A Sales Forecast Model for Short‐Life‐Cycle Products: New Releases at Blockbuster

Abstract

We develop, in this article, a sales model for movie and game products at Blockbuster. The model assumes that there are three sales components: the first is from consumers who have already committed to purchasing (or renting) a product (e.g., based on promotion of, or exposure to, the product prior to its launch); the second comes from consumers who are potential buyers of the product; and the third comes from either a networking effect on closely tied (as in a social group) potential buyers from previous buyers (in the case of movie rental and all retail products) or re‐rents (in the case of game rental). In addition, we explicitly formulate into our model dynamic interactions between these sales components, both within and across sales periods. This important feature is motivated by realism, and it significantly contributes to the accuracy of our model. The model is thoroughly tested against sales data for rental and retail products from Blockbuster. Our empirical results show that the model offers excellent fit to actual sales activity. We also demonstrate that the model is capable of delivering reasonable sales forecasts based solely on environmental data (e.g., theatrical sales, studio, genre, MPAA ratings, etc.) and actual first‐period sales. Accurate sales forecasts can lead to significant cost savings. In particular, it can improve the retail operations at Blockbuster by determining appropriate order quantities of products, which is critical in effective inventory management (i.e., it can reduce the extent of over‐stocking and under‐stocking). While our model is developed specifically for product sales at Blockbuster, we believe that with context‐dependent modifications, our modeling approach could also provide a reasonable basis for the study of sales for other short‐Life‐Cycle products.

Maximizing Revenue Through Two‐Dimensional Shelf‐Space Allocation

“…Chung () and Chung et al. () tested this model against the actual demand for 352 titles and found it to be remarkably accurate (the average is 0.96). Hence, we assume that each title's revenue potential is known and is an input to the current study.…”

An Improved Forecasting Algorithm for Spare Parts of Short Life Cycle Products Based on EMD-SVM

“…Casey et al (2012) developed a sales model of movie and game products at Blockbuster company [2]. They assumed that it contains three sales components: consumers who have already bought or rented the products, consumers who are going to buy or rent the products and the networking effect on closely linked potential buyers from previous buyers or re-rents.…”

A Viral Product Diffusion Model to Forecast the Market Performance of Products

“…They found that new product diffusion was not be affected by persuasive advertisement in the decision-making stage. Chung et al [5] provided a sales forecast model for short-lifecycle products, such as movies and games. Van Den Bulte and Joshi [18] discussed the role of influentials and imitators in the diffusion of new products.…”

“…The Bass model discusses at length the impact of the dynamic changes of innovators and imitators on the product sales. Based on the theoretical framework of the Bass model, a large number of product diffusion models are directly divided buyers, then analyze the changes of behaviors of these buyers in the decisionmaking process, and forecast product sales via these changes [4][5][6][7]. Although these models are effective for forecasting product sales, these models only present the changes of individual behaviors in the decision-making stage and fail to reveal how those buyers are formed.…”