We study the effects of a large U.S. hybrid robo-advisor on the portfolios of previously self-directed investors. Across all investors, robo-advising reduces idiosyncratic risk by lowering the holdings of individual stocks and active mutual funds and raising exposure to low-cost indexed mutual funds. It further eliminates investors’ home bias and increases investors’ overall risk-adjusted performance, mainly by lowering investors’ portfolio risk. We use a machine learning algorithm known as Boosted Regression Trees (BRT) to explain the cross-sectional variation in the effects of advice on portfolio allocations and performance. Finally, we study the determinants of investors’ sign-up and attrition.