Predictive Analytics Introductory Key Terms

Predictive Analytics Introductory Key Terms, Explained

Predictive Analytics Introductory Key Terms, Explained
Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.

This article compiles the key definitions included throughout PAW Founder Eric Siegel’s popular, award-winning book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Revised and Updated, 2016), which has been adopted as a textbook at over 35 universities—but reads like pop science, dubbed “The Freakonomics of big data.”

Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.

A mechanism that predicts a behavior of an individual, such as click, buy, lie, or die. It takes characteristics (variables) of the individual as input and provides a predictive score as output. The higher the score, the more likely it is that the individual will exhibit the predicted behavior.

Advanced machine capabilities that are by definition impossible to achieve since, once achieved, they have necessarily been trivialized (by way of being mechanized) and are therefore not impressive in the subjective sense of “intelligence,” so they no longer qualify. To put it another way, the word “intelligence” has no formal definition, so why use it in an engineering context? However… I still feel like IBM’s Watson seems truly intelligent when watching it play the TV quiz show Jeopardy!. I’m like, “Wow!” This definition is not an excerpt from the book Predictive Analytics, but it does summarize one of my conclusions in the book’s chapter on Watson.

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A type of predictive model that predicts the influence on an individual’s behavior that results from applying one treatment over another. Synonyms include: differential response, impact, incremental impact, incremental lift, incremental response, net lift, net response, persuasion, true lift, or true response model.

The uplift score output by and uplift model answers the question, “How much more likely is this treatment to generate the desired outcome than the alternative treatment?” For more information, see the article Personalization Is Back: How to Drive Influence by Crunching Numbers (which includes links for further reading at the end), Chapter 7 of Predictive Analytics, and, for more technical citations, the Notes corresponding to that chapter, which may be downloaded as a PDF at

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