Selling Online: The Amazon Effect and The Algorithm Economy
When you start making half of all your sales online, it only makes sense to close a large number of brick-and-mortar shops serving that amount of merchandise. Well, that’s exactly what a North American office supply superstore is doing in response to the Amazon effect. On March 6, Staples announced that it would close 225 stores by 2015, some 10% of its worldwide footprint, as it continues to move more of is business online. “With nearly half of our sales generated online today, we’re meeting the changing needs of business customers and taking aggressive action to reduce costs and improve efficiency, ” said Ron Sargent, Staples’ chairman and chief executive officer, in a statement.
Since most Zambians now shop online, it is only befitting to explain the logistics of the process. Additionally, there is also a large market gap waiting to be filled by online retailers in the e-commerce sector. Amazon has invented, and is continuously refining new ways to connect customers with solutions. The Amazon effect is essentially the transformation of the traditional thinking around customer satisfaction, distribution networks and operations. Thanks to Amazon that kind of traditional thinking has become obsolete.
We have now moved into an algorithm economy which seeks to solve the problem of abundance created by online shopping. According to The Atlantic, Amazon’s problem is that it wants you to keep shopping after you buy what you came for, even though you don’t need the vast majority of what they’ve got to sell.
Amazon’s storefront radically changes for each consumer, showing different pages to a gadget nerd, a romance novel reader, or a college student. Thus building a recommendation engine became necessary. Amazon built a unique patented recommendation formula for itself in the late 1990s. Rather than match customers to similar customers, the company built an index of items that customers tend to purchase together. Therefore, when you make a purchase, the site shows you products with high ratings and similar qualities based on that index. The key to the formula is that, it’s fast, it’s scalable, and it doesn’t need to know much about you. Naturally, the recommendation engine is based on products and not on people.
Ultimately, the recommendation engine reveals itself to be too robotic. For instance, if your last book purchase was “The Art of War”, then the next 20 recommendations will be books by Sun Tzu. According to an article, for the age of algorithms to succeed on its own terms, we have to embrace a new version of intimacy that felt natural with the local newspaper and corner shop clerk who knew our name. The machines have to know us!
