Targeted marketing is an increasing trend in advertising with companies attempting to send heavily customised mail shots to only that subset of customers identified as likely to be interested. This requires detailed demographic data on the target group and, when the customer is identified by virtue of having paid by credit card or ordered by mail, such data may be readily available. When customers are anonymous as, for example, at a supermarket checkout, Phenomenal Data Mining has been proposed as a methodology for making demographic and lifestyle inferences from analysis of captured point-of-sale data. Planners of Public Transport systems require an understanding of patterns of commuter behaviour. This is traditionally derived by expensive survey or diary methods. We show that there are underlying similarities between EPOS and pre-paid ticket data captured by the on-bus Wayfarer system used by the Dublin Bus company and investigate Phenomenal Data Mining as complementary low-cost methodology for analysing this data. We expect that it will be possible to make statistically valid identifications of commuter trips.