本季时尚界流行什么？大数据

    Editd公司每天和每周分别都会发布反映特定市场类别的新品和打折商品情况的零售报告。它的分析工具则致力于帮助业内人士追踪竞争情况，改进自己的产品规划。Editd还有一个虚拟的销售规划档案工具，可以帮你制定下一季的促销战略。

    使用Editd的最大好处之一，就是业内人士们不必再去“竞争性购物”（即调查竞争对手）了。比如Editd公司就有一个非常重视数据的客户，该公司的整支采购和销售团队每过六个星期就要专门抽出一周时间，到竞争对手的网站上搜集信息，比如他们有多少款紧身牛仔裤，每款定价多少钱等等。

    法勒表示：“他们要把这些数据汇总到Excel表格里，然后做成小册子在公司里散发。这就是他们接下来六个星期里的‘销售兵法’。”

    瓦茨表示，这种方法不仅非常耗时，而且“充满了危险，很多错误都可能发生。”在一些情况下，有些项目可能被重复计算，还有些时候，一些不同的数据收集方法可能被混用。

    在时尚业这样一个边界比较模糊的产业里，光是给产品分类就是一个不小的挑战。比如裤子就有长裤、七分裤、短裤等许多种类。瓦茨表示：“我们分析产品种类的方法也非常重要。我们使用了计算机视觉和自然语言处理程序给服装分类，比如‘这是一件印花连衣裙’或‘这是一件羊毛开衫’等等。对于我们的工作来说，统一分类标准，生成一个干净、一致的数据库是一个极为重要的部分。”

    法勒表示，Editd的用户现在只需要输入“羊毛开衫”几个字进行查询，不到一秒钟便可以获取结果。她还补充道，Editd的系统可以追踪到5000多万个SKU（注：SKU即‘库存最小单位’。对于服装业来说，某一款服装的某一个颜色的某一个尺码，即是一个SKU。）

    Editd的用户之一英国在线零售商Asos声称，使用了Editd的服务后，其2013年第四季度的销售额跃升了33%。这家公司尤其注重产品定价环节的改善，已经给予200多名员工进入Editd系统的权限。

    瓦茨表示：“这项技术以及它给行业带来的变革，使客户能够获得他们真正想要的东西，而不是由别人决定给他们什么东西。它使客户可以更加动态地掌控他们的时尚格调，也使市场更加高效、绿色。”

    100万个产品，1100万个SKU

    Editd并不是唯一一家试水大数据的时尚公司。英国时尚预测机构WGSN也想在这个市场上分一杯羹。WGSN去年刚刚推出了它的首个大数据服务Instock。

    WGSN称，它的数据库每天都从全球10000多个在线品牌和零售商那里搜集100多万个产品和1100多万个SKU数据。Instock本质上是一项零售分析服务，它恪守着同一种产品分类方法，旨在补充该公司被广泛使用的时尚趋势预测服务。

    该公司负责Instock业务的全球常务董事海伦•斯拉文表示：“我们对一件T恤、一条裙子或一件和服进行分类，并且将这种分类与它在WGSN Instock上的分类展示结合起来。”换句话说，它是一种统一的、端对端的分类方法。斯拉文指出，鉴于不同的公司对同一条产品线的命名可能存在差异，通过统一不同的命名口径，业内人士可以据此做出更有效的决策。

    目前已经有6000多个客户在使用WGSN的趋势服务。最新推出的Instock服务也已经在9个国家拥有了50名全球客户。除了女装、鞋类和配饰之外，WGSN还计划在本季继续补充童装和男装数据。另外，该公司还计划推出一项名叫Analysis+的服务，用于向用户提供定制数据和附加分析功能。

    斯拉文表示：“对于大数据和零售业来说，现在真是个非常令人兴奋的时代。通过提供大量更加有可操作性的见解，大数据正在彻底改变零售商对业务流程的看法。”

    Editd公司的瓦茨也认同这一点。“我们帮助零售商在正确的时间，以正确的价格，提供正确的产品。这在零售业可以说是惊天动地的事情。如果你做对了，它会为你带来一大笔财富。”（财富中文网）

    译者：朴成奎

    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.”