Predictive analytics, five years later

This week I am at Predictive Analytics World Boston to learn from leaders in statistics, data mining, and related analytical disciplines. Much has changed since I attended the inaugural PAW conference over five years ago but many of the core challenges and techniques remain the same. While it’s clear that demand for predictive analytics has increased significantly over the last several years—measured in part…
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Measuring marketing value through incrementality experiments

Last week Google released CausalImpact, an open-source r package that both improves and simplifies incrementality analysis of time series data. For marketers who do not always have the luxury of A/B tests but still want to be data-driven, this package should be a core part of the toolkit. Based on original research, the model establishes what’s called a…
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Survival analysis to understand customer retention

Survival analysis is a branch of models used in medical studies to measure time to outcomes or patient duration and other disciplines have similar techniques (e.g. duration analysis in economics). These techniques are easily translated to customer analytics: BarryAnalytics provides an introduction to the application of survival analysis in retention modeling. Most churn analyses focus…
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Nate Silver on prediction as art and science

Nate Silver’s presidential poll analysis cleaned up in 2008 with 49/50 states correct and 35/35 Senate races. Now he’s written a book on forecasting and unavoidable biases: In The Signal and the Noise, Silver looks at analysts in many fields, from weather to the economy to national security, and concludes that those who succeed at spotting…
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