5个零售业大数据带来巨大收益的实例(译文)注明：此译文已经在小象公众平台发表，转载请注明-小象学院，谢谢合作！
作者：Chuck Schaeffer
大数据正在为零售商们传递一些可观的成果。
Macy说他们的大数据程序是一个关键的竞争优势，指出大数据作为一个强有力的贡献因素，将零售店的销售额提高了十个百分点。Sterling Jewelers把上个休假期49%的销售额增长归功于大数据。Kroger的执行总裁David Dillon把他们的大数据程序视为自己的秘密武器。
麦肯锡通过五年多来对超过250个业务案例的分析，揭示了将大数据作为销售和营销策略中心的公司，他们的投资回报率提高了15到20个百分点的事实。但是尽管有一些惊人的回报和改变零售业游戏规则的可能，我们仍然要面对许多存在的阻碍。
从和许多零售业高管的会议中，我发现大数据正在得到越来越多的关注，但是同时大部分的高管都在为了共同的挑战而作斗争，像如何将大数据与实用案例结合，如何确定新的类型的数据（通用结构），还有如何得到提高决策质量的大数据。
大数据可以用来做任何事却并不能被直接使用。它是一种没有打包解决方案的颠覆性科技。当然，你可以获取大数据技术，但是如果没有理解和设想之前的那些隐藏数据是怎样被得到和应用于商业过程，商业挑战和机遇，大数据就会变成另外一个无法带来预期回报并且使用寿命短的冷板凳软件。
就我的经验来说，成功部署一个大数据解决方案应该首先从新信息中确定使用实例以及可收益商业决策。
当零售业大数据实例是你的创造性思维的所展现的一个函数时，这比做起来容易一些。为了促进这种思维，来一起看看下面的零售业大数据实例。
连锁酒店利用大数据来增加预订量
糟糕的天气会使旅行的人数减少，当然同时也使得晚上的住宿人数减少了。对于在酒店行业工作的人来说这并不是一个好消息。然而Red Roof酒店把这种趋势转化成了他们的优势。他们意识到被取消的航班会使旅客们处于困境并且需要一个地方来睡觉过夜。这个公司免费提供随意可得的天气和航班取消信息，这些信息是联合酒店和机场位置归纳所得。该公司还建立了一套算法，从众多变量中提取天气恶劣程度，旅游环境，当日时刻以及航班取消率作为因子计入该算法。 通过大数据的洞察力以及对于实例中将要使用移动设备旅客的识别，该公司利用Search，PPC，SoMoLo等搜索引擎给滞留旅客的目标移动设备发送广告，使消费者能更容易的订到周围的酒店。
大数据所带来的回报是引人注目的，利用大数据我们可以统计出平均每天有1%—3% 的航班会取消，折合平均每天有150到500个航班取消，或则说每天约有25000到90000的滞留旅客。红屋顶酒店运用大数据和地理的移动营销活动，使它的业务与从2013年到2014年增长了10% 。
披萨连锁店在天气不好的时候能赚更多的钱
类似于上面的例子，当消费者在恶劣天气或者停电的情况下无法做饭时，一个披萨连锁店就可以使用一个移动应用程序和移动营销技术提供优惠券来刺激他们消费。这个移动和定位营销活动大概得到了20%的反应率。
音乐经销商运用大数据来指定需求计划
EMI唱片公司可以用大数据来测量和预测产品需求。唱片公司发布音乐以后，可以利用自己的社交网络统计音乐点击率，另外也可通过流行音乐流媒体服务器，歌曲识别应用程序或“第二屏”社交媒体排序获得第三方监听模式数据。这些数据是人口，位置和亚文化群的聚合数据，音乐发行商可以很放心利用这些数据提供精确定位广告以及预测产品需求。对于其他零售商也可以通过社交网络获得聚合数据，以此来了解他们的新产品在新的或现有的市场的销售情况，甚至可会获悉公众对他们的产品评价以及他们公司的信誉度。
金融服务公司运用大数据获得新客户
由于在新客户群体中获得赢利率较低，金融服务公司也开始转向运用大数据，以便更好地识别哪些新客户会有更大的投资可能性。金融公司客户人口统计数据以及携带的第三数据是从eBureau购买的。数据服务提供消费者的职业、收入、年龄、零售历史和其他相关因素，以此得知他们会在本公司消费的几率。增强的数据集应用于一个算法，该算法可以识别对哪些新客户有必要再进行额外的投资，对哪些新客户不必再进行投资。应用该方法取得的成果是新客户获得率增加11%,同时公司销售相关费用降低了14.5%。
零售商创建怀孕检测模型
最近一个非常有名的零售商运用大数据的例子：零售商Target使用宝宝派对注册以及客户身份认证，来确定客户是否怀孕。Target客户身份认证是一个独特的消费ID，它可以追踪购买历史，信用卡使用，调查回复，客户支持事件，邮件点击率，网站访问等。该公司通过购买人口数据来补充消费者活动，这些数据包括：年龄，种族，教育，婚姻状况，孩子数量，估计收入、工作历史和生活事件 如你最后一次移动或如果你已经离婚或宣布破产。通过比较在婴儿淋浴注册的顾客的购买历史，零售商发现她们在怀孕期间购物习惯的改变情况。例如，在第一个20周，孕妇开始采购补充钙、镁和锌的产品。在怀孕中期，孕妇开始购买更大的牛仔裤和更大数量的洗手液，无香味的乳液、香水免费肥皂和棉花球，通常是买大袋的棉花球。总的来说，零售商可以确定孕妇经常购买的25种产品。
通过查看所有顾客目标的消费行为可以识别孕妇甚至是那些未发现怀孕的女性目标。目标是用这一发现创建了怀孕预测模型可以给顾客提供怀孕预测。然后零售商就可以传播婴儿产品根据怀孕的不同时间阶段推广给特定的细分顾客，这样不仅是那些女性购买婴儿产品也可以知道重要的生活事件可以改变顾客的整体消费习惯，目标是可以让经济消费从分析的时间开始，2002年的440亿美元增长到2010年的670亿美元。当零售商们还没有公开地关注这一计划。目标的负责人，GreggSteinhafel已经记录分享投资者的数据“着重关注项是可以吸引特定顾客段的项目和分类 ，如妈妈和孩子”很大程度上决定了零售商的成功。
是大数据还是旧经验
这些零售商的大数据的样例可以被多种方式推测，使用天气模型预测、商店销售结合互联网搜索数据、网页浏览模式、社交网络和工业预报去预测产品趋势，预告需求，精确定位顾客和优化价格和促销。
理解你的产品销售与其他未发现的因素之间的关系，这些因素有很多，比如天气、潮流文化、社会媒体潮流、你的竞争者和消费者的观点，这可以让你充分利用和挖掘有特定行为环境中的事件以提升财务绩效。
零售商利用大数据可以设计更能被消费者接受的产品，更好地参与和应对市场变化，更好地吸引消费者。这意味着更好地数据可以有更少的缺货，更高的访问购买率，更好地改变和采取商品结构的规划。
大数据并不只是服务大公司
零售业的思想领袖加里霍金斯认为，大数据可以创造一个零售垄断。在哈佛商业评论中，霍金斯指出了大数据可能“击溃除了最大的零售商所有其他零售商”。
他认为大型零售商有更大的IT预算和资源，可以利用大数据的机会，增加市场的优势，把较小的零售商基本上压迫到“便利店的角色”。
尽管霍金斯支持的论点以及大数据的确是切实提高营销地有利条件，可以提高产品的可用性和用户体验，从而超越零售业的其他竞争对手，但是我坚信新的零售强弱顺序将更少取决于零售商的IT预算而更多地倾向创新性和敏锐度。零售业正在发生深刻的变化，小企业往往比大的零售商更敏捷。正如达尔文教我们“不是最强也不是最聪明的物种可以生存。而是最能适应改变的那个可以笑到最后”。
翻译：秦何煜
王嫣
崔凤焦
校对：马延超
原文部分
Big Data in Retail Examples
By Chuck Schaeffer
5 Retail Big DataExamples with Big Paybacks
Big data is deliveringsome big results for retailers.
Macy's says that its bigdata program is a key competitive advantage and cites big data as a strongcontributing factor in boosting the department store's sales by 10 percent.Sterling Jewelers attributes a 49 percent increase in sales during the lastholiday season to big data. Kroger CEO David Dillon refers to his big dataprogram as his "secret weapon."
McKinsey analysis of morethan 250 engagements over a five year period revealed that companies that putdata at the center of the sales and marketing decisions improved theirmarketing ROI by 15 to 20 percent.
But despite some impressivepaybacks and what may be a game changer in the retail industry, plenty ofobstacles remain.
In meeting with a numberof retail executives I've found that Big Data is getting a lot of interest, butmost of these executives struggle with some common challenges – suchas how to align big data with use cases, how to identify new types of(generally unstructured) data and how to harvest big data for improved decisionmaking.
Big data is anything butout of the box. This is a disruptive technology without packaged solutions.Sure, you can acquire big data technology, but without understanding andhypothesizing how previously hidden data can be harvested and applied tobusiness processes, challenges or opportunities, big data becomes anothershelfware solution with a disappointing payback and short lifespan.
In my experience,successfully deploying a big data solution begins by identifying use cases andbusiness decisions which benefit from new information. This is easier said thandone as retail big data use cases are a function of your creative thinking. Tostimulate that thinking, consider the following retail big data examples.
Hotel Chain Uses Big Datato Increase Bookings
Bad weather reducestravel, which then reduces overnight lodging. That’s notgood news if you’re in the hotel business. However, Red Roof Inn turned thistrend on its head. The hotel chain recognized that cancelled flights leavetravelers in a bind and in need of a place to sleep overnight. The companysourced freely available weather and flight cancellation information, organizedby combinations of hotel and airport locations, and built an algorithm whichfactored weather severity, travel conditions, time of the day and cancellationrates by airport and airline among other variables. With its big data insights,and recognition that travelers will be using mobile devices for this use case,the company used Search, PPC and SoLoMo mobile campaigns to deliver targetedmobile ads to stranded travelers and make it easy for them to book a nearbyhotel.
This big data paybackis compelling. Flight cancellations average 1-3% daily, which translates into150 to 500 cancelled flights or around 25,000 to 90,000 stranded passengerseach day. With its big data and geo-based mobile marketing campaigns Red RoofInn achieved a 10% business increase from 2013 to 2014.
Pizza Chain Earns MoreDough in Bad Weather
Somewhat similar to theabove example, a pizza chain uses a mobile app and mobile marketing techniquesto deliver coupons based on bad weather or where power outages leave consumersunable to cook. This mobile and location-based marketing campaign achieves a20% response rate.
Music distributorApplies Big Data for Demand Planning
Record label EMI usesbig data to measure and forecast product demand. After distributing or leakingmusic, the company measures consumption on its own social networks andadditionally acquires third party listening pattern data from popular musicstreaming services, song identification apps or 'second screen' social mediacollators. The data is aggregated by demographics, locations and subculturesand helps the music distributor deliver pinpoint advertising and forecastproduct demand with a high confidence level. This concept is applicable toother retailers who can also aggregate feeds from social networks to build anunderstanding of how new products will be received by new or existing markets,or even how their products and company reputation are perceived among thepublic.
Financial ServicesCompany Scores New Clients
After incurring low winrates for new client acquisitions, a financial services firm turned to big datain order to better identify which new client opportunities warrant the mostinvestment. The company supplemented its customer demographic data with thirdparty data purchased from eBureau. The data service provider appended saleslead opportunities with consumer occupations, incomes, ages, retail historiesand related factors. The enhanced data set is then applied to an algorithmwhich identifies which new client leads should receive additional investmentand which should not. The result has been an 11 percent increase in new clientwin rates while at the same time the firm has lowered sales related expenses by14.5%.
Retailer CreatesPregnancy Detection Model
In a near infamousretail big data example, retailer Target correlated its baby-shower registrywith its Guest ID program in order to determine when a shopper is likelypregnant. Target's Guest ID is a unique consumer ID that tracks purchasehistory, credit card use, survey responses, customer support incidents, emailclick-throughs, web site visits and more. The company supplements the consumeractivities it tracks by purchasing demographic data such as age, ethnicity,education, marital status, number of children, estimated income, job historyand life events such as when you last moved or if you have been divorced orever declared bankruptcy.
By comparing shopperswho registered on the baby shower registry with the purchase history from theirGuest ID, the retailer discovered changes in shopping habits as the womanprogressed through her pregnancy. For example, during the first 20 weeks,pregnant women began purchasing supplements like calcium, magnesium and zinc.In the second trimester, pregnant women began buying larger jeans and largerquantities of hand sanitizers, unscented lotion, fragrance free soap and cottonballs; often extra-big bags of cotton balls. In total, the retailer identifiedabout 25 products purchased by pregnant women.
By applying thesepurchase behaviors to all shoppers Target was able to identify women who werepregnant even though these women had not notified Target – oroften anybody else – they were pregnant. Target used this discoveryto create a pregnancy prediction model which assigned a pregnancy predictionscore to shoppers. The retailer was then able to distribute baby productpromotions to a very specific customer segment, timed to stages of pregnancy,and the financial results were off the charts. Not only did these women makenew baby product purchases, but knowing that significant life events change aconsumers overall shopping habits, Target was able to grow its revenues from$44 billion in 2002 when the analysis started to $67 billion in 2010. While theretailer does not publicly comment on this program, Target's president, GreggSteinhafel, is on record sharing with investors that the company's"heightened focus on items and categories that appeal to specific guestsegments such as mom and baby" heavily contribute to the retailerssuccess.
Notwithstanding theconsumer privacy and public relations considerations which must be deliberated,this is a powerful lesson for retailers.
Go Big or Go Home
These retail big dataexamples can be extrapolated in many ways — from using weatherpatterns to predict in-store sales to combining data from web search trends,website browsing patterns, social networks and industry forecasts to predictproduct trends, forecast demand, pinpoint customers and optimize pricing andpromotions.
Understanding thecorrelation between your product sales and otherwise undetected factors such asthe weather, pop culture, social media trending, your competitors and consumersentiment can allow you to tap into these environmental events with specificactions that lead to improved financial performance.
Retailers that leveragebig data will design products that are more embraced by consumers, betteranticipate and respond to market shifts, and engage consumers with predictableresults. This means fewer stockouts, higher visit to buy ratios, bigger basketsizes and other performance measures that can be improved with better data.
Big Data Not Just ForBig Companies
Retail thought leaderGary Hawkins suggests that big data may actually create a retail oligopoly.Writing in the Harvard Business Review, Hawkins poses the likelihood that bigdata may "kill all but the biggest retailers." He suggests that largeretailers, with their larger IT budgets and resources, can capitalize on thebig data opportunity, increase market dominance and essentially relegatesmaller retailers to "the role of convenience stores."
Notwithstanding Hawkinswell supported argument as well as big data's very real opportunity to improvemarketing, product availability or the customer experience, and therebyoutperform retail competitors, it's my strong belief that the new retailpecking order will be less determined by the size of the retailer's IT budgetand more by the retailer's propensity toward innovation and agility. The retailindustry is incurring profound change and smaller businesses often show moreagility than larger retailers. As Darwin taught us "It is not thestrongest of the species that survives, nor the most intelligent that survives.It is the one that is most adaptable to change."