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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
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"Predictive Analytics" by Eric Siegel offers insights into the world of data science and how predictive models can be used to make informed decisions for businesses and individuals. Learn how to turn data into gold.
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Predictive Analytics
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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.
Predictive Analytics Review
Predictive Analytics (2013) by Eric Siegel is a must-read for anyone interested in harnessing the power of data to make accurate predictions and drive business decisions. Here's why this book stands out:
- Offers practical strategies and techniques for leveraging predictive analytics to gain a competitive edge and maximize opportunities.
- Provides real-world case studies that demonstrate the impact of predictive analytics across various industries, making it relevant and insightful.
- Engages readers with its fascinating exploration of the science behind predictive analytics and the ethical considerations surrounding its implementation.
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
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Predictive Analytics FAQs
What is the main message of Predictive Analytics?
The main message of Predictive Analytics is the power of data and how it can be used to make accurate predictions and drive decision-making.
How long does it take to read Predictive Analytics?
The reading time for Predictive Analytics varies depending on the reader's speed, but it typically takes several hours. However, the Blinkist summary can be read in just 15 minutes.
Is Predictive Analytics a good book? Is it worth reading?
Predictive Analytics is a valuable read for anyone interested in harnessing the power of data. It provides insights into how predictive analytics can revolutionize industries and drive success.
Who is the author of Predictive Analytics?
The author of Predictive Analytics is Eric Siegel.
What to read after Predictive Analytics?
If you're wondering what to read next after Predictive Analytics, here are some recommendations we suggest:
- Lean Analytics by Alistair Croll and Benjamin Yoskovitz
- Connected CRM by David S. Williams
- Uncle Tom's Cabin by Harriet Beecher Stowe
- Testing Business Ideas by David J. Bland and Alexander Osterwalder
- Flash Boys by Michael Lewis
- Financial Intelligence by Karen Berman & Joe Knigh with John Case
- Superintelligence by Nick Bostrom
- Mindset by Carol Dweck
- Power And Prediction by Ajay Agrawal
- Made to Stick by Chip Heath and Dan Heath