تصمیمات قیمت گذاری خرده فروشی و ساختار رقابتی رده محصول

This study addresses the use of demand forecasting techniques by retailers to support their decision making. Specifically, the authors propose a pricing decision support model for retailers to estimate optimal prices, whose output depends on the configuration of a supporting measurement model. The measurement model is a demand function that relates sales and prices within the category; optimal prices are those whose effects on demand and retail margins maximize the category's profitability. This investigation focuses particularly on the role of competitive structure, such that the authors consider two types of price competition asymmetries for demand forecasting: those depending on the brand (differential price effects) and those dealing with demand for competing brands (cross-price effects). By explicitly modeling competitive asymmetries in the demand function that underlies the decision support model, the authors assess implications for pricing decisions, sales, and profitability. The empirical application of the model to store-level, aggregated scanner data for two frequently purchased categories reveals the impact of an asymmetric competitive structure on demand forecasting and optimal pricing decisions. Furthermore, this article quantifies the costs of ignoring asymmetric competitive interactions in retailers' decision making.

The increasing power of retailers has become more apparent in the form of greater autonomy in their final pricing decisions. These autonomous retailer pricing decisions constitute a key element of marketing channel performance that can determine the profits of manufacturers [18], especially if the decisions come from large-scale retailers [20]. Therefore, it should come as no surprise that retailing research has focused primarily on category management and the favorable consequences of this management strategy compared with brand-centered management [45]. Authors cite the key role of category management in achieving more profitable retail pricing structures [3], yet no studies investigate the extent to which these positive consequences might depend on the implementation of the category management. Despite the importance of assessing and understanding the competitive structure of product categories for successful category management, prior research offers little empirical support. This article addresses this gap by analyzing how a greater understanding of the competitive structure within a product category may improve retail pricing decisions in the context of category management approaches. A full understanding of any competitive structure requires the analysis of competition asymmetries [8]; we consider the role of two types of price competition asymmetries. First, the impact of variations in a brand's price may differ depending on the brand, in that the price changes of different brands likely affect demand with greater or lesser intensity. Second, the impact of variations in a brand's price may differ across the various levels of demand for competing brands. The higher the substitutability of two brands, the greater the impact of their price changes. If retailers explicitly consider these asymmetries in their category pricing decision making, they may make more precise predictions about market responses to their pricing decisions and thereby improve their profitability. We propose a pricing decision-making model, based on aggregated scanner data in the context of frequently purchased categories. In this model, the optimal prices are those whose effects on demand and retail margins maximize the category's profitability; the output therefore depends on the configuration of a supporting measurement model, in which the demand function relates sales and prices within the category. Our proposed model explicitly details competitive asymmetries in the demand function, which indicates their implications for pricing decisions and thus for sales and profitability. Various authors have considered the problem of pricing decision optimization for retailers [10], [21], [25], [29], [31], [39] and [42]. In Table 1, we summarize the key contributions of these studies for our investigation. Although all these approaches recognize that the optimal prices for a product category maximize its expected profits, few consider competitive asymmetries explicitly when they specify the relationship between prices and sales. Rather, the effects they find result from different assumptions about consumer behavior. For example, some studies incorporate latent heterogeneity among consumers [10], [21] and [42], whereas others incorporate latent heterogeneity among stores [25], though in neither case can they isolate the role of competitive asymmetries from other price optimization determinants.Two studies formalize each brand's demand within a product category as a function of all brand prices to capture competitive asymmetries [29] and [31], but this procedure is not robust [42], because the model can produce unrealistic solutions, such as infinite or negative prices. To resolve this issue, we incorporate brand demand decomposition into category demand and market share. In other words, the price effect consists of purchase decision and brand choice decision effects; the latter reflect the competitive interaction among brands. Using market share models that possess logical consistency adds robustness to the demand function and enables more explicit modeling of the asymmetric price effects [12]. This article's contribution is twofold. First, we investigate the role of competitive structure in successful retail category management, especially the extent to which understanding competitive structures within a product category improves the profitability of the whole category as a result of improved pricing decisions. Our proposed model decomposes the price optimization problem of retailers into its relevant components (i.e., decision model, overall category demand model, and within-category market share model) and offers a way to understand the effects of the competitive structure (i.e., competitive asymmetries between brands). Second, our empirical application shows that the proposed decision support model can operate with the input of readily available, store-level, aggregated scanner data; we also provide a numerical example in which the consideration of competitive asymmetries changes the retailer's optimal decisions. Although our approach is based on assumptions subject to some limitations, it improves researchers' and practitioners' ability to make pricing decisions within a product category by accounting for competitive asymmetries. Our goal is to demonstrate that incorporating asymmetry in cross-price effects alters a retailer's pricing decision.

Tocontributeto existingknowledgeaboutthefactorsthatunderlie category management success in retail stores, we investigate the role of the category's competitive structure and propose a decision model that can indicate optimal category prices, according to scanner dataaggregatedtothestore-level.Optimalpricesmaximizeexpectedpro fi t through their effects on demand and margins. Moreover, by explicitly modelingdifferentialandcross-priceeffectsonbranddemand,wecan assess the role of an asymmetric competitive structure for optimal decisions, as well as quantify the economic consequences of ignoring this asymmetry. The cross-effects model that we propose includes various price effects between pairs of brands separately, which makeit more sophisticated than any existing explanatory con fi guration, such as those that distinguish price effects in terms of national and store brands, low- and high-priced brands, and so forth. The empirical application of our model to two frequently purchased productcategories,one perishable and onenonperishable,con fi rms the analytic possibilities of our model. The robust and aggregated nature of this proposal makes it easily applicable by any retail establishment at any time. The empirical application also clari fi es the consequences for the retailer if it makes its pricing decisions based solely on a simpli- fi ed interpretation of the category's competitive structure. Speci fi cally, greater competitive asymmetry will lead to higher cost estimates if the retailer ignores the competitive structure. Scanner data can provide the information needed to optimize marketing policies in a particular product category, though their ability to do so depends on the accuracy of the market response estimation. The key lies in the measurement models that support the decision model. Explicit modeling of asymmetric competitive effects involves variations in both the optimal prices and the expected results, so ignoring such variance across the categories that constitute a store's retail assortment, especially with regard to frequently pur- chased products, will mean lost pro fi ts.This study also con fi rms the importance of assessing categories to support retail category management. Retailers must attend to the competitive structures of their product categories to determine their optimal prices and thus their expected pro fi t. The same marketing actions applied to different brands may elicit different responses from consumers,whichishighlysigni fi cantfordeterminingcategory-speci fi c price structures. Similarly, rivalry between brands differs across the various pairs of brands in a category, but it affects the optimal price structure. Retailers therefore must recognize and understand the com- plex competitive structure that results from the balance of substitution and complementary relationships amongbrands within a category. Our fi ndings suggest retailers should rely on their own scanner data, input into sophisticated forecasting techniques and decision support models, to obtain optimal solutions. Because optimal category prices depend on many factors beyond the asymmetric competitive effects between brands, including market shares, intrinsic attractiveness, and unitary costs, we assert that no clear patterns for optimal prices exist. In other words, any in fl exible rule for fi xing prices can be misleading. Competitive asymmetries may alter the optimal pricing decision, so explicit modeling can improve pro fi tability. However, we consider only two kinds of asymmetries, namely, differential effects and crosseffects. An assessment of how the explicit consideration of speci fi c cross-effects may improve decision making is beyond of the scope of our analysis,though an approachsimilar to the onewe propose hereincould straightforwardly assess the economic impact of ignoring spe- ci fi c asymmetric competition effects (e.g., high versus low market share brands; private versus store brands). This proposal in turn could lead to an analytical management tool that is easy to integrate and use and effectively supports retail man- agement decisions. Such a tool would offer valuable bene fi ts for retailers. To design and implement the analytical tool for their speci fi c situation,the retailersneed to gatherscannerdata,along withsupport information that should be available from the retail manager (e.g., displays and feature advertising). Furthermore, they would need the support and cooperation of their retail distributors, which may require them to emphasize the positive implications of this tool, such as the advantages and potential bene fi ts of its applications, to managers. The basic information input therefore consists of the retail database and managerial judgments supplied by distributors. The implementation and calibration of this extended tool with actual data is key to con fi rming its validity, robustness, and reliability. With its various assumptions and limitations, our proposed model remains incomplete, but its development and empirical application indicate speci fi c areas that require more sophisticated modeling. In particular,wenotetheneedtoconnectmarketingdecisionstothecost structure, especially agreements withsuppliers(i.e., themanufacturer isastrategicplayerinpricingdecisions);theneedtoconsiderdifferent categories, including the complementary and substitutability effects across them, simultaneously; and the need to calculate the response of competing stores and the potential equilibrium state that may lead to an optimal decision. Furthermore, our empirical results must be interpreted with cautionduetoourdatalimitations.Becauseweignoreothermarketing mix variables, our estimations may be biased. Another source of bias in the estimation stems from the potential problems with price endogeneity, which we do not properly address. Several researchers have noted the need to consider endogeneity in fi rm marketing decisions in explanatory models of market response (e.g., [5] ), and sincethen,someresearchcontributionshavefocusedonincorporating the decision rules that govern fi rms' actions [11,23] . Another limitation pertains to the static nature of our proposed model. Practitioners understand any marketing decisions about a product category from a dynamic perspective; the lagged effects of prices, as well as other marketing variables, should prevent inter- pretations of prices as representative of time-isolated decisions. For example,Van Heerde andcolleagues [40,41] recognizethat thegrowth in sales caused by price promotions results from increased consump- tion and brand switching, as well as from consumers' anticipation of futurepurchases.Thus,anymaximizationofpro fi ts in acurrentperiod might come at the cost of future pro fi ts. Researchers should extend our investigation to determine the sequence of optimal decisions for a product category over an extended period of time.