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In political campaigning substantial resources are spent on votermobilization, that is, on identifying and influencing as many people aspossible to vote. Campaigns use statistical tools for deciding whom to target("microtargeting"). In this paper we describe a nonpartisan campaign that aimsat increasing overall turnout using the example of the 2004 US presidentialelection. Based on a real data set of 19,634 eligible voters from Ohio, weintroduce a modern statistical framework well suited for carrying out the maintasks of voter targeting in a single sweep: predicting an individual's turnout(or support) likelihood for a particular cause, party or candidate as well asdata-driven voter segmentation. Our framework, which we refer to as LORET (forLOgistic REgression Trees), contains standard methods such as logisticregression and classification trees as special cases and allows for a synthesisof both techniques. For our case study, we explore various LORET models withdifferent regressors in the logistic model components and differentpartitioning variables in the tree components; we analyze them in terms oftheir predictive accuracy and compare the effect of using the full set ofavailable variables against using only a limited amount of information. We findthat augmenting a standard set of variables (such as age and voting history)with additional predictor variables (such as the household composition in termsof party affiliation) clearly improves predictive accuracy. We also find thatLORET models based on tree induction beat the unpartitioned models.Furthermore, we illustrate how voter segmentation arises from our framework anddiscuss the resulting profiles from a targeting point of view.

Influencing elections with statistics: Targeting voters with logistic regression trees