Last week we looked at several metrics for assessing the performance of classification models – accuracy, receiver operating characteristics (ROC) curves, and lift (gains). In this week’s Brief we move beyond lift and cover uplift.
Marketers have long known that different messages to different people work better than one-size-fits-all. The statistical models that guide differentiated messages are termed […]
Predictor p-values in linear models are a guide to the statistical significance of a predictor coefficient value – they measure the probability that a randomly shuffled model could have produced a coefficient as great as the fitted value. They are of limited utility […]
Case Study
Second Opinions
Should employers or insurers require second opinions as a way to reduce disability costs? Read this Elder Research case study of how multiple analytic techniques were brought to bear on this problem.
Your instructor is Ken Strasma, director of targeting for the Kerry and Obama presidential campaigns, and co-founder and CEO of HaystaqDNA, a firm that provides predictive analytics and strategic consulting services for corporations, non-profits and membership organizations.
In this course you will learn how to:
Find voter/customer targets that are appropriate to a campaign phase
Design a survey instrument for use in a predictive model
Conduct an A-B test
Implement predictive models with voter data
Add uplift modeling to predict whether a person responds better with a “treatment”
See you in class!
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