Service Escape: Profiting from Customer Cancellations
Marketing Science
Advance selling and internet intermediary: travel distribution strategies in the e-commerce age
Proceedings of the ninth international conference on Electronic commerce
Demand forecasting of high-speed Internet access service considering unknown time-varying covariates
Computers and Industrial Engineering
Real-Time Evaluation of E-mail Campaign Performance
Marketing Science
Optimal Preorder Strategy with Endogenous Information Control
Management Science
Using online search data to forecast new product sales
Decision Support Systems
A pricing model for a service inventory system when demand is price and waiting time sensitive
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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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 that incorporates the basic concepts of new product diffusion, using a mixture of two distributions: one representing the behavior of innovators (i.e., those who place advance orders) and one representing the behavior of followers (i.e., those who wait for the mass market to emerge). The resulting mixed-Weibull model specification can accommodate a wide variety of possible sales patterns. This flexibility is what makes the model well-suited for an experiential product category (e.g., movies, music, etc.) in which we frequently observe very different sales diffusion patterns, ranging from a rapid exponential decline (which is most typical) to a gradual buildup characteristic of "sleeper" products. We incorporate product-specific covariates and use hierarchical Bayes methods to link the two customer segments together while accommodating heterogeneity across products. We find that this model fits a variety of sales patterns far better than do a pair of benchmark models. More importantly, we demonstrate the ability to forecast new album sales before the actual launch of the album, based only on the pattern of advance orders.