the Analytics Age, customer preference questions along with predictive recommendations are a routine part of the modeling process.  It is this feature that allowed Netflix to transcend standard distribution models and to transform into content manufacturers.  Customer behavior analytics results in new customer insights, new opportunities, new innovations, and better strategy formulation for delivering customer value.

In addition to lacking the foundation of a data infrastructure, Blockbuster lacked timely access to the vital data they needed to respond to the Netflix threat, and to formulate a competing business strategy. In general, most companies of that time, used outside research firms to provide industry insight. Analytics as we know it was a nascent discipline. So, most companies, excluding internet companies, had to make decisions based on industry data that had been aggregated over the course of one to two years.  The data was already outdated by the time a company acquired it. After studying industry reports, internal analysts would then go to their systems, and match the external data against their own internal reporting, which was also historical in nature.  Making predictions would have been extremely difficult, but it was this body of out-of-date (and out-of-context to the current marketplace) data, often rapidly cobbled together, that executives were expected to use as a resource for responding to marketplace opportunities and threats. As an online company, Netflix would have had a better grasp on instantaneous customer insights, as websites could track every page a customer visited, the time spent on a page, and each item selected.

The final Analytics Age deficit that Blockbuster faced was one of analytics. In 2006, Netflix introduced a competition, offering $1 million to anyone who could create an algorithm that beat their own internal cinematch algorithm.  As an internet company, Netflix constantly reshaped itself in response to both marketplace and technology shifts. There was just no way that a “bricks and mortar” company like Blockbuster could know customer wants and needs as well as Netflix. Ultimately, the “Blockbuster experience,” which included those late fees, just couldn’t compensate against an algorithm that had been programmed to provide customers with the perfect movie suggestion at the point of decision.

A great deal of scorn has been heaped on Blockbuster for their initial obstinacy in adapting to the shift in technology. However, to be fair, once the giant had awakened, they faced numerous challenges as they sought to compete in a new world of business without possessing the tools needed for success. Additionally, another major obstacle Blockbuster faced was the one that cost CEO John Antioco his job, the proxy fight with Blockbuster board member and activist shareholder, Carl Icahnn. Some posit that it was the departure of Antioco that ultimately led to Blockbuster’s fall. Whatever the reason behind the company’s failure, it is clear that Netflix was far more prepared for the changes taking place in the marketplace.  Their customer orientation and focus, along with access to customer behavioral data, allowed Netflix to triumph over Blockbuster with little more than an idea and an algorithm.  It is a sobering lesson about the havoc of technical disruption, and the importance of the role that technology plays in supporting the business infrastructure.
Blockbuster’s 2010 bankruptcy filing was not the end of their story.  In 2011, the company was purchased by DirecTV. After spending two years attempting to rebuild the brand, the company finally closed all remaining corporate stores, although a few franchise Blockbusters still exist.  After defeating Blockbuster, Netflix continued dominating the home entertainment market and have now surpassed Comcast and HBO in the number of subscribers.  At this writing, the company has approximately 33 million domestic subscribers and 10 million international subscribers.

It has been four years since Blockbuster declared bankruptcy.  Data technologies have become more accessible, and will become ubiquitous in the next stage of the technology lifecycle.  Now, more than ever, companies must have in place a data infrastructure with an integrated data AND analytics environment that allows for the analysis of huge amounts of data, integrated from dozens, hundreds, or even thousands of sources. Companies lacking such ability will find themselves at a competitive disadvantage.  Additionally, worker roles have begun to shift from knowledge-based, to analysis-based.  Analytics Age staff, including executives at every level of the organization, from CEO to subject matter experts, must be able to blend data analysis, business knowledge, strategic thinking, customer experience, and process management with the use of data and analytics tools. Attempting to do business in the Analytics Age without these basic tools is like Blockbuster trying to compete against Netflix, and we all know how that ended.