Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request is provided service before other waiting requests. The batching approach leads to sunk costs incurred when visitors wait for the next batch to begin. An optimal admission control policy is developed to prescreen new request arrivals. We show how the policy can be implemented efficiently when the revenue function is complex and there are a large number of requests that can be served in a batch. Numerical experiments show that the proposed approach leads to substantial improvements over a linear approximation of the concave revenue function. Interestingly, we find that the improvements in firm profits are not only (or primarily) due to the different service times that are obtained when using the nonlinear personalization functionthere is a ripple effect on the admission control policy that incorporates these optimized service times, which contributes even more to the additional profits than the service time optimization by itself.