As anglers become increasingly diverse, fisheries managers are challenged to find ways to satisfy users with divergent preferences while conserving a limited resource on a limited budget. With this management challenge in mind, this study combines previous angler specialization research with an importance–satisfaction analysis (ISA) to aid fisheries managers in better understanding users with divergent preferences. An ISA involves asking stakeholders to simultaneously evaluate the importance of salient attributes of their fishing experience and their satisfaction with those attributes, which allows for graphical depiction of each attribute in one of four quadrants with managerial implications: Concentrate Here, Keep up the Good Work, Possible Overkill, and Low Priority. The data were collected via a mail-administered survey that was sent to 4,000 licensed trout anglers who were residents of Georgia. The anglers were asked to evaluate 10 catch and 7 noncatch attributes as well as answer questions that were used to measure levels of angler specialization. A K-means cluster analysis was used to segment the 469 respondents into three groups based on their specialization level. Analysis of variance (ANOVA) was used to determine whether anglers’ preferences differed by level of specialization. The results revealed that specialization groups differed in the level of importance that they placed on 6 catch and 4 noncatch attributes and in their levels of satisfaction with 5 catch and 2 noncatch attributes. There were also differences among the groups in quadrant placement for both catch and noncatch attributes. The respondents agreed that habitat improvement initiatives, adding new fishable water, and clear signage of regulations were of relatively high importance, with habitat improvement initiatives as the only attribute to fall in the Concentrate Here quadrant for all three clusters. The average responses for importance and satisfaction were highest for the most specialized group and lowest for the least specialized group. Combining segmenting techniques, like cluster analysis with ISA, offers fisheries managers the opportunity to gauge different user groups’ perceptions of salient fishery attributes.