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<\/a><\/p>\nLet\u2019s fast forward ten years into the future.\u00a0 Big Data is 10,000x more advanced than it is right now.\u00a0 Companies use it to measure and manage customer satisfaction in every facet of its organization. \u00a0An imaginary company, Qualmart, is reviewing the costs associated with customer service.\u00a0 Big Data is streaming information from a thousand different sources \u2013 cash registers, Twitter, Facebook, loyalty programs, credit cards, employee files, product recalls, and anything else you can imagine.\u00a0 After correlating data from Facebook, Twitter, and its own returned merchandise receipts, it has found that Chinese consumers are 60% more likely to indicate negative experiences on the internet during a product return than the rest of the population.\u00a0 This information is an algorithmic compendium of data captured from credit card transactions, age\/sex\/racial data compiled from its loyalty programs, and complaints and negative feedback from Facebook posts, tweets, and employee-documented information from the actual return itself.\u00a0 As a result, a new corporate policy is issued for the customer service departments of Qualmarts located in Chinatowns of major cities to ensure that the return process is as quick and painless as possible, even if there is no real reason to honor the return.\u00a0 Well, as someone who is not from China, I don\u2019t think that seems fair.<\/p>\n
Let\u2019s look at it from a different perspective.\u00a0 Let\u2019s say that this retailer determines that white females under the age of 30 that live in Louisiana typically indicate negative experiences on the internet with respect to returns only 15% of the time.\u00a0 According to sales data captured from stores located in Louisiana, the most returned products are baby carriages, medium sized t-shirts for women that cost less than $15, and women\u2019s shoes in sizes 8 and 9.\u00a0 Customer loyalty cards suggest that sales are made by young, married white women who make less than $50,000 annually.\u00a0 The sales for this demographic are 15% of total store sales.\u00a0 However, the costs associated with the return policy of this demographic exceed 75% of total returns of all returned products.\u00a0 Since the percentages of returns in this hypothetical situation in Louisiana are from women under the age of 30, Qualmart issues a corporate policy in Louisiana to make the return process in Louisiana more cumbersome.<\/p>\n
It can get uglier.\u00a0 Let\u2019s take it a step further.\u00a0 Let\u2019s say that our retailer finds that 50% of their returns in Texas are from African-Americans. This information is compared against the internet browing habits of shoppers on qualmart.com; their customers tend to look at competing products on their website after purchasing the product in question.\u00a0 They decide that the return policy is an issue of \u201cbuyer\u2019s remorse\u201d and the impulse buy by the consumer was mitigated by other options available on the website.\u00a0 Qualmart decides that returns from African-Americans should be challenged.\u00a0 To ensure compliance with this new directive, they decide to incentivize their Texas employees: if only 35% of all attempted product returns are ultimately returned, they will all get $100 bonuses.\u00a0 This will have a disproportionate effect on African American consumers in the Texas region.\u00a0 However, Qualmart will save millions of dollars by instituting this new policy.\u00a0 And they have data to back up their decision-making.<\/p>\n
How about Big Data that has life-or-death repercussions?\u00a0 Big Data could prove out that Korean pilots are 35% more likely to experience a crash.\u00a0 Analyzing personnel data from all crashes originating from the US might reveal a disproportionate number of crashes can be attributed to pilots born in South Korea.\u00a0 However, it might be a causation that has deeper roots; it might be something specific to training or the types of aircraft they fly or cultural barriers.\u00a0 It could be that Korean pilots fly poorly designed aircraft.\u00a0 Maybe all of the aircraft shipped to Korea in a specific year came with faulty altimeters or something.<\/p>\n
The magic we are looking at is the promise of Big Data – companies are able to predict the behavior of customers even before they exercise the power of their wallet.\u00a0 If you\u2019re buying online, they can give you recommendation based upon your previous buying history or the buying history of people that fit your demographic so that you are fully satisfied with your purchase.\u00a0 The insight this provides retailers is almost frightening to consider.\u00a0 If 75% of people buy one type of widget, only to return it for one or two other styles of widget, what would that suggest?\u00a0 It is a problem with the first widget, obviously.\u00a0 Why even present that as an option?\u00a0 Why not drop the product and offer only the alternative?\u00a0 Even better, why not claim it is \u201cno longer available\u201d and offer the top two alternatives?\u00a0 After all, we can measure and manage the customer experience more precisely than ever before. We can make better predictions and smarter decisions. We can target interventions that surpass the gut instinct of executives that came before us, guided by data rather than intuition.<\/p>\n What we are looking at is cultural clich\u00e9s that are validated by empirical data. \u00a0\u00a0Once we overlay Big Data against predictive analytics, the result is (apologies for the pun) predictable.\u00a0 Customer segmentation is no longer just a function of increasing sales; it becomes a relevant factor in driving policies that discriminate between racial, gender, age, or socio-economic classes.<\/p>\n In America, we have laws that mitigate the effect of relevant data on the employment process.\u00a0 It is called \u201cdisparate impact<\/a>.\u201d\u00a0 In fact, slew of laws exist to mitigate the effect of data on hiring policies.\u00a0 For instance, it is illegal to refuse to hire a woman if she is\u00a0pregnant<\/a>.\u00a0 Similarly, you cannot refuse service to someone based upon a\u00a0protected class<\/a>.\u00a0 Even if you have empirical data to suggest that gay urban males are more likely to complain about service on social media than other customer segments, you cannot refuse to sell them a product.<\/p>\n But can you do the opposite? It is fair (or legal) to provide a protected class advantageous policies, such as reward programs, special discounts and promotions, or other preferential treatment, because of (or despite) their protected class? Should pregnant women get mommy discounts? Should veterans get discounted fares on airlines?\u00a0 Should gay urban males receive special discounts in order to mitigate the potential effects of negative social media comments? After all, theirs is a difference between fair and profitable.\u00a0 If companies were fair, we wouldn\u2019t have to worry about jobs being exported to China.\u00a0 Companies exist to make a profit.\u00a0 Although it is illegal to discriminate against a protected class, should it be illegal to give preferential treatment to one if it will result in higher profits?<\/p>\n I don\u2019t know what the first rule of Big Data is, because there are no real rules for Big Data.\u00a0 It is an endless sea of information that can be used for the forces of good or the forces of evil.\u00a0 We need to start thinking about Big Data issues as they relate to how our society functions and is governed: security, privacy, and advantageous or discriminatory policies.\u00a0 The potential for the abuse Big Data is enormous, even as it promises a better tomorrow.<\/p>\n<\/a><\/p>\n
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