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Article Excerpt
What are the drivers of retailer pricing tactics over time? Based on multivariate time-series analysis of two rich data sets, we quantify the relative importance of competitive retailer prices, pricing history, brand demand, wholesale prices, and retailer category-management considerations as drivers of retail prices. Interestingly, competitive retailer prices account for less than 10% of the over-time variation in retail prices. Instead, pricing history, wholesale price, and brand demand are the main drivers of retail-price variation over time. Moreover, the influence of these price drivers on retailer pricing tactics is linked to retailer category margin. We find that demand-based pricing and category-management considerations are associated with higher retailer margins. In contrast, dependence on pricing history and pricing based on store traffic considerations imply lower retailer margins.
Key words: retail-price drivers; retailer profits; time-series models; generalized forecast error variance decomposition
History: This paper was received July 21, 2004, and was with the authors 11 months for 3 revisions; processed by Marnik G. Dekimpe.
1. Introduction
In today's competitive environment, retailers face the complicated task of setting prices for many items. A typical grocery store in the United States carries around 31,000 items in approximately 600 product categories (Kahn and McAlister 1997). A recent article underscores the complexity of the pricing problem: "While most companies are savvy about cutting costs, few have figured out how much money they are giving up by using 'lunk-headed' pricing due to a lack of detailed information about market demand" (Business Week 2000). Also, the trade press suggests that retailers lack good tools for making pricing decisions (AMR Research 2000), as they have been slow to adopt sophisticated pricing models (Stores 2002). Therefore, the actual retail prices observed over time may differ greatly from model-recommended courses of action.
As a result, uncovering the drivers of retail prices is of great importance to marketing executives and academics. Surprisingly, there has been little empirical research in this area. Two notable exceptions are Chintagunta (2002) and Shankar and Bolton (2004). The former investigates category pricing behavior by decomposing retail prices into wholesale price, markup, additional promotional payments, retailer store brand objectives, and interretail competition for a single category in a single retail chain. Our study extends Chintagunta's (2002) work by using time-series models to develop empirical generalizations on the impact dynamics of cost-, customer-, company-, competitor-, market-, and category-drivers of retail prices over time and across brands, categories, and stores/chains. Shankar and Bolton (2004) use a cross-sectional design to study pricing strategies, focusing on price consistency, price-promotion intensity, price-promotion coordination, and relative brand price level. In contrast, we study dynamic pricing tactics with a focus on uncovering the drivers of retail prices over time.
From a modeling perspective, our study shares the basic VARX approach with Srinivasan et al. (2004). However, our research offers contributions in substantive, data, and methodological areas. First, Srinivasan et al. (2004) consider whether manufacturers or retailers benefit more from price promotions, while we focus on the drivers of retail prices across brands and categories over time. Second, they study which brand, category, and market conditions influence price-promotion elasticities and the allocation of their benefits across manufacturers and retailers. In contrast, we link the influence of price drivers on retailer pricing tactics to retailer category margin while controlling for brand and category characteristics. Our study also offers several methodological contributions that are discussed in [section]3.
Our research contributes to the existing literature on retail-price drivers by answering three unresolved questions: What are the drivers of retailer pricing tactics over time? To what extent do these drivers account for the variation in retail prices over time? And, finally, how does the relative influence of these different drivers affect retailer margins? We address these questions in three empirical steps. First, we estimate the dynamic interactions between retail prices and their drivers using time-series models. Next, we use generalized forecast error variance decomposition (GFEVD) to quantify the relative influence of these drivers on retailer pricing. Finally, we analyze the association between retailer profits (category gross margin) and the influence of the price drivers on pricing as identified in Step 2.
The paper is structured as follows: Section 2 describes the drivers of retailer pricing; [section]3 introduces the methodology; [section]4 presents the data; results in [section]5 show the relative prominence of price drivers; and [section]6 examines the link between the influence of these price drivers on retailer pricing tactics and retailer category margin. We conclude with managerial implications and suggestions for future research in [section]7.
2. Retail-Price Drivers
Previous marketing literature suggests that retail prices for a focal brand are affected by competitive retailer pricing and store traffic (e.g., Chintagunta 2002), pricing history of the focal brand (e.g., Krishna et al. 2001), demand for the focal brand (e.g., Pesendorfer 2001), wholesale prices of the focal brand (e.g., Krishna et al. 2001), and category-management considerations (e.g., Zenor 1994).
Competitive Retailer Activity
Competitive retailer activity is expected to influence retailer prices and performance. For instance, price promotions by competing retailers may reduce store traffic, inducing the retailer to lower prices (Chintagunta 2002, Hall et al. 1997). However, empirical evidence on the link between retail prices and store traffic/store switching is mixed. Chintagunta (2002) concludes retail prices have a weak impact on store traffic for the five brands under consideration in his study. Likewise, research by Walters and colleagues (e.g., Walters and MacKenzie 1988) indicates that the link is weak at best.
Pricing History
Empirical studies on price rigidity show that a large proportion of the variation in prices, often in excess of 90%, is driven by pricing history (Dutta et al. 2002). For example, past pricing actions--such as temporary price reductions--can boost sales, inducing the retailer to promote in subsequent periods even when it lowers retailer profits (Einhorn and Hogarth 1986, Srinivasan et al. 2004). Additional reasons for the dependency on pricing history include satisficing behavior due to limited information processing capacity (March and Simon 1958), formal budgeting rules that promote the status quo (Hulbert 1981), loss aversion (Tversky and Kahneman 1991), and decision anchoring (Plous 1993).
Recent experiments by Krishna et al. (2001) demonstrate that decision anchoring applies to retail pricing in the form of "a powerful tendency to rely on past prices in determining future prices" (Krishna et al. 2001, p. 1). When given a price history, the experiments' subjects set future prices too low, mostly because they give more weight to extreme observations (i.e., price deals) than to regular prices. This phenomenon reflects a perceptual averaging of past prices (Alba et al. 1999). Moreover, after a price promotion, retail prices take a longer time to revert back to their mean than do sales (Srinivasan et al. 2004). Finally, Kopalle et al. (1999), Dekimpe and Hanssens (1999), and Van Heerde et al. (2000) report that price promotions often lead to subsequent price promotions.
Given the convergent evidence from theory, experiments, and empirical analyses, we expect that retail prices for a focal brand will depend strongly on its past retail prices.
Brand Demand
Both marketing theory and practice suggest that a brand's level of demand is an important input into its pricing decisions. Indeed, a UK survey (Hall et al. 1997) reveals that retailers rate demand considerations as the most important price driver, ahead of wholesale prices and interretailer competition. In particular, low demand is often a motivation for remedial action, and (temporary) price reductions offer a quick fix to boost sales and meet performance quotas (Neslin 2002). Retailers understand the important relationship between price and demand and use that knowledge when setting retail prices (i.e., a brand's demand history affects its current and future prices).
Wholesale Prices
Both retailer surveys (Hall et al. 1997) and experimental studies (Krishna et al. 2001) confirm that costs are an important consideration for managers in setting retail prices. Almost half the marketing budget of consumer packaged goods manufacturers is allocated to trade deals (Cox Direct 1998). The extensive use of trade deals leads to frequent changes in wholesale prices and is an important determinant of retailer profitability (Economist 1992). Retail prices are not only affected by current but also by past wholesale prices, as retailers forward buy and anticipate trade deal patterns (Hall et al. 2002).
Category Management
The move towards category management (Progressive Grocer 2001) implies that retailers increasingly consider the demand, costs, and prices of competing brands in a joint decision-making process when setting prices for a focal brand (Zenor 1994). Retailers set prices for different brands to maximize total category profits (see, e.g., Raju et al. 1995), and prefer to promote only one brand at a time in a given category (Leeflang and Wittink 1992, Tellis and Zufryden 1995). Manufacturers' wholesale prices will affect the selection of the brand (Hall et al. 2002) and may in turn influence the retail prices of all brands in a specific category (Besanko et al. 2005).
3. Methodology
Our empirical analysis proceeds in three steps. First, we estimate the dynamic interactions between retail prices and their drivers using vector-autoregressive models with exogenous variables (VARX). Second, GFEVD is used to quantify the relative influence of these drivers on retailer pricing. Finally, the influence of these price drivers on retailer pricing tactics, identified in Step 2, is linked to retailer profitability. Table 1 provides references for further details.
Step 1. Vector-Autoregressive Model Specification. VARX models are well suited to measure retailpricing dynamics. (1) First, the endogenous treatment of marketing actions implies that they are explained by both past marketing actions and past performance variables. Second, VARX models are able to capture complex feedback loops that may impact retail prices over time. For instance, a price promotion in a given week may generate a high demand response, inducing the retailer to offer additional price promotions in subsequent weeks. Competing retailers may respond with price promotions to maintain store traffic. By capturing such feedback loops, VARX estimation yields a comprehensive picture of observable retail-price drivers (Pauwels 2004).
In our empirical analysis, we use two different data sources: The first contains store-level data from the Denver area and the second contains store-level data from the Dominick's retail chain in the Chicago area. The Denver database provides information on competitive retail prices but not wholesale prices, while the Dominick's database contains information on wholesale prices and store traffic but not competitive retailer prices (see [section]4 for further details).
For the Denver database, we estimate a sevenequation VARX model per product category per store, where the endogenous variables are the sales volume for the top two brands ([S.sub.i], i = 1, 2), an other-brands composite ([S.sub.3]), and the retail prices and competitive retail prices for the two major brands (RPi and C[P.sub.i], i= 1, 2). In addition to the intercept (a), we add five sets of exogenous control variables: (i) a deterministic-trend t to capture the impact of omitted, gradually changing variables, (ii) a set of dummy variables (HD) that equal one in the shopping periods around major holidays (Chevalier et al. 2003), (iii) four-weekly...
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