Turning Insights into Content Confidence with Synthetic Audiences
We recently sat down with EPAM’s Rob Ahnemann, Global Sitecore Practice Lead, to discuss the current challenges with content creation, the evolving role of audience validation, and how AI tools like synthetic audiences can reshape the way marketers approach their content strategy with confidence.
Q: What challenges are marketers facing today when it comes to content creation?
Ahnemann: One of the biggest challenges is the sheer scale of content variants. With personalization, multiple channels and the rise of generative tools, teams are producing more versions of the same idea than ever before. But traditional research methods, like focus groups or surveys, haven’t kept up. They’re great for a few key messages but can’t practically validate dozens or even hundreds of variants before launch. This creates a confidence gap: teams often have to choose between speed and evidence, which can leave them uncertain whether their content will resonate with their target audience.
We unpacked this gap and its challenges in a previous blog, but at a high level, it leaves marketing teams in a tough spot. Without early validation, teams often rely on post-launch analytics to understand what worked and what didn’t. But by then, it’s too late to make meaningful changes without significant cost and effort. This delay forces teams to make decisions based on assumptions rather than evidence, which can hurt engagement and trust with their target audience.
Q: Where do you see the opportunity here? How can marketers address these gaps?
Ahnemann: The key is to start validation earlier — bring it into the content-creation process itself. Imagine being able to test a headline, a page layout or even a call-to-action while you’re still drafting it.
AI is starting to make this more practical. One example is EPAM’s Synthetic Audiences capability within SitecoreAI Page Builder. It is a research tool that allows marketers to interact with AI-powered personas that simulate real target segments, creating a virtual audience for your content. The personas are governed by marketing and research teams to reflect actual customer characteristics — such as demographics, behavior patterns and regional context — enabling questions like “Does this message resonate?” or “Will this headline motivate action?” to be answered in real time. Marketers can conversationally learn how a headline reads or what feels unclear, and the personas can respond with specific, actionable insights and make content updates directly. It's like a focus group and a collaborator built into your authoring environment.
Marketers can conversationally learn how a headline reads or what feels unclear, and the personas can respond with specific, actionable insights and make content updates directly. It's like a focus group and a collaborator built into your authoring environment.
Q: How are synthetic audiences different from traditional research methods?
Ahnemann: Traditional research is often separate from the content creation process. It requires external testers, specialized tools and a significant amount of time. Synthetic audiences, on the other hand, can be integrated directly into the authoring workflow. It allows teams to work faster and more intuitively, without switching between tools.
Another key difference is governance. Synthetic audiences don’t need to be generic; they can be built, defined and managed by the organization so they reflect real customer contexts, business goals and the brand standards teams already know. In the case of SitecoreAI, because the system can also draw on any uploaded brand guidelines, the result is not just an audience simulation, but an audience that responds with an understanding of how your organization communicates. That makes the feedback more relevant, more actionable and better aligned to the realities of the brand.
Q: Can you share an example of how this works in practice?
Ahnemann: Sure! Let’s say a team is creating a landing page for an urban healthcare clinic. The initial copy might emphasize privacy and premium services, but a persona representing budget-conscious patients could flag that the language doesn’t align with their needs for affordability and transparency.
The persona might suggest alternative phrasing and explain why it’s a better fit so the author can then make targeted changes, request localized versions, and even see how the updates resonate all within the same interface. This kind of iteration happens in minutes, not weeks.
Q: What separates brands that consistently launch effective content from those that miss the mark?
Ahnemann: The clearest difference is when they seek evidence. Most organizations validate content after it's live — through analytics, post-campaign reviews or A/B tests that take weeks to reach significance. Category leaders have flipped that sequence. They build feedback into the creation process itself, so confidence is a precondition for publishing, not a byproduct of it.
In practice, this means structured audience personas, lightweight validation embedded in authoring workflows and clear governance over how insights get used. Research stops being a separate project and becomes part of how content gets made every day.
Most organizations validate content after it's live — through analytics, post-campaign reviews or A/B tests that take weeks to reach significance. Category leaders have flipped that sequence. They build feedback into the creation process itself, so confidence is a precondition for publishing, not a byproduct of it.
Q: What’s the biggest impact of synthetic-audience validation in the authoring process?
Ahnemann: The biggest impact is confidence that the content will interest the target audience. For the business, that means reduced need for costly reworks and content that is better aligned with audience expectations across regions and channels.
Striving for improved content confidence also fosters better internal collaboration between marketing and research teams. Validation becomes a routine part of the workflow rather than a separate project, which helps scale this capability as content programs grow.
Q: Where does human judgment come in when AI is generating audience feedback?
Ahnemann: AI is most useful as a starting point, not a final verdict. It surfaces patterns quickly — flagging messages that miss the mark for a particular audience segment, identifying language that doesn't match audience expectations, generating feedback across a large volume of variants and more. What it can't do is weigh those signals against competitive context, brand history, strategy and other nuanced perspectives.
The teams seeing the best results are clear about this boundary. They treat AI feedback as input to an informed decision, not a substitute for one. We also see that in leading AI tools. For example, Synthetic Audiences for SitecoreAI embeds audience simulation into the authoring environment to prompt better questions, not to answer them definitively. The marketer still interprets, prioritizes and decides. AI just makes that process faster and more grounded.
Q: What’s advice would you give organizations looking to adopt an approach with synthetic audiences?
Ahnemann: Knowing your target audience enables you to define your synthetic personas and governance framework. Insight quality directly depends on how well the personas reflect your actual audience.
You’ll also need to integrate validation into your existing workflows. The goal is to make it a natural part of the authoring process, not an extra step.
Finally, remember that synthetic personas are there to enhance human expertise, not replace it. The best results come from combining AI-driven insights with human judgment.
