Many of us have fond memories of Friday nights at Blockbuster, perusing shelves of VHS tapes and DVDs, debating whether to rent “Jurassic Park” for the 17th time or take a chance on that obscure indie film. But nostalgia couldn’t save this once-dominant brand from becoming the punchline of business school case studies. Blockbuster’s downfall offers fundamental lessons about the importance of sound strategy and the role data science can play in shaping it—or in Blockbuster’s case, how ignoring data can lead to a business horror story scarier than anything in their “Late Night” section.
The Meeting: A Desperate Grab for Relevance
Picture this: There I was, the head of analytics at a major ad agency, sitting across from Blockbuster’s C-suite in their Texas headquarters. The tension was thicker than the plot of a Christopher Nolan movie. The CEO, looking like he’d just watched his company’s stock price perform a Swan Lake death scene, outlined their strategy to turn the struggling company around, which included such straw-grasps as:
- Transform rental locations into retail stores selling merchandise.
- Branch out into selling tickets for live events.
As he spoke, I felt a growing sense of unease, like I was watching a car crash in slow motion. This wasn’t a strategy; it was a Hail Mary pass thrown by a quarterback who wasn’t sure which end zone was his.
The Problem: Misguided Desperation
Now, don’t get me wrong. Blockbuster’s team wasn’t stupid. They were desperate, and desperation can make even the smartest cookies crumble. Their core business was eroding faster than a sandcastle at high tide, thanks to Netflix’s DVD-by-mail business and the rise of streaming services. In their panic, they had broken the glass to grasp desperately at the emergency lever, concocting a strategy that was more wishful thinking than coherent plan.
Their approach was problematic for several reasons. First, Blockbuster had about as much experience in merchandising or event ticket sales as I do in neurosurgery. They were ignoring the real threat—the shift to digital streaming—like an ostrich with its head in the sand, if the sand was a pile of unsold DVDs. Transforming their stores would drain resources faster than a leaky bucket, leaving nothing for potential digital innovations. And let’s not forget they were trying to muscle into markets already crowded with specialized competitors—they were bringing a plastic knife to a gunfight.
The potential consequences were severe: wasted resources, confused customers, and a continued decline in their core business. Ultimately, this misguided strategy accelerated Blockbuster’s demise rather than saving it. It was like trying to bail out the Titanic with a teaspoon—energetic, but ultimately futile.
The Broader Context: A Common Pitfall
Blockbuster’s misstep isn’t unique in the business world. It’s like there’s a support group out there: “Welcome to Strategic Blunders Anonymous.” Consider an old client of mine, Kodak, who invented the digital camera but clung to their film business like it was the last life raft on the Titanic. They filed for bankruptcy in 2012, proving that even being first doesn’t matter if you’re running in the wrong direction. Then there’s Borders Books, who expanded aggressively into music and video, neglecting the growing e-book market. They ended up liquidating in 2011, a cautionary tale of what happens when you bring a bookshelf to an e-reader fight.
These cases share a common thread: wishful thinking masquerading as strategy, like a kid wearing their parent’s clothes and pretending to be an adult. It’s cute when a five-year-old does it, less so when it’s a billion-dollar corporation.
What is Strategy, Really?
So, if we know what strategy isn’t (a wish list, a Hail Mary, or a series of random tactics thrown at the wall to see what sticks), what is it? Strategy isn’t just a fancy word to make the C-suite feel important. It’s a cohesive framework for achieving a specific, well-defined goal. Think of it as the GPS for your business road trip—without it, you’re just driving around aimlessly, burning fuel and getting nowhere.
Richard Rumelt, in his book Good Strategy, Bad Strategy, outlines three key elements of a solid strategy. First, you need a clear diagnosis—understanding the challenges and opportunities facing your organization. It’s like going to the doctor; you can’t treat the disease if you don’t know what it is.
Next comes the guiding policy, a general approach to overcoming the challenges identified in your diagnosis. This is your North Star, providing a consistent direction for decision-making throughout the organization. Without it, your company is like a ship without a rudder, at the mercy of whatever wind happens to be blowing that day.
Finally, you need coherent actions—a set of coordinated steps designed to carry out your guiding policy. Without this, even the best policy remains just a good idea, like that gym membership you bought in January and haven’t used since February.
The Role of Data Science in Strategy
This is where data scientists come in, armed with our algorithms and our slightly unhealthy obsession with spreadsheets. We’re not just number crunchers or the folks you call when your computer acts up (that’s IT, for the last time!). We can play an essential role in developing and executing strategy.
We’re the Ghostbusters of the business world, but instead of proton packs, we carry powerful forecasting models to project future trends. We’re the reality check when the sales team starts spouting off about hockey stick growth projections that look more like a giraffe’s neck. We can set realistic KPIs and develop SMART goals, ensuring your objectives are more achievable than time travel and more specific than “do better.”
But wait, there’s more! We’re also your guides through the maze of data-driven decision making. We can provide insights from complex data sets that are clearer than a bell and more actionable than a triple espresso on a Monday morning. And when it comes to preparing for the future, our scenario planning capabilities make us the corporate world’s equivalent of fortune tellers, minus the crystal ball and questionable fashion choices.
Learning from Blockbuster’s Mistakes
What can we learn from Blockbuster’s ill-fated attempt to blockbust itself? First, stay true to your core. Blockbuster trying to sell t-shirts and concert tickets was like McDonald’s deciding to pivot into haute cuisine—stick to what you know. They could have leveraged their brand recognition to move into digital streaming, but instead, they zigged when they should have zagged.
Second, don’t ignore the elephant in the room, especially when that elephant is stomping all over your business model. Blockbuster should have tackled the shift to digital head-on, instead of pretending it was just a passing fad like pet rocks or low-rise jeans.
Third, use your data wisely. Blockbuster probably had more data than they knew what to do with, showing the rise of streaming. But having data and using it effectively are two different things—like owning a treadmill and actually using it to exercise instead of as an expensive clothes hanger.
Fourth, think long-term. Avoid short-term fixes for long-term problems. It’s like putting a band-aid on a broken leg. It might make you feel better for a moment, but it’s not solving the real issue. And come to think of it, it probably wouldn’t even make you feel better for that moment.
Finally, be realistic. Optimism is great, but too much of it is like too much sugar—it’ll give you a quick high before the crash. Blockbuster’s plan to suddenly dominate new markets was about as realistic as my plan to win an Olympic gold medal in figure skating (I can’t even ice skate).
The Power of Good Strategy
In the end, Blockbuster’s story serves as a powerful reminder of the importance of sound strategy in business. As data scientists, we have the tools and skills to contribute meaningfully to strategic discussions, grounding them in data-driven insights and realistic projections. We’re the voice of reason in a world that sometimes seems to have lost its mind, the ones who can tell you that no, the emperor really isn’t wearing any clothes. And we’re the digital tailor that can take the measurements to sew him some.
But remember, while data is super-extra-duper important, it’s not everything. Good strategy also requires creativity, industry knowledge, and the courage to make tough decisions. It’s about combining the art of business with the science of data, like an awkward make-out session between intuition and cold, hard facts.
So let’s learn from Blockbuster’s mistakes. Let’s harness the power of data, craft strategies that stand the test of time, and maybe, just maybe, avoid becoming the next cautionary tale in the business strategy hall of fame. After all, in the game of business, you either win and become a case study, or you still become a case study, just a much more depressing one.
Your move, C-suite.
For more columns from Michael Bagalman’s Data Science for Decision Makers series, click here.
Contributor
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Michael Bagalman brings a wealth of experience applying data science and analytics to solve complex business challenges. As VP of Business Intelligence and Data Science at STARZ, he leads a team leveraging data to inform decision-making across the organization. Bagalman has previously built and managed analytics teams at Sony Pictures, AT&T, Publicis, and Deutsch. He is passionate about translating cutting-edge techniques into tangible insights executives can act on. Bagalman holds degrees from Harvard and Princeton and teaches marketing analytics at the university level. Through his monthly column, he aims to demystify important data science concepts for leaders seeking to harness analytics to drive growth.
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