
Discover how algorithms predict who will click, buy, lie, or die in "Predictive Analytics" - the Freakonomics of big data. Used by 30+ universities and translated into 9 languages, it reveals why vegetarians miss fewer flights and how companies like Netflix see your future.
通过作者的声音感受这本书
将知识转化为引人入胜、富含实例的见解
快速捕捉核心观点,高效学习
以有趣互动的方式享受这本书
Imagine a father storming into a Target store, furious that the retailer was sending his teenage daughter baby-related coupons. Days later, he called back with a sheepish apology: his daughter was indeed pregnant - due in August. Target's algorithms had detected subtle changes in her purchasing patterns before she'd told her family. Welcome to the world of predictive analytics, where algorithms peer "through the previously impenetrable barrier between today and tomorrow." This technology transforms our digital footprints into forecasts of remarkable accuracy - predicting what we'll buy, whether we'll quit our jobs, default on loans, or even develop diseases. These aren't vague, generalized predictions like weather forecasts, but individualized insights applied across millions of people simultaneously. The power comes not from perfect accuracy, but from being consistently better than random guessing - what Eric Siegel calls "The Prediction Effect." When organizations can make slightly better decisions at massive scale, the cumulative impact transforms industries. Consider a direct marketing campaign: without predictive analytics, you'd randomly select recipients and expect a 1% response rate. But what if you could identify a segment three times more likely to respond? You'd triple your results with the same budget. This illustrates how even modest improvements in prediction accuracy create tremendous value. Credit card companies improving fraud detection by just 2% save millions annually. Healthcare providers predicting patient readmissions with 15% better accuracy significantly reduce costs while improving care. What makes this approach transformative is that predictions directly drive decisions: doctors reviewing high-risk patients, agents contacting customers likely to cancel, marketers targeting specific prospects. Amazon's recommendation engine drives 35% of their sales. Netflix saves $1 billion annually through predictive customer retention.
将《Predictive Analytics》的核心观点拆解为易于理解的要点,了解创新团队如何创造、协作和成长。
将《Predictive Analytics》提炼为快速记忆要点,突出坦诚、团队合作和创造力的关键原则。

通过生动的故事体验《Predictive Analytics》,将创新经验转化为令人难忘且可应用的精彩时刻。
随心提问,选择声音,共同创造真正与你产生共鸣的见解。

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