The 2008 election fundamentally changed political campaigning by revolutionizing the use of digital. Since then, political digital ad spend has grown at an average rate of 744% per presidential election cycle.
With the persistent and rapid rise of digital’s role in budgets and outcomes, campaigns that solely rely on the voter file for GOTV and persuasive messaging are discovering a critical challenge: when an offline voter file is on-boarded via a third party, which is essential to the process, up to 60% of high-propensity voters are lost in the offline to online match.
For years, the voter file was the gold standard for data-driven GOTV and engagement across all mediums and continues to provide the necessary fundamentals, such as demographics and party registration. A well modeled voter file also continues to be a necessary tool to guide doorknocking and phone efforts. However, for the modern political campaign looking to optimize digital budget, especially in tight races where a small number of votes can make all the difference, the voter file alone is unable to accurately identify your most valuable targets and maximize your scale of outreach to them.
Furthermore, campaigns in competitive battlegrounds home to elusive swing voters are playing a game of Russian roulette if targeting and messaging is solely based on voter file attributes like party registration.
Let’s take a deeper look at these pressing modern-day challenges and the solutions campaigns can leverage to go beyond the voter file and drive digital wins.
Challenge: Poor Data Modeling and On-boarded Voter Files
The obvious challenge with voter files and digital is that the data is collected offline. Next, the voter file is most often appended with publicly available consumer data from any variety of sources to help make a more informed prediction of the issues, candidates and agendas voters may support. If any single component of these models is built using stale, poor or dubious data sources, then the model’s integrity is sacrificed before the on-boarding process even begins. Once the onboarding is complete, typically household level rather than on an individual basis, you can find yourself with that offline-online match rate average of 40%. For a campaign looking to most wisely invest its finite resources, the question becomes: how do you maintain accuracy of targeting and regain that 60% lost reach?
Savvy digital strategists understand that the solution to overcoming this obstacle is to supplement the offline voter file with sophisticated lookalike modeling strategies and alternative methodologies that can provide true 1:1. Building models with real-time dynamic behavioral data and leveraging machine learning ensures their campaign is always working with the freshest data sets that can most accurately identify and target voters online.
The most efficient online strategy goes further than one-dimensional predictive modeling, such as browsing activity or 3rd party consumer data, to really get to the core of what drives a voter’s decision: their policy positions, values and motivations.
For example, at Resonate we believe the most effective way to obtain this information is to ask them. Our solution begins with leveraging massive survey waves, taken on a rolling basis throughout the year, that ask voters where they stand on the most relevant campaign issues and the values that drive everything from their politics to day-to-day decision making. We then fuse our proprietary survey information with these voters’ online behavioral activity, including massive contextual analysis, to develop highly accurate predictive models at census scale. A critical piece to the puzzle is the models update on a nightly basis with the 15B daily behavioral data points we observe, ensuring that individual-level sentiment shifts across the electorate are accounted for and we avoid the type of inaccuracies we see with point-in-time polling methods.
The end result of this methodology is campaigns can digitally access 90% of the U.S. electorate online and quickly identify and reach the right voters for their campaign through a combination of thousands of persuasive targetable attributes. Attribute combinations can include anything from the basics such as demographics or party registration, to deep-level insights revealing policy stances, issue engagement areas, candidate platform support and more.
Challenge: Blanket Targeting and Messaging in Competitive Races
In competitive battlegrounds where individual-level persuasion is key, solely relying on voter file attributes like party registration is a dangerous blanket approach that risks neglecting critical independent support. While turning out the party base is a necessity, having a strategy to identify, analyze, and reach persuadable swing voters is often the difference between winning and losing.
By supplementing voter file data with dynamic online behavioral models, campaigns can accurately identify those swing voters in their state/district and understand the policy positions and motivations likely to swing their vote. Equipped with this individual-level data, campaigns can engage issue-based swing voters with a personalized message that goes beyond party affiliation and speaks directly to their most relevant ballot box decision drivers.
Solution: Adapt and Win
Just as political digital ad spend has grown at an astronomical rate, so hasn’t the level of sophistication in voter data, analytics and online targeting readily available for campaigns. Given the severe limitations associated with solely relying on the voter file for targeting accuracy and message development, the fact that so many campaigns continue to ignore or understand the advantages of adding dynamic models presents an unprecedented opportunity for the campaigns that adapt to the future.
Resonate fuses the nation’s largest proprietary voter survey data with dynamic behavioral analysis to generate the most accurate, real-time insights and predictive modeling at scale. Our end-to-end data, analytics and digital media activation solution unifies strategy and action to drive campaign wins.