ACCA PM知识点：企业如何使用大数据

Big data refers to the large collections of data that may be analysed to reveal patterns,trends and associations,especially relating to human behaviour and interactions.Big data has already been explained in another article(Big data 1:What is big data?).This article will describe some real life examples of the use of big data for performance management and measurement purposes.

Performance management involves managing the organisation in order to ensure that it meets its objectives.Broadly,big data is relevant to performance management in the following ways:

Gaining insights(eg about customers’preferences)which can then be used to improve marketing and sales,thus increasing profits and shareholders’wealth.

Forecasting better(eg customer’s future spending patterns,when machines will need replacing)so that more appropriate decisions can be made.

Automating of high level business processes(eg lawyers scanning documents)which can lead to organisations becoming more efficient.

Providing more detailed and up to date performance measurement.

Examples of companies using big data

Netflix

Netflix began as a DVD mailing service and developed algorithms to help it to predict viewers’preferences and habits.Now it delivers films over the internet and can easily collect information about when movies are watched,how often films might be stopped and restarted,where they might be abandoned,and how users rate films.This allows Netflix to predict which films will be popular with which customers.It is also being used by Netflix to produce its own TV series,with much greater assurance that these will be hits.

Amazon

The world’s leading e-retailer collects huge amounts of information about customers’preferences and habits which allow it to market very accurately to each customer.For example,it routinely makes recommendations to customers based on products previously purchased.

Airlines

Airlines know where you’ve flown,preferred seats,cabin class,when you fly,how often you search for a flight before booking,how susceptible you are to price reductions,probably which airline you might book with instead,whether you are returning with them but didn’t fly out with them,whether car hire was purchased last time,what class of hotel you might book through their site,which routes are growing in popularity,seasonality of routes.They also know the profitability of each customer so that,for example,if a flight is cancelled they can help the most valuable customers first.

This information allows airlines to design new routes and timings,match routes to planes and also to make individualised offers to each potential passenger.

Target

Target is a large discount retailer in the USA.There is an often quoted story about their ability to predict when a customer is pregnant–frequently before the customer has informed her family.By looking at about 25 products it is claimed that they can create a pregnancy predictor.For example,early pregnancy often causes morning sickness so consumers would perhaps change to blander food and less perfumed shower gel.Why would Target be interested in knowing whether a consumer is pregnant?Well that person will require different products during the pregnancy then in a few months the baby will have its own product needs:nappies,baby shampoo and clothes.Early identification of pregnancy can allow Target to establish the shopping habits of the mother and perhaps even the preferences of the child.

Walmart’s Polaris search engine

Walmart is an American retailer that operates in 28 countries around the world.It is the world’s largest company based on revenues.Many of Walmart’s customers buy online through the company’s website.Walmart wanted to make sure that customers can find what they are looking for on its website,so it developed its Polaris search engine.If customers are looking for a particular product,they enter the description in a search box,and the website displays products which meet that description.

What is unusual about Polaris is the way it ranks the search results.It attempts to show the products that the customer is most likely to buy towards the top of the list.The algorithm takes into account many factors,including the number of likes that the product has on social media networks and how many favourable reviews it has.

The system also uses artificial intelligence to learn so that it can continually provide better search results.If a phrase has been entered that the engine did not initially understand,for example,the engine can‘learn’what that phrase meant based on what the customer actually bought.Thus the system was soon able to figure out that when a user entered‘House’into the search box,they were probably looking for merchandise connected with the TV series of that name,not furniture or other items for their house.If someone searches for‘Flats’,the engine has learned that they probably want to buy shoes,not apartments or flat screen TVs.

The metric that is used to measure the success of the website is customer conversion rate–the number of customers that actually buy a product after a search.It is estimated that the Polaris search engine has increased the conversion rate by between 10%and 15%.That is worth billions of dollars in extra revenue.

Beredynamic

Beredynamic is a manufacturer of high quality audio products such as microphones and headphones.The company is based in Germany,but has a wide international sales and distribution network.The company wanted to improve its analysis of sales.Most ad hoc reports required data to be extracted from its legacy systems into a spreadsheet where the reports would then be manually compiled.This was time consuming,leading to delays in producing the reports.The reports themselves were not always accurate either.【点击免费下载>>>更多ACCA学习相关资料】

The company developed a data warehouse that automatically extracts transactions from its existing ERP and financial accounting systems.The structure of this warehouse was carefully designed so that standard information is stored for each transaction such as product codes,country code,customer and region.This is supplemented by a web based reporting solution that enables managers to create their own reports,both standard and ad hoc,based on the data held in the warehouse.

The system allows the company to perform detailed analysis of sales,which helps it to identify trends in different products or markets.This leads to two business advantages.The first is that the sales and distribution strategy can be changed when demand changes in certain markets–for example,when sales of gaming headphones began to increase in Japan,the company introduced promotions for all its gaming products in that country,including a large advertising campaign and introduction of product bundles specially for the Japanese market.The second advantage is that production plans can quickly be changed as demand changes.If demand is falling,production is slowed to ensure that the company is not left with excessive inventory.If demand is expanding,production is increased to take advantage of higher sales.

The ability to provide more detailed analysis quickly can also be used for performance measurement and appraisal,for example,comparing actual sales with targets by region,assessing whether a promotion achieved the expected increase in profits.Such reports can be produced quickly based on real time data,meaning that management can respond quickly to any adverse variances.

The success of the new system is measured in terms of the growth in revenues and profits.While this seems simple,it has to be recognised that some growth would have been expected even if the system had not been implemented,so determining how much revenue growth has resulted from the greater analysis can be difficult.Assumptions need to be made.

Morton’s Steak House

A customer jokingly tweeted US chain Morton’s and requested that dinner be sent to the Newark airport where he was due to arrive late.Morton’s saw the tweet,realised he was a regular customer,pulled up information on what he typically ordered,figured out which flight he was on and then sent a waiter to meet him at the airport and serve him dinner.

Clearly this action was a publicity stunt which the restaurant hoped that their customer would publicise in future tweets.What it demonstrates is how easy it was for Morton’s to identify the customer who sent the tweet,and to ascertain what his favourite meal was.It also shows how companies like to influence social media users who have a large following as a means of increasing their own publicity.

It is difficult to measure the impact of interventions into social media.No doubt the happy customer would have communicated this story,and this may have improved the reputation of the restaurant,but it is very difficult to measure the impact of this on sales.

Conclusion

The cases above have shown how detailed analysis of data can be used in a number of different ways to improve the performance of an organisation.Big data can be used to understand customers and trends better,to provide insights into costs,and to make it easier for customers to find what they want on the website.Companies are likely to continue to identify innovative uses of the increasing volumes of data available to them,and analysis of big data is likely to grow in importance as an important strategic tool for many businesses.

Updated article extracted from articles by Ken Garrett,a freelance lecturer and writer,and Nick Ryan,a lead tutor for performance management subjects

翻译参考

大数据是指可以分析以揭示模式、趋势和关联的大量数据，尤其是与人类行为和交互相关的数据。大数据已经在另一篇文章中解释过（大数据1：什么是大数据？）。本文将描述一些将大数据用于绩效管理和衡量目的的真实案例。

绩效管理涉及管理组织以确保其实现其目标。从广义上讲，大数据通过以下方式与绩效管理相关：

获得洞察力（例如关于客户的偏好），然后可用于改善营销和销售，从而增加利润和股东财富。

更好地预测（例如客户未来的消费模式，何时需要更换机器）以便做出更合适的决策。

高级业务流程的自动化（例如律师扫描文档）可以使组织变得更有效率。

提供更详细和最新的性能测量。

使用大数据的公司示例

网飞

Netflix最初是一家DVD邮寄服务公司，并开发了算法来帮助它预测观众的偏好和习惯。现在，它通过互联网传送电影，并且可以轻松收集有关电影何时被观看、电影可能被停止和重新播放的频率、它们可能被放弃的位置以及用户如何评价电影的信息。这使Netflix能够预测哪些电影会受到哪些客户的欢迎。Netflix也正在使用它来制作自己的电视剧，从而更有把握地确保这些电视剧会成为热门。

亚马逊

这家世界领先的电子零售商收集了大量有关客户偏好和习惯的信息，这使其能够非常准确地向每个客户进行营销。例如，它经常根据之前购买的产品向客户提出建议。

航空公司

航空公司知道您飞往哪里、首选座位、舱位等级、何时飞行、您在预订前搜索航班的频率、您对降价的敏感程度、您可能改为预订哪家航空公司、您是否返回他们但没有和他们一起飞出去，上次是否购买了租车，您可以通过他们的网站预订什么级别的酒店，哪些路线越来越受欢迎，路线的季节性。他们还了解每位客户的盈利能力，因此，例如，如果航班取消，他们可以首先帮助最有价值的客户。

这些信息使航空公司能够设计新的航线和时间，将航线与飞机相匹配，并为每位潜在乘客提供个性化的优惠。

目标

Target是美国的一家大型折扣零售商。有一个经常被引用的故事是关于他们预测客户何时怀孕的能力——通常是在客户通知她的家人之前。通过查看大约25种产品，据称它们可以创建怀孕预测器。例如，早孕通常会导致孕吐，因此消费者可能会改用清淡的食物和香味较少的沐浴露。为什么Target会对了解消费者是否怀孕感兴趣？那么这个人在怀孕期间会需要不同的产品，然后在几个月后婴儿就会有自己的产品需求：尿布、婴儿洗发水和衣服。怀孕的早期识别可以让Target建立母亲的购物习惯，甚至可能是孩子的偏好。

沃尔玛的北极星搜索引擎

沃尔玛是一家美国零售商，在全球28个国家/地区开展业务。按收入计算，它是世界上最大的公司。沃尔玛的许多客户通过公司网站在线购买。沃尔玛希望确保客户可以在其网站上找到他们想要的东西，因此它开发了Polaris搜索引擎。如果客户正在寻找特定产品，他们会在搜索框中输入描述，网站会显示符合该描述的产品。

Polaris的不同寻常之处在于它对搜索结果进行排名的方式。它试图将客户最有可能购买的产品显示在列表的顶部。该算法考虑了许多因素，包括产品在社交媒体网络上的点赞数以及它有多少好评。

该系统还使用人工智能进行学习，以便不断提供更好的搜索结果。例如，如果输入了引擎最初不理解的短语，则引擎可以根据客户实际购买的内容“了解”该短语的含义。因此，系统很快就能够确定，当用户在搜索框中输入“房屋”时，他们可能正在寻找与该名称的电视剧相关的商品，而不是他们房屋的家具或其他物品。如果有人搜索“公寓”，引擎就会了解到他们可能想购买鞋子，而不是公寓或平板电视。

用于衡量网站成功与否的指标是客户转化率——搜索后实际购买产品的客户数量。据估计，Polaris搜索引擎将转化率提高了10%到15%。这价值数十亿美元的额外收入。

贝瑞动力

Beredynamic是麦克风和耳机等高品质音频产品的制造商。该公司总部位于德国，但拥有广泛的国际销售和分销网络。该公司希望改进对销售的分析。大多数临时报告需要将数据从其遗留系统中提取到电子表格中，然后手动编译报告。这很耗时，导致报告的制作出现延误。报告本身也不总是准确的。

该公司开发了一个数据仓库，可以从其现有的ERP和财务会计系统中自动提取交易。该仓库的结构经过精心设计，以便为每笔交易存储标准信息，例如产品代码、国家代码、客户和地区。这是一个基于网络的报告解决方案的补充，该解决方案使管理人员能够根据仓库中保存的数据创建自己的标准和临时报告。

该系统允许公司对销售进行详细分析，这有助于它识别不同产品或市场的趋势。这带来了两个商业优势。首先是当某些市场的需求发生变化时，销售和分销策略可以改变——例如，当游戏耳机在日本的销量开始增加时，该公司在该国推出了所有游戏产品的促销活动，包括大量广告专门针对日本市场的产品组合的活动和介绍。第二个优点是生产计划可以随着需求的变化而迅速改变。如果需求下降，生产就会放缓，以确保公司不会留下过多的库存。如果需求扩大，则增加产量以利用更高的销售额。

快速提供更详细分析的能力还可以用于绩效衡量和评估，例如，将实际销售额与区域目标进行比较，评估促销是否实现了预期的利润增长。此类报告可以根据实时数据快速生成，这意味着管理层可以对任何不利差异做出快速响应。

新系统的成功是通过收入和利润的增长来衡量的。虽然这看起来很简单，但必须认识到，即使没有实施该系统，也会有一些增长，因此确定更深入的分析带来了多少收入增长可能很困难。需要做出假设。

莫顿牛排馆

一位顾客开玩笑地在美国连锁店Morton's上发了推文，要求将晚餐送到他原定迟到的纽瓦克机场。莫顿看到这条推文，意识到他是一个常客，提取了他通常订购的东西的信息，找出他乘坐的航班，然后派一名服务员在机场迎接他并为他提供晚餐。

显然，这一行动是一个宣传噱头，餐厅希望他们的顾客在未来的推文中宣传。它展示了Morton's能够轻松识别发送推文的客户，并确定他最喜欢的食物是什么。它还显示了公司如何影响拥有大量追随者的社交媒体用户，以此作为增加自身宣传的手段。

很难衡量干预对社交媒体的影响。毫无疑问，快乐的顾客会传达这个故事，这可能提高了餐厅的声誉，但很难衡量这对销售的影响。

结论

上述案例显示了如何以多种不同方式使用详细的数据分析来提高组织的绩效。大数据可用于更好地了解客户和趋势，提供对成本的洞察，并使客户更容易在网站上找到他们想要的东西。公司可能会继续发现越来越多的可用数据的创新用途，并且大数据分析作为许多企业的重要战略工具可能会变得越来越重要。

更新文章摘自自由讲师兼作家Ken Garrett和绩效管理学科的首席导师Nick Ryan的文章

以上翻译仅供参考，请以ACCA官网内容为准！

【ACCA PM知识点：处理和分析大数据】