在“走进电子商务大数据”中,我们对数据增长及其对电子商务商家带来的影响进行了基本介绍。本文将更详细地解释大数据并展示其在电子商务网站上最常见的应用。
对于大数据的定义有许多种，而我最喜欢的定义是：“一种难以用传统数据库和软件进行处理和分析的数据。”
大数据的4V’S
4V’S表示大数据面临的4个挑战即：数据量，速度，类型和价值。
数据量的挑战源于大多数企业产生的数据远远超过了其系统预设的处理能力。
速度的挑战来自于公司的数据分析或者数据储存运行速度比其数据产生速度慢。这可能由于顾客点击网站或者每秒产生成千上万笔的销售交易。
数据类型的挑战来自于需要处理不同类型的数据以得到所需的观点。比如，这可能包括同时分析来自社交网络，数据库和在同一时间的客户服务呼叫记录的数据。
价值的挑战来自于从数据中提取出有价值的观点，这是所有V中最重要的一个挑战。一个公司通常可以收集所有的数据，但是提出正确的问题以从数据中获得价值则是一个巨大的挑战。
大数据在在线零售商的六个应用
大多数小企业认为，大数据分析只能应用于规模较大的公司。而事实上，在小企业与大企业的竞争中数据分析的作用举足轻重。尤其是在线零售商与他们的客户进行实时互动时，大数据分析显的尤为重要。但需要注意的是，对于大数据集的处理可能会增加网站的加载时间，而网站加载速度变缓则会损害购物过程的每一个环节。
大数据在在线零售商的6个应用：
个性化。消费者在同一个零售商那里的购物方式是不同的。这些来自于多触点的数据应该进行实时处理和分析，以提供给购物者个性化的购物体验，包括购物内容和促销活动。例如，不能把忠实顾客和新顾客同等对待。对于忠实顾客，购物体验应该个性化,以奖励他们的忠诚。但是，购物体验应该看起来具有吸引力,从而能够吸引新的顾客。
动态定价。如果产品在多个网站上存在价格竞争，就应该采用动态定价。这需要从多个渠道收集数据，例如竞争对手的定价，产品销售，区域偏好和顾客行动以确定合适的价格完成销售。像亚马逊这样的大企业已经支持此功能。克服这一挑战将会为企业带来巨大的竞争优势。
客户服务。出色的客户服务是一个电子商务网站成功的关键。Zappos和Netflix是具备卓越的客户服务的典型。但是大数据给客户服务带来了挑战，因为大数据需要收集每一次与顾客进行接触的数据以用来服务此顾客。如果想要保持优质的客户服务，零售商则需要克服这一挑战。例如，如果客户通过在线商店的联系表格向商家表达自己的不满，并持续关注Tweet，在商家提前知道这些信息的情况下,顾客呼叫客户服务，对商家来说并非坏事。由于他们有所准备，顾客的问题可以得到更快的解决，使得顾客感到被重视。
欺诈行为管理。更大的数据集有助于提高欺诈侦测。但是，它需要适当的基础设施，以实时检测欺诈行为。这将营造一个更安全的环境来运行业务和提高盈利能力。大多数在线零售商需要处理他们的销售交易以应对欺诈模式。如果检测不能实时完成，可能无法及时识破骗子的诡计。
供应链的可视性。顾客希望获得他们订单确切的情况，状态以及位置信息。但如果有多个第三方参与到供应链中，零售商想要获得这些信息就会变的复杂。但是，如果想要保持顾客的满意，零售商就需要克服这一挑战。如果一个顾客购买了延期交货的产品，他就希望获知产品的状态。这就需要企业的业务，仓储，运输等功能部门与供应链中的任何第三方，相互之间进行沟通。实现这项功能的最好办法是进行逐步的微小改进。
预测分析。无论何种规模的分析对于在线零售商来说都是重要的。如果没有分析就很难维持业务。大数据帮助企业未卜先知，提前预料到将要发生的事情。这就是所谓的预测分析，现在已经成为了许多企业的重要工具。这方面一个很好的例子是预测某个产品下一季的收益。知道这点后，商家可以更好的管理他们的库存成本并避免产品脱销。
6 Uses of Big Data for Online Retailers
In “Understanding Big Data for Ecommerce,” we provided a primer on the growth of data and its implications for ecommerce merchants. This article will add to that post by explaining Big Data in more detail and presenting its most common uses for ecommerce sites.
There are many definitions of Big Data. My favorite is: “Data that is difficult to process and analyze using traditional database and software techniques.”
The 4 V’s of Big Data
The challenges associated with Big Data are the “4 V’s”: Volume, Velocity, Variety, and Value.
The Volume challenge exists because most businesses generate much more data than what their systems were designed to handle.
The Velocity challenge exists if a company’s data analysis or data storage runs slower than its data generation. This could be because of customer clicks on your website or thousands of sales transactions every second — a good problem to have.
The Variety challenge exists because of the need to process different types of data to produce the desired insights. This could include, for example, analyzing data from social networks, databases and customer service call records at the same time.
The Value challenge applies to deriving valuable insights from data, which is the most important of all V’s in my view. A company can usually collect all the data but the challenge is to ask the right questions to get value from it.
6 Uses of Big Data for Online Retailers
Most small merchants think that Big Data analysis is for larger companies. In fact, it is important for small businesses, too, as they attempt to compete with the larger ones. This becomes even more important as online retailers interact with their customers in real time. Note, however, that handling large sets of data can increase a site’s load time. A slow site harms every aspect of the shopping process.
Here are six uses of Big Data for online retailers.
Personalization. Consumers shop with the same retailer in different ways. Data from these multiple touch points should be processed in real-time to offer the shopper a personalized experience, including content and promotions.
For example, do not treat loyal customers the same as new ones. The experience needs to be personalized to reward loyal customers. It should look attractive and “sticky” for new customers.
Dynamic pricing. You need dynamic pricing if your products compete on price with other sites. This requires taking data from multiple sources, such as competitor pricing, product sales, regional preferences, and customer actions to determine the right price to close the sale. Large merchants like Amazon already support this functionality. Overcoming this challenge will give your business a huge competitive advantage.
Customer service. Excellent customer service is critical to the success of an ecommerce site. Zappos and Netflix are examples of terrific customer service. But Big Data has made customer service a challenge by requiring seemingly every interaction with a shopper to be used for serving that shopper. To continue to excel at customer service, online retailers need to overcome this challenge.
For example, if a customer has complained via the contact form on your online store and also tweeted about it, it will be good to have this background when he calls customer service. This will result in the customer feeling valued, creating a quicker resolution.
Managing fraud. Larger data sets help increase fraud detection. But it requires the right infrastructure, to detect fraud in real-time. This will lead to a safer environment to run your business and improved profitability.
Most online retailers need to process their sales transactions against defined fraud patterns, for detection. If it’s not done in near real-time, it could be too late to catch the fraudsters.
Supply chain visibility. Customers expect to know the exact availability, status, and location of their orders. This can get complicated for retailers if multiple third parties are involved in the supply chain. But, it is a challenge that needs to be overcome to keep customers happy.
A customer who has purchased a backordered product would want to know the status. This will require your commerce, warehousing, and transportation functions to communicate with each other and with any third-party systems in your supply chain. This functionality is best implemented by making small changes gradually.
Predictive analytics. Analytics is crucial for all online retails, regardless of size. Without analytics it is difficult to sustain your business. Big Data has helped businesses identify events before they occur. This is called “predictive analytics.” Predictive analytics is becoming an important tool for many businesses.
A good example of this is predicting the revenue from a certain product in the next quarter. Knowing this, a merchant can better manage its inventory costs and avoid key out-of-stock products.
原文发布时间为：2014-08-11
本文来自云栖社区合作伙伴“大数据文摘”，了解相关信息可以关注“BigDataDigest”微信公众号
