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