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2014 年 2 月 11 日
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Modern Retail Architectures Built with Hadoop

作者:
Justin Sears

This is the fourth in our series on modern data architectures across industry verticals. Others in the series are:

We’ve probably all heard the famous quote by John Wanamaker, the father of modern advertising: “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

Wanamaker would love Apache Hadoop for retail applications, because it diminishes (or eliminates) the dilemma he described.

在 Hadoop 与现代零售运营集成后,可大幅降低获取、汲取、存储和分析数据的成本。

现在 Wanamaker 的崇拜者可分析足够的数据来对实证零售数据进行统计性的可靠观察,而不是通过客户群体、店内调查或重点人群来无端推测什么因素可推动销量提升。

以下参考架构图展示了我们了解的客户所采用的方法组合,无论他们销售的是汽车、梯子、衬衫还是鞋子。

零售 2

With their Hadoop modern data architectures, retail companies of all sorts can execute use cases like the five following. These are five of the most common ways that retailers do Hadoop.

构建客户的全方位概览

零售商跨多个渠道与客户交互,而客户交互和购买数据却通常位于各个数据孤岛中。很少有零售商能够准确地将最终的客户购买行为与营销活动及在线浏览行为关联。

Apache Hadoop gives retailers a 360° view of customer behavior. It lets them store data longer, join it with other data sets, and identify phases of the customer lifecycle. Better customer analytics helps to increase sales, reduce inventory expenses and retain the best customers.

分析品牌情绪

企业缺乏可靠的方式来跟踪其品牌健康状况。它们难以分析如何进行广告宣传、竞争对手的动向、产品发布或影响品牌的新案例。内部品牌研究显得缓慢、成本高昂并且存在缺陷。

Apache Hadoop enables quick, unbiased snapshots of brand opinions expressed in social media. Retailers can analyze sentiment in real-time from Twitter, Facebook, LinkedIn or industry-specific social media streams. With better understanding of customer perceptions, they can align their communications, products and promotions.

促销本地化和个性化

可在地理上确定其移动订户的零售商可提供本地化和个性化的促销。这需要同时与历史和实时流媒体数据关联。

Apache Hadoop 将数据聚合到一起,以较低成本将提供给移动设备的促销本地化和个性化。零售商可开发移动应用将符合客户偏好和地理位置的本地活动和销售通知客户(甚至可细致到特定商店的特定部分)。

在 2013 年假日购物季节,Macy’s 使用 Apple 的 iBeacons 技术在两个旗舰店推出了测试。该文章描述了具体情况:“一段时间之后,Macy’s 也可以逐个部门的方式提示购物者,可能在客户处于鞋类部分时告知他们在售的运动鞋,甚至可推荐附近的产品。”

优化网站

Online shoppers leave billions of clickstream data trails. Clickstream data can tell web retailers the web pages customers visit and what they buy (or what they don’t buy). But at scale, the huge volume of unstructured weblogs is difficult to ingest, store, refine and analyze for insight. Relational databases are not suited to store this clickstream data.

Apache Hadoop can store all web logs, for years, at a low cost. Web retailers use information in that data to understand user paths, do basket analysis, run A/B tests and prioritize site updates. This improves online conversion and revenue.

Redesign Store Layouts

In-store layout and product placement affect sales. Retailers often hire extraneous staff to make up for a sub-optimal store layout (e.g. “Are you finding what you need?”). Brick-and-mortar stores lack “pre-cash register” data about what in-store shoppers do before they buy.

In-store sensors, RFID tags & QR codes can fill that data gap, but they generate a lot of data. Apache Hadoop can store that huge volume of unstructured sensor and location data. Once analyzed, the resulting intelligence allows retailers to reduce costs and simultaneously improve customer in-store satisfaction. This improves same-store sales and customer loyalty.

Watch our blog in the coming weeks for reference architectures in other industry verticals.

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