Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and 𝜏²-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference cues, capturing how diverse shopper types—from price-conscious planners to trend-seeking explorers—navigate product search and selection. We further design a baseline of ShopperAgents that operate under persona guidance to simulate realistic, goal-oriented shopping interactions. To evaluate these agents, we propose new metrics including Persona Fidelity, Persona-Query Alignment, and Path Consistency. Together, Our ShopperBench provides a testbed for studying personalized and context-aware shopping intelligence, bridging the gap between human-centered e-commerce behavior and agent-based simulation.