This paper takes stock of methods to profile the unemployed in public employment services (PESs) in OECD countries, in order to single out suitable approaches for PES in emerging economies. Profiling should enable PESs to segment jobseekers into groups with similar risk of work-resumption, and in turn to determine their level of access to different levels of treatment. In our framework PESs rely to a varying extent on (i) case worker discretion and on (ii) data-intensive approaches. On one hand of the spectrum, PESs may allocate interventions on a first-come-first-serve basis according to broad eligibility criteria (age, unemployment duration). This is likely to either induce deadweight loss or to delay treatment. Most often case managers judgment, steered by qualitative guidelines, also plays a role. In this case outcomes depend strongly on the available time and capacity of case managers. An alternative approach is to exploit data about jobseekers to determine the probability of work-resumption according to a statistical model, which then allows the identification of customers most likely to need active labor market interventions. We argue that for PES in emerging economies that show limited case management experience and high customer load, statistical profiling could be a suitable tool to maximize the impact of their scarce resources.