As platforms collect more user data, they can tailor algorithms to better match users. At the same time, on matching platforms, users pay to be matched by the platform, while the platform makes money as long as it does not match them. This paper analyzes the matching rule of a profit-maximizing monopoly platform when the incentives between users and the platform are misaligned. I demonstrate that frequently studied matching rules, such as random matching and PAM, can be suboptimal for the platform. Contrary to the intuition that more data about users might improve matching efficiency and speed, I show that more data allows the platform to design a matching rule that strategically lowers match quality to increase search time and thus profits, leading to unnecessary delays and potentially inefficient matches. Finally, I provide explanations for why platforms induces search for users: complexity-constraint pricing, targeted advertising or the presence of overconfident users.