Category management (CM) has become a widespread trade practice in recent years. A category manager’s decision problem is complex and multifaceted owing to demand dependencies across products and across time. Extant research on CM has typically focused on one or the other of these dependencies, but seldom both. The authors address this research gap by presenting a competition framework that reconciles cross-sectional breadth (large numbers of stockkeeping units in any given period) with longitudinal depth (demand effects across time). The endeavor is to offer retailers a general, realistic, and practical CM approach by comprehensively accounting for competitive effects. The authors demonstrate their approach using real-world data in the beer category for a midsize grocery chain in the northeastern United States. After determining the optimal weekly prices for the entire assortment over 23 weeks, the authors report a profit yield that is 3.30% more than in the benchmark logit model and substantially more than in the retailer’s current everyday low price policy.