Template-Type: ReDIF-Paper 1.0
Author-Name: van Nierop, J.E.M.
Author-Name-Last: van Nierop
Author-Name: Fok, D.
Author-Name-Last: Fok
Author-Name-First: Dennis
Author-Name: Franses, Ph.H.B.F.
Author-Name-Last: Franses
Author-Name-First: Philip Hans
Author-Person: pfr226
Title: Sales Models For Many Items Using Attribute Data
Abstract: Sales models are mainly used to analyze markets with a
 fairly small number of items, obtained after aggregating to the
 brand level. In practice one may require analyses at a more
 disaggregate level. For example, brand managers may be interested
 in a comparison across product attributes. For such an analysis
 the number of relevant items in the product category make commonly
 used sales models difficult to use as they would contain too many
 parameters.
 
 In this paper we propose a new model, which allows for the
 analysis of a market with many items while using only a moderate
 number of easily interpretable parameters. This is achieved by
 writing the sales model as a Hierarchical Bayes model. In this way
 we relate the marketing-mix effectiveness to item characteristics
 such as brand, package size, package type and shelf position. In
 this specification we do not have to impose restrictions on the
 competitive structure, as all items are allowed to have different
 own and cross elasticities. The parameters in the model are
 estimated 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 items
 or 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 of
 the model. We apply the model to a ketchup scanner data set with 
 23 items at the chain level. Our results indicate that the model
 fits the sales of most items very well.
Creation-Date: 2002-09-02
File-URL: https://repub.eur.nl/pub/220/erimrs20020902120413.pdf
File-Format: application/pdf
Series: RePEc:ems:eureri
Number: ERS-2002-65-MKT
Classification-JEL: C44, M, M31
Keywords: Markov Chain Monte Carlo, SKU level analysis, attribute data, hierarchical bayes, sales models
Handle: RePEc:ems:eureri:220