No data scientist? No problem: How low-code AI platforms like Akkio can help
AI is increasingly important to all industries, including political fundraising, as found by Sterling Data Company.
There will be over 1,000 elections in the United States in 2022 at the state level and above. And as of June 30, 2022, six fundraising committees associated with the Democratic and Republican parties reported raising a total of $1.3 billion. That’s a lot of political campaigning and money.
Raising and spending that money effectively on campaigns is where specialist companies like Sterling Data Company come in. Sterling is a national fundraising-focused Democratic political data company. Regardless of your political preferences, Sterling’s use of artificial intelligence is instructive for virtually any organization seeking to gain a competitive advantage.
Low code, big donors
The fact that Sterling is increasingly reliant on AI to drive its business isn’t particularly surprising: who isn’t using AI today? But what is surprising is how Sterling Data uses AI. For starters, they don’t hire data scientists or AI specialists. Instead, using Akkio, a no-code SaaS AI platform, they simply upload Excel spreadsheets to the cloud. Despite the simplicity of this approach, Sterling’s AI-focused capabilities are comparable to those of much larger – and more expensive to hire – companies with dedicated teams of data scientists and AI experts.
So how does it work?
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Sterling works with candidates from the city council level all the way up to congressional races. Among more than 1,000 clients, they consult for Beto O’Rourke’s run in the Texas gubernatorial campaign. To support their work, Sterling has built a database of over 30 million campaign donors over the years, with each donor defined by no less than 500 different variables such as amount donated, voting record, age, magazine subscriptions and more.
According to Sterling CEO Martin Kurucz, each U.S. congressional district averages up to 50,000 donors, with an average of around 17,000.
“How do candidates find people who will care about their cause? Kurucz asked. “That’s where we come in. We have a ton of variables and we’re trying to figure out who the most likely people are.”
The data analytics component is only part of the extraordinarily complex challenges of developing candidate fundraising strategies, Kurucz said. There are many different analytical models for different campaign scenarios, such as whether a politician is a longtime candidate in a given race.
To show how AI can have a profound impact on the outcome, Kurucz walked me through a hypothetical race for a congressional candidate in Minnesota. The candidate provides data that he has collected himself. From this list, coupled with Sterling’s own database, and given the budget the campaign needs to spend on fundraising, Sterling needs to know who is most likely to donate.
Working with over 500 variables per name, Kurucz gives Akkio parameters for a model it wants to generate that will bring up the most likely donors. Depending on the size of the dataset, Akkio returns results in 30 seconds to 30 minutes.
“I haven’t found anything else that can do that, not even close,” Kurucz said. “The resulting pattern is unique to this candidate. Now you can deploy it to predict “zero”, not a donate, or “one”, a likely donate. And we test it and revise the model. Even better, you can test it back.
Templates created by Akkio perform up to 400% better than templates created the usual way, he said. Its top models have historically shown more than 100% ROI in three months for candidates. Akkio models can be ROI positive within a month.
That’s the power of AI. But the magic is that a no-code approach puts that power in the hands of a daily practitioner who no longer needs expensive data scientists to get those results.
Kurucz says he can build and run his AI models on an airplane on his laptop.
“Spending on slate analysis in political campaigns is skyrocketing,” he said. “But only a few companies have all the pieces of the puzzle to make it work – increasingly powered by AI. So that’s where most things are heading.
Disclosure: I work for MongoDB, but the opinions expressed here are my own.