When a firm allows the return of previously purchased merchandise, it provides customers with an option that has measurable value. Whereas the option to return merchandise leads to an increase in gross revenue, it also creates additional costs. Selecting an optimal return policy requires...

We propose and test a new approach for modeling consumer heterogeneity in conjoint estimation based on convex optimization and statistical machine learning. We develop methods both for metric and choice data. Like hierarchical Bayes (HB), our methods shrink individual-level partworth estimates...

We introduce methods from statistical learning theory to the field of conjoint analysis for preference modeling. We present a method for estimating preference models that can be highly nonlinear and robust to noise. Like recently developed polyhedral methods for conjoint analysis, our method is...

We develop a brand choice model with learning based on the Kalman filter methodology. The model enables us to separate the effects of contemporaneous marketing promotions from the impact of the perceived quality valuation accrued through product usage over time. We also account for idiosyncratic...

We introduce the idea of a massively categorical variable, a variable such as zip code that takes on too many values to treat in the standard manner. We show how to use a massively categorical variable directly as an explanatory variable. As an application of this concept, we explore several of...

Marketers have long struggled with developing forecasts for new products before their launch. We focus on one data source—advance purchase orders—that has been available to retailers for many years but has rarely been tied together with postlaunch sales data. We put forth a duration model...