【正文】 ation offered a great opportunity for one of their newest datadriven weapons … predictive technology. ? A week ahead of the storm‘s landfall, Linda M. Dillman, WalMart‘s chief information officer, pressed her staff to e up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes‘ worth of shopper history that is stored in WalMart‘s data warehouse, she felt that the pany could ?start predicting what‘s going to happen, instead of waiting for it to happen,‘ as she put it. (Hays, 2020) 7 Example: Hurricane Frances ? Consider why datadriven prediction might be useful in this scenario. ? It might be useful to predict that people in the path of the hurricane would buy more bottled water. Maybe, but this point seems a bit obvious, and why would we need data science to discover it? ? It might be useful to project the amount of increase in sale due to the hurricane, to ensure that local WalMart are properly stocked. ? Perhaps mining the data could reveal that a particular DVD sold out in the hurricane‘s path – but maybe it sold out that week at WalMarts across the country, not just where the hurricane landing was imminent. 8 Example: Hurricane Frances ? The prediction could be somewhat useful, but is probably more general than Ms. Dillman was intending. ? It would be more valuable to discover patterns due to the hurricane that were not obvious. ? To do this, analysts might examine the huge volume of WalMart data from prior, similar situations (such as Hurricane Charley) to identify unusual local demand for products. 9 Example: Hurricane Frances ? From such patterns, the pany might be able to anticipate unusual demand for products and rush stock to the stores ahead of the hurricane‘s landfall. Indeed, that is what happened. ? The New York Times (Hays, 2020) reported that:‖…the experts mined the data and found that the stores would indeed need certain productsand not just the usual flashlights. ―We didn‘t know in the past that strawberry PopTarts increase in sales, like seven times their normal sales rate, ahead of a hurricane‘, Ms. Dillman said in a recent interview. ―And the prehurricane topselling item was beer.‖ 10 Example: Predicting Customer Churn ? How are such data analyses performed? Consider a second, more typical business scenario and how it might be treated from a data perspective. ? Assume you just landed a great analytical job with MegaTelCo, one of the largest telemunication firms in the United States. ? They are having major problem with customer retention in their wireless business. In the midAtlantic region, 20% of cell phone customers leave when their contracts expire, and it is getting increasingly difficult to acquire new customers. ? Since the cell phone market is now saturated, the huge growth in the wireless market has tapered off. 11 Example: Predicting Customer Churn ? Communications panies are now engaged in battles to attract each other‘s customers while retaining their own. ? Customers switching from one pany to another is called churn, and it is expensive all around: one pany must spend on incentives to attract a customer while another pany loses revenue when the customer departs. ? You have been called in to help understand the problem and to devise a solution. ? Attracting new customers is much more expensive than retaining existing ones, so a good deal of marketing budget is allocated to prevent churn. 12 Example: Predicting Customer Churn ? Marketing has already designed a special retention offer. Your task is to devise a precise, stepbystep plan for how the data science team should use MegaTelCo‘s vast data resources to decide which customers should be offered the special retention deal prior to the expiration of their contract. ? Think carefully about what data you might use and how they would be used. Specifically, how should MegaTelCo choose a set of customers to receive their offer in order to best reduce churn for a particular incentive budget? Answering this question is much more plicated than it may seem initially. 13 Data Science, Engineering, and DataDriven Decision Making ? Data science involves principles, processes, and techniques for understanding phenomena via the (automated) analysis of data. ? In this book, we will view the ultimate goal of data science as improving decision making, as this generally is of direct interest to business. 14 Data Science, Engineering, and DataDriven Decision Making ? Figure 11 places data science in the context of various other closely related and data related processes in the anization. ? It distinguishes data science from other aspects of data processing that are gaining increasing attention in business. Let‘s start at the top. 15 Data Science, Engineering, and DataDriven Decision Making ? Datadriven decisionmaking (DDD) refers to the practice of basing decisions on the analysis of data, rather than purely on intuition. ? For example, a marketer could select advertisements based purely on her long experience in the field and her eye for what will work. Or, she could base her selection on the analysis of data regarding how consumers react to different ads. ? She could also use a bination of these approaches. DDD is not an allornothing practice, and different firms engage in DDD to greater or lesser degrees. 16 Data Science, Engineering, and DataDriven Decision Making ? Economist Erik Brynjolfsson and his colleagues from MIT and Penn‘s Wharton School conducted a study of how DDD affects firm performance (Brynjolfsson, Hitt, amp。 ?同樣的邏輯也適用其他領(lǐng)域的應(yīng)用 : 直接行銷、線上廣告、信用評(píng)分、舞弊偵測(cè)、產(chǎn)品推薦等。張忠謀認(rèn)為創(chuàng)新不要狹隘，他說：「諾貝爾獎(jiǎng)的創(chuàng)新，就只是一個(gè)金字塔，而創(chuàng)業(yè)的創(chuàng)新，是要把多個(gè)金字塔的頂端連結(jié)，做聯(lián)想。 ? 優(yōu)勢(shì)：事先根據(jù) 2,900 萬(wàn) Ne