[Webinar Recap] How to Validate and Operationalize Synthetic Data in B2B Research 

Is synthetic data just hype, or a practical step forward for your research strategy?

Synthetic data has entered the chat, and a familiar tension comes with it. On one hand, your team is under pressure to move faster, cut costs, and bring new ideas. On the other hand, there’s hesitation: Can we trust it? Is it biased? Will stakeholders buy in? 

That push-pull is precisely what we tackled in our May 2025 webinar with Greenbook, hosted by NewtonX COO Leon Mishkis, with our guest Steven Rencher, Senior Market Research Manager at BILL. 

Our conversation offered a clear, actionable roadmap for B2B researchers ready to experiment with and scale synthetic data, without sacrificing quality or trust.

Whether you’re already piloting synthetic sample or just building your case, here’s what you need to know.

What is synthetic data, and why does it matter now?

Synthetic data is artificially generated data that replicates the structure and statistical properties of real-world data. You can use it to supplement survey responses when it’s difficult, costly, or sensitive to gather large enough samples. 

In B2B research, niche audiences and tight budgets can constrain your feasibility. With synthetic sample, you can simulate high-quality responses based on verified trends, instead of introducing unverified responses just to hit a quota. As Steven put it, “I’d rather have a 1,000-person sample with 30% synthetic data than unknown amounts of fraudulent data I can’t control.”

We first explored this idea in our October 2024 webinar with Greenbook. Back then, many were still skeptical. Today, NewtonX clients are actively piloting and scaling synthetic sample. 

If you’re nodding along, you might be ready to take that step.

The challenge: Breaking through the trust barrier

Although there’s growing interest among the insights crowd, skepticism still lingers in other parts of the organization. In our work with researchers, we often see them encounter three stakeholder archetypes:

• The senior leadership pusher: Ready to innovate and wants to cut costs, but doesn’t necessarily provide clear direction on how to make it happen
• The skeptic researcher: Concerned about bias, hallucination, and disruption to established methodologies
• The stuck early adopter: Curious and excited about synthetic data, but struggling to get momentum in a conservative research organization

No matter which archetype you’re working with, the key to progress is trust. We’ve found that a structured, low-risk pilot is the best way to build trust in AI. 

The solution: Launching a risk-free pilot

A well-designed pilot helps you test synthetic data without disrupting your existing workflows or taking on unnecessary risk. In the webinar, Leon shared a simple playbook to follow:

1. Identify a real use case

Start with a project where traditional methods might fall short due to tight timelines, budget gaps, or difficult-to-reach segments.

2. Address stakeholder concerns early

Bring skeptics into the process from the beginning. Validate definitions, success metrics, and guardrails for your data. 

3. Design and run the pilot 

Base both traditional and synthetic methods on the same questionnaire. 

4. Evaluate results and iterate

Consider and compare. Did the results turn out as expected?

Take BILL, for example. Steven shared that doing a pilot was the “linchpin,” the key to earning greater buy-in and investment. He ran an A/B test with NewtonX comparing traditional and synthetic methods, with BILL providing the questionnaire and data outputs, and NewtonX programming the questions, training their model, and generating the augmented data. 

The future: A roadmap for wide-scale adoption

Once you’ve earned trust through your pilot, you can begin integrating AI into your research workflows. In our view, that process starts with ensuring you have a foundation of AI-ready insights. 

As you’ve likely heard before, garbage in yields garbage out. Any synthetic data must have high-quality, verified data as its foundation. One way we achieve this at NewtonX is helping our clients move from online panel sample—with fraud rates of 30%—to verified sample that can be trusted. 

Then, with AI-ready data, you can start building. In the webinar, Leon shared two additional steps: 

1. Define vision and value: Articulate AI’s purpose in your organization, and consider which project types will benefit from it most.

2. Develop your roadmap: Collaborate with partners to chart an AI innovation path, prioritizing projects and resource allocation based on strategic fit and potential impact. Your goal is strategically integrating AI as a tool in your research toolkit. Consider:
   • Where do AI and synthetic data fit within your research portfolio?
   • What are your high-impact projects?
   • Which projects require granular segmentation?

From there, you keep iterating and building, testing in low-risk ways, and scaling the tests that perform well. 

NewtonX Augmented Data—the future of B2B research

At NewtonX, we believe synthetic data is no longer a science experiment. With the right partner, it’s a research accelerator—making it faster, safer, and more cost-effective to uncover insights from even the toughest audiences.

Curious how NewtonX Augmented Data could fit into your next project? Let’s talk.