Will traditional policy-oriented ads help elect Vice President Kamala Harris, or will those rooted in storytelling be more persuasive in shifting voters’ opinions in her favor? Which approach can carve a path to victory with key voters?
Ground Media set out to better understand which advertising approach would most effectively shift voter opinion for the 2024 Presidential Election, especially among critical swing state voters essential to the Harris campaign.
Using Swayable’s platform to measure messaging impact, Ground Media chose two ads to test: one focused on immigration and border policy and the other on Kamala Harris’ background and character. Check out the research report below, powered by Swayable’s RCT technology, to see which ad positively impacted key voters and which messaging is cause for concern.
Methodology
This monadic RCT survey experiment was conducted on Swayable over 24 hours between August 27th and 28th, 2024, with responses from 4,105 U.S. consumers. Questions were asked of a general population sample using Swayable’s proprietary online platform.
The sample frame is U.S. smartphone users in all 50 states with active internet connections who are users of popular mobile and web apps that make up Swayable’s network of respondent partners. Respondents are solicited from partner apps with non-monetary reward offers for their participation. Respondents were recruited with an approximately even ratio of men and women imposed via separate quotas for each. This is a “non-probability sample” (in the conventional terminology of public opinion research, although this team believes this concept is not meaningful since truly random sampling of the population is not possible via any methodology). To correct for over/under-sampling, all samples are post-stratified to the general U.S. population using cross-tabulations accounting for factors including age, ethnicity, gender, educational attainment, and geography, based on the latest available data from the U.S. Census Bureau and Swayable’s proprietary population modeling. Margins of error quoted are based on response distribution statistics and sample sizes and are calculated independently for each result.
This research was conducted and self-funded by Swayable.