Since the AUC for your model exceeds 0.50, your model predicts the outcome better than chance. However, you might also specify the PROC LOGISTIC MODEL statement option, CTABLE, to print a classification table of your outcomes including the sensitivity, and the specificity and plot the receiver-operatiing characteristic curve of your model (cf., the documentation). Then you can consider the trade-offs between higher sensitivity and lower specificity (finding most of the customers likely to purchase tennis tickets but also. finding many who are unlikely to purchase these tickets) and between lower sensitivity an higher specificity (finding fewer customers likely to purchase tennis tickets but also eliminating many of those who are unlikely to purchase these tickets).
