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A Hierarchical Bayesian Forecasting Model for Innovative Products–in the case of American Films
Movie,New product prediction,Bass diffusion model,Hierarchical Bayesian model,Seemingly unrelated regression,
|Publication Year :||2019|
Nowadays, the market environment is changing rapidly. Enterprises using traditional operation methods could no longer adapt to the dynamic market, so they need to adjust the resources and strengthen their ability of problem solving. Before launching new products, companies should focus on the scale of demand (market potential) and whether it is spreading rapidly. Therefore, it is necessary to develop an accurate sales forecasting model to find the most consistent product cycle for each product, which will effectively reduce the risk of the company investment and assist in the formulation of marketing strategies.
As the advance in technology makes people change their lifestyles, and with the fast-past life and stressful work environment, leisure activities become indispensable. Among different kinds of leisure activities, seeing movies is the fastest and the most effective way for modern people. This study will use American films as the object of empirical analysis, predicting the sales volume of each film through the innovative product sales forecasting model, and expect to provide a reference for the movie industry to predict the sales of new products. The purpose of this study is to propose factors that influence the diffusion pattern of American film sales, so as to construct different sales forecasting models, and then compare the predictive validity of different models.
In recent years, the Bass diffusion model has been continuously developed by using different kinds of effective statistical methods. If we add various important variables that affect the prediction model, the model will become more precise and stable. Therefore, this study is based on the best model which is derived from the previous literature, and adds the hierarchical Bayesian model to do the analysis. Under these two model prediction systems, the attributes of the innovative products (production budget, MPAA rating, film type, main cast, star score, expert rating, number of comments before release, holiday effect) are considered in the models. After comparing the advantages and disadvantages, we can find the best prediction model. According to the research conclusion, the best prediction model for the sales of innovative products is the hierarchical Bayesian model.
|Appears in Collections:||統計碩士學位學程|
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