Voter$-$Persuasion.cv is the dataset for this case study.

Note: Our thanks to Ken Strasma, President of HaystaqDNA and director of

targeting for the $2004$ Kerry campaign and the $2008$ Obama campaign, for the

data used in this case and for sharing the information in the following writeup.

Background

When you think of political persuasion, you may think of the efforts that political

campaigns undertake to persuade you that their candidate is better than the other

candidate. In truth, campaigns are less about persuading people to change their

minds and more about persuading those who agree with you to actually go out

and vote. Predictive analytics now plays a big role in this effort, but in $2004,$ it

was a new arrival in the political toolbox.

Predictive Analytics Arrives in US Politics

In January of $2004,$ candidates in the US presidential campaign were compet$-$

ing in the lowa caucuses, part of a lengthy state$-$by$-$state primary campaign that

culminates in the selection of the Republican and Democratic candidates for

president. Among the Democrats, Howard Dean was leading in national polls

The low caucuses, however, are a complex and intensive process attracting only

the most committed and interested voters. Those participating are not a repre$-$

sentative sample of voters nationwide. Surveys of those planning to take part

showed a close race between Dean and three other candidates, including John

Kerry.

Kerry ended up winning by a surprisingly large margin, and the better than

expected performance was due to his campaign's innovative and successful use

of predictive analytics to learn more about the likely actions of individual voters.

This allowed the campaign to target voters in such a way as to optimize perfor$-$

mance in the caucuses. For example, once the model showed sufficient support

in a precinct to win that precinct's delegate to the caucus, money and time could

be redirected to other precincts where the race was closer.