The Moneyball Approach to Web Analytics: Companies that Crush the Competition
September 29, 2011
With Moneyball hitting the theaters this past weekend, I thought I’d get this out there before it becomes just another clichéd Hollywood sports movie:
Moneyball was the one book that most changed how I think about the world.
Forget the fact that I’m a huge baseball fan, because the book wasn’t even really about baseball, or even sports. It was about using cold, hard facts to form an opinion about the world rather than gut instincts, conventional wisdom, ‘expert’ opinions, or any other ambiguously reliable authority.
This message has stuck with me through the years, pushing me towards fields where I thought there was an opportunity to gain greater understanding through open-minded, unbiased analysis of the data. While the specific inefficiencies discussed in the book have long since closed, the lessons are just as relevant today as they were when I read them as a junior in high school. The technical ability to make data-driven decisions, it seems, will always precede the widespread willingness to employ them.
Which brings us to web analytics. The dawn of the internet opened a vast new world of consumer feedback for businesses to mine. Ironically, instead of leveraging their scale and resources to stay ahead of the game in analytics, many industry leaders used their footprint as a crutch, sticking to their increasingly archaic qualitative assessments. Generally this meant decision making by what Avinash Kaushik disparagingly calls the “HiPPO” method, for “Highest Paid Person’s Opinion.” This reluctance to embrace the power of web analytics left the door open for a new breed of analytically-savvy companies to topple them.
Here are three internet retailers that I believe have used the ‘Moneyball’ culture of analytical decision making to crush their less sophisticated competitors:
Amazon.com
In the Moneyball analogy, there’s no question that Amazon is the Oakland A’s of the retail world, and this chart proves it. Founder Jeff Bezos, a computer science major, navigated his company through the wreckage of the bursting tech bubble, using his initial success with books to expand into virtually every corner of the consumer economy. What allowed them to succeed where so many others failed was their pioneering work in personalized recommendations, continually tweaking their algorithms to offer more relevant products to their visitors and keep them shopping.
Nearly twenty years after its launch, the company is still innovating. In a letter to shareholders, Bezos wrote, “…while many of our systems are based on the latest in computer science research, this often hasn’t been sufficient: our architects and engineers have had to advance research in directions that no academic had yet taken.” In the process, they’ve brought cloud computing to the masses, proved that free shipping can be a profitable model, and spawned the e-book industry, all in the face of deep skepticism from onlookers. The driving force behind all of this is a fundamentally analytical approach to decision making that has remained a step above its competitors.
Netflix
From the moment Netflix launched in 2002, it was on a collision course with Blockbuster, which possessed the lion’s share of global movie rentals and a monster presence of 8,500 locations globally. Watching Netflix’s business model gain traction, Blockbuster responded in 2006 with a mail-order service of its own, with a lower price point than Netflix, and the added support of its massive global footprint. So given their greater resources and household brand, why didn’t Blockbuster eviscerate Netflix?
The answer, of course, is analytics. Because their customers were requesting too many new releases, Blockbuster mail-order subscribers often faced delays on the titles they wanted. Netflix addressed this problem with sophisticated recommendation algorithms, which better distributed the demand for titles across their vast library and cured the availability problem. The company, as anyone with an account can tell you, also relentlessly gathers feedback from their customers via automated emails, using that information to pinpoint and address weaknesses in their service. Perhaps most tellingly, the company puts their money where their mouth is on their dedication to analytics by offering a million dollar prize in 2009 to the group that improved most on Netflix’s recommendation algorithm. The result is a much better user experience, built almost entirely on quantitative customer feedback.
Vistaprint
Offering your core product for free sounds like a great way to lose a bunch of money in a hurry. Vistaprint, however, has made the strategy work by cross-selling and up-selling their way to profits. Primarily a seller of business cards, the company has mastered the art of suggesting just the right complimentary product at just the right time to get their SMB customers to fill their basket with more than just free business cards. They do this with a strong dedication to trial, error, and of course, analysis.
Simultaneously, the company takes a precise analytical approach to keeping costs down despite their growing number of products. Their investor presentation is loaded with analytical insights about the company, such as the fact that a package of business cards takes only 13 seconds of labor to create, and they can pump out as many as 3,400 per production hour. This analytical mentality is a big reason why their unique active customer now results in about $46 of gross profits per year, despite the fact that the majority originally stopped by for some free business cards.
All of these companies were initially at a disadvantage relative to their larger competitors in both resources and brand awareness, but all of them harnessed the power of analytics to eventually dominate their respective industries. If you can think of other examples, and I’m sure there are many more, please be sure to let me know in the comments.
For another blog on the topic, visit:
http://www.mediapost.com/publications/?fa=Articles.showArticle&art_aid=159061
To read up on web analytics, see: