Simulations create decision environments, not just data
Editor’s note: Kelly McKnight is executive director at Verve.
I’ve been talking about simulations in the context of insights for over three years. My team is often asked what we mean by simulations, how they differ from synthetic data and why the distinction matters when it comes to making better decisions. So, let’s break it down.
Four modes of insight
We can group today’s research and insight approaches into four boxes.
Representation – traditional research
Representation is the goal of traditional research: sampling, quotas and method design all work to mirror the population. This foundation delivers trustworthy segmentations, surveys, qualitative insight and personas rooted in lived experience. But these outputs are static, slow to update, siloed and limited in scale – out of step with how quickly customer expectations now shift. And layered on top is a well-documented respondent quality crisis.
Representation remains the most trusted and low-risk foundation for decision-making, but its value is increasingly limited as traditional research struggles to keep pace with today’s rapid decision cycles.
Imitation – synthetic data, synthetic personas and GPTs
Imitation tools – including synthetic datasets, synthetic personas and GPT-style generative models – generate artificial versions of people or markets, producing fast, scalable outputs. But as Ipsos warns (registration required), imitation tools can be “plausible yet completely wrong,” reproducing patterns in existing data rather than the complexity of human behavior. They’re helpful for early drafting or hypothesis development, but risky for real decisions as most synthetic systems aren’t validated, lack cultural grounding and can’t reliably confirm truth.
That makes imitation tools high risk and low value for strategic use.
Replication – digital twins
Digital twins aim for precision: detailed, data-rich reconstructions of individuals, systems or markets. They’re excellent for modeling operational flows or forecasting where inputs and rules remain stable – Google’s Digital Twin guidance reinforces this. Their drawback when applied to market research is rigidity. They’re costly, slow to evolve and unable to absorb cultural change, emotional nuance or the day-to-day variability of human behavior. Meaning they can never be fully rounded or reliably up to date.
They replicate what is, but struggle with what could be, limiting their value in an industry where uncertainty and imperfect data increasingly define decision-making.
Generation – simulations
Simulations sit in the generation space, where insight doesn’t just mirror the world but creates new understanding. A simulation is a dynamic, adaptive system that blends validated human data, cultural context and machine reasoning. It doesn’t imitate people or replicate systems; it performs like reality, evolving as conditions shift. Recent Stanford research shows that simulations grounded in human interviews can replicate behavior with 85% human-level accuracy – far beyond synthetic shortcuts.
By combining validated inputs, traceable logic, continual updates and human oversight, our simulations remain transparent, auditable and fit for decision-making. Built to behave like the real world, simulations allow teams to safely test ideas, explore consequences and see decisions play out in context. And because they evolve with new evidence, they stay aligned with changing expectations and emerging behaviors. The result: uniquely low-risk, high-value intelligence for decisions that matter.
Think of a flight simulator. A pilot doesn’t just read a dashboard, they step into an environment that behaves like the aircraft, practicing maneuvers before taking off. Simulations work the same way: not dashboards or static models, but training cockpits for decision-making. You can try strategies, see outcomes unfold and rehearse the future before it arrives.
Simulations and the marketing research industry
Traditional research tells us what was. Most AI tools simply speed up what we already do. But evidence-based decision-making is failing, largely because insight still isn’t embedded where decisions actually happen. Simulations create something different: a decision environment that behaves like the real world.
Insight stops being retrospective and becomes an active tool for shaping what comes next.
This shift is needed now. ESOMAR’s 2025 Insight Activation work shows fewer than half of business decisions today are evidence-based, largely because insight remains static; trapped in decks, dashboards and disconnected studies rather than embedded in the workflows where decisions happen.
Simulations correct this. Instead of producing knowledge that decays on delivery, they embed intelligence inside the environments where teams think, plan and act. They enable continuous learning, rapid experimentation and cumulative knowledge, a living system rather than isolated projects.
For researchers, the role expands: from reporting findings to designing, calibrating and governing intelligent systems. And for the industry, expectations rise – insight shouldn’t just describe the world, it should help teams understand how it behaves and how their choices influence it.
Simulations move us from explaining the past to rehearsing the future, a change with the potential to redefine the purpose, value and impact of insight altogether.
And I think that’s incredibly exciting.