Product line extensions and competitive market interactions: An empirical analysis

Kadiyali, Vrinda; Vilcassim, Naufel J.; Chintagunta, Pradeep K.

doi:10.1016/s0304-4076(98)00066-9

Cited by 108 publications

(67 citation statements)

References 24 publications

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“…Gametheoretic work also has shed substantial light on the forms of competitive pricing behavior that are commonly found in consumer packaged goods categories and their implications (e.g., Rao et al 1995, Lal andPadmanabhan 1995). Recently, researchers have used the industrial organization paradigm to estimate demand and reaction functions simultaneously (Kadiyali et al 1999).…”

Section: Competitive Reaction and Time Horizonmentioning

confidence: 99%

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

1999

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The authors report the findings from an exploratory investigation of the use of UPC scanner data in the consumer packaged goods industry in the U.S. The study examines the practitioner community's view of the use of scanner data and compares these views with academic research. Forty-one executives from ten data suppliers, packaged goods manufacturers, and consulting firms participated in wide-ranging, inperson, interviews conducted by the authors. The interviews sought to uncover key questions practitioners would like to answer with scanner data, how scanner data is applied to these questions, and the industry's perspective regarding the success that the use of scanner data has had in each area.The authors then compare and contrast practitioners' views regarding the resolution of each issue with academic research. This produces a 2 ‫ן‬ 2 classification of each question as "resolved" or "unresolved" from the perspectives of industry and academia. Along the diagonal of the 2 ‫ן‬ 2, issues viewed as unresolved by both groups are important topics for future research. Issues deemed resolved by both groups are, correspondingly, of lower priority. In the offdiagonal cells, industry and academics disagree. These topics should be given priority for discussion, information exchange, and possible further research.Practitioners reported that scanner data analysis has had the most success and been most widely adopted for decision making in consumer promotions (i.e., coupons), trade promotions, and pricing. For example, logit and regression models applied to scanner data have revealed very low average consumer response to coupons which has directly led to reduced couponing activity. Managers also reported high levels of comfort with and impact from analyses of trade promotions and price elasticities. While industry views most of the issues in these areas to be resolved, academic research raises concerns about a number of practices in common commercial use. These include price threshold analysis and trade promotion evaluation using baseline and incremental sales.In product strategy, advertising, and distribution management, practitioners reported that the use of scanner data has had more limited development, success, and impact. In the case of new product decisions, scanner data use has been slow to develop due to the inherent limitations of historical data for these decisions and a heavy reliance on traditional primary research methods. In advertising, scanner data is widely analyzed with models, but confusion among practitioners is very high due to controversies about methods (e.g., what level of data aggregation is best) and conflicting results. In distribution and retail management, scanner data use has tremendous potential but a mixed track record to date. Thus, practitioners view the use of scanner data as unresolved for most issues in product strategy, advertising, and distribution. This view is largely, though not entirely, consistent with academic research, which has only begun to address many of the key quest...

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Section: Competitive Reaction and Time Horizonmentioning

confidence: 99%

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

1999

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“…However, relatively little is known empirically about how product assortment composition varies across stores within a supermarket chain or across supermarket chains. Most existing empirical studies have focused on assortment size rather than assortment composition (Bayus and Putsis 1999, Putsis and Bayus 2001, Kadiyali et al 1999, Draganska and Jain 2005, Watson 2009, Richards andHamilton 2006, Misra 2008).…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of Assortment Similarities Across U.S. Supermarkets

2010

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An empirical analysis of assortment similarities across U.S. supermarketsHwang, M.; Bronnenberg, Bart; Thomadsen, R. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. T his paper examines pairwise assortment similarities at U.S. supermarkets to understand how assortment composition and size are related to underlying factors that describe local store clientele, local competitive structure, and the retail outlets' characteristics. The top-selling items, which cumulatively make up 50% of sales, are sold at nearly every store, but other items are viewed as optional. We find that, within states, supermarkets owned by the same chain carry similar assortments and that the composition of their clientele and the presence of competing stores have effects on assortment similarity that are an order of magnitude smaller than ownership structure. In contrast, we find that, across states, supermarkets owned by the same chain do version their assortment. We explain this difference using extant work on the minimal efficient scale of supermarkets and on local demand effects. Furthermore, we investigate the distribution and role of regional brands. We find that regional brands are primarily distributed by small regional chains or independent stores. "Value" regional brands are primarily distributed by supermarket firms without store brands, whereas the distribution of "premium" regional brands is unrelated to the presence of store brands. We discuss our findings in the context of modeling assortment decisions and manufacturers designing distribution policies.

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“…Further, firms are assumed to form expectations of others' reactions to changes in both price and product line. In other words, we estimate cross-variable conduct parameters similar to Kadiyali, Vilcassim and Chintagunta (1999) so that we can test for whether firms respond to changes in price with changes in product Similarly, the first order condition with respect to variety, or product-line length for retailer i is given by:…”

Section: Morey Et Al (2001) Develop a Model Of Fishing-trip Demand Bmentioning

confidence: 99%

Rivalry in Price and Variety among Supermarket Retailers

Richards

Hamilton

2006

American J Agri Economics

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Abstract:Variety is a key competitive tool used by retailers to differentiate themselves from rivals. Theoretical models of price and variety competition suggest that both firm and product heterogeneity are key determinants of price and variety strategies, but none test this hypothesis in a rigorous way. This study provides the first empirical evidence on supermarket retailers' combined price and variety strategies using a nested CES modeling framework. Unlike other discrete-choice models of product differentiation, the NCES model is sufficiently general to admit both corner and interior solutions in both store and product choice. The model is estimated using chain-level scanner data for four major grocery chains in a large, urban West Coast market. The results show that retailers do indeed use both price and variety strategies to compete for market share. Retailers tend to follow relatively similar, moderately cooperative pricing strategies, but tend to diverge in the extent to which they follow cooperative strategies in variety.

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Product line extensions and competitive market interactions: An empirical analysis

Cited by 108 publications

References 24 publications

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

An Empirical Analysis of Assortment Similarities Across U.S. Supermarkets

Rivalry in Price and Variety among Supermarket Retailers

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