You have been predicted — by companies, governments, law-enforcement, hospitals and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.
Why? For good reason: Predicting human behavior combats financial risk, fortifies healthcare, reduces spam, toughens crime-fighting and boosts sales.
How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.
Predictive analytics is the science that unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but even lousy predictions can be extremely valuable.
In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World co-founder Eric Siegel reveals the power and perils of prediction:
- What unique form of mortgage risk Chase Bank predicted before the recession.
- Predicting which people will drop out of school, cancel a subscription or get divorced before they are even aware of it themselves.
- Why early retirement decreases life expectancy and vegetarians miss fewer flights.
- Five reasons organizations predict death, including one health insurance company.
- The way U.S. Bank and European wireless carrier Telenor calculate how to most strongly influence each customer.
- How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.
- How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free.
- What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia.
A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining who to call, mail, investigate, incarcerate, set up on a date, or medicate.
Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?
Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
Keywords: General & Introductory Business & Management, advanced analytics, AI, algorithmic trading, analytical decision making, analytics, artificial intelligence, big data, black box trading, business analytics, business intelligence, business rules, CRM, CRM analytics, customer analytics, customer profiling, customer relationship management, customer segmentation, data analysis, data analytics, data driven business, data driven decision making, data driven decisioning, data driven decisions, data mining, data science, data warehousing, database analytics, database marketing, datamart, decision analytics, decision automation, decision science, decision support, decision trees, demand forecasting, direct marketing, ensemble models, enterprise decision management, Eric Siegel, expert systems, forecasting, fraud detection, healthcare analytics, hr analytics, human resource analytics, incremental lift modeling, machine learning, market segmentation, marketing analysis, marketing analytics, net lift modeling, operations research, predictive analytics, probability, quantitative methods, response modeling, risk modeling, segmentation, social media analytics, statistical analysis, statistical modeling, statistical techniques, statistics, true lift modeling, true-lift modeling, uplift modeling, web analytics, workforce analytics