Political Campaigns and Big Data

TLDR

Modern political campaigns build detailed citizen databases, relying mainly on behavioral responses rather than consumer data, to inform strategy, but widespread use creates a coordination game that limits efficient partitioning of the electorate due to legal and political constraints. Campaign analysts use these data to build individual‑level predictive models estimating likelihoods of political behaviors, support, and responsiveness to targeted interventions. Predictive scores have grown rapidly since 2004 and can provide substantial gains for campaigns that effectively deploy them.

Abstract

Modern campaigns develop databases of detailed information about citizens to inform electoral strategy and to guide tactical efforts. Despite sensational reports about the value of individual consumer data, the most valuable information campaigns acquire comes from the behaviors and direct responses provided by citizens themselves. Campaign data analysts develop models using this information to produce individual-level predictions about citizens' likelihoods of performing certain political behaviors, of supporting candidates and issues, and of changing their support conditional on being targeted with specific campaign interventions. The use of these predictive scores has increased dramatically since 2004, and their use could yield sizable gains to campaigns that harness them. At the same time, their widespread use effectively creates a coordination game with incomplete information between allied organizations. As such, organizations would benefit from partitioning the electorate to not duplicate efforts, but legal and political constraints preclude that possibility.