Forecasting dynamic market share relationships

Terui, Nobuhiko

doi:10.1108/02634500010318610

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Then we examine the out of sample performance of multi-step forecasts. One advantage of a Bayesian approach is forecasting because it offers a complete operational [25,39] suggests using the Bayesian predictive density for linear market-share time series models. The optimal predictor with the minimum mean squared error is known as the conditional expectation * Y tþh ¼ EfY tþh j Y tÀ1 ; Y tÀ2 ; .…”

Section: Model Estimationmentioning

confidence: 99%

Forecasting model with asymmetric market response and its application to pricing of consumer package goods

Terui

Imano

2005

Appl Stoch Models Bus & Ind

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SUMMARYThis paper presents a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable}price promotion}by threshold models. As a threshold variable to generate a mechanism for different market responses, we use the counterpart to the concept of a price threshold applied to a representative consumer in a store. A Bayesian approach is taken for statistical modelling because of advantages that it offers over estimation and forecasting. The proposed model incorporates the lagged effects of a price variable. Thereby, myriad pricing strategies can be implemented in the time horizon. Their effectiveness can be evaluated using the predictive density. We intend to improve the forecasting performance over conventional linear time series models. Furthermore, we discuss efficient dynamic pricing in a store using strategic simulations under some scenarios suggested by an estimated structure of the models. Empirical studies illustrate the superior forecasting performance of our model against conventional linear models in terms of the root mean square error of the forecasts. Useful information for dynamic pricing is derived from its structural parameter estimates. This paper develops a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable}price promotion}by the threshold models.

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Section: Model Estimationmentioning

confidence: 99%

Forecasting model with asymmetric market response and its application to pricing of consumer package goods

Terui

Imano

2005

Appl Stoch Models Bus & Ind

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“…Here, Monte Carlo integration via synthetic random numbers is adopted. It has previously been applied successfully in time series analysis by, for example, Terui (1991, 1993), and Terui (2000), and by other authors cited in those papers. A full description of the method and algorithm are available from the author (terui@econ.tohoku.ac.jp).…”

Section: Mip 223mentioning

confidence: 99%

“…In contrast, time-series analysis defines the dynamic system for the data generation process, and its predictive usefulness has been recognized in many disciplines. Terui (2000) proposed the dynamic VAR (vector autoregressive) model for market-share time series, built on competitive relationships between the market shares themselves and their dynamics over time, and provided an automatic prediction system. This model does not link market share with marketing mix variables because its purpose is to construct a complete automatic system independent of uncertain knowledge about future orbits of marketing mix variables for other brands.…”

Section: Introductionmentioning

confidence: 99%

Measuring delayed and long‐term effects of pricing decisions on market share

Terui

2004

Marketing Intelligence &Amp; Planning

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Market-share analysis focuses on the competitive interrelations between products or brands. Marketing activity may affect the performance of a company's own product and that of its competitors not only within a single time horizon but also over several extended periods. Starting from a static market-share analysis model, the dynamic relationships of market shares between competitive brands are described by multiplicative competitive interaction (MCI) time-series models, in which the problem of logical consistency for estimated shares is resolved. A Bayesian shrinkage estimator solution is applied to the further problem of model-induced collinearity in cross-differential MCI models. Dynamic elasticity is defined and used to measure the delayed and long-term effects of marketing mix variables on market shares. The dynamic relationships of future market shares are predicted by means of predictive density. Strategic simulations are conducted under several scenarios for marketing planning. It is argued that the new dynamic model proposed here, applied to daily national or store tracking data, provides useful insights into dynamic competitive relationships in the marketplace, to the benefit of corporate planners, marketing directors, brand managers and retail strategists.

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Monitoring new product introductions with sell‐through data from channel partners

Salmi¹,

Holmström²

2004

Supply Chain Management: An International Journal

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Manufacturers of consumer goods need to see the market situation and end customer demand in order to efficiently allocate production capacity and procure materials. However, the difficulty to obtain timely and accurate demand data from the point‐of‐sales (POS) calls for alternative solutions to be developed. This paper presents a solution that is based on readily available sell‐through data from channel partners, such as distributors, to monitor what happens on the market in product introduction situations. The difficulty with using demand information from distributors rather than the POS is the bullwhip effect that distorts demand as you move upstream in the supply chain. The paper presents how this problem can be overcome by using market mix views of product categories and simple smoothing techniques.

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Forecasting dynamic market share relationships

Cited by 3 publications

References 24 publications

Forecasting model with asymmetric market response and its application to pricing of consumer package goods

Forecasting model with asymmetric market response and its application to pricing of consumer package goods

Measuring delayed and long‐term effects of pricing decisions on market share

Monitoring new product introductions with sell‐through data from channel partners

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