A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e., shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ``components'' on forecasting performance. Furthermore, we systematically explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data.