Digital Customer Twins Have Nothing To Do With Agents
There is a growing tendency to attach the word agent to almost anything involving AI. Digital Customer Twins are beginning to suffer the same fate. A customer twin is often described as an AI agent representing a customer. It is not. Give it a persona, some purchase history and perhaps a demographic profile. Ask it what it thinks about a new proposition. Let thousands of these agents talk to one another. Aggregate their answers. You have something interesting. But you do not have a Digital Customer Twin. The distinction matters because the purpose of a Digital Customer Twin is not to imitate a customer. It is to model how customer behaviour changes when the business changes something. That is a very different problem.
Digital Twins and the Rise of Decision Intelligence
When NASA engineers faced problems during the Apollo missions, they could not simply experiment on a spacecraft hundreds of thousands of miles from Earth. Instead, they relied on corresponding systems on the ground to understand what was happening, evaluate potential responses and anticipate the consequences of different decisions before acting. The principle was simple but powerful: if you can create an accurate representation of a complex system, you can explore possible futures before making changes in the real world.
Run Your Customer Decisions Like An F1 Team
There is a reality many businesses need to face. Customer value propositions decisions often worth tens of millions are made on gut feel, simple modelling of first order effects or long testing cycles that all impact real world return. There is a lot to be learned from F1.