A Bayesian Model to Forecast New Product Performance in Domestic and International Markets

This paper attempts to shed light on the following research questions: When a firm introduces a new product (or service) how can it effectively use the different information sources available to generate reliable new product performance forecasts? How can the firm account for varying information availability at different stages of the new product launch and generate forecasts at each stage? We address these questions in the context of the sequential launches of motion pictures in international markets. Players in the motion picture industry require forecasts at different stages of the movie launch process to aid decision-making, and the information sets available to generate such forecasts vary at different stages. Despite the importance of such forecasts, the industry struggles to understand and predict sales of new movies in domestic and overseas markets. We develop a Bayesian modeling framework that predicts first-week viewership for new movies in both domestic and several international markets. We focus on the first week because industry players involved in international markets (studios, distributors, and exhibitors) are most interested in these predictions. We draw on existing literature on forecasting performance of new movies to formulate our model. Specifically, we model the number of viewers of a movie in a given week using a Poisson count data model. The number of screens, distribution strategy, movie attributes such as genre, and presence/absence of stars are among the factors modeled to influence viewership. We employ a hierarchical Bayes formulation of the Poisson model that allows the determinants of viewership to vary across countries. We adopt the Bayesian approach for two reasons: First, it provides a convenient framework to model varying assumptions of information availability; specifically, it allows us to make forecasts by combining different sources of information such as domestic and international market-specific data. Second, this methodology provides us with the entire distribution of the new movie's performance forecast. Such a predictive distribution is more informative than a point estimate and provides a measure of the uncertainty in the forecasts. We propose a Bayesian prediction procedure that provides viewership forecasts at different stages of the new movie release process. The methodology provides forecasts under a number of information availability scenarios. Thus, forecasts can be obtained with just information from a historical database containing data on previous new product launches in several international markets. As more information becomes available, the forecasting methodology allows us to combine historical information with data on the performance of the new product in the domestic market and thereby to make forecasts with less uncertainty and greater accuracy. Our results indicate that for all the countries in the data set the number of screens on which a movie is released is the most important influence on viewership. Furthermore, we find that local distribution improves movie sales internationally in contrast to the domestic market. We also find evidence of similar genre preferences in geographically disparate countries. We find that the proposed model provides accurate forecasts at the movie-country level. Further, the model outperforms all the extant models in the marketing literature that could potentially be used for making these forecasts. A comparison of root mean square and mean absolute errors for movies in a hold out sample shows that the model that combines information available from the different sources generates the lowest errors. A Bayesian predictive model selection criterion corroborates the superior performance of this model. We demonstrate that the Bayesian model can be combined with industry rules of thumb to generate cumulative box office forecasts. In summary, this research demonstrates a Bayesian modeling framework that allows the use of different information sources to make new product forecasts in domestic and international markets. Our results underscore the theme that each movie is unique as is each country—and viewership results from an interaction of the product and the market. Hence, the motion picture industry should use both product-specific and market-specific information to make new movie performance forecasts.Hierarchical Bayes, New Products, Motion Pictures, International Markets, Forecasting

Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets

We develop a demand model for technology products that captures the effect of changes in the portfolio of models offered by a brand as well as the influence of the dynamics in its intrinsic preference on that brand's performance. To account for the potential correlation in the preferences of models offered by a particular brand, we use a nested logit model with the brand (e.g., Sony) at the upper level and its various models (e.g., Mavica, FD, DSC, etc.) at the lower level of the nest. Relative model preferences are captured via their attributes and prices. We allow for heterogeneity across consumers in their preferences for these attributes and in their price sensitivities in addition to heterogeneity in consumers' intrinsic brand preferences. Together with the nested logit assumption, this allows for a flexible substitution pattern across models at the aggregate level. The attractiveness of a brand's product line changes over time with entry and exit of new models and with changes in attribute and price levels. To allow for time-varying intrinsic brand preferences, we use a state-space model based on the Kalman filter, which captures the influence of marketing actions such as brand-level advertising on the dynamics of intrinsic brand preferences. Hence, the proposed model accounts for the effects of brand preferences, model attributes and marketing mix variables on consumer choice. First, we carry out a simulation study to ensure that our estimation procedure is able to recover the true parameters generating the data. Then, we estimate our model parameters on data for the U.S. digital camera market. Overall, we find that the effect of dynamics in the intrinsic brand preference is greater than the corresponding effect of the dynamics in the brand's product line attractiveness. Assuming plausible profit margins, we evaluate the effect of increasing the advertising expenditures for the largest and the smallest brands in this category and find that these brands can increase their profitability by increasing their advertising expenditures. We also analyze the impact of modifying a camera model's attributes on its profits. Such an analysis could potentially be used to evaluate if product development efforts would be profitable.econometric models, hi-tech marketing, advertising, product line attractiveness, product development, nested logit models, Kalman filter

Investigating consumer choice processes for experience goods : an econometric analysis

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : DO 5582 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Price elasticity dynamics over the product life cycle : a study of consumer durables

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : DO 4650 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc