Amazon was an early adopter of analysing customer purchasing patterns and suggesting products based on previous purchases. Here is an article from Fortune that gives a real life example of predictive analysis in action.
When Amazon recommends a product on its site, it is clearly not a coincidence.
At root, the retail giant’s recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they’ve rated and liked, and what other customers have viewed and purchased. Amazon calls this home-grown math “item-to-item collaborative filtering,” and it’s used this algorithm to heavily customize the browsing experience for returning customers. A gadget enthusiast may find Amazon web pages heavy on device suggestions, while a new mother could see those same pages offering up baby products.
Judging by Amazon’s success, the recommendation system works. The company reported a 29% sales increase to $12.83 billion during its second fiscal quarter, up from $9.9 billion during the same time last year. A lot of that growth arguably has to do with the way Amazon has integrated recommendations into nearly every part of the purchasing process from product discovery to checkout. Go to Amazon.com and you’ll find multiple panes of product suggestions; navigate to a particular product page and you’ll see areas plugging items “Frequently Bought Together” or other items customers also bought. The company remains tight-lipped about how effective recommendations are. (“Our mission is to delight our customers by allowing them to serendipitously discover great products,” an Amazon spokesperson told Fortune. “We believe this happens every single day and that’s our biggest metric of success.”)
Amazon also doles out recommendations to users via email. Whereas the web site recommendation process is more automated, there remains to this day a large manual component. According to one employee, the company provides some staffers with numerous software tools to target customers based on purchasing and browsing behaviour. But the actual targeting is done by the employees and not by machine. If an employee is tasked with promoting a movie to purchase like say, Captain America, they may think up similar film titles and make sure customers who have viewed other comic book action films receive an email encouraging them to check out Captain America in the future.
Amazon employees study key engagement metrics like open rate, click rate, opt-out — all pretty standard for email marketing channels at any company — but lesser known is the fact that the company employs a survival-of-the-fittest-type revenue and mail metric to prioritize the Amazon email ecosystem. “It’s pretty cool. Basically, if a customer qualifies for both a Books mail and a Video Games mail, the email with a higher average revenue-per-mail-sent will win out,” this employee told Fortune. “Now imagine that on a scale across every single product line — customers qualifying for dozens of emails, but only the most effective one reaches their inbox.”
The tactic prevents email inbox’s from being flooded, at least by Amazon. At the same time it maximizes the purchase opportunity. In fact, the conversion rate and efficiency of such emails are “very high,” significantly more effective than on-site recommendations. According to Sucharita Mulpuru, a Forrester analyst, Amazon’s conversion to sales of on-site recommendations could be as high as 60% in some cases based off the performance of other e-commerce sites.
Still, although Amazon recommendations are cited by many company observers as a killer feature, analysts believe there’s a lot of room for growth.”There’s a collective belief within the e-commerce industry that Amazon’s recommendation engine is a suboptimal solution,” says Mulpuru. Trisha Dill, a Well’s Fargo analyst, says it’s hard to fault Amazon for their recommendations, but she also says the company has a lot of work to do in offering users items more relevant to them. As an example, she points to a targeted email she received pushing a chainsaw carrying case. (She doesn’t own a chainsaw.)
Besides refining the accuracy of recommendations themselves, Amazon could explore more ways to reach customers. Already, the company has begun selling items previously sold in bulk that were too cost-prohibitive to ship individually like say, a deck of cards or a jar of cinnamon. Customers may buy them, but only if they have an order totalling $25 or over. But the company could actively recommend these add-on products during check-out when an order crosses that pricing threshold, much like traditional supermarkets have impulse-purchase items like gum and candy bars at the register.
At that point, the Amazon customer, just as they would in the supermarket, might think, “It’s just a few more bucks. Why not?”