Many of our clients have built up a rich body of research over time. A common question we hear is how to get more value from that existing data, rather than constantly running new studies.
One of our current priorities is helping clients do exactly that.
As part of this, we’re introducing a small number of AI-assisted tools into our research approach. These are always used alongside traditional research methods, never as a replacement.
- A digital twin approach, which uses real consumer data to model likely responses to new ideas or claims within specific audiences.
- A predictive modelling tool that explores how different features or benefits may influence emotional response, evaluation, and choice.
Many current predictive approaches in marketing rely on generative AI techniques to create synthetic data and model consumer behaviour (Madanchian, 2024). These methods can be powerful for exploring potential outcomes, but they are often limited by the assumptions built into the models (you can read more about this in our previous blog post here).
Our approach uses digital twins built from observed data, keeping simulations based on real behaviour (Hornik & Rachamim, 2025). This allows us to model likely reactions to new ideas or claims while reducing unnecessary repetition and helps focus live research where it adds the most value.
Clients don’t need to become experts in AI to use these approaches. Our in-house AI team handles the technical side, focusing on translating outputs into clear, practical brand implications, led by our CEO, Dr Eamon Fulcher, who holds a PhD in AI from Imperial College London.
As ever, our focus remains on robust research, clear thinking, and helping clients make confident decisions.
If this approach interests you or you have a current problem you need to solve, please get in touch here.
References:
Hornik, J., & Rachamim, M. (2025). AI-enabled consumer digital twins as a platform for research aimed at enhancing customer experience. Management Review Quarterly. https://doi.org/10.1007/s11301-025-00527-3
Madanchian, M. (2024). Generative AI for Consumer Behavior Prediction: Techniques and Applications. Sustainability, 16(22), 9963. https://doi.org/10.3390/su16229963
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