位置:首页 > 英语六级 > 六级资讯 > 正文
英语六级阅读 亚马逊商业推广经验
日期:2012-08-07 16:26


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?




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》。



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。)



  • navigatevi. 航行,驾驶,操纵 vt. 航行,驾驶
  • relevantadj. 相关的,切题的,中肯的
  • faultn. 缺点,过失,故障,毛病,过错,[地]断层 vt.
  • solutionn. 解答,解决办法,溶解,溶液
  • engagementn. 婚约,订婚,约会,约定,交战,雇用,(机器零件等)
  • recommendvt. 建议,推荐,劝告 vt. 使成为可取,使受欢迎
  • multipleadj. 许多,多种多样的 n. 倍数,并联
  • delightn. 高兴,快乐 v. (使)高兴,(使)欣喜
  • performancen. 表演,表现; 履行,实行 n. 性能,本事
  • candyn. 糖果 vt. 用糖煮,使结晶为砂糖 vi. 结晶为