Analytics lessons from Amazon

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For much of history, most commerce has been fairly reactive. Think about it. Consumers decided that they needed a new tchotchke and walked or drove to a store to actually buy it.

Sure, there have been variations on the theme. These have included mail orders, deliveries, cold-calling, door-knocking, and, relatively recently, online purchases. For the most part, though, a consumer made a conscious decision to part with his or her money. Adam Smith would be proud.

The arrival of the Web, smartphones, and cloud computing have turned traditional commerce on its head. Things that were previously impossible are now commonplace. You can subscribe to a physical newspaper or magazine, but why not beef jerky? Executives at Amazon asked themselves that very question and, voilà! Subscribe and save was born.

But we ain't seen nothin' yet. What about purchases based upon what you might want or haven't realized you want? They're coming. As The Wall Street Journal reported in January of 2014:

Amazon says it may box and ship products it expects customers in a specific area will want—based on previous orders and other factors… According to the patent, the packages could wait at the shippers’ hubs or on trucks until an order arrives. If implemented well, this strategy has the potential to take predictive analytics to the next level, allowing the data-savvy company to greatly expand its base of loyal customers.

With such vast amounts of information, prediction becomes easier, if not easy. As I write in The Age of the Platform, Amazon has garnered a tremendously deep and wide trove of data on its nearly 300 million customers. Equipped with this such detailed information, the idea of keeping even potentially perishable items near a customer's home doesn't seem so crazy. The company's data-oriented culture is nothing short of legendary. It's tracking every click, sale, return, and page view. Beyond the transactional stuff, rest assured that its use of analytics is second to none.

Via machine learning and extensive use of customer data, Amazon offers increasingly relevant product suggestions to its customer base. Much like Netflix and Google, its recommendation algorithm becomes smarter over time. This makes the company more money, not that its leadership has ever minded operating at razor-thin margins.

Simon Says: Get Amazonian

Truth be told, learning specific techniques from Jeff Bezos's company isn't easy. Amazon is famously opaque in its moves, something that has earned the ire of investors since its inception.

Many of us would love more details, but consider the following: Even without knowing how it does what it does, we know why. The company understands the cardinal importance of data, both big and small. Case in point: Amazon is "addicted" to A/B testing. No matter how much data Amazon has collected, it wants more—and not just for the sake of collecting it. Data is a means to critical ends: understanding customers, making better business decisions, and thwarting competitors. (Find out what companies are doing to manage their data more effectively in this Information Management e-book, Data Management: What You Need to Know and Why).

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About Author

Phil Simon

Author, Speaker, and Professor

Phil Simon is a keynote speaker and recognized technology expert. He is the award-winning author of eight management books, most recently Analytics: The Agile Way. He consults organizations on matters related to strategy, data, analytics, and technology. His contributions have appeared in The Harvard Business Review, CNN, Wired, The New York Times, and many other sites. He teaches information systems and analytics at Arizona State University's W. P. Carey School of Business (Department of Information Systems). He also runs 5marbles, an Agile software-development shop.

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