Asking the Customer by Asking the DatabaseLaudon, KC & Laudon, JP 2013, Management information systems: Managing the digital firm, 13th edn,Pearson Education Ltd, England.What's the best way to find out what your customers want? If you're a large business with millionsof customers, its impossible to ask each one face to face. But thanks to modern data managementand data mining technology, you can "ask" each one by mining your customer database. The resultsare dazzling and perhaps a bit unsettling as well.Customer databases typically contain data such as a customer's name, address and history ofpurchases. These databases include records of the company's communication with its owncustomers or customer "lists" purchased from other organisations, including charity donation forms,application forms for free products or contests, product warranty cards, subscription forms andcredit application forms. Today, these databases are starting to include customer data gatheredfrom social media, mobile, web and email transactions.Until fairly recently, large companies, such as Forbes, did not look closely at their own data. Forbesis an American publishing and media company that publishes the bi-weekly Forbes businessmagazine and maintains an extensive website. Its worldwide print and online readership numbersover 45 million people. Forbes' business model relies heavily on advertising to supplement revenuefrom paid subscriptions, so it is constantly looking for ways to help its advertisers reach Forbesreaders more effectively.For many years, the various entities in the Forbes media empire employed third party researchservices to analyse their customer data. These services compiled information about Forbes readersby selecting and analysing subsets of the reader population as a way of learning about the entirereadership. Decisions were made on the basis of what could be predicted about the "average"reader. But readers are individuals, and what Forbes and its advertisers really wanted was to findout what each individual customer was doing with Forbes publications.Enter corporate databases and data mining. Forbes did maintain extensive data on its individualsubscribers and web site visitors from magazine subscriptions and visits to its websites. It justneeded to make better use of the data. Management realised it could actually learn details abouteach of its individual readers by examining Forbes' entire reader population using the data it hadalready accumulated on a regular basis.Forbes started to use SAP BusinessObjects software to analyse its own readership data, examiningvariables of greatest interest to its advertisers. Forbes claims it can now understand each individualwho interacts with its brand. Whether that person is a subscriber or registered website visitor,Forbes has some knowledge of that individual's demographics, values and lifestyle as well as howthat person has interacted with Forbes over the years. These details help Forbes' advertisers targettheir campaigns more precisely and also help Forbes' publications increase their circulation.Monster.com, one of the world's largest job listing sites, used business intelligence analytics to scaleback its broad based brand advertising in favour of a more targeted multichannel approach. Most ofMonster's revenue comes from employers who pay to post job listings and to search its résumédatabase. Job seekers can post résumés and search listings free of charge.A typical campaign starts with email but in the past Monster had been sending generic messagesabout itself and its services to large groups of companies. The recent recession and highunemployment rates inspired the company to look for a more cost effective approach.Monster now tries to find new employers and candidates using much more personalised email,direct main, social engagement and prioritised telemarketing using IBM's Unica enterprise marketingmanagement tools. A typical campaign now starts with a personalised email message to targetedsegments, Monster's best prospects being human resources decision makers in large companies ingrowth industries such as health care and technology. Monster uses SAS statistical modellingsoftware to identify existing and prospective customers who are most likely to purchase job listingsand other services in a specific quarter. A Unica marketing database maintains data on when theemail campaigns ran, email recipients, who responded to the email messages and who clickedthrough. By analysing these data, Unica is able to generate mailing lists based on criteria such aspast response behaviour and an "opportunity score".Monster's business intelligence (BI) analytics tools examine attributes such as industry, company sizeand location to score the data and target a subset of around 1,000 human resources (HR) executivesidentified as top prospects who might merit special treatment, such as an expensive direct mailpromotion or even a gift. For example, to promote its new Power Résumé Search service, Monstersent the leading prospects global positioning system (GPS) devices, combined with promotionalmessages describing the service as a GPS for finding job candidates.Monster also uses its Unica data to initiate interactions with select prospects through LinkedIn andother business oriented social networks. If it has a target list of 1,000 executives, Monster tries toengage 50 to 100 of them using social media.Behaviours tracked through Unica also help Monster's sales force target prioritised telemarketingfollow-up calls. For instance, any customer who has opened and clicked on more than one email willmost likely receive a call.Diapers.com, the largest online specialty retailer for baby products, started out in 2005 as abootstrap operation by entrepreneurs Marc Lore and Vinit Bharara. At that time, the companydidn't have any historical data to predict how new mothers would behave and decision s were basedon the co-founders' firsthand knowledge that new parents would be attracted to overnight shippingof essential items and reliable customer service. The company's business strategy focussed oncultivating customer loyalty by touting cheaper items such as infant formula and baby powder toentice customers into purchasing higher end goods such as car seats or baby swings that could beconveniently shipped in the same box.Diapers.com's parent company Quidsi, owned by Amazon.com, now uses predictive analytics withmore than five years of historical data on customer spending to calculate how much each buyer willspend over that person's lifetime as a customer. Geographic location and product choices areimportant variables. The analytic data drive the company's marketing budget for different customerdemographics.Like other retailers with low profit margins from online sales, Diapers.com does not make anythingoff the first purchase. If a customer does not shop on a repeat basis, Diapers.com doesn't believethat customer is worth having. According to Marc Lore, once Quidsi calculates the profit it will makefrom each customer over a lifetime, it knows how much its willing to spend to acquire and retainthat customer. In 2010, the average Diapers.com customer cost about $40 to acquire, but thatperson would contribute an average of $70 to the company's bottom line over the lifetime ofpurchases from the company.Target, the second largest US discount retail chain, has been able to take predictive analysis ofcustomers to new heights by incorporating scientific findings about habit formation. For decades,Target has amassed vast quantities of customer data. It assigns each shopper a Guest ID number,which is a unique code for keeping track of everything an individual customer purchases. The GuestID is linked to data about whether a shopper uses a coupon or credit card, mails in a refund, fills outa survey, responds to an email, visits Target's website or calls Target's customer help line.Also linked to the Guest ID is demographic information such as a customer age, marital status,number of children, residential location, whether the customer recently moved, what credit cardsthe customer uses and what websites to the customer visits. Target can purchase data about yourjob history, ethnicity, magazine subscriptions, whether you've declared bankruptcy, what brands ofcoffee and paper towels you prefer, your political leanings, charitable contributions and the numberof cars you own.Target's predictive analytics department is further pushing to increase sales by using findings abouthabit formation, which show that 45 percent of the choices people make every day are based onhabits rather than conscious decision making. Purchases for mundane products such as soap,toothpaste and paper towels are typically made habitually, with no complex decision making. Forsuch habit driven purchases, special displays, product promotions and coupons have little impact.But when customers are going through a major life event such as moving to a new town or expectinga baby, their shopping habits become more flexible and open to intervention by marketers.Target was able to mine its customer data to identify about 25 products, such as unscented lotionand cotton balls that, when analysed together, resulted in a pregnancy prediction score with anestimated due date for each shopper. Shoppers with high prediction scores were more open topurchasing a whole array of products from Target that they had previously bought out of habit fromother retailers. A Target statistician created a pregnancy prediction score for every female shopperin its national database and was able to come up with a list of tens of thousands of women whowere most likely to be pregnant. If these women could be enticed into a Target store to buy babyrelated products, they might be open to buying groceries, toys and clothing from Target as well.When Target started sending coupons for baby items to customers according to their pregnancyprediction scores, the company quickly found out that this made people uncomfortable, eventhough it was strictly complying with federal and state privacy laws. So instead of sending peoplewith high pregnancy prediction scores books of coupons solely for baby items, Target tried to makethe baby ads look random by mixing them with ads for things it knew pregnant women would neverbuy. For example, Target might put a coupon for wine glasses next to infant clothes to make itappear that all the products were selected by chance. Target found a pregnant woman would use itscoupons as long as she thinks she hasn't been spied on.Target may want to keep its pregnancy predication score formula a closely guarded secret, but thereare many companies selling pregnancy and baby related products that might be interested inpurchasing this method. Under current privacy laws, there's nothing to prevent Target from sharingsuch information with retailers and other organisations that are not part of the Target family butwant to provide customers with special offers of their own.Questions1. Why would a customer database be so useful for the companies described in this case?2. What would happen if these companies had not kept their customer data in databases?3. How did better data management and analytics improve each company's businessperformance? Give examples of two decisions that were improved by mining thesecustomer databases.4. Outline the audience centred communication model and show how it applies to the case.5. Are there any ethical issues raised by mining customer databases? Explain.