只用了8年時間，在線零售商Stitch Fix就將業務做得風生水起。每年有超過320萬名消費者通過它的服務購買牛仔褲、羊毛衫和手鏈等服裝和飾品。

與傳統網購平臺不同的是，Stitch Fix的訂閱用戶會通過快遞收到成箱的服裝和飾品，收件的頻率多高都可以。在注冊的時候，用戶需要回答一長串的問題，比如他們喜歡的著裝風格和體型等等。然后Stitch Fix的計算機算法和造型師們會根據這些信息，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。

現在，Stitch Fix的CEO卡特里娜·萊克正在為公司下一階段的發展奠定基礎。她想利用Stitch Fix強大的數據分析能力，更準確地預測消費者想要購買和保留哪些商品，以創造更多的業務。

萊克對《財富》雜志表示：“我們正在研究如何通過個性化推薦，為你的衣櫥貢獻更多合適的衣服，讓你可以在各種不同的場合穿著。”

StitchFix會對每名用戶的數據進行分析，然后生成一份個人檔案，然后它會利用可視化手段，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，所以它對每個用戶的推薦都是極其細致的，而不是籠統地分成幾個大類。

在華爾街看來，Stitch Fix創造新的收入來源的速度還不夠快。截至10月中旬，該公司的股價已經較2019年的最高點下跌了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爭對手復制了它高度個性化的電商模式。

零售業目前正在發生的劇變，也給帶來了很多挑戰。

最近，Stitch Fix公司推出了一項叫做“Shop Your Looks”（意為“選購你的造型”）的新功能。這也是該公司做的一項重要的試驗。它會在已經寄送給用戶的推薦商品的基礎上，再向用戶推薦一些用來搭配的單品。比如用戶在收到一件夾克衫后，可能會馬上又收到一封電子郵件，建議他們再買一副太陽鏡，專門來搭配這件夾克。

該公司希望Shop Your Looks能夠扮演一個“穿搭小能手”的角色，繼續勾起用戶的購買欲，并且提高他們訪問Stitch Fix的頻率。不過萊克也表示，她也意識到這個功能是有風險的，大家很可能會覺得Stitch Fix只不過是另一個用“推薦商品”向消費者狂轟亂炸的普通網購平臺罷了。

在這種模式下，消費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，但是絕對不會像亞馬遜或eBay那樣顯示出無窮無盡的搜索結果。到目前為止，在使用過該功能購買商品的人中，有60%購買了不止一件。

從業務成績上看，Stitch Fix可以說是喜憂參半。在截至今年8月3日的12個月間，它的營收入較上年同期飆升29%，達到15.8億美元，實現利潤3690萬美元。不過它的利潤卻較上年同期下降了18%，這與Stitch Fix在打造新服務上投入重資不無關系。

Stitch Fix必須向那些緊張的投資者證明，它有能力繼續吸引新用戶，同時向現有用戶賣出更多的商品。與此同時，它還要面臨同業者們對庫存服裝瘋狂地打折銷售帶來的壓力。

KeyBanc Capital Markets公司的分析師艾德·伊魯瑪指出，來自亞馬遜的壓力，也是Stitch Fix面臨的一個“長期隱患”。亞馬遜自稱是個“能買一切”的網購平臺，它的服裝業務整體上增長很快，今年6月，亞馬遜還推出了自家的個性化購物服務，使Stitch Fix直接成了它瞄準的靶子。

除此之外，Stitch Fix還有一個勁敵——諾德斯特龍（Nordstrom）的Trunk Club，這也是一個偏高端的定制購物服務。與此同時，Instagram和Pinterest等社交媒體服務也對各大電商平臺越來越友好了，這些都讓本已十分復雜的在線零售業增添了新的變數。

這意味著Stitch Fix必須不斷提高其技術的準確度。Stitch Fix擁有一支約3000名真人造型師組成的團隊，他們會根據計算機算法的分析結果，決定應該往寄給用戶的包裹里放入哪些衣服。

為了優化公司的數據分析能力，Stitch Fix去年還推出了一項名為Style Shuffle的新服務，它每次會向用戶展示一款有可能上架的新品，然后讓用戶進行投票。通過該工具收集的信息，有助于Stitch Fix更準確地對用戶進行推薦。到目前為止，該功能已經反饋了大約30億次用戶的評價信息。

與此同時，Stitch Fix也在努力擴大對服裝的選擇。目前，該平臺的服裝主要來自一些小品牌。而現在，一些大牌服裝也已經逐漸登陸了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據，好知道用戶喜歡什么。同時這些信息也有助于Stitch Fix更準確地預測市場對其自營服裝品牌的需求。而自營品牌已經日益成為該公司業務中至關重要的一部分。

在萊克看來，關注數據是她的唯一選擇。

她表示：“如果一個人沒有收到他們喜歡的東西，他們就會不再使用Stitch Fix。能否為人們提供個性化的服務，這對我們來說是一個事關生死存亡的問題，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 “latent 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 “long-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，可以讓計算機算法和真人造型師來幫你決定。

“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。她的鼠標光標此刻正停在一張褪色的藍色牛仔褲的圖片上。

我從來沒買過破洞的牛仔褲，所以我不知道該怎么回答她。好在萊克的MacBook電腦上有一個軟件，它已經代表我做出了一個有根據的猜測——我有74%的可能性會喜歡這條褲子。于是我告訴她：“好的”。這位CEO用鼠標點了一下圖片，把這條褲子添加到了我的購物箱里（也就是Stitch Fix定期寄送給用戶的個性化包裹）。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。”她指著一件黑色的夾克說。很顯然，我應該買一件掛在衣柜里——根據Stitch Fix的軟件，我有62%的幾率會買它。

Stitch Fix為用戶挑選衣服不僅靠算法，也靠藝術。該公司的造型師們在為用戶挑衣服時也是有發言權的。今天，萊克就讓我看到了這個過程的幕后環節，并且用我的Stitch Fix個人檔案，為我現場搭配了一個“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都會將一名用戶與一名造型師進行配對，在這個過程中，它會考慮到地理位置和時尚偏好等變量（我們可以跳過這部分了，因為在這次演示中，萊克親自擔任了我的造型師）。然后，選中的造型師會進入用戶的個人賬戶，對系統算法認為符合客戶品味的預選衣物進行評估。

在這個過程中，系統會對大量數據進行分析，包括用戶的個人檔案（比如我已經告訴Stitch Fix，不要給我發送帶有動物圖案的衣服）、購買歷史（我可能口頭上說自己喜歡大膽一點的顏色，但實際上買得最多的還是黑色的）等等。設計師對最終的選擇仍然有發言權，并且可以推翻系統的建議。

萊克表示：“這些有助于設計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送一件低評分的商品。

我也親自看到了這種情況的發生。我讓萊克給我找幾雙靴子。她點擊進入了這個類別，但系統顯示，即便是評分最高的靴子，被我喜歡的幾率也只有4%。萊克說道：“我們已經給你寄了11雙鞋了，但你只留下了兩雙。”（于是我們決定跳過靴子的部分。）

幾天后，一個“箱子”被快遞員送到了我家門口，里面還有這位CEO的一封信。“這只是為了好玩——這里都是我們根據預測，認為你會喜歡的東西。”事實證明，萊克的眼光和她的公司的算法確實厲害——我留下的三件衣服，恰好是系統認為我最有可能留下的那三件。另外，沒錯，我現在超喜歡破洞牛仔褲的。（財富中文網）

本文另一版本登載于《財富》雜志2019年11月刊，標題為《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.”