Predictive Analytics and the 2024 Presidential Election: A Study of Key Candidate Attributes That Predict Election Results in the 2024 Presidential Election

Abstract

Marketing is a key component in elections with voters. Political marketing is an important component factor and influence on how voters choose political candidates. The purpose of this study was to examine key candidate attributes and predictive analytics that influenced voter behavior in the 2024 Presidential Election. The Political Marketing Candidate Attribute Scale (PMCAS) was developed specifically for this research on political marketing and voter behavior. This study is the result of a four-year research project on how political candidates win or lose elections based on predictive analytics, which included local and state elections and the 2024 Presidential Election. The results of study reveal the key predictor variables that influenced voter behavior for candidates for local and state elections as well as the presidential election. The researchers had five national samples (N = 1,774)  in the U.S. that were used for this research on political marketing and candidate attributes.

For this study, the researchers examined 30 candidate attributes that are key indicators in predicting elections wins. We used three statistical tests to measure a candidate’s attributes that influence voter behavior. The results of this four-year study revealed three key factors that influence voter behavior based on candidate attributes.  First, we identified the top ten candidate attributes that predict voter behavior and predict candidate wins in an election. Second, we found five key demographic variables that are a significant predictive influence on voter behavior and election wins.  Lastly, we found that voters are highly influenced by visual attributes with candidates compared to other attributes. The implication for marketers is that political marketing efforts can be predicted using statistical models and marketing model frameworks.

Keywords: Political marketing, predictive analytics, marketing models, political campaigns,

marketing frameworks, political marketing infrastructure.

DOI: 10.7176/JMCR/96-04

Publication date: February 28^th 2026