Sales Models for Many Items Using Attribute Data

28 Pages Posted: 17 Feb 2003

See all articles by Erjen van Nierop

Erjen van Nierop

Carnegie Mellon University - David A. Tepper School of Business; Erasmus Research Institute of Management (ERIM); Tinbergen Institute

D. Fok

Econometric Institute - Erasmus University Rotterdam; Erasmus Research Institute of Management (ERIM); Tinbergen Institute Rotterdam

Philip Hans Franses

Erasmus University Rotterdam (EUR) - Department of Econometrics

Date Written: February 2002 9,

Abstract

Sales models are mainly used to analyze markets with afairly small number of items, obtained after aggregating to thebrand level. In practice one may require analyses at a moredisaggregate level. For example, brand managers may be interestedin a comparison across product attributes. For such an analysisthe number of relevant items in the product category make commonlyused sales models difficult to use as they would contain too manyparameters.In this paper we propose a new model, which allows for theanalysis of a market with many items while using only a moderatenumber of easily interpretable parameters. This is achieved bywriting the sales model as a Hierarchical Bayes model. In this waywe relate the marketing-mix effectiveness to item characteristicssuch as brand, package size, package type and shelf position. Inthis specification we do not have to impose restrictions on thecompetitive structure, as all items are allowed to have differentown and cross elasticities. The parameters in the model areestimated using Markov Chain Monte Carlo techniques.As a by-product this model allows to make predictions of sales levels and marketing-mix effectiveness of new to introduce itemsor of attribute changes. For example, one can assess the impact of changing the packaging from plastic to glass, on sales and price elasticity. Besides entering and changing products, our model also allows for items to leave the market.We consider the representation, specification and estimation ofthe model. We apply the model to a ketchup scanner data set with 23 items at the chain level. Our results indicate that the modelfits the sales of most items very well.

Keywords: sales models, attribute data, SKU level analysis, hierarchical bayes, Markov Chain Monte Carlo

JEL Classification: M, M31, C44

Suggested Citation

van Nierop, Erjen and Fok, Dennis and Franses, Philip Hans, Sales Models for Many Items Using Attribute Data (February 2002 9,). ERIM Report Series Reference No. ERS-2002-65-MKT. Available at SSRN: https://ssrn.com/abstract=371016

Erjen Van Nierop

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Erasmus Research Institute of Management (ERIM)

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Tinbergen Institute ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Dennis Fok

Econometric Institute - Erasmus University Rotterdam ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands
+31 10 408 1333 (Phone)
+31 10 408 9162 (Fax)

Tinbergen Institute Rotterdam ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Philip Hans Franses

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands
+31 10 408 1278 (Phone)
+31 10 408 9162 (Fax)

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