Menu
Close
.svg)
Pharmaceutical marketing research is in a state of continuous evolution. While many tools have come and gone, we now find ourselves exploring something fundamentally different — synthetic respondents. In other words, simulated customers powered by AI personas designed to behave like real physicians, patients, caregivers, or payers.
Earlier today, I had a fascinating conversation with one of our most trusted analytics partners, Tim O'Rourke. He and his team are running early experiments comparing the responses of human participants with AI-generated personas. The idea is not to replace traditional research, but to explore whether synthetic respondents can approximate real-world data under very specific and controlled conditions. This head-to-head comparison isn’t just intellectually exciting — it represents a thoughtful approach grounded in scientific rigor.
Let’s be clear: we are not anywhere near ready for prime time. I’ve floated the concept of synthetic respondents with two other clients, and both responded with a mix of curiosity and deep skepticism. Their reaction, paraphrased, was something like: “The foundational purpose of marketing research is to hear from our customers — not from simulated or synthetic ones.” And they’re right. No one is suggesting we abandon real voices in favor of digital ones.
But the very fact that synthetic respondents are even being considered is notable. It speaks to an industry facing a tsunami of pressure — to deliver faster insights, at lower cost, and with greater agility. Traditional methods, while rigorous, can be time-consuming and expensive. We’ve all seen promising segmentation studies put on pause or abandoned altogether because of resource and time constraints. If synthetic data can help offset these challenges — even in a directional or supplementary way — then it becomes a conversation worth having.
Per my conversation with Tim, the methodology being explored involves running synthetic responses side-by-side with actual research to evaluate whether AI-generated data can produce anything even remotely credible. It begins with persona creation — often using the same detailed segmentation slides we already rely on in our work. These personas are then fed into a large language model using carefully crafted prompts, allowing us to simulate multiple respondent types, each with its own tone, context, and variability.
Of course, AI isn’t a customer. It doesn’t feel, prefer, or decide like a human does. But if trained well, it can simulate enough behavioral patterns to provoke questions, challenge assumptions, and maybe even point us in new directions. At the very least, it offers a chance to stress-test our thinking before the first real interview ever takes place.
Believe me, this isn’t a call to replace traditional research. It’s an invitation to explore new tools and methods, cautiously and collaboratively. If synthetic respondents can’t pass the most basic test — aligning with human responses and natural variability within a sample — then the experiment ends there. But if they can show even rough fidelity, they might just become another tool in the broader research kit.
For now, this remains an exploratory space. But it’s one that’s moving fast, and the more open we are to testing these ideas — with the right guardrails — the better prepared we’ll be when the time comes to embrace them.
