只用了8年時(shí)間,，在線零售商Stitch Fix就將業(yè)務(wù)做得風(fēng)生水起。每年有超過(guò)320萬(wàn)名消費(fèi)者通過(guò)它的服務(wù)購(gòu)買牛仔褲,、羊毛衫和手鏈等服裝和飾品,。

與傳統(tǒng)網(wǎng)購(gòu)平臺(tái)不同的是，Stitch Fix的訂閱用戶會(huì)通過(guò)快遞收到成箱的服裝和飾品,，收件的頻率多高都可以,。在注冊(cè)的時(shí)候，用戶需要回答一長(zhǎng)串的問題,，比如他們喜歡的著裝風(fēng)格和體型等等,。然后Stitch Fix的計(jì)算機(jī)算法和造型師們會(huì)根據(jù)這些信息，選擇給用戶寄送哪些商品,。用戶可以留下他們喜歡的,，付完錢后再將剩下的商品寄回去,。

現(xiàn)在，Stitch Fix的CEO卡特里娜·萊克正在為公司下一階段的發(fā)展奠定基礎(chǔ),。她想利用Stitch Fix強(qiáng)大的數(shù)據(jù)分析能力,，更準(zhǔn)確地預(yù)測(cè)消費(fèi)者想要購(gòu)買和保留哪些商品，以創(chuàng)造更多的業(yè)務(wù),。

萊克對(duì)《財(cái)富》雜志表示：“我們正在研究如何通過(guò)個(gè)性化推薦,，為你的衣櫥貢獻(xiàn)更多合適的衣服，讓你可以在各種不同的場(chǎng)合穿著,?！?/p>

StitchFix會(huì)對(duì)每名用戶的數(shù)據(jù)進(jìn)行分析，然后生成一份個(gè)人檔案,，然后它會(huì)利用可視化手段,，制作一份“潛在造型地圖”。每張“地圖”都包含了幾百件官方推薦的衣服,，所以它對(duì)每個(gè)用戶的推薦都是極其細(xì)致的,，而不是籠統(tǒng)地分成幾個(gè)大類。

在華爾街看來(lái),，Stitch Fix創(chuàng)造新的收入來(lái)源的速度還不夠快,。截至10月中旬，該公司的股價(jià)已經(jīng)較2019年的最高點(diǎn)下跌了30%,。這一方面是由于吸引和保留用戶需要更高的成本,，另一方面也是由于有競(jìng)爭(zhēng)對(duì)手復(fù)制了它高度個(gè)性化的電商模式。

零售業(yè)目前正在發(fā)生的劇變,，也給帶來(lái)了很多挑戰(zhàn),。

最近，Stitch Fix公司推出了一項(xiàng)叫做“Shop Your Looks”（意為“選購(gòu)你的造型”）的新功能,。這也是該公司做的一項(xiàng)重要的試驗(yàn),。它會(huì)在已經(jīng)寄送給用戶的推薦商品的基礎(chǔ)上，再向用戶推薦一些用來(lái)搭配的單品,。比如用戶在收到一件夾克衫后,，可能會(huì)馬上又收到一封電子郵件，建議他們?cè)儋I一副太陽(yáng)鏡,，專門來(lái)搭配這件夾克,。

該公司希望Shop Your Looks能夠扮演一個(gè)“穿搭小能手”的角色，繼續(xù)勾起用戶的購(gòu)買欲,，并且提高他們?cè)L問Stitch Fix的頻率,。不過(guò)萊克也表示，她也意識(shí)到這個(gè)功能是有風(fēng)險(xiǎn)的,，大家很可能會(huì)覺得Stitch Fix只不過(guò)是另一個(gè)用“推薦商品”向消費(fèi)者狂轟亂炸的普通網(wǎng)購(gòu)平臺(tái)罷了,。

在這種模式下,，消費(fèi)者最多只能夠在線看到30到40件推薦商品——雖然選擇也不少了，但是絕對(duì)不會(huì)像亞馬遜或eBay那樣顯示出無(wú)窮無(wú)盡的搜索結(jié)果,。到目前為止,，在使用過(guò)該功能購(gòu)買商品的人中，有60%購(gòu)買了不止一件,。

從業(yè)務(wù)成績(jī)上看,，Stitch Fix可以說(shuō)是喜憂參半。在截至今年8月3日的12個(gè)月間,，它的營(yíng)收入較上年同期飆升29%,，達(dá)到15.8億美元，實(shí)現(xiàn)利潤(rùn)3690萬(wàn)美元,。不過(guò)它的利潤(rùn)卻較上年同期下降了18%,，這與Stitch Fix在打造新服務(wù)上投入重資不無(wú)關(guān)系。

Stitch Fix必須向那些緊張的投資者證明,，它有能力繼續(xù)吸引新用戶,，同時(shí)向現(xiàn)有用戶賣出更多的商品。與此同時(shí),，它還要面臨同業(yè)者們對(duì)庫(kù)存服裝瘋狂地打折銷售帶來(lái)的壓力,。

KeyBanc Capital Markets公司的分析師艾德·伊魯瑪指出，來(lái)自亞馬遜的壓力,，也是Stitch Fix面臨的一個(gè)“長(zhǎng)期隱患”,。亞馬遜自稱是個(gè)“能買一切”的網(wǎng)購(gòu)平臺(tái)，它的服裝業(yè)務(wù)整體上增長(zhǎng)很快,，今年6月,，亞馬遜還推出了自家的個(gè)性化購(gòu)物服務(wù)，使Stitch Fix直接成了它瞄準(zhǔn)的靶子,。

除此之外,，Stitch Fix還有一個(gè)勁敵——諾德斯特龍（Nordstrom）的Trunk Club，這也是一個(gè)偏高端的定制購(gòu)物服務(wù),。與此同時(shí),，Instagram和Pinterest等社交媒體服務(wù)也對(duì)各大電商平臺(tái)越來(lái)越友好了，這些都讓本已十分復(fù)雜的在線零售業(yè)增添了新的變數(shù),。

這意味著Stitch Fix必須不斷提高其技術(shù)的準(zhǔn)確度,。Stitch Fix擁有一支約3000名真人造型師組成的團(tuán)隊(duì),，他們會(huì)根據(jù)計(jì)算機(jī)算法的分析結(jié)果,，決定應(yīng)該往寄給用戶的包裹里放入哪些衣服。

為了優(yōu)化公司的數(shù)據(jù)分析能力,，Stitch Fix去年還推出了一項(xiàng)名為Style Shuffle的新服務(wù),，它每次會(huì)向用戶展示一款有可能上架的新品,，然后讓用戶進(jìn)行投票。通過(guò)該工具收集的信息,，有助于Stitch Fix更準(zhǔn)確地對(duì)用戶進(jìn)行推薦,。到目前為止，該功能已經(jīng)反饋了大約30億次用戶的評(píng)價(jià)信息,。

與此同時(shí),，Stitch Fix也在努力擴(kuò)大對(duì)服裝的選擇。目前,，該平臺(tái)的服裝主要來(lái)自一些小品牌,。而現(xiàn)在，一些大牌服裝也已經(jīng)逐漸登陸了Stitch Fix,，比如New Balance和Madewell等等,。這些大品牌之所以如此看中Stitch Fix，在一定程度上也是為了分享數(shù)據(jù),，好知道用戶喜歡什么,。同時(shí)這些信息也有助于Stitch Fix更準(zhǔn)確地預(yù)測(cè)市場(chǎng)對(duì)其自營(yíng)服裝品牌的需求。而自營(yíng)品牌已經(jīng)日益成為該公司業(yè)務(wù)中至關(guān)重要的一部分,。

在萊克看來(lái),，關(guān)注數(shù)據(jù)是她的唯一選擇。

她表示：“如果一個(gè)人沒有收到他們喜歡的東西,，他們就會(huì)不再使用Stitch Fix,。能否為人們提供個(gè)性化的服務(wù)，這對(duì)我們來(lái)說(shuō)是一個(gè)事關(guān)生死存亡的問題,，這是我們的生命線,。”

In just eight years, online retailer Stitch Fix has created a flourishing business. More than 3.2 million shoppers use its service annually to buy merchandise from jeans to wool sweaters to bracelets.

Unlike with conventional online retailers, customers subscribe to Stitch Fix to receive boxes of apparel and accessories, or “fixes,” as often as they want. When signing up, clients answer a long list of questions about the kind of clothes they like and their body type—information that the company’s algorithms and human stylists use to choose which items to send. Customers keep and pay for what they like, and send the rest back.

Now Stitch Fix CEO Katrina Lake is laying the groundwork for her company’s next chapter. She wants to tap Stitch Fix’s data-crunching prowess to even more accurately predict what shoppers want to buy and keep, and to drum up more business between so-called fixes.

“We are trying to figure out how we can use personalization to deliver more parts of your closet so that you can use those items for all occasions,” Lake tells Fortune.

StitchFix analyzes data to generate an individualized profile for each customer, which it visualizes in a “l(fā)atent style map”. Each map is comprised of hundreds of suggested pieces of clothing, constructing an extremely nuanced picture of each user—versus pigeonholing into overly general categories.

For Wall Street, Stitch Fix’s push for new revenue sources can’t come soon enough. As of mid-October, its shares were down 30% from their 2019 high, owing both to the rising cost of attracting and retaining customers and to rivals’ copying its personalized approach to e-commerce.

The ongoing upheaval in the retail industry makes Stitch Fix’s latest push that much more challenging.

One crucial test for the company is Shop Your Looks, a feature that suggests additional items to customers to complement what Stitch Fix sends them in their fixes. For example, clients who keep a jacket sent to them may later receive a suggestion via email that they buy a pair of sunglasses to go with it.

The hope is that Shop Your Looks will prompt an impulse buy between boxes and get customers to visit Stitch Fix more often. Lake recognizes the risk of making Stitch Fix just another online retailer that bombards shoppers with an exhausting list of “suggested items.”

That means the e-tailer shows at most 30 to 40 suggested items online—a lot of choice, but not the endless scroll shoppers see in Amazon’s or eBay’s search results. So far, 60% of people who have bought an item using this feature have bought more than one.

In terms of its business, Stitch Fix is getting mixed results. In the 12 months ended Aug. 3, its revenue soared 29% to $1.58 billion compared with the preceding year. During that period, the company had a profit of $36.9 million. But that profit was down 18% from the previous year, as Stitch Fix spent heavily to build out new services.

Stitch Fix must show nervous investors that it can continue to attract new customers and sell more to existing ones, all while grappling with the apparel industry’s rampant discounting of overstocked clothing.

As KeyBanc Capital Markets analyst Ed Yruma points out, Stitch Fix also faces “l(fā)ong-term concerns” related to Amazon. The self-proclaimed Everything Store’s overall apparel business is growing rapidly, and in July it debuted its own personal shopping service—putting Stitch Fix directly in its crosshairs.

As if that’s not enough, Stitch Fix has a serious rival in Nordstrom’s Trunk Club, a slightly higher-end bespoke shopping service. Meanwhile, Instagram and Pinterest have both made their services friendlier to online retailers, adding a new wrinkle to what is already a complex retail environment.

That means Stitch Fix must keep improving the accuracy of its technology. An army of some 3,000 human stylists uses what the algorithm spits out to help decide what to include in customer fixes.

Style Shuffle, a feature added last year that shows customers prospective products one at a time and lets them vote on each, is part of the company’s effort to improve its data crunching. The information collected through the tool—some 3 billion ratings have been submitted—helps make customer suggestions more accurate.

Meanwhile, Stitch Fix is also working on expanding its clothing selection, which is heavy on smaller brands. Big-name clothing makers have gradually come on board, including New Balance and Madewell. Part of the pitch is that Stitch Fix can share data with them about what customers like. That kind of information also helps Stitch Fix more accurately predict demand for its own clothing brands, an increasingly crucial part of its business.

For her part, Lake doesn’t see any choice but to focus on data.

“If somebody is not receiving things that they love, they’re going to stop [using Stitch Fix],” she says. “We live and die by our ability to personalize for people. That is our lifeblood.”?

我的私人購(gòu)物顧問

衣柜“鬧饑荒”,，但是不知道該買什么,？Stitch Fix的CEO告訴我們，可以讓計(jì)算機(jī)算法和真人造型師來(lái)幫你決定,。

“你想要一條破洞牛仔褲嗎,？”Stitch Fix公司的CEO卡特里娜·萊克問道。她的鼠標(biāo)光標(biāo)此刻正停在一張褪色的藍(lán)色牛仔褲的圖片上,。

我從來(lái)沒買過(guò)破洞的牛仔褲,，所以我不知道該怎么回答她。好在萊克的MacBook電腦上有一個(gè)軟件,，它已經(jīng)代表我做出了一個(gè)有根據(jù)的猜測(cè)——我有74%的可能性會(huì)喜歡這條褲子,。于是我告訴她：“好的”。這位CEO用鼠標(biāo)點(diǎn)了一下圖片，把這條褲子添加到了我的購(gòu)物箱里（也就是Stitch Fix定期寄送給用戶的個(gè)性化包裹）,。然后我們又接著看起了外套,。

“哇，這一件很適合舊金山的天氣,?！彼钢患谏膴A克說(shuō)。很顯然,，我應(yīng)該買一件掛在衣柜里——根據(jù)Stitch Fix的軟件,，我有62%的幾率會(huì)買它。

Stitch Fix為用戶挑選衣服不僅靠算法,，也靠藝術(shù),。該公司的造型師們?cè)跒橛脩籼粢路r(shí)也是有發(fā)言權(quán)的。今天,，萊克就讓我看到了這個(gè)過(guò)程的幕后環(huán)節(jié),，并且用我的Stitch Fix個(gè)人檔案，為我現(xiàn)場(chǎng)搭配了一個(gè)“箱子”,。

My Own Personal Shopper

Stitch Fix’s CEO shows what it’s like to let algorithms and human stylists choose your wardrobe. By Michal Lev-Ram

“Do you want a ripped denim?” asks Katrina Lake, CEO of online styling service Stitch Fix, her computer cursor hovering over an image of faded blue jeans.

I’ve never actually owned a pair of pants with premade holes, so I’m unsure how to answer. Lucky for me, the software Lake is running on her MacBook has already spit out an educated guess on my behalf: There’s a 74% chance that I’ll like this particular garment. I tell her yes, and the CEO clicks on the image, adding it to my “fix” (the personalized box of five items Stitch Fix sends to its clients). We move on to outerwear.

“Ooh, this one is good for San Francisco weather,” she says, pointing to a black jacket. Apparently, it belongs in my closet—?I have a 62% chance of keeping it, according to Stitch Fix’s software.

It’s not just algorithms that pick clothes for customers at Stitch Fix; it’s also art. The company’s human stylists have a say when creating fixes for customers. Today, Lake is giving me a behind-the-scenes look at the process—and using my real-life Stitch Fix profile to put together a real-life fix for me.

它的工作原理是這樣的：在每次推薦之前，Stitch fix都會(huì)將一名用戶與一名造型師進(jìn)行配對(duì),，在這個(gè)過(guò)程中,，它會(huì)考慮到地理位置和時(shí)尚偏好等變量（我們可以跳過(guò)這部分了，因?yàn)樵谶@次演示中,，萊克親自擔(dān)任了我的造型師）,。然后，選中的造型師會(huì)進(jìn)入用戶的個(gè)人賬戶,，對(duì)系統(tǒng)算法認(rèn)為符合客戶品味的預(yù)選衣物進(jìn)行評(píng)估,。

在這個(gè)過(guò)程中，系統(tǒng)會(huì)對(duì)大量數(shù)據(jù)進(jìn)行分析,，包括用戶的個(gè)人檔案（比如我已經(jīng)告訴Stitch Fix,，不要給我發(fā)送帶有動(dòng)物圖案的衣服）、購(gòu)買歷史（我可能口頭上說(shuō)自己喜歡大膽一點(diǎn)的顏色,，但實(shí)際上買得最多的還是黑色的）等等,。設(shè)計(jì)師對(duì)最終的選擇仍然有發(fā)言權(quán)，并且可以推翻系統(tǒng)的建議,。

萊克表示：“這些有助于設(shè)計(jì)師在深思熟慮后做出正確的選擇,。”她還表示,，如果顧客明確要求,，造型師也可以給用戶發(fā)送一件低評(píng)分的商品,。

我也親自看到了這種情況的發(fā)生。我讓萊克給我找?guī)纂p靴子,。她點(diǎn)擊進(jìn)入了這個(gè)類別，但系統(tǒng)顯示,，即便是評(píng)分最高的靴子,，被我喜歡的幾率也只有4%。萊克說(shuō)道：“我們已經(jīng)給你寄了11雙鞋了,，但你只留下了兩雙,。”（于是我們決定跳過(guò)靴子的部分,。）

幾天后,，一個(gè)“箱子”被快遞員送到了我家門口，里面還有這位CEO的一封信,?！斑@只是為了好玩——這里都是我們根據(jù)預(yù)測(cè)，認(rèn)為你會(huì)喜歡的東西,?！笔聦?shí)證明，萊克的眼光和她的公司的算法確實(shí)厲害——我留下的三件衣服,，恰好是系統(tǒng)認(rèn)為我最有可能留下的那三件,。另外，沒錯(cuò),，我現(xiàn)在超喜歡破洞牛仔褲的,。（財(cái)富中文網(wǎng)）

本文另一版本登載于《財(cái)富》雜志2019年11月刊，標(biāo)題為《Stitch Fix利用算法向你推薦穿搭》,。

譯者：樸成奎

Here’s how it works: Before a fix is started, Stitch Fix’s technology pairs a customer with a stylist, taking into account variables like location and fashion preferences (we’ve skipped that step because Lake has been designated as my stylist for this demo). Then the selected stylist accesses the client’s account to review a preselected assortment of clothes that the system’s algorithm has deemed to be in line with that shopper’s taste.

A lot of data feeds into this computerized curation, including a customer’s profile (I’ve told Stitch Fix not to send me “critter” prints, for example) and purchase history (I may say that I want bold colors but tend to keep black tops). The stylist still has say over the final selection and can override the system’s suggestions.

“It helps the stylist thoughtfully make the right choices,” Lake says of the technology, adding that stylists can send shoppers an item with a low score if the shopper specifically asks for it.

I see this play out in real time when I ask Lake to find me some boots. When she clicks into the category, though, the highest-ranked boots are listed as having only a 4% likelihood of ending up in my closet. “We’ve sent you 11 pairs of shoes, and you’ve only kept two,” Lake says. (We decide to skip the boots.)

A few days later, my fix arrived on my doorstep, along with a note from the CEO. “Just for fun, here are our predictions on what you’ll like!” wrote Lake, noting for each item the statistical probability that I will. As it turned out, the combination of Lake’s eye and her company’s algorithms was a winner: The three garments I kept all happened to have the highest likelihood of my keeping them—and, yes, I’m now the proud owner of ripped denim.

A version of this article appears in the November 2019 issue of Fortune with the headline “Stitch Fix Thinks Outside the Box.”