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Modern spam is ultimately driven by product sales: goods purchased by customers online. However, while this model is easy to state in the abstract, our understanding of the concrete business environment—how many orders, of what kind, from which customers, for how much—is poor at best. This situation is unsurprising since such sellers typically operate under questionable legal footing, with “ground truth” data rarely available to the public. However, absent quantifiable empirical data,“guesstimates” operate unchecked and can distort both policy making and our choice of appropriate interventions. In this paper, we describe two inference techniques for peering inside the business operations of spam-advertised enterprises: purchase pair and basket inference. Using these, we provide informed estimates on order volumes, product sales distribution, customer makeup and total revenues for a range of spam-advertised programs.