Tag Archives: pricing

  • Pricing Strategies for Hybrid Bundles: Analytical Model and Insights

    Meyer and Shankar

    by Jeffrey Meyer and Venkatesh Shankar

    This article is forthcoming in Journal of Retailing

    Retailers are increasingly offering hybrid bundles — products that combine both good(s) and service(s). Some hybrid bundles, such as Lowe’s flooring that combines flooring material (good) and flooring installation (service) are sold in traditional stores, while others, such as Best Buy’s bundle that includes a computer (good) and tech support (service) are also offered online. The pricing strategy of a hybrid bundle is critical to its success. While pricing strategies for a goods bundle have been well-studied, those for a services bundle have been underexplored. Hybrid bundles, which fundamentally differ from bundles of goods or bundles of services, primarily with regard to quality variability and scalability, have received even less attention. Drawing from the pricing and bundling literatures for both goods and services, we develop an analytic model of optimal pricing for hybrid bundles by a monopolist retailer. We derive and illustrate many useful propositions, several of which are counter-intuitive. Our results show that an increase in quality variability of the service is associated with a higher optimal hybrid bundle price and a lower optimal price of the good, but a lower overall bundle profit. Our findings also reveal that the optimal price of the service (good) in a hybrid bundle is higher (lower) when the good has diminishing unit cost and the service has constant unit cost (i.e., the good is more scalable than the service). Our results also show that higher unit costs incurred to achieve lower service quality variability can result in higher (lower) profits when the cost increase is low (high). We discuss important implications of these insights for researchers and practitioners.

  • Price Levels and Price Dispersion Within and Across Multiple Retailer Types: Further Evidence and Extension

    Ancarani_Shankar_JAMS_2004

    by Fabio Ancarani and Venkatesh Shankar

    This article was published in the Journal of Academy of Marketing Science, 32 (Spring 2004), 176-187.

    In this paper, we develop hypotheses on how prices and price dispersion compare among three types of retailers, pure-play Internet, bricks-and-mortar (traditional), and bricks-and-clicks (multichannel) retailers and test them through an empirical analysis of data on the book and compact disc categories inItalyduring 2002. Our results, based on an analysis of 13720 price quotes, show that when posted prices are considered, traditional retailers have the highest prices, followed by multichannel retailers and pure-play e-tailers, in that order. However, when shipping costs are included, multichannel retailers have the highest prices, followed by pure-play e-tailers and traditional retailers, in that order.  With regard to price dispersion, pure-play e-tailers have the highest range of prices, but the lowest standard deviation.  Multichannel retailers have the highest standard deviation in prices with or without shipping costs.  These findings suggest that online markets offer opportunities for retailers to differentiate within and across the retailer types.

  • Relating Price Sensitivity to Retailer Promotional Variables and Pricing Policy: An Empirical Analysis

    Shankar_Krishnamurthi_JR_1996

    by Venkatesh Shankar and Lakshman Krishnamurthi

    This article was published in the Journal of Retailing, 72 (3, 1996), 249-272.

    There is substantial evidence for variation in price sensitivity of products across stores and chains.  Understanding the relationships between price sensitivity and promotional variables (such as price cut, feature advertising, and display), and between price sensitivity and pricing policy (Everyday Low Pricing [EDLP] and High Low Pricing [HLP]) is particularly important to retailers.  We develop hypotheses on the relationships between regular price elasticity and retailer promotional variables, and between regular price elasticity and retailer pricing policy.  We test these hypotheses by analyzing the variation of regular price elasticity of a frequently purchased consumer packaged brand across stores, both within and across chains, through a multistage regression analysis.  In the first stage of our analysis, we use a mixed double-log model to estimate the sales response function for the brand in each store using time series data.  In the second stage, we explain the differences in the estimated regular price elasticities across stores within a chain by a process function model.  In the final stage, the differences across all stores and chains are explained through an aggregate process function model.  We extend the literature by separating regular (long-run) price elasticity from promotional (short-run) elasticity, and by studying the influence of both strategic and tactical retailer variables on regular price elasticity in a single framework within and across chains.  Our results for the brand analyzed show that a higher level of display and feature advertising together is associated with a lower level of regular price elasticity in EDLP stores and that an EDLP policy is associated with a higher level of regular price elasticity, whereas an HLP policy is related to a lower level of regular price elasticity.

  • An Empirical Analysis of Determinants of Retailer Pricing

    Shankar_Bolton_MS_2004

    by Venkatesh Shankar and Ruth N. Bolton

    This article was published in Marketing Science, 23 (Winter 2004), 28-49.

    This paper empirically investigates the determinants of retailers’ pricing decisions with a focus on competitor factors. We classify the different types of pricing strategies based on four underlying dimensions.  These dimensions are price consistency, price-promotion intensity, price-promotion coordination, and relative brand price.  We develop and estimate a simultaneous equation model of how each of the underlying dimensions of retailers’ pricing strategies is influenced by variables representing the market, chain, store, category, brand, customer and competition. Our empirical analysis is based on optical scanner data that describe 1364 brand-store combinations from six categories of consumer packaged goods in fiveU.S.markets over a two year time period. The four underlying pricing dimensions are statistically related to: (1) competitor price and deal frequency (competitor factors), (2) storability and necessity (category factors), (3) chain positioning and size (chain factors), (4) store size and assortment (store factors), (5) brand preference and advertising (brand factors), and (6) own price and deal elasticities (customer factors).  Competitor factors explain the most variance in retailer pricing strategy, followed by category and chain factors.  Only in the cases of price-promotion coordination and relative brand price, do category and chain factors explain much variance in retailer pricing.  Store, brand and customer factors capture an insignificant proportion of explained variance in retailer pricing. These findings are useful to retailers in profiling alternative pricing strategies.  They can also help manufacturers make informed decisions about the levels of marketing support spending for their brands that are appropriate for different retailers.  We outline the managerial implications based on the results.

  • Price Dispersion on the Internet: A Review and Directions for Future Research

    Pan_Ratchford_Shankar_JIM_2004

    by Xing Pan, Brian T. Ratchford, and Venkatesh Shankar

    This aricle was published in the Journal of Interactive Marketing, 18 (Autumn 2004), 116-135.

    The explosive growth in Internet retailing has sparked a stream of research on online price dispersion, defined as the distribution of prices (such as range and standard deviation) of an item with the same measured characteristics across sellers of the item at a given point in time.  In this paper, we review the empirical and analytical literatures on online price dispersion and outline the future directions in this research stream.  We address the issue of whether price dispersion is greater or smaller online than offline, examine whether price dispersion on the Internet has changed over time, discuss multi-channel retailing and measurement of price dispersion, explore why Internet price dispersion exists, and investigate the drivers of online price dispersion.

     

  • Can Price Dispersion in Online Markets be Explained by Differences in E-Tailer Service Quality?

    Pan_Ratchford_Shankar_JAMS_2002

    by Xing Pan, Brian T. Ratchford, and Venkaesh Shankar

    This article was published in the Journal of the Academy of Marketing Science, 30 (Fall 2002), 433-445.

    It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets.  This suggests that price dispersion–the distribution of prices of an item indicated by measures such as range and standard deviation—of an item with the same measured characteristics across sellers of the item at a given point in time for identical products sold by e-tailers online (on the Internet) should be smaller than it is offline, but some recent empirical evidence reveals the opposite.  A study by Smith et al. (2000) speculates that this is due to heterogeneity among e-tailers in such factors as shopping convenience and consumer awareness.  Based on an empirical analysis of 105 e-tailers comprising 6739 price observations for 581 items in eight product categories, we show that online price dispersion is persistent, even after controlling for e-tailer heterogeneity.  Our general conclusion is that the proportion of the price dispersion explained by e-tailer characteristics is small. This evidence is contrary to the hypothesis that search costs in online markets are low, or that online markets are highly competitive.  The results also show that after controlling for differences in e-tailer service quality, prices at pure play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.

     

  • An Empirically Derived Taxonomy of Retailer Pricing and Promotion Strategies

    Bolton_Shankar_JR_2003

    by Ruth N. Bolton and Venkatesh Shankar

    This article was published in the Journal of Retailing, 79 (2003), 213-224.

    Most research categorizes grocery retailers as following either an EDLP or a HiLo pricing strategy at a store or chain level, whereas this paper studies retailer pricing and promotions at a brand-store level. It empirically examines 1,364 brand-store combinations from 17 chains, 212 stores and six categories of consumer package goods in five U.S. markets.  Retailer pricing and promotion strategies are found to be based on combinations of four underlying dimensions:  relative price, price variation, deal intensity and deal support.  At the brand-store level, retailers practice five pricing strategies, labeled exclusive, moderately promotional, HiLo, EDLP, and aggressive pricing.  Surprisingly, the most prevalent pricing strategy is characterized by average relative brand price, low price variation, medium deal intensity, and medium deal support.  The findings provide some initial benchmarks and suggest that retailers should closely monitor their competitors’ price decisions at the brand level.