Digital Twins Are the New A/B Test—But Most Executives Can’t Define Them

A quant + qual report on how leaders across marketing, innovation, and market research are racing to adopt digital twins—without a clear definition, playbook, or path to impact.

Digital twins and synthetic data are among the hottest buzzwords in research and innovation right now. Industry panels spew them as the next big leap. LinkedIn feeds are full of bold predictions about their potential. But beneath the hype lies a striking disconnect:

In a survey of 100 B2B leaders—ranging from CMOs to Global Project Directors to VP of Ops—2/3s said they’re eager to apply digital twins. But nearly 60% also confessed they don’t really know what the term means.

That disconnect is the biggest opportunity (and risk) in research today.

Why Definitions Matter

“Digital twin” has meant different things to different industries for decades.

• Engineering: GE used twins of jet engines to predict failures before they happened. 
• Biotech: Stripe’s Patrick Collison spoke about simulating a “virtual cell” before real-world testing. 
• Now: The concept is expanding into marketing, product innovation, and audience discovery. 

But our research shows that when asked to define digital twins, executives often defaulted to vague or outdated ideas.

And it’s not surprising. Tech jargon comes and goes. In marketing, consumer insights, and innovation especially, the buzzwords pile up faster than anyone can realistically keep track of.

When we asked executives, “Have you heard of the following terms?” only 24% said they were familiar with digital twins.

Chart 1: Which tech terms industry leaders are familiar with

Yet, in the same survey, 41% cited “pressure to do more with fewer resources” as one of their biggest challenges.

Chart 2: Top challenges industry leaders are facing today

The disconnect is clear: the urgency is high, but the knowledge is low.

When we asked senior leaders to define digital twins, the answers were all over the map. Not completely wrong—but not entirely right in the context of marketing, insights, and innovation either.

Table 1: How industry leaders define digital twins

These takes reveal the sea of misinformation surrounding the term. They’re not totally off base but they miss the critical opportunity: digital twins in marketing and insights aren’t about objects or sci-fi replicas—they’re about modeling audience behavior.

Consider A/B testing: you run two versions of a message, see which performs better, and optimize. But that only tells you what worked. What if you could also understand why it worked—what emotion it triggered, what context shaped the response, or how that idea might perform in a completely different scenario?

Until recently, that level of insight required focus groups or surveys—slow, costly, and outdated the moment they’re done. Validated digital twins change the game: they offer instant, evolving feedback that grows alongside your strategy.

What Digital Twins Really Are

A digital twin is a virtual version of a real customer—or an entire customer segment—built from real data. Think of your A/B testing, your focus group, your analytics tools, social listening platform, ChatGPT—all wrapped into one person—a digital twin. Unlike a flat persona or generic AI chatbot, a validated twin is an AI-powered persona grounded in first-party data, not guesses.

Take Talkhouse Encore, a premium canned cocktail brand preparing to expand beyond New York. Like most growth-stage CPG companies they faced the challenge of how to test flavors, pricing, and positioning without the time and cost of traditional pilots or focus groups. Using Panoplai’s digital twins, Talkhouse discovered that taste was the strongest purchase driver with their target consumer—a finding that reshaped their entire go-to-market approach.

This resulted in a faster expansion path, retail placements across multiple states, and national coverage in MarketWatch that elevated brand credibility as they scaled.

Where Synthetic Data Comes In

Digital twins are powered by real, first-party data. But what happens when the data you need doesn’t exist yet? That’s where synthetic data comes in. Synthetic data is AI-generated information that mirrors the statistical properties of real data. Done right, it’s not random, not manipulated, and not “made up.” It’s built to fill gaps, scale insights, and unlock scenarios your original dataset didn’t capture—like how consumers might respond to a new product, a cultural shift, or a message no one’s seen before.

Together, digital twins and synthetic data mark a shift from theory to practice. So what does the data reveal about where the market really stands?

What the Data Tells Us About the Future of Digital Twins:

The paradox is clear: leaders are excited (67% are very interested), but uninformed (60% don’t understand). That gap is where marketers and researchers can lead—by learning, educating, and experimenting early. 

The challenges align. 41% say they’re asked to do more with less, and 30% struggle to keep up with AI and tech. Digital twins and synthetic data are designed for exactly these pain points: efficient insights, faster testing, and predictive power without costly trial and error.

The barriers are real. Too many still think “digital twins” means factory modeling, or dismiss synthetic data as “fake.” That branding problem is slowing adoption more than the technology itself.

The opportunity is urgent. Marketers and researchers who connect digital twins to ROI—campaign testing, audience simulation, data-gap filling—won’t just win buy-in. They’ll redefine what agile, predictive research looks like.

Where This is Headed & Why Early Movers Win

The future of research won’t be a choice between old and new. It will be hybrid—traditional surveys and qualitative depth, powered by validated twins and synthetic data. The result? Faster timelines, stronger rigor, and predictive power that outpaces the market.

But here’s the catch: the gap between interest and understanding is closing fast. History shows that research tends to resist change—whether it was the move to online surveys, mobile panels, or automation. Early skeptics eventually scrambled to catch up, often after losing ground.

Early adopters will shape the rules of the game. They’ll set the standards, establish best practices, and prove value before the market matures. Late adopters will be forced to follow on someone else’s terms.

So what can you do now?

1. Learn in context. Explore digital twins in market research—not just as engineering or IT jargon (read our all-in-one digital twin guide).
2. Validate relentlessly. Ask where the data comes from, how it’s tested, and what biases may remain. 
3. Pilot, then scale. Start small, prove value, and expand thoughtfully. 

The tools are already here. The question is whether our industry embraces them by choice, or waits until necessity forces its hand. Marketers and researchers: the next move is yours