Transcript
Dynamic demand spaces should be built from multiple different sources. Survey data has historically been the bedrock of any demand spaces framework. To make them dynamic, you can augment them by layering in future facing signals. Things like search and social trends, sales data, and macroeconomic shifts such as demographics or birth rates. These inputs reveal which faces are growing, which are static, and which are in decline. You can also use AI techniques such as synthetic respondents and digital twins of your original survey set to revisit and refresh your demand spaces without running further studies from scratch. This approach can also be applied in retrospect to existing data sets. So when culture shifts like the current impact of GLP-1 drugs in food and drink, frameworks can be remapped to reflect the new reality. To recap, future casting combined with regular refreshes help us create dynamic demand spaces designed to unlock value for years to come.
