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Blink 3 of 8 - The 5 AM Club
by Robin Sharma
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Predictive Analytics summary
Eric Siegel
The Power to Predict Who Will Click, Buy, Lie, Or Die
17 mins
Table of Contents
Predictive Analytics
summarized in 7 key ideas
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Key idea 1 of 7
Predictive analytics can help you lower your risks and make safer decisions.
Every time a company invests in an expensive marketing campaign, they’re taking a risk; there’s always a chance the campaign might fail and millions of dollars will disappear down the drain. However, when predictive analytics are used, a company can reduce that risk.
The purpose of predictive analytics, or PA, is to study human behavior and get a sense of how people will respond to certain situations, such as seeing an advertisement.
It does this by taking into consideration a wide variety of statistics and human characteristics, all of which are focused on understanding individual, as opposed to general, behaviors. So you wouldn’t use PA to determine which advertisement has the broadest appeal; you’d use it to determine the likeliest responses of specific people to specific advertisements.
More precisely: once you enter all your variables, you’re given a predictive score. Now, this score doesn’t tell you the future as much as it tells you how probable certain individual reactions will be.
For example, let’s say you want to know which online ad people in the United States will be most tempted to click on while searching for grants and scholarships. The more variables you supply, such as age, gender and email domain, the more precise the predictive score will be.
These predictive scores are useful to organizations that want to know the best demographics to target for certain discount offers and advertisements, as well as for organizations that want to know which stocks to buy or people to audit.
The predictive model used in PA is more dynamic than other models since it’s based on machine learning, which means it can change, grow and adapt based on the kind of data it is given. And it’s more accurate than other predictive tools since it uses backtesting, which takes old data to determine how accurate your results will be.
So, if you’re trying to predict whether the S&P Index is going to go up or down in a year’s time, with backtesting, you can feed it old data from 1990 to see how accurate it is about the S&P in 1991.
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What is Predictive Analytics about?
Predictive Analytics (2016) provides a helpful introduction to a complex and fascinating field. Learn how data gets crunched so that people can make more informed decisions, a practice that has drastically altered the way the world conducts its research and runs its businesses. Siegel offers an enlightening glimpse at the wide-ranging areas that have been forever changed, from marketing to health care, banking to artificial intelligence.
Best quote from Predictive Analytics
Ensemble modeling is often cited as the most important advance in predictive analytics to occur in this century.
—Eric Siegel
Who should read Predictive Analytics?
- Business students interested in applied analytics
- Readers interested in economics
- Tech geeks curious about artificial intelligence
Categories with Predictive Analytics
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