Finding the right audience is hard. Finding an audience that barely exists in traditional research panels is even harder.
That’s the challenge agencies face when clients want insights into highly specific consumer groups, such as politically independent news subscribers or niche investor communities. Recruiting those audiences can take weeks and cost thousands of dollars, often slowing product launches, media planning and audience development efforts.
That’s the problem StatSocial hopes to address with Digital Twins, a new product launching today. The tool allows brands, agencies and publishers to simulate audience research using AI-generated profiles built from real behavioral data.
The platform creates anonymized digital representations of audiences using StatSocial’s behavioral graph, which maps interests, affinities, media consumption habits and other attributes across hundreds of millions of consumers. Users can build audience segments, ask questions, test creative concepts and evaluate products without recruiting traditional focus groups.
One of the earliest testers is Shepherd, an audience strategy consultancy that has worked with StatSocial for more than eight years.
For Shepherd, the appeal of Digital Twins is accelerating its research capabilities.
Turning audience profiles into audience conversations
Shepherd has long used StatSocial’s behavioral data to help clients understand difficult-to-reach audiences.
“We work with really highly niche audiences,” said Dean McBeth, managing partner and co-founder at Shepherd. “No one has these panels, but what Stat provided was hundreds of millions of people anonymized across tens of thousands of data points. We could get those behaviors and start to understand what people were doing online without asking them.”
That behavioral data helped Shepherd build audience profiles and identify patterns across media consumption, interests and purchasing behavior. Digital Twins adds another layer by allowing the agency to interrogate those audiences directly.
“It was such a natural extension,” McBeth said. “We already had the audience. We were already creating them on their platform and studying them. So it was such a natural extension to go, ‘Oh, we can ask them stuff now.’”
StatSocial’s CEO, David Barker, told AdExchanger that the technology is a way to place hundreds of audience members into a virtual room and ask questions at scale. Users can upload creative assets, test messaging or conduct free-form interviews with AI-generated audience members whose responses are informed by behavioral data.
Unlike many AI research tools that rely on synthetic data or survey panels, Barker says Digital Twins starts with real audience behaviors.
“We start with the digital twins and then do the AI responses on top,” Barker said. “It’s a much more granular level of data.”
Testing Digital Twins before going to market
One of Shepherd’s early use cases involved a news and entertainment company exploring whether audiences would pay for a new editorial offering.
The company wanted to move beyond its mix of personalities and video content by layering in written editorial. Traditionally, answering that question would require audience modeling, recruitment, surveys and interviews with a cohort that doesn’t really exist in standard research panels.
Instead, Shepherd matched the publisher’s first‑party subscriber data with StatSocial’s behavioral graph, then used Digital Twins to segment core subscribers, casual users and prospects, and to test reactions to different content concepts. StatSocial’s data had already shown that many of these users paid creators via Patreon and other online subscription services, suggesting a baseline willingness to pay if the offer was positioned correctly.
The results challenged some assumptions.
Core audience members liked the concept but weren’t the most eager to pay. Prospective users showed a greater willingness to pay, particularly when the product was framed more closely to the creator‑driven content they were already supporting. Those findings helped Shepherd rethink positioning and pricing before investing in broader testing.
“It gives us some really solid understanding of how these audiences might react to a potential product or service,” McBeth said. “The work that we would have had to do would have taken weeks and taken time to recruit those people.”
Shepherd is also using Digital Twins with a fin tech company that combines chores, allowances, safety tracking and family calendaring for “household CFO” and with a long‑standing financial publisher serving institutional and DIY investors who are expensive and time‑consuming to recruit.
In each case, the common challenge is access.
“It’s a really hard audience to recruit,” McBeth said. “Being able to go directly into asking questions is huge.”
Can AI research match reality?
The biggest question surrounding AI-powered research tools is whether the answers can be trusted.
McBeth said Shepherd has spent much of its testing period validating Digital Twins against historical research, first-party customer data and live surveys. So far, the agency has seen strong alignment between the digital models and real-world responses.
“We’re actually getting a lot of the same feedback,” McBeth said. “There isn’t a big delta between how they’re responding as a twin and how we’re seeing them show up in real life.”
That doesn’t mean traditional research disappears. Shepherd still uses surveys and qualitative interviews when clients need additional validation. Instead, the agency views Digital Twins as a faster way to test assumptions. It can help identify promising opportunities and determine where deeper research is worth the investment.
As audience segmentation becomes more specialized and marketers face growing pressure to move faster, tools that reduce the time between insight and action could become increasingly valuable.
