Run Real Experiments
(RCT) methodology to measure how creative impacts
consumer perception before launch.
What Swayable Measures—and
Why It Matters
Swayable’s methodology is designed to answer the most important question in marketing: does your creative change minds?
RCT experiments generate evidence for strategic business decisions.
Measure causal impact on brand lift, consideration, and purchase intent
Compare performance of different creative assets objectively
Understand who the creative persuades vs. the engagement it creates
Make data-driven decisions prior to taking campaigns live
Inside Randomized Controlled Trials (RCTs)
Swayable deploys randomized controlled trials (RCTs) to pre-test messages and creative and identify what stories change minds and who they persuade, before campaigns are launched.
RCTs are used in any context where real evidence matters. In healthcare, they are required to approve new drugs. In marketing, they are the most rigorous way to isolate whether creative causes changes in attitudes, rather than simply correlating with them.
Swayable runs highly efficient RCT survey experiments, pairing rapid respondent acquisition with post-stratification reweighting to maximize statistical power while maintaining scientific rigor.
How Swayable Runs RCTs for Global Brands
Mobile-first respondent acquisition
Swayable recruits respondents from a network of mobile apps with the largest possible user bases, including puzzles, games, and social networking. Our agreements with app publishers and their agents allow us to recruit users to take part in our tests in return for non-cash incentives, generally in-app currencies.
Data integrity
Two structural advantages are responsible for the reliability of our results. First, we treat respondents fairly. They opt in to all tests, so it is a demonstrable fact that we start with much higher quality data than market research industry standard practices. Second, the RCT design inherently minimizes the impact of invalid answers, because random allocation ensures they occur as often in the control group as the test groups, canceling each other out.
Population modeling
With millions of respondents having participated in Swayable tests, the platform’s population modeling adds significant statistical power to all test results. In typical deployments, this gain is the equivalent of at least doubling the sample size at no additional cost in time or money. For results on smaller segments, gains are typically many times greater.
Proving Creative Impact
Swayable computes the Swayable Impact Score by comparing average responses in each test group to the control group baseline.
If a message is successful, responses in the test group will be higher than the baseline. If it has no effect, there will be no meaningful change.
In simple terms, the Impact Score shows whether a piece of creative actually moves opinion—and by how much—compared to doing nothing.
This forced exposure RCT design ensures that nearly everyone who sees the creative completes the survey, making results more reliable than pre/post surveys or in-channel ad platform tests, which often lose the majority of viewers after exposure.
Creative impact matters because ineffective creative cannot be compensated for with better reach or engagement alone. The role of creative is to change attitudes—not just generate views.
Built on High-Quality Data
Strengthened by Modeling
Swayable invests heavily in data integrity and respondent quality.
Respondents are recruited through a large network of mobile apps, including games and social platforms, and opt in to participate in tests in exchange for non-cash incentives. This opt-in model results in higher-quality responses than many traditional research practices.
The RCT design further minimizes the impact of invalid responses, as random assignment ensures noise appears equally across test and control groups, effectively canceling out bias.
In addition, Swayable’s AI population modeling—trained on millions of prior responses—adds significant statistical power to every test. In typical deployments, this modeling provides the equivalent of doubling the sample size at no additional cost, with even greater gains for smaller audience segments.
The result is faster, more reliable evidence teams can confidently use in high-stakes decisions.
