Integrated measures of sales, merchandising, and distribution

Little, John D. C.

doi:10.1016/s0167-8116(98)00015-9

Cited by 23 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We originally coded promotions as dummy variables -a zero or one if a promotion is present for any UPC. The final promotion variable used in estimation is a weighted index of the promotions run across UPCs within a product line (e.g Little 1998)…”

mentioning

confidence: 99%

Anomalies in Estimates of Cross-Price Elasticities for Marketing Mix Models: Theory and Empirical Test

Bonfrer

Berndt

Silk³

2006

View full text Add to dashboard Cite

We investigate the theoretical possibility and empirical regularity of two troublesome anomalies that frequently arise when cross-price elasticities are estimated for a set of brands expected to be substitutes. These anomalies are the occurrence of: (a) negatively signed cross-elasticities; and (b) sign asymmetries in pairs of cross price elasticities. Drawing upon the Slutsky equation from neoclassical demand theory, we show how and why these anomalies may occur when cross elasticities are estimated for pairs of brands that are substitutes. We empirically examine these issues in the context of the widely used Multiplicative Competitive Interaction (MCI) and Multinomial Logit (MNL) specifications of the fully extended attraction models (Cooper and Nakanishi 1988). Utilizing a database of store-level scanner data for 25 categories and 127 brands of frequently purchased branded consumer goods, we find that about 18% of a total of 732 cross elasticity estimates are negative and approximately 40% of the 366 pairs of cross elasticities are sign asymmetric. Finally, we find that the occurrence of negatively signed cross elasticities can be partially explained by a set of hypothesized relationships between cross-price elasticities and brand share and elasticities of income and category demand. * 1 I What is not well understood is how often and under what circumstances that negative cross elasticities arise. It is also not well known how this relation translates into

show abstract

mentioning

confidence: 99%

Anomalies in Estimates of Cross-Price Elasticities for Marketing Mix Models: Theory and Empirical Test

Bonfrer

Berndt

Silk³

2006

View full text Add to dashboard Cite

show abstract

“…One can mention: store service level (Ingene 1982), store layout (Jain and Mahajan 1979), pricing (e.g. EDLP vs. Hi-Lo), promotions (Dhar and Hoch 1997;Gijsbrechts et al 2003;Little 1998), merchandising space and assortment (Dhar et al 2001;Mittelstaedt and Stassen 1999;Simonson 1999;Sirohi, McLaughlin and Witink 1998) and shelf space allocation (Bultez and Naert 1988;Bultez et al 1989;Campo et al 2000;Corstjens and Doyle 1981;Desmet and Renaudin 1998). The relation between these marketing variables and the shop performance is not linear.…”

Section: Traditional Methods To Evaluate Retail Outlet Performancementioning

confidence: 99%

Retail network performance evaluation: a DEA approach considering retailers' geomarketing

Vyt

2008

The International Review of Retail, Distribution and Consumer R

View full text Add to dashboard Cite

Stores develop customized assortments based on localization characteristics. In terms of performance analysis, these changes make it more difficult to analyse store performance. This adjustment procedure entails an internal benchmarking which needs small samples to control regional and assortment effects. This paper explores the influence of competitive environment and store neighbourhood characteristics on store efficiency by using three data envelopment analysis (DEA) models. This research is illustrated by using real data from a French supermarket retail chain at the product category level. It is shown that the two-steps DEA model is a relevant analysis tool to take into account location aspects. It presents a discriminant power strong enough to derive managerial implications with small samples.

show abstract

“…Hence, consumers might decide to substitute their favorite brand within the same store using available brands' merchandising variables as switching cues, depending upon the degree of their store loyalty. In this context, in-store merchandising variables are described in the retailing literature mainly as in-store pricing, shelf space allocation/shelving and advertisement (Little 1998;Kim and Staelin 1999;Boatwright et al 1999). However, advertising will be used along with in-store advertisement as one of the in-store switching cues of the OOS item because of the uncertainty in the OOS situation for many consumers in this study.…”

Section: In-store Merchandising Activitiesmentioning

confidence: 99%

Reducing the Out-of-Stock Costs in a Developing Retailing Sector

Kucuk

2004

J. of Int. Consumer Marketing

View full text Add to dashboard Cite

Because of the unavailability of sufficient distribution systems and technology in many developing nations, retailers must often solve the out-of-stock (OOS) problem alone within their stores, rather than relying on their suppliers. However, many OOS studies have shown that OOS costs are determined by consumer responses, which are mainly stimulated by brand and store loyalty, to the OOS item. In this study's context, the influences of in-store merchandising and store attractiveness factors on consumers' brand and store loyalties in OOS situations were explored in Turkey's developing retailing sector. The Ordered Logit Model was determined to be the most appropriate for testing influences on 544 consumers' responses to OOS item in three different size stores. The results showed that in-store pricing generally influences store loyal consumers' brand-switching decisions in three stores for a specific soft drink in addition to the influence of in-store advertisement in larger size stores. Also, consumers' store-searching decisions may be influenced by the assortment richness of alternative stores in the OOS situation. Therefore, in order to avoid OOS costs, retailers should work to improve store loyalty by helping with in-store merchandising support

show abstract

Integrated measures of sales, merchandising, and distribution

Cited by 23 publications

References 5 publications

Anomalies in Estimates of Cross-Price Elasticities for Marketing Mix Models: Theory and Empirical Test

Anomalies in Estimates of Cross-Price Elasticities for Marketing Mix Models: Theory and Empirical Test

Retail network performance evaluation: a DEA approach considering retailers' geomarketing

Reducing the Out-of-Stock Costs in a Developing Retailing Sector

Contact Info

Product

Resources

About