The crowdsourcing movement has spawned a host of successful efforts that organize large numbers of globally-distributed participants to tackle a range of tasks. While many demand-driven crowd marketplaces have emerged (like Amazon Mechanical Turk, often resulting in workers that are essentially replace-able), we are witnessing the rise of supply-driven marketplaces where specialized workers offer their expertise. In this paper, we present a comprehensive data-driven measurement study of one prominent supply-driven marketplace – Fiverr – wherein we investigate the sellers and their offerings (called "gigs"). As part of this investigation, we identify the key features distinguishing "super sellers" from regular participants and develop a machine learning based approach for inferring the quality of gigs, which is especially important for the vast majority of gigs with little feedback.
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