Determinants of Store-Level Price Elasticity

Hoch, Stephen J.; Kim, Byung-Do; Montgomery, Alan L.; Rossi, Peter E.

doi:10.2307/3152107

Cited by 276 publications

(146 citation statements)

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“…Using this data, retailers (and researchers) could analyze market baskets crosssectionally -item co-occurrences, complements and substitutes, cross-category dependence etc (see, e.g., Blattberg et al 2008;Russell and Petersen 2000); analyze aggregate sales and inventory movement patterns by SKU (e.g., Anupindi, Dada and Gupta 1998 in a vending machine scenario); compute elasticities for prices and shelf space at the different levels of aggregation such as category, brand, SKU etc (e.g., Hoch et al 1995 on store-level price elasticities; Bijmolt et al…”

Section: New Sources Of Datamentioning

confidence: 99%

The Role of Big Data and Predictive Analytics in Retailing

Bradlow

Gangwar

Kopalle

et al. 2017

Journal of Retailing

353

181

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The paper examines the opportunities in and possibilities arising from Big Data in retailing, particularly along five major data dimensions -data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of Big Data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.2

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Section: New Sources Of Datamentioning

confidence: 99%

The Role of Big Data and Predictive Analytics in Retailing

Bradlow

Gangwar

Kopalle

et al. 2017

Journal of Retailing

353

181

View full text Add to dashboard Cite

show abstract

“…We also add two market-level demographic variables calculated from census data: INCOME (log of medium household income in a market) and POPDEN (population density in a market, or the size of the population per square kilometers/1000). Both INCOME and POPDEN relate to a market's product variety; the former indicates potential income constraints on purchasing behavior (Hoch et al 1995), and the latter affects the market's demand level and relative profitability (Watson 2009). …”

Section: Methodsmentioning

confidence: 99%

Managing Product Variety and Collocation in a Competitive Environment: An Empirical Investigation of Consumer Electronics Retailing

Ren

et al. 2011

Management Science

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Product variety is an important strategic tool that firms can use to attract customers and respond to competition. This study focuses on the retail industry and investigates how stores manage their product variety, contingent on the presence of competition and their actual distance from rivals.Using a unique data set that contains all Best Buy and Circuit City stores in the United States, the authors find that a store's product variety (i.e., number of stock-keeping units) increases if a rival store exists in its market but, in the presence of such competition, decreases when the rival store is collocated (within one mile of the focal store). Moreover, collocated rival stores tend to differentiate themselves by overlapping less in product range than do non-collocated rivals. This smaller and more differentiated product variety may be due to coordinated interactions between collocated stores. In summary, this article presents evidence of both coordination and competition in retailers' use of product variety.

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“…None of the analysis directly incorporates the possibility that a price zone might react partially independently to wholesale prices when it sets a brand's retail price. This is done because Hoch et al (1995), Montgomery (1997), and Chintagunta et al (2003) all tell us that DFF responded to wholesale promotions with a single chainwide retail promotional price, i.e., that DFF price zones did not react independently or even partially independently to wholesale promotions.…”

Section: -Fold Replication Yields Significantmentioning

confidence: 99%

“…What is critical to our argument is that differences in a brand's regular retail price across price zones are not reactions to wholesale prices. Hoch et al (1995) tell us that, though DFF set different "regular" (i.e., unpromoted) retail prices for a brand in different price zones, it set the same promotional retail price for the brand in all price zones. Montgomery (1997) explains that DFF did this in order to maintain its overall image and positioning.…”

Section: How Dff Sets Pricesmentioning

confidence: 99%

“…The point of Hoch et al's (1995) paper was that DFF should base its price zones on grocery shoppers' price sensitivities rather than on the fierceness of retail competition around a store. Hoch and Purk (1993) report the results of pricing experiments done by DFF and the University of Chicago, Graduate School of Business (GSB), which confirm the short-run profitability of exploiting differences in grocery shoppers' price sensitivities in different stores.…”

Section: How Dff Sets Pricesmentioning

confidence: 99%

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Comment—Cross-Brand Pass-Through: Fact or Artifact?

McAlister

2007

Marketing Science

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Cross-brand pass-through implies that a retailer responds to wholesale promotional support from a target brand by changing the retail prices of competitive brands. Besanko et al. (2005) model a target brand's retail price as a function of its own and other brands' wholesale prices using 780 observations (15 price zones × 52 weeks) and take the many significant coefficients for other brands' wholesale prices that they find as evidence of cross-brand pass-through. Because price zones do not react independently to wholesale prices when they set a brand's retail price, Besanko et al.'s (2005) estimation overstates the number of independent observations by a factor of 15. When we correct for this overstatement of independent observations, we find that the number of stable, significant coefficients for other brands' wholesale prices is lower than one would expect by chance. We conclude that there is no evidence of cross-brand pass-through in the 11 categories analyzed by Besanko et al. (2005).

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Determinants of Store-Level Price Elasticity

Cited by 276 publications

References 0 publications

The Role of Big Data and Predictive Analytics in Retailing

The Role of Big Data and Predictive Analytics in Retailing

Managing Product Variety and Collocation in a Competitive Environment: An Empirical Investigation of Consumer Electronics Retailing

Comment—Cross-Brand Pass-Through: Fact or Artifact?

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