For 40% of market researchers, marketers, and innovators, their #1 challenge is doing more with less resources.
And yet, most people still aren’t sure what they actually are. Or worse—they think they know, and end up trusting the wrong ones.
Or worse—they think they know, and end up trusting the wrong ones. Like any powerful tool, digital twins can be a breakthrough or a liability. It all depends on how they’re built—and how you use them.
Whether you're in marketing, research or innovation, digital twins will be impacting your field in the next 5 years. Why? They tackle one of your toughest challenges: doing more with less—without compromising quality.
Panoplai Founder + CEO says: you may have heard the term “digital twin” before—but maybe not in the context of market research. The concept is simple: a digital twin is a virtual replica of something that exists in the real world.
For example, General Electric creates digital twins of jet engines to simulate conditions and prevent failures before they happen. More recently, Patrick Collison, CEO of Stripe, described how the Arc Institute is developing a “virtual cell,” using AI to model biological processes in a simulated environment before any physical experiments take place.
A digital twin is a virtual version of a real customer—or an entire customer segment—created using real data. Think of it as your ideal customer, brought to life digitally. You can chat with them, test ideas, and understand how they think and what they want. It’s like having a go-to customer on call, 24/7—ready to ideate with you, stress-test your campaigns, or validate product ideas before launch.
While people may use these terms slightly differently, the core idea is the same: An AI-powered persona built on real human data—not guesses.
Step 1: Start with a real human
We begin by selecting an actual survey respondent. This person isn’t randomly scraped — they’ve opted in, answered thoughtfully, and represent a real-world customer or audience segment.
The segment can be as niche or broad as you’d like.
For example: middle-class mom in Spain who likes to read.
Step 2: Get more information from them
After we’ve decided who exactly to target, we gather a mix of 25+ quantitative data through a pre-screening process including:
Then, we ask questions to these actual respondents through a survey with custom qualitative and quantitative questions to gauge what exactly you want to know from real people. We use sentiment analysis AI features to understand not just who they are and how they think, but also how they feel.
Thus building a well-rounded profile.
Step 3: Narrow down your audience — segment-level or persona-level — to dive deeper
This creates a twin that doesn't just look smart — it actually thinks like your customer.
Step 4: Keep your twin up to date
Your audience evolves — so should your twin. Panoplai twins update continuously with:
We collaborate with clients to define sustainable ingestion and update plans.
We collaborate with clients to define sustainable ingestion and update plans.
Chat with a twin to test hypotheses
Before your idea hits the real world, pressure-test it with someone who thinks like your target customer.
Use it when:
Real example:
ChestPal Pro, a smart stethoscope startup—had traction in Europe but wanted to expand to the U.S. market. To do so, they needed fast, credible insights from frontline healthcare providers (an audience that's expensive and difficult to reach). So, they turned to Panoplai's digital twin solutions. Read the story here.
Craft messaging that resonates
Simulate audience response at scale
Real example:
HubSpot, a long-standing client of ours, needed a dynamic representation of their buyer persona—one that could evolve with their customers. So, they turned to Panoplai, enabling the creation of "Growth Gabby." Read the story here.
Segment-level testing and comparison
Real Example:
TripAdvisor needed to understand the unique (and often generalized) preferences and behaviors of young black travelers. Their findings resulted in a black traveler report—earning them 50M+ combined reach from media outlets while lowering research cost by 40% (check out their story here).
Ideation on-demand
When you need raw, unfiltered consumer data
Use real respondents when you're collecting new data from scratch—like gauging awareness of a totally unfamiliar concept or brand. For example, running a benchmark brand awareness study for the first time? Use live survey fielding.
When regulatory or compliance requirements demand it
Some industries—like finance, healthcare, and pharma—may require directly sourced human data for compliance and audit reasons. For example, clinical trial perception research under FDA oversight.
When you don't trust the foundation
If the twin wasn’t built on verified, first-party data, the insights could be biased, inaccurate, or worse—hallucinated. For example, you’re using a “black-box” persona with no transparency about where the data came from.
When you want emotional texture that's hard to simulate
While digital twins are great at mimicking sentiment, sometimes you need to feel the emotion—tone, pauses, nuance—that only real human interviews can capture. For example, deep-dive ethnographies or in-home interviews for lifestyle brands.
How could I use digital twins as an innovator?
Build better products by simulating real-world reactions before launch.
Example: Pre-launch feedback with a segment twin
Other applications:
digital twin for marketers
How could I use digital twins as a marketer?
Digital twins help you pressure-test messaging before it goes live — so you can spend less time guessing and more time resonating.
Example: Copy test with a Gen Z Creator twin
Other applications:
digital twins for researchers
How could I use digital twins as a researcher?
Use digital twins to validate hypotheses, explore emerging trends, and understand audiences in real time — without waiting weeks for new studies.
Example: Test a new product with a segment twin
Other applications:
digital twin for researchers
