Algorithms increasingly assist consumers in making their purchase decisions across a variety of markets; yet little is known about how humans interact with algorithmic advice. We examine how algorithmic, personalized information affects consumer choice among complex financial products using data from a randomized, controlled trial of decision support software for choosing health insurance plans. The intervention significantly increased plan switching, cost savings, time spent choosing a plan, and choice process satisfaction, particularly when individuals were exposed to an algorithmic expert recommendation. We document systematic selection — individuals who would have responded to treatment the most were the least likely to participate. A model of consumer decision-making suggests that our intervention affected consumers’ signals about both product features (learning) and utility weights (interpretation).