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 (AMZN) calls this homegrown 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 behavior. 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 inboxes 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 totaling $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?

当亚马逊（Amazon）在网站上向你推荐商品时，它绝非无的放矢。

从根本上讲，这家零售巨头的推荐系统推荐的基础是一系列基本元素：用户过去购买过哪些商品；他们的虚拟购物车里有什么；哪些商品被他们评价或“赞”过；其它用户浏览及购买了哪些东西。亚马逊把这套自主研发的算法称为“从项目到项目的协同过滤算法”。依靠这套算法，亚马逊向回头客们提供了深度定制的浏览体验。数码爱好者们会发现亚马逊上满是新潮电子产品的推荐，而新妈妈们在相同的位置看到的却是婴幼儿产品。

亚马逊如今大获成功，推荐系统想必功不可没。2012年第二财季，亚马逊营收达到了128.3亿美元，与去年同期的99亿美元相比大涨了29%。毫无疑问，如此惊人的增长肯定离不开推荐系统。亚马逊将其深度整合到购物流程的方方面面，从商品发掘到结账付款，几乎无处不在。登录Amazon.com，你会看到许多商品推荐板块；点入某个商品的网页，“人气组合”与“（浏览了该商品的）用户还购买了其它商品”等栏目赫然在目。不过，亚马逊对推荐系统的效率守口如瓶。【亚马逊的一位发言人向《财富》杂志（Fortune）表示，“我们的任务是取悦用户，让他们在不经意之间发现美妙的产品。我们相信快乐每天都会出现，这是我们衡量成功的标准。”】

亚马逊还能通过电子邮件发送推荐。虽然亚马逊网站的推荐系统绝大部分依靠自动化，但至今仍有某些部分需要人工大量参与。亚马逊的一名员工表示，公司提供了许多软件，它们能根据用户的购买和浏览行为筛选目标用户。不过，最终目标的确认仍依靠人工而非机器。如果一名员工负责推销一部电影，例如《美国队长》（Captain America），那么他也许会想到其它类似电影，他要确保观看过别的卡通改编动作电影的用户都能收到亚马逊的邮件，以鼓励他们登陆亚马逊购买《美国队长》。

亚马逊员工研究邮件阅读率、点击率、退出率等关键参与指标——这可谓任何公司电子邮件营销渠道的标准做法——但鲜为人知的是，亚马逊按照邮件营收率等指标，对邮件生态系统进行优胜劣汰式优先级排序。一位员工对《财富》称：“这种功能很了不起。基本上，如果某位客户既有资格收到书籍类的推销邮件，又有资格收到视频游戏类的推销邮件，那么（亚马逊最终将向他发送）能带来平均营收更高的那类邮件。想象一下，在每一条产品线上，客户都有资格收到数十封电子邮件，但他们最终收到的只会是效果最佳的那封。”

这一策略能防止（客户的）收件箱被亚马逊的广告邮件塞满，同时将购买机会最大化。事实上，此类邮件的转化率和效率“非常高”，比网站推荐的效率要高得多。调研公司Forrester分析师苏察瑞塔•穆尔普鲁称，根据其他电子商务网站的业绩，在某些情况下，亚马逊网站推荐的销售转化率可高达60%。

虽然很多亚马逊观察员将推荐视为其杀手级应用，但分析师们相信它还有很大的提升空间。穆尔普鲁说：“电子商务行业的普遍看法是，亚马逊的推荐引擎是一个次优选项。”富国银行（Well's Fargo）分析师穆普鲁•特里沙•蒂尔表示，虽然亚马逊的推荐几乎无可挑剔，但在向用户提供相关性更高的产品方面，它仍有很多工作要做。比如说，她就收到过一封推销电锯便携箱的邮件。（但她并没有电锯。）

除了提升推荐本身的准确性外，亚马逊还可以探索更多争取用户的途径。目前，该公司已经开始销售之前都是成批出售的商品，比如说一副扑克牌或一罐肉桂，这些商品单独配送的成本过高。只有当客户订单金额大于等于25美元，才能购买这些商品。但在客户结账时，假如订单金额超过这一门槛，亚马逊可以积极推荐这些附加产品，这与传统超市在收银处摆放口香糖和糖果等冲动消费商品十分相似。

那时，亚马逊的顾客会想：“也没多几块钱。干吗不买呢？”和他们在超市的反应如出一辙。