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Assessment of accurate market size and early adoption patterns is essential to strategic decision making of managers involved in new-product launches. This article proposes methodology that explains changes in parameter estimates of the Bass model, (coefficient of innovation), (coefficient of imitation), and (market penetration rate) by direction of "extra-Bass" skew in the data, or equivalently, by underlying heterogeneity of the population. This research shows significantly opposite patterns of these parameter estimates, depending on skew of the diffusion curve detected by a generalized model, i.e., the gamma/shifted Gompertz (G/SG) model, which embeds the Bass model as a special case. The G/SG model originally presented in Bemmaor (1994) is based on two assumptions: (1) Individual-level times to first purchase are distributed shifted Gompertz and (2) individual-level propensity to buy follows a gamma distribution across the population. We assume that the scale parameter of the shifted Gompertz distribution is constant across consumers. The advantage the G/SG model has over alternative diffusion models such as the nonuniform influence model is that its cumulative distribution function takes a closed-form expression. In line with Van den Bulte and Lilien (1997), we analyze these opposite patterns from simulated data using the G/SG model as the true model and 12 real adoption data sets. The patterns are: (1) as the level of censoring decreases, the estimates of and decrease and those of increase when data exhibit more right skew than the Bass model and (2) the estimates of and increase and those of q decrease when data exhibit more left skew than the Bass model. For the simulated data, we manipulated four dimensions: (1) "extra-Bass" skew in the data, (2) ratio , (3) speed of diffusion, and (4) error variance. Both results of the simulated data and the real adoption data sets confirm the existence of two opposite patterns of parameter estimates of the Bass model depending on "extra-Bass" skew. When the model is correctly specified with simulated data, estimates of increase and those of decrease for both the Bass and the G/SG models. The estimates of increase as one adds data points only for the G/SG model. No significant tendency in parameter estimates of was detected for the Bass model. As for ill-conditioning issues, systematic changes in the parameter estimates of the G/SG model can be substantially larger in some cases than those obtained with the Bass model, even though the data were generated by taking the G/SG model as the true one. Therefore, model complexity can aggravate the tendency for parameters to change systematically as one adds data points. The forecasting results from the simulated data show the supremacy of the G/SG model. It provides more accurate results than the Bass model in the one-step ahead, two-step ahead, and three-step ahead forecasts. With the real data set, the G/SG model provides more accurate one-step ahead forecasts than the Bass model, but the model's forecasting performance det...

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One of the most managerially useful constructs that emerge from the stochastic modelling of brand choice is that of conditional expectations. In this paper the conditional expectations are derived for a generalization of the NBD model, called the beta binomial/negative binomial distribution (BB/NBD) model, first described by Jeuland, Bass and Wright. The model, developed to jointly represent the product class purchase and brand selection processes, is also particularly appropriate for analyzing panel data when not every purchase occasion is recorded. The conditional expectations for the NBD model increase linearly with the number of previous purchases made by an individual. Therefore the degree of nonlinearity of the conditional expectations in our more complicated model allows us to assess the robustness of NBD results when in fact the basic assumptions are not true. This paper gives the simple conditions when the NBD will exactly or approximately represent the BB/NBD; and describes the qualitative deviations between these two models when they are not identical.