Estimating the SCAN⁎PRO model of store sales: HB, FM or just OLS?

Andrews, Rick L.; Currim, Imran S.; Leeflang, Peter S. H.; Lim, Jooseop

doi:10.1016/j.ijresmar.2007.10.001

Cited by 39 publications

(23 citation statements)

References 24 publications

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“…A well know example is the SCAN*pro model and its extensions which decompose sales for a brand into own-and cross -brand effects of price, feature advertising, aisle displays, week effects, and store effects (Wittink et al, 1988;Foekens et al, 1994;Van Heerde et al, 2000Andrews et al, 2008).…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Fildes

Huang

2016

European Journal of Operational Research

136

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In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts, but no research has put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra-and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 14.3 percent over the model using only the SKU's own predictors. But of the improvements achieved, 88.1 percent of it comes from the intra-category information, and only 11.9 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.

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Section: Sku Sales Forecastingmentioning

confidence: 99%

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Fildes

Huang

2016

European Journal of Operational Research

136

View full text Add to dashboard Cite

show abstract

“…In recent years, two streams of research for estimating sales response models based on storelevel data have evolved: on the one hand, researchers have proposed hierarchical Bayesian (HB) store sales models allowing for heterogeneity of marketing effects across stores (e.g., Blattberg and George, 1991;Montgomery, 1997;Boatwright et al, 1999;Montgomery and Rossi, 1999;Hruschka, 2006b;Andrews et al, 2008). While some of these studies have shown that considering heterogeneity can improve model fit, the accuracy of sales forecasts, or expected profits (e.g., Montgomery, 1997;Hruschka, 2006b), recent research of Andrews et al (2008) has demonstrated rather marginal improvements in fit and predictive performance from incorporating store heterogeneity.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

“…While some of these studies have shown that considering heterogeneity can improve model fit, the accuracy of sales forecasts, or expected profits (e.g., Montgomery, 1997;Hruschka, 2006b), recent research of Andrews et al (2008) has demonstrated rather marginal improvements in fit and predictive performance from incorporating store heterogeneity. One possible reason for this latter finding is that the HB models mentioned above assume a strictly parametric functional form thereby limiting the scope for model calibration to an a priori fixed parametrization.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

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Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits

Lang

Steiner

Weber

et al. 2015

European Journal of Operational Research

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“…5 -8 For example, in a recent study, Andrews et al . 9 use a mixture regression model approach to account for unobserved heterogeneity in the well-known SCAN * PRO model. Through this fi t and prediction accuracy have been increased, compared to an aggregate-level analysis.…”

Section: Introductionmentioning

confidence: 99%

Market segmentation with mixture regression models: Understanding measures that guide model selection

Sarstedt

2008

J Target Meas Anal Mark

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Marko Sarstedtis an assistant professor at the Institute for Market-based Management at the Ludwig-Maximilians-University Munich. His research interests include research methodology, especially in the fi elds of fi nite mixture modelling and partial least-squares path analysis. Keywords information criteria , fi nite mixture models , unobserved heterogeneity , mixture regression , FIMIX-PLS, customer satisfactionAbstract Owing to their considerable potential for market segmentation studies, mixture regression models have recently received increasing attention from both academics and practitioners. One fundamental diffi culty with their application is related to the problem of model selection, that is, the choice regarding the number of segments. Retaining the correct number of segments is, however, crucial as many managerial decisions depend on this decision. Since the proper number of segments is unknown in real-world applications, a thorough understanding of measures that guide the model selection decision is of fundamental importance. Based on a simulation study, this paper addresses the issue by evaluating how the interaction of the most important infl uencing factors for the measures ' success -sample and segment size -affects the performance of four of the most widely used criteria for assessing the correct number of segments in mixture regression models. For the fi rst time, the quality of these criteria is evaluated with regard to a wide spectrum of possible constellations. Furthermore, relative and absolute performances are analysed in respect of outside criteria. Recommendations on criterion selection are thereafter deduced from the results when a certain sample size is given. These recommendations also help to establish the sample size that is needed in order to guarantee an accurate decision based on a specifi c criterion. An application based on customer satisfaction data illustrates the relevance of the fi ndings. In conclusion, theoretical and managerial implications are provided.

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Estimating the SCAN⁎PRO model of store sales: HB, FM or just OLS?

Cited by 39 publications

References 24 publications

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits

Market segmentation with mixture regression models: Understanding measures that guide model selection

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