A Population-Growth Model for Multiple Generations of Technology Products

In this paper, we consider the demand for multiple successive generations of products and develop a population growth model that allows demand transitions across multiple product generations, and takes into consideration the effect of competition. We propose an iterative descent method for obtaining the parameter estimates and the covariance matrix, and show that the method is theoretically sound and overcomes the difficulty that the units-in-use population of each product is not observable. We test the model on both simulated sales data and Intel’s high-end desktop processor sales data. We use two alternative specifications for product strength in this market – performance, and performance/price ratio. The former demonstrates better fit and forecast accuracy, likely due to the low price-sensitivity of this high-end market. In addition, the parameter estimate suggests that, for the innovators in the diffusion of product adoption, brand switchings are more strongly influenced by product strength than within-brand product upgrades in this market. Our results indicate that compared to the Bass model, the Norton-Bass model, as well as the Jun-Park choice-based diffusion model, our approach is a better fit for strategic forecasting which occurs many months or years before the actual product launch.