This study proposes a new customer lifetime model: the gamma/Gompertz distribution (G/G). The advantage of this model relative to the well-known Pareto distribution is twofold: (i) its probability density function can exhibit a mode at zero or an interior mode, and (ii) it can be skewed to the right or to the left. We combine the G/G with a negative binomial distribution (NBD) and obtain the moments of the distribution of the number of transactions over (0, T] and (T, T+T * ]. Out of six data sets, the G/G/NBD model provides a notable improvement in the log-likelihood over the Pareto/NBD model in four data sets. It can indicate substantial differences in expected residual lifetimes compared to the Pareto/NBD and induce a retention rather than acquisition policy. On the average, the G/G/NBD exhibits slightly better forecasts of the mean number of transactions than the Pareto/NBD. This paper was accepted by Pradeep Chintagunta, marketing.

This article investigates how private firms respond to potential entry from public firms. This paper uses a data set of over 3,000 U.S. cable TV systems to present evidence consistent with entry deterrence. Incumbent cable TV firms upgrade faster when located in markets with a potential municipal entrant. However, the same systems are then slower to offer new products enabled by the upgrade, suggesting upgrades in these markets occur for strategic reasons. Incumbent cable systems also upgrade faster in response to municipal entry threats than to private entry threats. Understanding how private firms respond to potential entry from public firms is especially important in light of recent U.S. government entry into several industries. This paper was accepted by Bruno Cassiman, business strategy.

The Norton–Bass (NB) model is often credited as the pioneering multigeneration diffusion model in marketing. However, as acknowledged by the authors, when counting the number of adopters who substitute an old product generation with a new generation, the NB model does not differentiate those who have already adopted the old generation from those who have not. In this study, we develop a generalized Norton–Bass (GNB) model that separates the two different types of substitutions. The GNB model provides closed-form expressions for both the number of units in use and the adoption rate, and offers greater flexibility in parameter estimation, forecasting, and revenue projection. An appealing aspect of the GNB model is that it uses exactly the same set of parameters as the NB model and is mathematically consistent with the later. Empirical results show that the GNB model delivers better overall performance than previous models both in terms of model fit and forecasting performance. The analyses also show that differentiating leapfrogging and switching adoptions based on the GNB model can help gain additional insights into the process of multigeneration diffusion. Furthermore, we demonstrate that the GNB model can incorporate the effect of marketing mix variables on the speed of diffusion for all product generations. This paper was accepted by Pradeep Chintagunta, marketing.

We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the “price”) of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds of a quality comparable with state-of-the-art approaches, but in a fraction of the time required for those approaches. As a by-product, it yields lower bounds (and suboptimal exercise policies) that are substantially superior to those produced by state-of-the-art methods. The PO method thus constitutes a practical and desirable approach to high-dimensional pricing problems. Furthermore, we develop an approximation theory relevant to martingale duality approaches in general and the PO method in particular. Our analysis provides a guarantee on the quality of upper bounds resulting from these approaches and identifies three key determinants of their performance: the quality of an input value function approximation, the square root of the effective time horizon of the problem, and a certain spectral measure of “predictability” of the underlying Markov chain. As a corollary to this analysis we develop approximation guarantees specific to the PO method. Finally, we view the PO method and several approximate dynamic programming methods for high-dimensional pricing problems through a common lens and in doing so show that the PO method dominates those alternatives. This paper was accepted by Wei Xiong, stochastic models and simulation.