Predictive Analytics Book Summary - Predictive Analytics Book explained in key points
Listen to the Intro

Predictive Analytics summary

Eric Siegel

The Power to Predict Who Will Click, Buy, Lie, Or Die

4.1 (66 ratings)
17 mins
Table of Contents

    Predictive Analytics
    Summary of 7 key ideas

    Audio & text in the Blinkist app
    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.

    Want to see all full key ideas from Predictive Analytics?

    Key ideas in Predictive Analytics

    More knowledge in less time
    Read or listen
    Read or listen
    Get the key ideas from nonfiction bestsellers in minutes, not hours.
    Find your next read
    Find your next read
    Get book lists curated by experts and personalized recommendations.
    Shortcasts New
    We’ve teamed up with podcast creators to bring you key insights from podcasts.

    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
    example alt text

    Who should read Predictive Analytics?

    • Business students interested in applied analytics
    • Readers interested in economics
    • Tech geeks curious about artificial intelligence

    About the Author

    Eric Siegel is a world-renowned leader in the field of predictive analytics and the founder of the Predictive Analytics World Conference Series. A former Columbia University professor, he’s also the executive editor of the Predictive Analytics Times.

    © Eric Siegel: Predictive Analytics copyright 2016, John Wiley & Sons Inc. Used by permission of John Wiley & Sons Inc. and shall not be made available to any unauthorized third parties.

    Categories with Predictive Analytics

    Books like Predictive Analytics

    People ❤️ Blinkist
    Sven O.

    It's highly addictive to get core insights on personally relevant topics without repetition or triviality. Added to that the apps ability to suggest kindred interests opens up a foundation of knowledge.

    Thi Viet Quynh N.

    Great app. Good selection of book summaries you can read or listen to while commuting. Instead of scrolling through your social media news feed, this is a much better way to spend your spare time in my opinion.

    Jonathan A.

    Life changing. The concept of being able to grasp a book's main point in such a short time truly opens multiple opportunities to grow every area of your life at a faster rate.

    Renee D.

    Great app. Addicting. Perfect for wait times, morning coffee, evening before bed. Extremely well written, thorough, easy to use.

    People also liked

    Start growing with Blinkist now
    28 Million
    Downloads on all platforms
    4.7 Stars
    Average ratings on iOS and Google Play
    Of Blinkist members create a better reading habit*
    *Based on survey data from Blinkist customers
    Powerful ideas from top nonfiction

    Try Blinkist to get the key ideas from 7,000+ bestselling nonfiction titles and podcasts. Listen or read in just 15 minutes.

    Start your free trial