Bayesian multiscale models exploit variants of the “decouple/recouple'' concept to enable advances in forecasting and monitoring of increasingly large-scale time series. Recent and current applications include financial and commercial forecasting, as well as dynamic network studies. I overview some recent developments via examples from applications in large-scale consumer demand and sales forecasting with intersecting marketing related goals. Two coupled applied settings involve (a) models for forecasting daily sales of each of many items in every supermarket of a large national chain, and (b) models for understanding and forecasting customer/household-specific purchasing behavior to informs decisions about personalized pricing and promotions on a continuing basis. The multiscale concept is applied in each setting to define new classes of hierarchical Bayesian state-space models customized to the application. In each area, micro-level, individual time series are represented via customized model forms that also involve aggregate-level factors, the latter being modelled and forecast separately. The implied conditional decoupling of many time series enables computational scalability, while the effects of shared multiscale factors define recoupling to appropriately reflect cross-series dependencies. The ideas are of course relevant to other applied settings involving large-scale, hierarchically structured time series.