Learning from Under Armor's failed Big Data push [Finance Friday]
A clear example of how ignoring fundamentals to recklessly pursue buzz words leads to financial ruin
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I’m going to start with a mini-rant:
Data is not the new gold. Data is not the new oil. Data/AI is not going impact the world to anywhere the same degree as electricity, agriculture, antibiotics, the death of Julius Caeser or any of the other historically significant events.
People that claim otherwise need a reality check. AI is cool. Data can be extremely valuable. In many cases, Data-Driven decisions and AI will change the way things are done and enable world-changing inventions. However, to do so, it must be part of a larger process; used with a lot of thought and planning. AI or even high-quality Data are meaningless if they don’t serve a larger purpose. AI for AI’s sake is an exercise in futility. Ignore it, and you end up like our boy here-
Rant over.
The story we go over today will be an illustration of this. Under Armor (a sporting apparel chain for those not familiar) was once the cool new kid on the block, ready to take on the incumbents Nike and Adidas. They had core customer differentiation, promising growth, and a dedicated consumer base. They created a lot of buzz as a high-quality solution for the more hardcore athletes. Unfortunately, since then, they have seen a remarkable fall from grace- culminating in being charged by defrauding their own investors by the SEC.
So what does this have to do with AI/Data? Let’s get right their failed Big Data push and how this led to this collapse.
Under Armor’s Failed Digitization Push
Background- Under Armor had so much going for it. They had great relationships with their retailers, strong branding with athletes, a lot of attention, customers loved their products. Under Armour experienced impressive revenue growth, averaging 40% every year, even during a recession, establishing itself as a major player in the athletic industry. Under Armour's footwear business experienced significant growth, going from $300M in 2013 to a billion-dollar business by 2016, thanks to the success of their SpeedForm shoes. However, they never fully lived up to their promise, face-planting spectacularly as mentioned prior. So what went wrong?
The Turning Point- Around 2015(notice how that shows up as the start of their fraud), UA decided they had to do something big. At this time, Big Data and Mobile Apps were all the rage, so the Under Armor upper management drank the kool-aid and got jiggy. They splashed serious cash to make major acquisitions on apps-
Unfortunately, these didn’t pan out so well. Under Armour sold off MyFitnessPal for $345M shut down Endomondo. To add insult to injury, Under Armor failed to capitalize on the first mover digitization advantage, with Nike’s app doing very well despite being late to the space. “Doing away with two of its apps also means that Under Armour’s digital fitness business will be shrinking at a time when competitors like Nike and Lululemon are investing heavily in the sapce. Lululemon announced in July it was acquiring Mirror, a device that allows users to stream workouts and personal training classes for a fee, for $500 million. Nike, meanwhile has reported triple-digit growth during the pandemic for both its main Nike shopping app as well as its Nike Train Club app, which it made free in the spring in order to encourage more people to download it.”
What went wrong- UA was one of the first victims of the Big Data hype cycle. They went on a 700 Million Dollar Shopping Spree on a simple rationale- more data about people in fitness might help them improve their product offerings and reach more customers. This seems valid- but keep in mind that everything comes with an opportunity cost. It’s not that this idea wouldn’t work, but that for 700 million Dollars there were many better expansion strategies. They could have approached their digitization push strategically, investing into acquisitions and R&D with a clear vision of how actions would improve business (one set of common sense recommendations is given in this simple 2016 writeup)-
Instead, Under Armor ignored their core fundamentals, pursuing instead a strategy of buzz words, burning money, and constantly moving goal posts. In a nutshell- Under Armor became a Silicon Valley startup.
Why today’s leaders should care- UA is a classic case of putting the cart before the horse. Too many groups/business AI leaders are blindly rushing to adopt AI into their processes. If you throw enough money at the problem, someone like me can definitely find a way to improve your key metrics with AI. However, take a second to think about whether that’s really the best solution for you. These systems can be extremely costly to design, maintain, and run. This is true even compared to traditional software systems, as Dr. Chris Walton, Senior Applied Science Manager at Amazon, touched upon here. Really take some time to define your expectations, sketch out a strategy, and identify important points. Otherwise, you’ll just be throwing money at the sink. If you’re still excited about implementing AI into your systems- shoot me a message (links at the end), and I’ll give you a hand.
If you want to know more about Under Armor, my recommendation is the following video by the exceptional Modern MBA. He covers a lot of important data points and has some hard-hitting lines. Can’t recommend it enough.
That is it for this piece. I appreciate your time. As always, if you’re interested in working with me or checking out my other work, my links will be at the end of this email/post. If you like my writing, I would really appreciate an anonymous testimonial. You can drop it here. And if you found value in this write-up, I would appreciate you sharing it with more people. It is word-of-mouth referrals like yours that help me grow.
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Wonder how many companies will have the same fate, but because of their "gold rush" to join in the AI revolution, rather than actually thinking about how to integrate these tools into the business!
This is a fantastic analysis and highlights exactly the pitfalls I've tried to help others avoid in the data space. Mo' Data ain't always better. The bigger issue is when they just collect data hoping answers will fall out. It's the thing that drives me nuts about Data Science, specifically their oft-heard mantra "Give me your data and I'll give you answers." No crap, tortured data will tell you anything.
But data science, and Under Armor forgot where they should have started. That starting point is with 'Why?"
https://www.polymathicbeing.com/p/what-data-science-forgot