Sales data often only represents a part of the demand for a service product owing to constraints such as capacity or booking limits. Unconstraining methods are concerned with estimating true demand from such constrained sales data. We address the frequently encountered situation of observing only a few sales events at the individual product level and propose variants of small demand forecasting methods to be used for unconstraining. The usual procedure is to aggregate data; however, in that case we lose information on when restrictions were imposed or lifted within a given booking profile. Our proposed methods exploit this information and are able to approximate convex, concave or homogeneous booking curves. Furthermore, they are numerically robust due to our proposed group-based parameter optimization. Empirical results on accuracy and revenue performance based on data from a major car rental company indicate revenue improvements over the best practice benchmark of more than 1%.