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Dec 2, 2022

How Artificial Intelligence Swayed The Midterm Elections – And Will Become A Permanent Feature Of Democracy

Source: Forbes
Author: Craig Smith

Artificial Intelligence helped give Democrats a fundraising edge this election season, allowing the blue party to retain control of the Senate and blunt a Republican takeover of the House.

While there were many other factors at play, the Democrats relied more heavily on AI in finding donors, according to professional fundraisers, bringing in more money from individual, small-dollar donors than their Republican rivals.



“AI will play an increasingly important role in democracy going forward,” said Martin Kurucz, who runs Sterling Data Company, a Democratic data technology company in the fundraising space.

Data analytics have been used in political fundraising for more than a decade, and bots – automated systems that post on social media – have long spread political messages on a large scale. But using AI to identify donors and maximize outreach efforts is relatively new.

On the left side of the aisle, fundraisers are using artificial intelligence to predict who’s likely to donate to their cause, while on the Republican side, fundraisers are using artificial intelligence to maximize the efficiency of outreach to established lists of donors. While the Republican approach may raise more dollars per donor, they have fewer donors to raise from.

Consider John Fetterman, the Democrat who won a senate seat in Pennsylvania against Republican Mehmet Oz. According to the Federal Election Commission, Fetterman raised more than $55 million in 2022 compared with just over $15 million by Mehmet Oz. More than half of Fetterman’s haul came from individual donors giving $200 or less, while only about a third of Oz’s contributions were from such small-dollar donors.

Mr. Fetterman, whose campaign used AI, was not alone. Most statewide Democrats outraised their GOP counterparts by large multiples in the small-dollar space.

Federal Elections Commission data from the 2022 election cycle show that Democratic donor organizations such as the Democratic Congressional Campaign Committee, or DCCC, spent around $8.4 million on digital acquisition, while raising $85.6 million from unitemized small dollar contributions – about ten times what they spent. At the same time, Republican organizations such as the National Republican Congressional Committee spent around $32 million on digital acquisition and raised less than double that amount in unitemized small dollar contributions.

While small-dollar donors on the left are typically more affluent than their Republican counterparts, the Democrat advantage is more than just demographics.

For most of the past decade, digital ads have been the prevalent means to find donors. But fundraising has shifted to email and mobile acquisition as digital advertising returns withered following Apple’s 2021 privacy measures that limited the tracking capabilities of digital advertisers. A few firms soon realized that AI-driven donor targeting was the most effective and fastest means of email and mobile acquisition.

“No data analyst in the world can sort through tens of thousands of potential donors and figure out which is most likely to give money,” said Kurucz, whose firm helped both the DCCC and Fetterman, “but AI can do this.”

“We have reached a point where anyone who can use a spreadsheet can use machine learning for data-driven decision-making,” said Jonathon Reilly, cofounder of Akkio.

AI models can spot larger trends, predict online donations, and recalibrate targeting in seconds, allowing campaigns to tailor their outreach in near real-time.

“We generate more revenue per donor than Democrats across the board,” said Tom Newhouse, vice president of digital marketing at Convergence Media, but added that Apple’s iOS changes have hurt Republicans’ donor prospecting strategies.

Sterling Data uses a no-code machine-learning website,, which allows users to drag and drop their data spreadsheets into the site. On the backend, Akkio analyzes the data and gives the user options for what they want to predict. In Sterling Data’s case, that is each person’s likelihood of giving money.

Sterling Data has built up a database of about 30 million donors with 500 or more columns of information on everything from the kind of car each donor drives to which Netflix shows they watch. It runs a subset of the data through an Akkio model trained to spot likely donors for a particular candidate. The result is a prospective donor list ranked from most likely to least likely to give, allowing Sterling Data to focus their efforts without wasting time and money on the wrong people.

“The ease of using AI-ranked lists means data firms selling donor prospects curated by AI can offer pricing that undercuts the rest of the market,” said Jacob Geers, who until recently served as managing director of Veracity Media, a digital strategy agency.

AI tools like Akkio now allow organizations to scale in ways that were once impossible, crunching millions of data points in seconds and prioritizing the actions to take for maximum effect.

“We have reached a point where anyone who can use a spreadsheet can use machine learning for data-driven decision-making,” said Jonathon Reilly, cofounder of Akkio. “Our platform enables non-technical users to extract value from their data.”

Sterling Data then gives the prioritized contact list to the candidate’s team, who contact donors in the most efficient way possible.

“The whole point is predicting who will actually donate if my candidate reaches out,” Kurucz said, adding that Akkio’s model builds prospective donor lists that consistently raise twice as much as lists built with other methods.

Over the last cycle, AI-driven donor acquisition provided by vendors like Sterling Data has changed industry wide expectations for the speed of a return on investment, said Kevin Massey, a partner in Momentum Campaigns, a digital fundraising agency. In past cycles, he said, it was standard to expect around 6 months or more to go by before an email acquisition paid off in full for a campaign. “But with these new AI-driven acquisitions, we’ve seen that increase rapidly to one to three months,” Massey said.

The faster payoff allows campaigns to react more quickly and be smarter with their acquisition budgets because they’re seeing results faster.

Sterling Data spends thousands of hours calling potential high-dollar donors. The firm uses another form of AI called natural language processing to read call notes and glean new details about those donors. Emails are reserved for potential small-dollar donors. Sterling Data works with Democrats up and down the ballot, from congressional to city council candidates.

“Targeting at scale is really hard and getting a hold of the donor, whether that’s through an email or a phone number, is even harder,” Kurucz said.

There are many different data sources from which to aggregate information on potential donors. “The more information you get on potential donors, the better the model is,” said Kurucz.

Kurucz believes the no-code approach will spread because it puts the power in the hands of a daily practitioner who no longer needs expensive data scientists to achieve results. Kurucz noted that he can create and run his AI models using Akkio on a plane on his laptop.

Convergence Media’s Newhouse says they use AI features in an email and SMS platform called Iterable to optimize the timing or mode of outreach, whether by email or text message, for example, which leads to higher donations per donor. But he said Republicans have yet to coalesce around a central set of tech tools that are shared across the board.

“To broaden the Republican donor base, Republicans need to embrace artificial intelligence or and data modeling to identify new donors,” he said.

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