POSデータの時系列モデリングによる知識発見 : 新製品投入の消費者価格反応変化に及ぼす影響の解析

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The number of competing-brands changes by new products entry. The new product introduction is endemic among consumer packaged goods firm and is an integral component of their marketing strategy. As a new products entry affects markets, there is a pressing need to develop market response model that can adapt to such changes. In this paper, we develop a dynamic model that capture the underlying evolution of the buying behavior associated with the new product. This extends an application of a dynamic linear model, which is used by a number of time series analyses, by allowing the observed dimension to change at some point in time. Our model copes with a problem that dynamic environments entail: changes in parameter over time and changes in the observed dimension. We formulate the model with framework of a state space model. We realize an estimation of the model using modified Kalman filter/fixed interval smoother. We find that new products entry (1) decreases brand differentiation for existing brands, as indicated by decreasing difference between cross-price elasticities; (2) decreases commodity power for existing brands, as indicated by decreasing trend; and (3) decreases the effect of discount for existing brands, as indicated by a decrease in the magnitude of own-brand price elasticities. The proposed framework is directly applicable to other fields in which the observed dimension might be change, such as economic, bioinformatics, and so forth.
2007-11-01

POSデータの時系列モデリングによる知識発見 : 新製品投入の消費者価格反応変化に及ぼす影響の解析

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