【導讀】Theterms―datascience‖and―datamining‖oftenareused

　　

【正文】 id not have the appropriate data to model profitability with the goal of offering different terms to different customers. No one did. ? Since banks were offering credit with a specific set of terms and a specific default model, they had the data to model profitability (1) for the terms they actually have offered in the past, and (2) for the sort of customer who was actually offered credit (that is, those who were deemed worthy of credit by the existing model). 42 Data and Data Science Capability as a Strategic Asset ? What could Sig Bank do? They brought into play a fundamental strategy of data science: acquire the necessary data at a cost. In Sig‘s case, data could be generated on the profitability of customers given different credit terms by conducting experiments. Different terms were offered at random to different customers. ? This may seem foolish outside the context of dataanalytic thinking: you‘re likely to lose money! This is true. In this case, losses are the cost of data acquisition. The data analytic thinker needs to consider whether she expects the data to have sufficient value to justify the investment. 43 Data and Data Science Capability as a Strategic Asset ? So what happened with Sig Bank? As you might expect, when Sig began randomly offering terms to customers for data acquisition, the number of bad accounts soared. ? Sig went from an industryleading ―chargeoff ‖ (壞帳 ) rate (% of balances went unpaid) to almost 6% chargeoffs. ? Losses continued for a few years while the data scientists worked to build predictive models from the data, evaluate them, and deploy them to improve profit. 44 Data and Data Science Capability as a Strategic Asset ? Because the firm viewed these losses as investments in data, they persisted despite plaints from stakeholders. ? Eventually, Sig‘s credit card operation turned around and became so profitable that it was spun off to separate it from the bank‘s other operations, which now were overshadowing the consumer credit success. 45 Data and Data Science Capability as a Strategic Asset ? Fairbanks and Morris became Chairman and CEO and President and COO, and proceeded to apply data science principles throughout the business—not just customer acquisition but retention as well. ? When a customer calls looking for a better offer, data driven models calculate the potential profitability of various possible actions (different offers, including sticking with the status quo), and the customer service representative‘s puter presents the best offers to make. ? Fairbanks and Morris‘s new pany Capital One grew to be one of the largest credit card issuers in the industry with one of the lowest charge off rates. In 2020, the bank was reported to be carrying out 45,000 of these ―scientific tests‖ as they called them. 46 兩種由資料推動的決策 ?Type 2: 藉由分析數(shù)據(jù)將決策的精準度提升一點，但卻可以得到可觀的效益，因為這類的決策需要經(jīng)常重複做，或是有廣泛的影響 ?電信公司預測客戶合約到期時是否會跳槽 : 每個月會有成千上萬的客戶合約到期，任何ㄧ位都有可能離開。如果我們針對任ㄧ位此種客戶，能較準確的估算出如果投注行銷資源在其身上會有多少的獲利，那我們就可以套用在所有客戶而得到大的效益。同樣的邏輯也適用其他領域的應用 : 直接行銷、線上廣告、信用評分、舞弊偵測、產(chǎn)品推薦等。 ?預測信用卡客戶的獲利率 : 以便將最好的方案提供給最佳的客戶，甚至將其他銀行的優(yōu)值客戶吸引過來 。 47 Data and Data Science Capability as a Strategic Asset ? The idea of data as a strategic asset is certainly not limited to Capital One, nor even to the banking industry. ? Amazon was able to gather data early on online customers, which has created significant switching costs: consumers find value in the rankings and remendations that Amazon provides. Amazon therefore can retain customers more easily, and can even charge a premium (Brynjolfsson amp。 Smith, 2020). ? Harrah‘s casinos famously invested in gathering and mining data on gamblers, and moved itself from a small player in the casino business in the mid1990s to the acquisition of Caesar‘s Entertainment in 2020 to bee the world‘s largest gambling pany. 48 Data and Data Science Capability as a Strategic Asset ? The huge valuation of Facebook has been credited to its vast and unique data assets (Sengupta, 2020), including both information about individuals and their likes, as well as information about the structure of the social work. ? Information about work structure has been shown to be important to predicting and has been shown to be remarkably helpful in building models of who will buy certain products (Hill, Provost, amp。 Volinsky, 2020). ? It is clear that Facebook has a remarkable data asset。 whether they have the right data science strategies to take full advantage of it is an open question. ? In the book we will discuss in more detail many of the fundamental concepts behind these success stories, in exploring the principles of data mining and dataanalytic thinking. 49 □ DataAnalytic Thinking ? Analyzing case studies such as the churn problem improves our ability to approach problems ―dataanalytically.‖ ? When faced with a business problem, you should be able to assess whether and how data can improve performance. We will discuss a set of fundamental concepts and principles that facilitate careful thinking. We will develop frameworks to structure the analysis so that it can be done systematically. ? Understanding the fundamental concepts, and having frameworks for anizing dataanalytic thinking not only will allow one to interact petently, but will help to envision opportunities for improving datadriven decisionmaking, or to see dataoriented petitive threats. ? Firms in many traditional industries are exploiting new and existing data resources for petitive advantage. They employ data science teams to bring advanced technologies to bear to increase revenue and to decrease costs. 50 DataAnalytic Thinking ? Increasingly, managers need to oversee analytics teams and analysis projects, marketers have to anize and understand datadriven campaign