推銷高手亞馬遜的秘密

????當(dāng)亞馬遜（Amazon）在網(wǎng)站上向你推薦商品時(shí)，它絕非無的放矢。

????從根本上講，這家零售巨頭的推薦系統(tǒng)推薦的基礎(chǔ)是一系列基本元素：用戶過去購(gòu)買過哪些商品；他們的虛擬購(gòu)物車?yán)镉惺裁矗荒男┥唐繁凰麄冊(cè)u(píng)價(jià)或“贊”過；其它用戶瀏覽及購(gòu)買了哪些東西。亞馬遜把這套自主研發(fā)的算法稱為“從項(xiàng)目到項(xiàng)目的協(xié)同過濾算法”。依靠這套算法，亞馬遜向回頭客們提供了深度定制的瀏覽體驗(yàn)。數(shù)碼愛好者們會(huì)發(fā)現(xiàn)亞馬遜上滿是新潮電子產(chǎn)品的推薦，而新媽媽們?cè)谙嗤奈恢每吹降膮s是嬰幼兒產(chǎn)品。

????亞馬遜如今大獲成功，推薦系統(tǒng)想必功不可沒。2012年第二財(cái)季，亞馬遜營(yíng)收達(dá)到了128.3億美元，與去年同期的99億美元相比大漲了29%。毫無疑問，如此驚人的增長(zhǎng)肯定離不開推薦系統(tǒng)。亞馬遜將其深度整合到購(gòu)物流程的方方面面，從商品發(fā)掘到結(jié)賬付款，幾乎無處不在。登錄Amazon.com，你會(huì)看到許多商品推薦板塊；點(diǎn)入某個(gè)商品的網(wǎng)頁(yè)，“人氣組合”與“（瀏覽了該商品的）用戶還購(gòu)買了其它商品”等欄目赫然在目。不過，亞馬遜對(duì)推薦系統(tǒng)的效率守口如瓶。【亞馬遜的一位發(fā)言人向《財(cái)富》雜志（Fortune）表示，“我們的任務(wù)是取悅用戶，讓他們?cè)诓唤?jīng)意之間發(fā)現(xiàn)美妙的產(chǎn)品。我們相信快樂每天都會(huì)出現(xiàn)，這是我們衡量成功的標(biāo)準(zhǔn)。”】

????亞馬遜還能通過電子郵件發(fā)送推薦。雖然亞馬遜網(wǎng)站的推薦系統(tǒng)絕大部分依靠自動(dòng)化，但至今仍有某些部分需要人工大量參與。亞馬遜的一名員工表示，公司提供了許多軟件，它們能根據(jù)用戶的購(gòu)買和瀏覽行為篩選目標(biāo)用戶。不過，最終目標(biāo)的確認(rèn)仍依靠人工而非機(jī)器。如果一名員工負(fù)責(zé)推銷一部電影，例如《美國(guó)隊(duì)長(zhǎng)》（Captain America），那么他也許會(huì)想到其它類似電影，他要確保觀看過別的卡通改編動(dòng)作電影的用戶都能收到亞馬遜的郵件，以鼓勵(lì)他們登陸亞馬遜購(gòu)買《美國(guó)隊(duì)長(zhǎng)》。

????亞馬遜員工研究郵件閱讀率、點(diǎn)擊率、退出率等關(guān)鍵參與指標(biāo)——這可謂任何公司電子郵件營(yíng)銷渠道的標(biāo)準(zhǔn)做法——但鮮為人知的是，亞馬遜按照郵件營(yíng)收率等指標(biāo)，對(duì)郵件生態(tài)系統(tǒng)進(jìn)行優(yōu)勝劣汰式優(yōu)先級(jí)排序。一位員工對(duì)《財(cái)富》稱：“這種功能很了不起。基本上，如果某位客戶既有資格收到書籍類的推銷郵件，又有資格收到視頻游戲類的推銷郵件，那么（亞馬遜最終將向他發(fā)送）能帶來平均營(yíng)收更高的那類郵件。想象一下，在每一條產(chǎn)品線上，客戶都有資格收到數(shù)十封電子郵件，但他們最終收到的只會(huì)是效果最佳的那封。”

????這一策略能防止（客戶的）收件箱被亞馬遜的廣告郵件塞滿，同時(shí)將購(gòu)買機(jī)會(huì)最大化。事實(shí)上，此類郵件的轉(zhuǎn)化率和效率“非常高”，比網(wǎng)站推薦的效率要高得多。調(diào)研公司Forrester分析師蘇察瑞塔?穆爾普魯稱，根據(jù)其他電子商務(wù)網(wǎng)站的業(yè)績(jī)，在某些情況下，亞馬遜網(wǎng)站推薦的銷售轉(zhuǎn)化率可高達(dá)60%。

????雖然很多亞馬遜觀察員將推薦視為其殺手級(jí)應(yīng)用，但分析師們相信它還有很大的提升空間。穆爾普魯說：“電子商務(wù)行業(yè)的普遍看法是，亞馬遜的推薦引擎是一個(gè)次優(yōu)選項(xiàng)。”富國(guó)銀行（Well's Fargo）分析師穆普魯?特里沙?蒂爾表示，雖然亞馬遜的推薦幾乎無可挑剔，但在向用戶提供相關(guān)性更高的產(chǎn)品方面，它仍有很多工作要做。比如說，她就收到過一封推銷電鋸便攜箱的郵件。（但她并沒有電鋸。）

????除了提升推薦本身的準(zhǔn)確性外，亞馬遜還可以探索更多爭(zhēng)取用戶的途徑。目前，該公司已經(jīng)開始銷售之前都是成批出售的商品，比如說一副撲克牌或一罐肉桂，這些商品單獨(dú)配送的成本過高。只有當(dāng)客戶訂單金額大于等于25美元，才能購(gòu)買這些商品。但在客戶結(jié)賬時(shí)，假如訂單金額超過這一門檻，亞馬遜可以積極推薦這些附加產(chǎn)品，這與傳統(tǒng)超市在收銀處擺放口香糖和糖果等沖動(dòng)消費(fèi)商品十分相似。

????那時(shí)，亞馬遜的顧客會(huì)想：“也沒多幾塊錢。干嗎不買呢？”和他們?cè)诔械姆磻?yīng)如出一轍。

????譯者：項(xiàng)航

????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?"