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DISH "Viewers to Voters" model predicts
Further, the Viewers to Voters model forecasts a Republican majority
victory in the
"With so much focus around national polling, we thought it'd be
interesting to see if we could find a correlation between how our
customers interact with DISH and how they may vote," said
Developed in partnership by DISH's Data Science and Media Sales divisions, Viewers to Voters is the first project completed under the company's extra-curricular initiative, "Wisdom of Crowds." DISH's Wisdom of Crowds program will explore the potential relationships between TV-viewing behaviors and real world actions.
Viewers to Voters additionally analyzed the relationship between programming watched and political affiliation. The model determined that customers who watched more sports, religious or family-oriented television were more likely to vote Republican. Examples include NFL Football, GEB America's "Giving Hope, Sharing Life" and PBS' "Sesame Street."
In contrast, customers who watched more series/specials, education or
music-oriented television were more likely to vote for
DISH collects anonymized, aggregated viewership data from millions of set-top boxes across its national footprint. Working with terabytes of viewership data, DISH's Data Science team took a two-step approach to developing a predictive model for the 2016 presidential election.
The first stage of the model identified the relationship between the shows DISH customers watched in 2014 with the state-by-state outcomes of the 2014 House elections. A wide assortment of variables were analyzed including the types and amounts of programming customers viewed across nine different Nielsen-defined genres (e.g. sports, movies, education and series/specials). The second stage of the model then identified the relationship between House party control and presidential party affiliation dating back to 1932.
DISH took this approach to accommodate its relatively young historical viewership data set that dates back only to 2014, when the last House election occurred. The Data Science team did not draw from any outside polls to reach its conclusions. The group expects to enhance its model by introducing hundreds of additional variables for future projects.
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