CONCLUSÕES

Within the wide field of studies considering the impact of price reductions, we focus on those studies that analyze profit (see Table 2.1). All five studies are based on offline data from the U.S. The most recent data set used in these studies is 16 years old (Ailawadi et al. 2006), while the others are based on data from the 1980s and 1990s. Two out of five studies (Srinivasan et al. 2004; Dawes 2012) rely on the data set of Dominick’s Finer Foods, which is a former U.S.

grocery chain based in the Chicago area. The profit calculations based on this data set, as well as the data set used by Mulhern and Leone (1991), include brand-specific wholesale prices but no manufacturer funding. Ailawadi et al. (2006) and Walters and MacKenzie (1988) do not include brand-specific margins for cross effects but assume an average margin for the competing brands. Most studies focus on a small subset of brands, e.g., assessing the profit impact of private-label brands (Dawes 2012).

Walters and MacKenzie (1988) start this stream of research by applying a structural equation model to analyze the store profitability of two grocery supermarkets using weekly data, including specific margin information from the years 1983 to 1985. They focus on the impact

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of three different promotional schemes (loss leader, in-store price specials, and double couponing) on profit, sales, and traffic. The results indicate that, depending on the promotional scheme, profit is affected in diverse ways. For loss leaders, profit is impacted through traffic rather than sales, and for couponing the opposite is true, while in-store price specials have no impact. As a result, they highlight the importance of building store traffic. Brand-specific analyses are not part of their study, as they take on a category perspective.

Mulhern and Leone (1991) focus on the impact of cross-category relations on profit with a demand model in log sales. Two complementary categories with four brands each from a grocery chain are analyzed. For each category, a system of seemingly unrelated regressions on brand level is estimated. The analysis reveals both substitution effects within category, as well as complementary effects across categories. The authors use cost data on wholesale prices without promotional allowances. Importantly, they do not consider a second-stage regression to obtain factors that drive profitability.

Srinivasan et al. (2004) take a more holistic view by quantifying and explaining the impact of price promotions on both manufacturer and retailer revenue, as well as retailer traffic and profits, using a vector autoregressive model. Their data source is the database of Dominick’s Finer Foods (DFF). The authors include weekly scanner data from 1989 to 1994 in 21 categories. The focus is on the three best-selling brands per category. In line with literature, a positive impact of promotions on retailer and manufacturer sales is found. Regarding revenue, promotions are more attractive to manufacturers than to retailers as a result of a strong post-promotion dip for retailers. The vector autoregressive models on retailer category margin reveal a negative impact for most brands. In a second stage, the authors explain those effects using brand and category characteristics. For retailer margins, market share, promotional frequency, and promotional depth, they identify a negative association with profit. The authors consider wholesale prices but no promotional allowances, which are now a major component in the promotional relationships between manufacturers and retailers. According to the authors, promotional support by manufacturers started in 1994; therefore, they restrict their data to the period ending in 1994.

Ailawadi et al. (2006) take on a decompositional approach on promotion level using scanner data from CVS, a major US drug retailer, from the year 2003. The promotional sales increase is decomposed into its components, i.e., consumption from other periods, other stores, and other brands, as well as a cross-category effect (halo effect). For the CVS data, the authors find

19 that 45 percent of the gross lift comes from brand switching within the store, which is not considered as being incremental. For those brand-switching movements, the authors use an average category margin, i.e., not considering margin differences per brand. Another 10 percent comes from future periods, and the remaining 45 percent is considered to be incremental lift coming from other stores, new users, or increased consumption. Additionally, a positive cross-category impact is found. Most of the promotions are not profitable for the retailer.

Furthermore, the decomposition restricts brand switching to substitution. Potential complementary or category expansion effects are found in the halo effect. The profit impact of the halo effect is calculated using the average store margin. For profit, cross-brand impact is substantial (Ailawadi et al. 2006). In a second-step regression Ailawadi et al. (2006) analyze a high number of correlates, for which they find opposing effects for sales versus profit. “Deep, featured promotions on high ‘consumer-pull’ brands generate high net unit impact, but they are also the ones for which the retailer's promotional margin is substantially lower than regular margin, resulting in lower net profit impact” (Ailawadi et al. 2006, p. 520). In line with Srinivasan et al. (2004), net profit impact, discount depth, and share have a negative effect on profit, although Ailawadi et al. (2006) include promotional funding by manufacturers.

Dawes (2012) focuses on promotional impact on a more granular level, with a demand model.

The authors analyze cannibalization between different sizes of the same brand based on the same data and time period as Srinivasan et al. (2004). Regarding profit, the focus is on the private-label brands of the retailer alone. The authors report a negative impact of promotions on private-label profits. Table 2.1: Literature Overview on Price Promotions' Profit Impact

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Given the substantial changes in retailing in recent decades (e.g., online retailing, the surge in private labels, the increasing relevance of manufacturer allowances), we add to the existing literature by including the growing importance of temporary price reductions and considering correlates of the monetary impact of such price promotions. On top of that, we seek to overcome existing limitations in the literature and base profit calculations on brand-specific margins for cross effects, including manufacturer allowances.