The methods used to identify beneficiaries of programmes aiming to address persistent poverty and shocks are subject to frequent policy debates. Relying on panel data from Niger, this paper analyses the performance of different targeting methods that are widely used by development and humanitarian actors and explores how they can be applied as part of an adaptive social protection (ASP) system. The methods include proxy-means testing (PMT), household economy analysis (HEA), geographical targeting, and combined methods. Results show that PMT performs better in identifying persistently poor households, while HEA performs better in identifying transiently food insecure households. Geographical targeting is particularly efficient in responding to food crises, which tend to be largely covariate in nature. Combinations of geographical, PMT, and HEA approaches may be used as part of an efficient and scalable ASP system. Results motivate the consolidation of data across programmes, which can support the application of alternative targeting methods tailored to programme-specific objectives.