The News Revenue Hub is using AI to help newsrooms better understand why readers donate and how to turn those insights into stronger fundraising strategies.

By combining clean first-party data with widely available AI tools, the non-profit has helped publishers extract meaning from thousands of donor comments that previously went unused, explained Sophie Ho, director of product and insights at News Revenue Hub in San Diego, during recent INMA Webinar How to Use AI to Gather First-Party Data.

The result is faster analysis, clearer messaging decisions, and more sustainable fundraising practices for small- and mid-sized newsrooms. What once required weeks of manual work can now be done in hours, without sacrificing editorial judgment, Ho said.

Founded in 2016, the News Revenue Hub supports news organisations by providing donation management tools and hands-on consulting focused on reader revenue. Over the past decade, it has partnered with more than 70 newsrooms and helped raise over US$150 million directly from audiences, Ho said.

In 2025, through a partnership with the American Journalism Project’s Product + AI Studio, News Revenue Hub worked with several newsrooms to test how AI could assist with real, newsroom-ready tasks. One of those experiments focused on fundraising data: specifically, the free-text “reasons for giving” that donors leave when they contribute, Ho said.

For many newsrooms, those comments are emotionally rich but operationally difficult to analyse. Small teams rarely have the time to read thousands of responses line by line. The goal of the experiment was not to automate decisions but to surface patterns that could inform strategy while keeping humans in control.

What the analysis revealed

Using AI to analyse nearly 9,000 donor comments from Cityside, a Bay Area non-profit that operates Berkeleyside, Oaklandside, and Richmondside, News Revenue Hub uncovered donors were motivated less by individual beats or reporters and more by the newsroom’s overall mission and community impact.

While coverage areas like local government, education, and dining appeared in comments, they were secondary to broader expressions of trust and civic value.

The analysis also showed donors who left written reasons tended to give higher recurring donations, though those comments alone were not a reliable predictor of long-term giving behaviour. Seasonal patterns emerged clearly, with year-end fundraising driving the highest volume of donations and donor messages. AI made it possible to identify these themes quickly and consistently.

“This was about surfacing patterns, not replacing judgment,” Ho said. “We wanted to understand what donors were really responding to at scale. AI helped us get there faster, but the interpretation and decisions stayed with the people who know their communities best.”

Understanding donor motivation at scale

The core value of the project was its ability to turn unstructured text into usable insight. Donor comments often contain personal stories and emotional feedback, but without analysis, they remain anecdotal.

“Reasons for giving are incredibly powerful, but they’re also overwhelming when you’re short on time,” she said. “AI allowed us to step back and see the bigger picture of why people support these newsrooms, instead of relying on a handful of memorable comments.”

By categorising motivations and emotional themes, the analysis gave fundraising teams a high-level view they had never had before.

A key principle of the experiment was using AI as a tool to extend capacity, not replace staff or editorial decision-making. The entire analysis process took roughly one hour once the data was prepared.

“This wasn’t about building custom tools or hiring engineers,” she said. “It was about clean data, thoughtful questions, and using existing AI tools in a disciplined way that connects directly to real decisions.”

Since the Cityside experiment, News Revenue Hub has replicated the analysis with other publishers. Some used it to refine messaging for donors who had moved away from their communities. Others used it to onboard new staff by quickly explaining long-term fundraising trends.

For News Revenue Hub, the takeaway is not that AI is a silver bullet, but that it can unlock value from data newsrooms already have.

“What excites me most is the efficiency,” she said. “What used to take days or weeks can now happen in a couple of hours. For small teams, that time savings can make the difference between reacting and actually being strategic.”

Banner art by Adobe Stock Viktor.

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