????Editd公司每天和每周分別都會發布反映特定市場類別的新品和打折商品情況的零售報告。它的分析工具則致力于幫助業內人士追蹤競爭情況，改進自己的產品規劃。Editd還有一個虛擬的銷售規劃檔案工具，可以幫你制定下一季的促銷戰略。

????使用Editd的最大好處之一，就是業內人士們不必再去“競爭性購物”（即調查競爭對手）了。比如Editd公司就有一個非常重視數據的客戶，該公司的整支采購和銷售團隊每過六個星期就要專門抽出一周時間，到競爭對手的網站上搜集信息，比如他們有多少款緊身牛仔褲，每款定價多少錢等等。

????法勒表示：“他們要把這些數據匯總到Excel表格里，然后做成小冊子在公司里散發。這就是他們接下來六個星期里的‘銷售兵法’。”

????瓦茨表示，這種方法不僅非常耗時，而且“充滿了危險，很多錯誤都可能發生。”在一些情況下，有些項目可能被重復計算，還有些時候，一些不同的數據收集方法可能被混用。

????在時尚業這樣一個邊界比較模糊的產業里，光是給產品分類就是一個不小的挑戰。比如褲子就有長褲、七分褲、短褲等許多種類。瓦茨表示：“我們分析產品種類的方法也非常重要。我們使用了計算機視覺和自然語言處理程序給服裝分類，比如‘這是一件印花連衣裙’或‘這是一件羊毛開衫’等等。對于我們的工作來說，統一分類標準，生成一個干凈、一致的數據庫是一個極為重要的部分。”

????法勒表示，Editd的用戶現在只需要輸入“羊毛開衫”幾個字進行查詢，不到一秒鐘便可以獲取結果。她還補充道，Editd的系統可以追蹤到5000多萬個SKU（注：SKU即‘庫存最小單位’。對于服裝業來說，某一款服裝的某一個顏色的某一個尺碼，即是一個SKU。）

????Editd的用戶之一英國在線零售商Asos聲稱，使用了Editd的服務后，其2013年第四季度的銷售額躍升了33%。這家公司尤其注重產品定價環節的改善，已經給予200多名員工進入Editd系統的權限。

????瓦茨表示：“這項技術以及它給行業帶來的變革，使客戶能夠獲得他們真正想要的東西，而不是由別人決定給他們什么東西。它使客戶可以更加動態地掌控他們的時尚格調，也使市場更加高效、綠色。”

????100萬個產品，1100萬個SKU

????Editd并不是唯一一家試水大數據的時尚公司。英國時尚預測機構WGSN也想在這個市場上分一杯羹。WGSN去年剛剛推出了它的首個大數據服務Instock。

????WGSN稱，它的數據庫每天都從全球10000多個在線品牌和零售商那里搜集100多萬個產品和1100多萬個SKU數據。Instock本質上是一項零售分析服務，它恪守著同一種產品分類方法，旨在補充該公司被廣泛使用的時尚趨勢預測服務。

????該公司負責Instock業務的全球常務董事海倫?斯拉文表示：“我們對一件T恤、一條裙子或一件和服進行分類，并且將這種分類與它在WGSN Instock上的分類展示結合起來。”換句話說，它是一種統一的、端對端的分類方法。斯拉文指出，鑒于不同的公司對同一條產品線的命名可能存在差異，通過統一不同的命名口徑，業內人士可以據此做出更有效的決策。

????目前已經有6000多個客戶在使用WGSN的趨勢服務。最新推出的Instock服務也已經在9個國家擁有了50名全球客戶。除了女裝、鞋類和配飾之外，WGSN還計劃在本季繼續補充童裝和男裝數據。另外，該公司還計劃推出一項名叫Analysis+的服務，用于向用戶提供定制數據和附加分析功能。

????斯拉文表示：“對于大數據和零售業來說，現在真是個非常令人興奮的時代。通過提供大量更加有可操作性的見解，大數據正在徹底改變零售商對業務流程的看法。”

????Editd公司的瓦茨也認同這一點。“我們幫助零售商在正確的時間，以正確的價格，提供正確的產品。這在零售業可以說是驚天動地的事情。如果你做對了，它會為你帶來一大筆財富。”（財富中文網）

????譯者：樸成奎

????Editd issues daily and weekly retail reports to highlight new and discounted products in chosen market categories. Its analytics tools are intended to help industry professionals track the competition and refine their own product planning. A visual merchandising archive helps shape promotion strategies for upcoming seasons.

????One of the biggest benefits of using Editd is that industry professionals no longer need to “comp shop,” short for competitive shopping, to research the competition. At one of Editd’s more data-driven customers, the entire buying and merchandising team used to stop work for one week every six to spend the time visiting competitors’ websites for information —how many types of skinny jeans are on offer, for example, and how they were priced.

????“They’d put together the reports in Excel, then the booklets were bound and distributed around the company,” Fowler says. “That was their playbook for the next six weeks.”

????Not only was the process time-consuming, but it was “fraught with danger,” Watts says. “So many errors creep into things.” In some cases, items might get double-counted. In others, different data collection methodologies might be used.

????In a boundary-blurring business like fashion, categorizing products across retailers is another challenge. Pants, capris, or shorts—or something else entirely? “The way we analyze the kinds of products and the categories of products is very important,” Watts says. “We use computer vision and natural language processing to understand, for example, ‘This is a floral dress’ or ‘This is a cardigan.’ Unifying that and making it one consistent, clean data set is an incredibly important part of what we do.”

????Today, an Editd user can simply run a query on cardigans, for example, and receive results in under a second, Fowler says. More than 50 million SKUs are tracked by the system, she adds.

????One Editd customer, the British online retailer Asos, credits the company’s services for the 33% jump in sales it saw in the last quarter of 2013. The company gave 200 of its employees access to the Editd system with a particular focus on improving the pricing of its goods.

????“What this technology and the changes to the industry are unlocking is the ability for customers to have exactly what they want and not necessarily what’s been decided for them,” Watts says. “It lets consumers be more fluid with their tastes and it lets the market be more efficient and more green.”

????A million products, 11 million SKUs

????Editd isn’t the only fashion-focused company dipping its toes in the big-data waters. Vying for a share of the market is the British trend forecaster WGSN, which just last year launched its own first big-data offering, Instock.

????WGSN claims its dataset has more than a million products and 11 million SKUs each day from more than 10,000 global online brands and retailers. Instock, essentially a retail analytics service, is intended to complement its widely used trend-forecasting service by adhering to the same product-categorization taxonomy.

????“We link the taxonomy from the trend side in terms of how we categorize a specific shirt or dress or kimono and how we track it coming through and being presented in WGSN Instock,” explains Helen Slaven, global managing director for Instock. It’s a single, end-to-end taxonomy, in other words. By unifying the many ways in which different companies might interpret the same product line, industry professionals can make more effective decisions, she says.

????More than 6,000 customers use WGSN’s trend service today. The newer Instock service counts almost 50 global clients in nine countries. This season, WGSN plans to complement its existing data on womenswear, footwear, and accessories with information on kids’ apparel and menswear. A new service called Analysis+ will offer custom cuts of the data and the option of additional analysis.

????“It’s a really exciting time for big data and retail,” Slaven says. “By providing a lot more actionable insight, it’s completely changing the way retailers think about their process.”

????Watts, of Editd, agrees. “We help retailers have the right product at the right price and the right time,” he says. “That’s the kingmaking thing in retail. When you get that right, it unlocks a fortune.”