Microtargeting and electorate segmentation: Data mining the American National Election Studies

Gregg R. Murray, Anthony Scime

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Business marketers widely use data mining for segmenting and targeting markets. To assess data mining for use by political marketers, we mined the 1948 to 2004 American National Elections Studies data file to identify a small number of variables and rules that can be used to predict individual voting behavior, including abstention, with the intent of segmenting the electorate in useful and meaningful ways. The resulting decision tree correctly predicts vote choice with 66 percent accuracy, a success rate that compares favorably with other predictive methods. More importantly, the process provides rules that identify segments of voters based on their predicted vote choice, with the vote choice of some segments predictable with up to 87 percent success. These results suggest that the data mining methodology may increase efficiency for political campaigns, but they also suggest that, from a democratic theory perspective, overall participation may be improved by communicating more effective messages that better inform intended voters and that motivate individuals to vote who otherwise may abstain.

Original languageEnglish (US)
Pages (from-to)143-166
Number of pages24
JournalJournal of Political Marketing
Volume9
Issue number3
DOIs
StatePublished - Aug 20 2010
Externally publishedYes

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Keywords

  • Campaign strategy
  • Classification
  • Data mining
  • Domain expert
  • Domain knowledge
  • Microtargeting
  • Political marketing
  • Vote choice
  • Voting behavior

ASJC Scopus subject areas

  • Sociology and Political Science
  • Marketing

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