Machine Learning Times
Machine Learning Times
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Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
Today’s AI Won’t Radically Transform Society, But It’s Already Reshaping Business
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A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...
The AI Playbook: Providing Important Reminders to Data Professionals
 Originally published in DATAVERSITY. This article reviews the new...
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9 years ago
White Paper – Immediate Access

 

Uplift Modeling: Predictive Analytics Can’t Optimize Marketing Decisions Without It written by PAW Founder Eric Siegel, Ph.D., and sponsored by Pitney Bowes Business Insight
Thank you for your interest in the white paper, “Uplift Modeling: Predictive Analytics Can’t Optimize Marketing Decisions Without It,” written by PAW Founder Eric Siegel, Ph.D., and sponsored by Pitney Bowes Business Insight.
 

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WHITE PAPER DESCRIPTION:

To drive business decisions for maximal impact, analytical models must predict the marketing influence of each decision on customer buying behavior. Uplift modeling provides the means to do this, improving upon conventional response and churn models that introduce significant risk by optimizing for the wrong thing. This shift is fundamental to empirically driven decision making. This convention-altering white paper reveals the why and how, and delivers case study results that multiply the ROI of predictive analytics by factors up to 11.

For upcoming presentations on uplift modeling at Predictive Analytics World, and other additional related resources, see this article.

For a broader introduction to uplift modeling, see also the final chapter of Eric Siegel’s book, Predictive Analytics.

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