The march of the machines: predictive models have broken out of the supply chain and are heading straight into marketing territory

Every brand and vendor performs some kind of demand forecasting. In the last few years, data science has gone massively mainstream, and new machine-learning techniques have dramatically improved the accuracy of forecasting in the supply chain. The impact has been pretty stark in retail, where a three per cent improvement in forecast accuracy translates to an average two per cent increase in profit margin for the retailer. By putting groups of algorithms to work on forecasting's knottiest problems, some of the most notoriously unpredictable demand patterns have been cracked – and that's had an unexpected side-effect: some companies have started applying these forecasting techniques in novel and unexpected ways, particularly product marketing. To understand why, we need to look at what's previously been out of reach to forecasters.