Predictive Analytics and the Presidential Election: Key Candidate Attributes that Predict Voter Behaviour in the 2020 Presidential Election | Chapter 5 | New Ideas Concerning Arts and Social Studies Vol. 4

 

Marketing is a key component in elections with voters. Political marketing is an important component and factor influencing how voters choose political candidates. The purpose of this study was to examine key candidate attributes and predictive analytics that influenced voter behaviour in the 2020 Presidential Election. The Political Marketing Candidate Attribute Scale (PMCAS) was developed specifically for this research on political marketing and voter behaviour. 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 2020 Presidential Election. The results of the study reveal the key predictor variables that influenced voter behaviour for candidates for local and state elections, as well as the presidential election. The researchers had four national samples (n = 146), (n = 758), (n = 1,016), and (n = 1,324) 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 election wins. Here, three statistical tests were used to measure a candidate’s attributes that influence voter behaviour. The results of this four-year study revealed three key factors that influence voter behaviour based on candidate attributes.  First, the top ten candidate attributes that predict voter behaviour and predict candidate wins in an election were identified. Second, five key demographic variables are found to be a significant predictive influence on voter behaviour and election wins.  Lastly, it was found that voters are highly influenced by the visual attributes of candidates compared to other attributes. The implication for marketers is that political marketing efforts can be predicted using statistical models and marketing model frameworks.

Author(s) Details

D. Anthony Miles
Miles Development Industries Corporation®, USA.

 

Joshua Garcia
Palo Alto College, USA.

 

Wanda Goodnough
Ashford University, USA.

 

Dt Ogilvie
Rochester Institute of Technology, USA.

 

Eniola Olagundoye
Texas Southern University, USA.

 

E.L. Seay
Albany State University, USA.

 

Nathan Tymann
Grand Canyon University, USA.

 

Robin Shedrick
Wright2Learn LLC, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/nicass/v4/5811