How price promotions work: A review of practice and theory

CHAPTER

How price promotions work: A review of practice and theory

9

Eric T. Andersona,∗ , Edward J. Foxb a Kellogg b Cox

School of Management, Northwestern University, Evanston, IL, United States School of Business, Southern Methodist University, Dallas, TX, United States ∗ Corresponding author: e-mail address: [email protected]

Contents 1 Introduction ...................................................................................... 2 Theories of price promotion ................................................................... 2.1 Macroeconomics ................................................................... 2.2 Price discrimination ............................................................... 2.2.1 Inter-temporal price discrimination ........................................ 2.2.2 Retail competition and inter-store price discrimination ................ 2.2.3 Manufacturer (brand) competition and inter-brand price discrimination .................................................................. 2.3 Demand uncertainty and price promotions .................................... 2.4 Consumer stockpiling of inventory .............................................. 2.5 Habit formation: Buying on promotion ......................................... 2.6 Retail market power ............................................................... 2.7 Discussion .......................................................................... 3 The practice of price promotion .............................................................. 3.1 Overview of trade promotion process ........................................... 3.2 Empirical example of trade rates ................................................ 3.3 Forms of trade spend ............................................................. 3.3.1 Off-invoice allowances ....................................................... 3.3.2 Bill backs ....................................................................... 3.3.3 Scan backs ..................................................................... 3.3.4 Advertising and display allowances........................................ 3.3.5 Markdown funds .............................................................. 3.3.6 Bracket pricing, or volume discounts ..................................... 3.3.7 Payment terms ................................................................ 3.3.8 Unsaleables allowance ....................................................... 3.3.9 Efficiency programs........................................................... 3.3.10 Slotting allowances............................................................ 3.3.11 Rack share ..................................................................... 3.3.12 Price protection................................................................ 3.4 Some implications of trade promotions ........................................ Handbook of the Economics of Marketing, Volume 1, ISSN 2452-2619, https://doi.org/10.1016/bs.hem.2019.04.006 Copyright © 2019 Elsevier B.V. All rights reserved.

498 502 502 503 504 505 507 508 510 511 512 513 513 514 515 518 518 519 519 520 520 521 521 521 521 521 521 522 522

497

498

CHAPTER 9 How price promotions work: A review

3.5 Trade promotion trends ........................................................... 3.6 Planning and tracking: Trade promotion management systems............. 4 Empirical literature on price promotions .................................................... 4.1 Empirical research – an update ................................................. 4.1.1 Promotional pass-through ................................................... 4.1.2 Long-term effects of promotion............................................. 4.1.3 Asymmetric cross-promotional effects .................................... 4.1.4 Decomposition of promotional sales....................................... 4.1.5 Advertised promotions result in increased store traffic ................ 4.1.6 Trough after the deal ......................................................... 4.2 Empirical research – newer topics .............................................. 4.2.1 Price promotions and category-demand ................................. 4.2.2 Cross-category effects and market baskets .............................. 4.2.3 Effectiveness of price promotion with display ........................... 4.2.4 Coupon promotions ........................................................... 4.2.5 Stockpiling and the timing of promotions ................................ 4.2.6 Search and price promotions ............................................... 4.2.7 Targeted price promotions................................................... 4.3 Macroeconomics and price promotions ........................................ 4.4 Promotion profitability ............................................................ 5 Getting practical ................................................................................. 5.1 Budgets and trade promotion adjustments .................................... 5.2 Retailer vs. manufacturer goals and issues .................................... 5.3 When decisions happen: Promotion timing and adjustments ............... 5.4 Promoted price: Pass-through ................................................... 5.5 Durable goods price promotion .................................................. 5.6 Private label price promotions ................................................... 5.7 Price pass through................................................................. 6 Summary .......................................................................................... References............................................................................................

522 523 526 526 526 527 528 529 530 531 531 532 532 533 534 534 535 535 536 537 537 538 539 541 542 543 544 545 545 545

1 Introduction Price promotions, which are temporary price changes offered by a seller, are recognized by academics as an important source of price variation (Klenow and Malin, 2010). A simplistic view would hold that price promotions are unilateral decisions by a seller to respond to immediate changes in supply or demand. For economists, it is important to understand the extent to which price promotions conform to what Robert Hall referred to as the Keynesian sticky price paradigm of “call options with unlimited quantities” (Klenow and Malin, 2010). While some price promotions do conform to this paradigm the vast majority do not. Most price promotions involve (i) joint, or coordinated, decision-making among retailers and manufacturers, and (ii) long-term planning. This has important implications for marketing, industrial organization, and macroeconomics which we will highlight in this chapter. The financial transfers from manufacturers to retailers that lead to most price promotions, which is known as trade spend, represent a substantial part of the global

1 Introduction

economy. For consumer packaged goods (CPG), trade spend by manufacturers is estimated to be as much as $500 billion globally, which represents more than half of the marketing budgets of CPG retailers (Corstjens and Corstjens, 1995, p. 239; Gómez et al., 2007). Price promotions are also critical for driving demand in many durable goods markets, such as automobiles, and in retail markets, such as sporting goods, apparel, and electronics. Among academics, there has been a heavy emphasis on CPG and retail markets due to the wide availability of data. We focus much of our attention on CPG markets but broaden our discussion in the final section to address durable goods markets. Within CPG, many studies have shown that, though a small percentage of items are price promoted at any given time, together they represent a disproportionate fraction of overall sales. ACNielsen found that 42.8% of US grocery store sales in 2009 were price promoted products. That same year, 40.4% of US drug store sales were found to be price promoted products (Felgate et al., 2012). The importance of price promotions is not limited to US markets. ACNielsen found that 12% to 25% of European retail grocery retail revenues in 2004 came from price promoted products (Gedenk et al., 2006). A senior CPG leader that we interviewed for this chapter noted that price promotions are commonplace all over the world, in both developed and emerging markets. Trade spend represents the second largest category of CPG manufacturer expenditures, after cost of goods (Gómez et al., 2007, p. 410). In a recent publication based on a survey of managers, Acosta (2012a, 2016) reported that trade spend averages 10% to 25% of manufacturer revenue (i.e., revenue derived from retailers, not point-ofsale revenue). In addition, a study by Boston Consulting Group (2012) reported that trade spend was 17.3% of revenues for nine large firms with a total of fifty billion in sales. Gartner (2015) reports that “upwards of 25%” of revenue is spent on trade promotions. The research reported in this chapter provides corroborating evidence that support these estimates.1 To many academics, price promotions are synonymous with “sales” or price discounts. There has been considerable debate among macroeconomists about whether to include or exclude “sales” from various metrics like the CPI or PPI, the impact of “sales” on the duration or stickiness of prices, and whether promotional price changes should be expected to be related to broader macroeconomic activity (e.g., Bils and Klenow, 2004; Nakamura and Steinsson, 2008; Klenow and Malin, 2010; Anderson et al., 2017). While we recognize the importance of these issues, our goal

1 To add further credibility to these metrics, we reviewed financial reports of CPG manufacturers. Many publicly traded CPG manufacturers report trade spend as an accrued liability in their financial statements. For example, P&G reported marketing and promotion accrued liabilities of $2.9 billion in 2015. If trade funds take 90 days to settle, this implies an annual trade spend of 4 x $2.9 billion = $11.6 billion. In 2015, P&G reported net sales of $76.2 billion and our rough calculation would suggest that trade spend is 11.6/76.2 = 15%, which is consistent with Acosta’s findings. In the same 2015 financial report, P&G reported $8.3 billion in advertising expenditure (11% of sales), which is also consistent with the Acosta surveys.

499

500

CHAPTER 9 How price promotions work: A review

in this chapter is to provide institutional details about the practice of price promotions to allow experts in these areas to arrive at more informed answers. To microeconomists and empirical marketing academics, price promotions provide variation in price that is fundamental to demand estimation (Nevo, 2000). In far too many papers, it is common to claim that “prices are endogeneous” and authors then pursue various solutions, including instrumental variables. This chapter provides institutional details about how price promotion decisions are made. For example, few academics are aware of the roles of scanbacks, bill backs, off-invoice allowances, trade rates, accrual accounts, and deal sheets. Such details will allow researchers to more carefully assess whether and why there is a price endogeneity problem. Again, we don’t want to diminish the issue of price endogeneity but we do hope that our chapter allows researchers to make more informed assessments. With this as a backdrop, we have four broad goals for the chapter. First, we highlight the theoretical foundations of price promotions, drawing on literature from both economics and marketing. We believe that theoretical models of price promotions are incomplete and no single model integrates four key features: a. Vertical Channel: manufacturers and retailers influence price promotions via a planned, negotiated process. b. Price Discrimination: manufacturers and retailers have incentives to price discriminate due to demand heterogeneity. c. Competition: manufacturers and retailers operate in oligopolistic markets. d. Consumer Behavior: stockpiling by consumers and habit formation. Existing theoretical models of price promotions fail to incorporate all of these features, which may cloud our understanding of how markets function. Second, we describe the mechanisms by which price promotions are implemented in practice, based on the experiences of retailers and manufacturers. We will show that price promotions are typically a planned, negotiated process in a vertical channel that involves both retail and manufacturer competition and price discrimination in a market with strategic consumers. Third, we summarize the recent empirical literature on price promotions. The last major review of this literature was published in marketing by Blattberg et al. (1995). Blattberg and Neslin’s (1990) seminal book on sales promotion was updated more recently (Neslin, 2002). Given this history, the focus of our empirical literature review is on published work between 1995 and 2018, though we do review foundational research prior to 1995. We attempt to highlight where empirical findings are consistent or inconsistent with economic models of price promotions. We believe that many of the current inconsistencies may be attributed to not capturing the institutional factors that we highlight in this chapter. Fourth, we identify gaps between academic and practitioner perspectives with an eye towards increasing the impact and relevance of academic research. To that end, we summarize key findings from numerous depth interviews with managers and identify academic research opportunities. Integration of academic thought leadership

1 Introduction

with practical problems has been a hallmark of research in marketing and economics for decades. Our hope is that this chapter helps to advance this mission. Before proceeding, we want to broadly address two important questions. First, why do price promotions exist? In particular, one might ask how price promotions benefit manufacturers and retailers. Second, how important are financial flows related to price promotions compared to other marketing activities, like advertising? To answer the first question, we offer both short-term and long-term perspectives. In the short-term, price promotions increase the sales of promoted products (see Blattberg et al., 1995 for a summary of the evidence). Interestingly, the resulting benefits accrue to manufacturers and retailers differently (Srinivasan et al., 2004; Ailawadi et al., 2007), reflecting their differing incentives. Increased sales of promoted products benefit manufacturers primarily by attracting consumers to switch from competing brands. To a lesser extent, manufacturers also benefit because price promotions cause consumers to accelerate their purchases and stockpile—buying before, or for a longer consumption horizon, than they would have otherwise (Gupta, 1988; Bell et al., 1999). Purchase acceleration and stockpiling benefit manufacturers by effectively precluding consumers from switching to competing brands on purchase occasions foregone. Finally, manufacturers may also benefit if price promotions increase consumption rates (Assuncao and Meyer, 1993), thus increasing demand for products in the category. Retailers benefit similarly from higher category demand but typically not from brand switching, which is the primary source of manufacturers’ benefit. Purchase acceleration and stockpiling may benefit retailers if they discourage shopping at competing retailers (Walters, 1991). Importantly, retailers also benefit from price promotions that prompt consumers to make incremental visits to their stores, not only because those consumers purchase the promoted products, but also because they make other planned purchases (Kollat and Willett, 1967), unplanned purchases (Walters, 1991), and purchases of complementary products (Manchanda et al., 1999). These “basket building effects” are the primary benefit for retailers. The long-term effects of price promotion are decidedly less beneficial. The primary drawback is that consumers “learn” to buy on promotion, which makes them more price sensitive and more responsive to future promotions (Papatla and Krishnamurthi, 1996; Mela et al., 1997; Jedidi et al., 1999). If consumers are trained to “buy on deal” then sellers face increasingly price-sensitive consumers. For manufacturers, an adverse long-term outcome of price promotions is the reduction of product differentiation and brand equity (Jedidi et al., 1999; Sriram and Kalwani, 2007). Manufacturers may benefit from price promotions causing consumers to try new products, but this benefit is offset by the long-term drawbacks (Ataman et al., 2008). In sum, price promotions offer short-term benefits but typically exact a long-term cost from both manufacturers and retailers. Regarding the importance of price promotions, a recent study by Boston Consulting Group (2012) noted that trade spend by large CPG firms averaged 12 times their R&D budget. Further, trade spend was 1.5 times the size of the typical advertising budget. For many CPG manufacturers, trade spend dominates advertising spend. As a specific example, Kraft publicly reported advertising expenses of $652 million while

501

502

CHAPTER 9 How price promotions work: A review

P&G reported advertising spending of $9 billion in 2014. If trade spend is roughly 15% of revenue (Acosta, 2012a, 2012b) then Kraft spent approximately $2.7 billion and P&G spent $12.4 billion on trade. In sum, while academics have recognized the importance of topics like innovation and advertising, trade spend is, on average, larger for CPG firms but largely neglected by researchers.

2 Theories of price promotion Before reviewing empirical findings, we will discuss the theoretical foundations of price promotions from macroeconomics, microeconomics, and marketing. To clarify our discussion, we distinguish retail promotions from trade promotions. Retail promotions include price promotions that retailers offer to consumers as incentives to make purchases. Trade promotions are promotions that manufacturers offer to retailers as incentives to offer retail promotions. These definitions suggest that trade promotions precede retail promotions and, perhaps more importantly, imply that retail promotions are a consequence of trade promotions. Manufacturer-retailer interactions require more complicated models. As we will argue, this complexity has limited the development of trade promotion theory and has resulted in theoretical models that are not necessarily consistent with empirical observations.

2.1 Macroeconomics We start with the macroeconomics literature, which has attempted in recent decades to reconcile models of the economy with pricing patterns observed in grocery stores and other retailers of fast-moving consumer goods. The emphasis on this industry is due, at least in part, to availability of data from syndicators such as IRI and Nielsen, and from the Bureau of Labor Statistics (BLS). Recently, scraped data from web sites has become another relevant data source. Much of the emphasis in this literature is on documenting the consistency between observed prices and theoretical macroeconomic models. In contrast to microeconomic and marketing models, which we will address later, macroeconomic models typically focus on understanding the broader economy. All of the macro models that we are aware of abstract away from the vertical channel (i.e., manufacturer and retailer) and consider a single firm. In addition to widespread availability of data, the CPG industry may have attracted interest among macroeconomists because of “moderate” price adjustment costs. For example, retail petroleum has very low costs of adjusting prices but business to business markets have relatively high adjustment costs. To some extent, CPG is an interesting laboratory for testing sticky price models. Eichenbaum et al. (2011) studied data from a large retailer in the CPG industry and demonstrated that a variant of the Dixit-Stiglitz model of monopolistic competition is broadly consistent with empirical prices, including price promotions. In contrast, they found that menu cost models, such as Golosov and Lucas (2007) and Burstein and Hellwig (2007), are generally inconsistent with empirical pricing pat-

2 Theories of price promotion

terns. A key inconsistency is that the latter models imply that prices are less volatile than marginal costs; empirically, the opposite is observed. They also found that the Calvo (1983) model, which is among the most widely used pricing models in macroeconomics, is inconsistent with the data. Klenow and Kryvtsov (2008) distinguished sticky price models, which vary with time (“time dependent”), from menu cost models (“state dependent”). Timedependent models, such as Calvo (1983) and Taylor (1980), imply that firms adjust prices randomly or every N periods. State-dependent models, such as Dotsey et al. (1999) and Golosov and Lucas (2007), assume that prices adjust with the state of the economy. One key metric that Klenow and Krystov focused on is how these models explain price inflation. They decomposed the variance in inflation into two parts: the intensive margin and extensive margin of adjustment. With BLS data that is used to construct the Consumer Price Index, they showed that the variance in price inflation is largely due to the intensive margin (i.e., the size of price changes) rather than the extensive margin (i.e., the fraction of items with price changes). Time dependent models, such as Calvo and Taylor, assume that all variance in inflation is explained by the intensive margin and don’t allow for any variation in the extensive margin. While time dependent models are broadly consistent with the decomposition, they cannot explain the fact that some adjustment occurs on the extensive margin. In contrast, state dependent models can potentially explain inflation variance on both margins of adjustment. However, simulations of Dotsey et al. (1999) show that most of the variance in inflation is on the extensive margin, which is inconsistent with the data. In addition, state dependent models like Golosov and Lucas (2007) fail to explain the sizable number of small price changes observed in the BLS data. Klenow and Kryvtsov (2008) briefly mentioned that newer macroeconomic models can explain more of the empirical facts. For example, Kehoe and Midrigan (2007) and Midrigan (2011) allow menu costs to vary in magnitude for regular versus sale prices, which leads to greater consistency with observed prices in the BLS data. While there is substantial evidence of menu costs when setting prices (Slade, 1998; Levy et al., 1997; Anderson and Simester, 2010), standard menu cost models (i.e., state dependent models) may need to be more flexible to explain observed prices.

2.2 Price discrimination A common view in microeconomics and marketing is that price promotions are used to price discriminate among end-users or consumers. Interestingly, none of the practitioners that we consulted while preparing this chapter mentioned price discrimination as a rationale for price promotions. Thus, while academics widely cite price discrimination to explain price promotions, practitioners apparently do not. A central premise of models that lead to variability in pricing over time (among sellers or products) is heterogeneity in supply and/or demand. Our review of the literature shows that heterogeneity in demand is the primary mechanism in most of these models. This may be due to the observation that cost heterogeneity among sellers may not be sufficient to generate price variation, as high cost suppliers would eventually be driven out of the market.

503

504

CHAPTER 9 How price promotions work: A review

We categorize the literature into three ways that price discrimination may arise: (1) inter-temporal, (2) inter-store, or (3) inter-brand. In the case of inter-temporal price discrimination, some shoppers are able to wait for a price promotion to be offered, when they will be able to buy at a low price. Other shoppers are unable to wait for a future price promotion so they will buy at the current price, often not a low promoted price. Inter-temporal price discrimination implicitly assumes that consumers strongly prefer to shop at a particular store and purchase a particular brand, so they will not switch. The case of inter-store price discrimination is conceptually similar, but assumes that shoppers have a uniformly strong brand preference. In this case, however, switchers are willing to buy from whichever store advertises a lower promoted price on their preferred brand. Loyals will pay whatever price their preferred retailer sets for that brand, often not a low promoted price. In the case of inter-brand price discrimination, consumers have a strong preference to shop at a given store, but some consumers are willing to switch brands to take advantage of price promotions while others are not. Switchers will generally purchase whichever brand the store offers at a low promoted price; loyals will purchase their preferred brand at whatever price the store offers.

2.2.1 Inter-temporal price discrimination Varian (1980) is often credited with introducing price discrimination as a theoretical basis for price promotion. He analyzed the case of a monopolist retailer facing some consumers who are informed about prices and others who are not. This might reflect the fact that only some consumers read retailers’ feature ads. Owing to the discrete nature of demand, the solution to this model is a mixed strategy equilibrium with a mass point where consumers are indifferent between buying and not buying. While the model treats all prices equivalently, the maximum price is interpreted as the regular price and the continuous distribution of lower prices as “sale” or promotional prices. A limitation of Varian’s model is its focus on a single firm that sets prices, which abstracts away from the vertical channel (e.g., retailer and manufacturer). In addition, the model predicts that price promotions follow a continuous distribution, conditional on the price being less than the regular price. This is inconsistent with empirical evidence that many brands are promoted at a small set of prices that are predictable (i.e., not random). As we will show later, one common strategy is to simply repeat the price promotion from a previous year. Despite its limitations, Varian’s seminal work motivated various demand-based explanations for price promotions including Narasimhan (1984), Raju et al. (1990), Rao (1991), Simester (1997), Anderson and Kumar (2007), and Sinitsyn (2008). Based on these theoretical studies, Rao et al. (1995) concluded that “competitive promotions are [emphasis ours] mixed strategies” (p. G96). In the spirit of Varian’s original work, these extensions typically assumed discrete demand (e.g., loyal and switching consumer segments) and interpreted mixed pricing strategies as price promotions. While this interpretation is intuitive and convenient, these models suffer from the same limitations as Varian’s original work.

2 Theories of price promotion

More recent price discrimination models incorporate retail competition and yield more nuanced equilibrium solutions. Díaz et al. (2009) developed a sequential model of price competition in a retail duopoly. In the first stage, the retailers simultaneously choose a regular, or “list” price; in the second stage, the retailers simultaneously choose a discount. They found a subgame perfect equilibrium in which both retailers play pure strategies. An implication of this work is that retail price promotions are not random, but rather reflect strategic decisions about regular and discounted prices. This is more consistent with empirical evidence and practitioners’ self-described decision-making processes.

2.2.2 Retail competition and inter-store price discrimination The literature reviewed in the previous section focuses primarily on inter-temporal discrimination.2 Marketing researchers have also focused on inter-store price discrimination. While retail competition is incorporated in some models of inter-temporal price discrimination, it plays a broader role in models in which the retailer’s objective is to drive traffic to the store (Hess and Gerstner, 1987). Marketing researchers have documented that price promotions may lead to store switching (Bell and Lattin, 1998). A long-held strategy for many retailers is to drive store traffic with the expectation that a customer will buy other items on a shopping trip (Richards, 2006). Many retailers fear that, if they do not price promote, they will not be able to generate customer excitement and trips to their stores or web site. In theory, retailers could increase revenue by increasing trip frequency, increasing spending per trip, or both. In practice, it is generally easier for retailers to grow via increased trip frequency than larger basket size (Simester et al., 2009). When selecting which items to promote, those with broad appeal among consumers are more desirable because they can efficiently generate store traffic. This tilts the playing field towards large brands with greater market share. A second factor is the magnitude of the price promotion. Saving ten cents on a low-priced item is unlikely to affect a store trip; saving hundreds of dollars on an expensive item is. In grocery stores, diapers are frequently used to drive store traffic because they are relatively expensive and are in high-demand from families with young children (Gönül and Srinivasan, 1996). If discounting a single item, like diapers, does not provide enough incentive to visit a store, then offering discounts on many items simultaneously can be an effective strategy (Lal and Matutes, 1994). In either case, promoted items typically have some degree of durability (i.e., lasting for weeks or months). To generate regular, weekly visits from the same consumer, a retailer may rotate the items that are discounted from period to period (Anderson and Kumar, 2007). In addition, retailers may differentiate themselves by promoting different brands, Pepsi

2 Two of the price discrimination models, Díaz et al. (2009) and Braido (2009), analyzed duopoly/ oligopoly markets and so incorporated inter-store as well as inter-temporal price discrimination.

505

506

CHAPTER 9 How price promotions work: A review

versus Coca-Cola for example, or different brand-packages, such as Pepsi 2 liter bottles versus Pepsi 24 pack cans (Anderson et al., 2004; Cao, 2011). These intuitions for competitive, multi-product retailers have been captured in various theoretical models. Braido (2009) incorporated retail competition and other factors in a very general model of price discrimination. The model accommodates both symmetric and asymmetric retail competition, multiple products, and arbitrary cost functions. In contrast to demand-based explanations in the Varian tradition, this model relies on costs rather than varying levels of consumer price sensitivity to drive price variation. Braido showed the existence of a Nash equilibrium in mixed strategies with an endogenous sharing rule. He proceeded to identify scenarios in which prices are necessarily random, which is interpreted as retail price promotions. This work represents a flexible, supply-side variation on the price discrimination explanation of retail price variation. Rhodes (2014) investigated important issues of pricing and promotion for multiproduct retailers selling to consumers who incur a search cost to learn prices. This reflects a perhaps outdated reality that consumers must visit a store to learn product prices at any particular time. Rhodes found that a retailer that offers more products should charge lower prices, though with little discounting. On the other hand, a retailer that offers fewer products should charge higher prices with deeper discounts. Generalizing to the case of competing multiproduct retailers,3 he found that competing retailers should charge a high regular price with occasional random discounts. Sinitsyn (2012) developed a model of competition between retail firms that sell complementary products, and investigated coordination of price promotions for those products.4 In his model, retailers face consumers who are either loyal, and so would rather purchase complementary products from their preferred retailer, or non-loyal. Analysis of the model for various combinations of parameter values showed that most equilibria involve discounting complementary products at the same time. Sinitsyn’s result suggests that multi-category retailers selling to loyal and non-loyal consumers should generally synchronize their price promotions. Shelegia’s (2012) model of multiproduct retailer price competition allows for not just complementary products, but also for products to be substitutes or independent in use. Using a duopoly model in which retailers sell two products to consumers of differing loyalty levels (see, for example, Sinitsyn, 2008), he showed that the equilibrium solution involves mixed strategies for both products’ prices. Only in the case of complementary products should product prices be related; in the cases of substitutes and independence, product prices should be unrelated. It is worth noting that Pesendorfer (2002) had previously conducted a detailed empirical study of synchronization in multiproduct retailers’ price promotions.

3 The case of competing multiproduct retailers was limited to just two products per retailer. 4 Note that this model assumes generic sellers offering products directly to consumers. The results are

applicable to retailers as sellers.

2 Theories of price promotion

2.2.3 Manufacturer (brand) competition and inter-brand price discrimination Researchers have also rationalized price promotions as a competitive tool for brands. Price promotions typically have a short-term impact on brand switching (Gupta, 1988; Bell et al., 1999), effectively stealing share from competitors. However, as discussed previously, the long-term benefits of price promotions for brands are less clear (Anderson and Simester, 2004). Lal and Villas-Boas (1998) extended Varian’s framework to include multiple manufacturers and retailers, and also addressed the case of retailers selling exclusive products. Their model assumes two competing single-product manufacturers selling through two competing retailers. The retailers sell to both loyal consumers and switchers. First considering the case in which manufacturers sell exclusively to one retailer, Lal and Villas-Boas found that incorporating retailers in the channel of distribution raises prices and lowers competition compared to direct consumer sales. They also found consistent retailer pass-through of trade promotions (i.e., manufacturer discounts), but that retailer margins increase with trade promotions. Interestingly, trade promotions should occur more frequently than retailer price promotions. Lal and Villas-Boas also analyzed the non-exclusive case in which manufacturers sell their products to both retailers—this is more common in practice. In this case, the lower-priced brand will always have more manufacturer discounts and the retailer will, in turn, adopt a mixed strategy in setting the retail price. Across the two cases, the most compelling result is that larger trade promotions should result is a lower percentage pass-through of those promotions by retailers. Rao (1991) modeled promotional decisions by manufacturers (vs. retailers) in a multistage game. This work recognizes the role of manufacturers in incentivizing retail price promotions and allows for asymmetric competition between a national brand and a private label. Each manufacturer chooses a single regular price, then the depth(s) of discount from regular price (many different discounts could be chosen), then the frequency of discounts. The manufacturers face consumers who differ in terms of their preference for the national brand, resulting in different degrees of price sensitivity. Rao’s primary finding is that, in equilibrium, the weaker private label manufacturer is unlikely to offer incentives for price promotion. Manufacturer inventory considerations have also been shown to play a strategic, competitive role in price promotions. Lal et al. (1996) specifically addressed forward buying by retailers that is induced by manufacturer trade promotions. They developed a dynamic model with a monopolist retailer to investigate manufacturer competition via trade promotions. Manufacturers offer branded products; consumers are assumed to vary in their brand (but not store) loyalties. Analysis of the model shows that forward buying is profitable for both retailers and manufacturers. Retailers benefit because forward buying enables them to stockpile products when trade deals are offered. Manufacturers benefit because forward buying causes the frequency of trade deals to be reduced. As we will discuss later, this has become less of an issue in practice as manufacturers have shifted towards trade promotions designed to eliminate forward buying. In related work, Cui et al. (2008) showed that price promotions may

507

508

CHAPTER 9 How price promotions work: A review

FIGURE 1 Theoretical rationales for price promotions.

allow manufacturers to price discriminate among large, dominant retailers and small retailers based on their inventory holding costs and ordering practices. Cao (2011) developed a relatively robust model, albeit with a monopolist retailer, to explain a set of empirical observations about retail price promotions. His model specifies duopoly manufacturers selling multiple products to the aforementioned single retailer, which in turn resells them to consumers with heterogeneous reservation prices. The combination of reservation price heterogeneity and the retailer’s monopoly power is sufficient to support an equilibrium that matches the empirical observations about price promotions. Specifically, Cao determined that the monopolist retailer should employ a pure strategy of price promotions if the low-reservation price segment is attractive enough. The low-reservation price segment is also sufficient for the competing manufacturers to employ a mixed strategy in offering trade promotions.

2.3 Demand uncertainty and price promotions The price discrimination models reviewed above typically require heterogeneity in demand to yield firm policies recognizable as price promotions. Demand uncertainty provides another rationale for price promotions, and Fig. 1 provides a comprehensive taxonomy of these various theories.5 The first branch of the taxonomy in Fig. 1 divides theories into those with known versus uncertain demand. When demand is known, we further divide explanations depending on whether demand has a temporal component, and so is time-driven, or

5 We thank Greg Shaffer, University of Rochester Simon School of Business, for bringing this framework to our attention, which is from Dolan and Simon (1996).

2 Theories of price promotion

not. Demand for seasonal goods, like cranberries (holiday), snow shovels (winter), and swimwear (summer) are time driven, which may lead to inter-temporal price discrimination as in Section 2.2.1. Demand for non-seasonal goods such as laundry detergent and coffee makers do not have naturally time-varying demand. When demand information is known and not time driven, the firm may offer price promotions to induce trial, accelerate purchases, and/or take advantage of built-up demand. One rationale for inducing trial is that, while the firm may know the true quality or value of its product, consumers may not. By incentivizing purchases, price promotions can therefore lead to consumers learning about quality, which in turn leads to future purchases (Erdem and Keane, 1996). A firm can also use price promotions to shift or accelerate purchases from the future into the present. For example, if the price discount is deep enough, consumers may be willing to purchase enough carbonated beverages for the next several weeks. Research has shown that purchase acceleration can boost short-term sales but may not have a long-term impact; in other words, firms often steal from their own future demand (Anderson and Simester, 2004; Srinivasan et al., 2004). In consumer packaged goods, Gupta (1988) and Bell et al. (1999) found that purchase acceleration is generally small in magnitude compared to brand switching (discussed in more detail later in this section). Finally, firms may offer price promotions to take advantage of a “build up” in demand of low value consumers who are unwilling to pay a high price (Conlisk et al., 1984; Sobel, 1984; Pesendorfer, 2002). Such a strategy may fail if high value consumers are strategic and wait for price discounts. Fairness can also be a concern if firms skim the market and charge higher prices initially while offering price promotions later (Anderson and Simester, 2008). When demand is known and is time driven, we are in a situation of peak load pricing. Price promotions or discounts may be used to shift the timing of consumer purchases. This type of pricing is more common when there is a fixed capacity constraint, such as a restaurant with a limited number of seats. Price promotions may be used to encourage consumers to dine on a weekday rather than weekend, for example. A critical link between price discrimination and capacity constraints is illustrated by Anderson and Dana (2009), who showed that price discrimination is not profitable for a monopolist, absent a quality constraint. Anderson and Dana’s model integrates work by Stokey (1979), who focused on the demand-side, and Salant (1989), who focused on the supply side, on whether price discrimination is profitable for a monopolist. Surprisingly, we often observe retailers and manufacturers promoting items during peak season. For example, it is very common to offer price promotions during holiday periods. Peak load pricing implies the opposite: prices should rise in peak demand periods (holding supply fixed). In consumer packaged goods, this puzzle was examined empirically by Chevalier et al. (2003). They found that prices tend to fall during peak holiday periods and attribute this to loss-leader pricing behavior on the part of retailers. When demand is unknown, price promotions may be offered to either learn about demand—demand probing—or to engage in yield management (Misra et al., 2019).

509

510

CHAPTER 9 How price promotions work: A review

A/B testing of price promotions is now extremely common and such tests often allow managers to answer the question: “Do I have the right promoted price?” Many online retailers regularly conduct A/B price promotion tests. Yield management is well known for airline pricing, but is also implicitly used for fashion goods, which are highly seasonal and for which demand is uncertain. Retailers launch such products with high prices and, if the item does not sell, the price is then reduced. In both A/B testing and yield management, the goal is to learn about demand and then respond with an optimal price. One difference is that, in A/B testing, price promotions may be inputs to the learning process (e.g., low and high price test conditions); in yield management, price promotions may be outcomes (e.g., a low price in response to low demand). Excess supply is a rationale for price promotions that follows from demand probing or yield management. When retailers or manufacturers find themselves facing excess supply, it is common to offer a price promotion. Excess supply can occur for many reasons, including overly optimistic demand forecasts as well as unanticipated value decay, spoilage or obsolescence (e.g., Lazear, 1986; Pashigian, 1988; Pashigian and Bowen, 1991; Rakesh and Steinberg, 1992). For example, in the U.S. car industry there are regular audits of inventory and, when thresholds are exceeded, either consumer cash or dealer cash are offered by manufacturers to induce lower prices (Busse et al., 2006). When products are in excess supply, the price promotions may be permanent rather than temporary, and remain until all excess units are sold. This is particularly true for seasonal goods.

2.4 Consumer stockpiling of inventory Another possible explanation for retail price promotions is that they influence consumers’ purchase timing and quantity, which is a specific form of inter-temporal price discrimination (see Section 2.2.1). Among the first to study this phenomenon were Blattberg et al. (1981), who proposed an inventory-theoretic model of retail pricing. The link between consumer stockpiling and price promotions has been examined subsequently by numerous researchers, including Jeuland and Narasimhan (1985), Assuncao and Meyer (1993), Bell et al. (2002), and Hendel and Nevo (2013). In their pioneering work, Blattberg et al. (1981) posited that promotions are a mechanism to shift inventory-carrying costs between retailer and consumer. In their inventory control model, a monopolist retailer sells a single product with an exogenous regular price.6 There are two types of consumers: high holding cost and low holding cost. This typology might reflect the difference between consumers who live in apartments with limited pantry space and consumers who live in larger homes with less binding constraints. Both retailer and consumers try to minimize their inventory costs, trading off acquisition costs (“cost of goods” for the retailer; “retail price” for the consumer) against holding costs. Analysis of this model shows that consumers

6 Blattberg et al. further assumed no manufacturer incentives, i.e., trade promotions.

2 Theories of price promotion

should stockpile products when prices are discounted, and that higher-demand products should be discounted less deeply but more often. Finally, the frequency of discounts should increase with holding costs and demand. An accompanying empirical analysis found support for this model, preferring it to an alternative model of retail price promotions as a mechanism for stimulating consumer trial. Hong et al. (2002) analyzed a model with n identical retailers selling to two types of consumers, loyals, and switchers. Consumers hold different levels of inventory, depending upon the time since their last purchase. Analyzing this model for subgame perfect Markov equilibria (in which decisions depend only on inventories) results in the finding that retailer prices should have negative serial correlation. This is because consumer stockpiling when prices are low depresses demand in the following period. More generally, stockpiling was found to increase retail price competition. Guo and Villas-Boas (2007) investigated stockpiling with a two-period model in which differentiated retailers face consumers with varying preferences for given products. Importantly, both retailers and consumers are strategic in this model. Consumers with relatively high preferences for a product are more likely to stockpile, thereby taking themselves out of the market in the second period. In equilibrium, Guo and Villas-Boas found that retailers should not discount deeply in order to avoid consumer stockpiling. This finding is consistent with Hong et al. (2002) in that consumer product storability, and with it the opportunity for consumers to stockpile, increases retail price competition. On the other hand, retailers have an incentive not to offer price discounts sufficient to trigger widespread stockpiling. The net effect should be to reduce the depth of promotional price discounts. More recently, Hendel and Nevo (2013) tested a similar inventory-theoretic model empirically using storable consumer goods.7 They found that consumers who store more product are more price sensitive. Further, they calculated that retail price promotions, as implemented, enable retailers to recover a substantial proportion of the possible gains from price discrimination.

2.5 Habit formation: Buying on promotion Once consumers are habituated to buying on price promotion and shopping for weekly deals, removing price promotions can be extremely difficult for both manufacturers and retailers. Models of habit formation can explain such behavior. Rozen (2010) formalized a model of habit formation and distinguished between habits that persist and those that are responsive. If consumer preferences are responsive, then there exists a compensating stream of utility such that a consumer can be weaned from his/her habits. Standard economic models assume that consumers respond to price promotions because of monetary savings, hence monetary benefit is the source of their habit. Yet

7 Hendel and Nevo (2013) assumed product storability, but further assumed that product can be stored for only a fixed period.

511

512

CHAPTER 9 How price promotions work: A review

several puzzles or counter-examples from the marketing literature suggest that monetary savings alone cannot explain consumer behavior (e.g., Dhar and Hoch, 1996; Hoch et al., 1994; Inman et al., 1990; Schindler, 1992). Chandon et al. (2000) developed a framework to reconcile much of this work. They proposed that consumers broadly enjoy three types of hedonic benefits and three types of utilitarian benefits from price promotions. A model with forward-looking consumers who are sufficiently patient and have price expectations can be consistent with the habit of “buying on deal.” Consumers may rationally form price expectations that “deals are expected to happen” when past price promotions are offered in the market. If a consumer is sufficiently patient, then that consumer will find it optimal to wait and buy only when a price is lower than some threshold. Models of habit formation with price expectations typically require that price promotions have been offered in the past (and become a habit) or that consumers believe they will be offered in the future (and are expected). An equilibrium that arises from these models might be characterized as a prisoner’s dilemma. If a firm had never offered a price promotion, then the habit may not have been formed and there would be no expectation of a future deal. Once consumers start buying on deal, it can be very difficult or costly to change their behavior and eliminate price promotions. In other words, the compensating stream of utility required to change consumer habits can be very large. The experiences of JCPenney (JCP) illustrate this problem (Mourdoukoutas, 2017). CEO Ron Johnson discovered that JCP was offering more than 365 promotions each year, which he believed was excessive. To address this issue, JCP dropped this promotional approach and offered lower regular prices every day instead. The drastic reduction in price promotions had a devastating effect on the business, as many consumers stopped shopping at JCP because they had become habituated to buying on deal. Johnson’s tenure as CEO ended after seventeen months and Mike Ullman, who succeeded him, quickly reinstated price promotions to lure customers back to JCP.

2.6 Retail market power Another view of price promotions is that they are the result of retail market power. For example, if manufacturers cannot profitably sell direct to consumers, then retailers may hold some degree of market power due to their ability to efficiently reach those consumers. Under this view, trade funds (i.e., payments from manufacturers to retailer) can be viewed as a financial transfer from a manufacturer to a retailer to obtain access to the retailer’s scarce resources—in other words, its customers. While this view is somewhat pessimistic, it is not without merit. Most retail markets are oligopolies and retailers have some degree of market power due to their geographic location. Further, conditional on a consumer visiting a store, a retailer has even more market power due to its ability to control prominent in-store locations, such as end-of-aisle displays. Price promotions that generate huge increases in vol-

3 The practice of price promotion

ume are typically located in these prominent in-store locations. Under this view, price promotions can be viewed as part of an auction or negotiation by the retailer to sell its most valuable, scarce assets each week. While this view is plausible, we are not aware of any papers that explicitly take this perspective.

2.7 Discussion In sum, there are numerous theories of price promotion and, to the best of our knowledge, no single theory explains all of the empirical observations that we will discuss later in this chapter. While we do not propose a specific theory in this chapter, we would argue that a complete explanation must account for these major factors: a. Vertical Channel: manufacturers and retailers influence price promotions via a planned, negotiated process. b. Price Discrimination: manufacturers and retailers have incentives to price discriminate due to demand heterogeneity. c. Competition: manufacturers and retailers operate in oligopolistic markets. d. Consumer Behavior: purchase acceleration and stockpiling by consumers, as well as habit formation. To date, no single model that we know of incorporates all these factors. Perhaps the one common theme among these models is that they have considered many different rationales grounded in price discrimination. In this sense, the literature is quite long on a set of theoretical possibilities. But many of the early models abstract away from the vertical channel and consider only a single firm. Numerous models also consider monopoly markets, ignoring competition entirely or focusing on competition at either the manufacturer or retailer level. As we will document later in this chapter, the nature of vertical contracts and price promotion planning among retailers and manufacturers has also been largely ignored in theoretical models. Finally, while habit formation is a well-understood concept, we have few, if any, theoretical models that seriously tackle the implications of habit formation in the context of price promotions.

3 The practice of price promotion As noted in the opening paragraph of this chapter, it is important for economists to understand whether wholesale and retail prices conform to what Robert Hall referred to as the Keynesian sticky price paradigm of “call options with unlimited quantities” (Klenow and Malin, 2010). We believe that this interpretation is unlikely due to the process by which promotional prices are set in most markets. The goal of this section is to describe the process by which regular and promoted prices are determined in the packaged goods industry but we believe the key properties of (i) coordination and (ii) planning among retailers and manufacturers generalize to many other markets.

513

514

CHAPTER 9 How price promotions work: A review

3.1 Overview of trade promotion process In practice, price promotions are co-created by manufacturers and retailers. Manufacturers offer trade promotions (e.g., trade funds) to retailers as an inducement to offer, in turn, retail promotions (in particular, price promotions) to consumers. In this section, we provide an overview of the trade promotion process from the perspectives of both manufacturer and retailer. Trade promotions are incentives that manufacturers offer retailers in return for marketing, merchandising, selling activities, and scarce resources (such as shelf space, display space, and advertising pages) that differentially benefit the manufacturer’s products and brands. A broader interpretation also includes incentives for retailers to transport and warehouse products and pay for them in ways that improve manufacturer efficiency. Under the umbrella of trade promotions, manufacturers also offer outcome-based incentives; i.e., payments for selling the manufacturer’s products through to consumers. Incentives for these activities take various forms, which we describe in more detail later in this chapter. The money that manufacturers allocate to these incentives is known as trade spend. While manufacturers have long been concerned about “bang for the buck” from trade spend8 and profitability of individual trade promotions,9 these concerns have not been reflected in reduced manufacturer profitability.10 Manufacturers’ trade spend is allocated to retailers using a two-step process (Gómez et al., 2007). First, a fixed amount of trade spend is budgeted for each retailer. For large manufacturers, these budgets are typically based on accruals (which we explain later) but budgets can also be lump-sum amounts. Trade spend budgets determine how much the manufacturer may spend in incentives for each retailer over the course of a year (in some cases, a quarter or a month). Second, throughout the budgeted period, money is allocated to specific incentive payments and performance requirements for individual promotional events, each negotiated between manufacturer and retailer. When the money allocated to a promotional event is spent, the remaining trade spend budget is reduced. Manufacturers often determine the amount of trade spend allocated to a retail account based on a percentage of the revenues generated from that retailer. This process is known as accrual of promotional funds, and the percentage is known as the accrual rate or trade rate. The budgeted trade spend for the current year is often based on accrual from revenues during the previous year. The rationale for setting budgets in this manner is to align incentives. If a retailer supports a manufacturer’s product line and generates more revenue, there is an increase in next year’s trade spend for that retailer. In contrast, if a retailer does not support a brand then there may be a subsequent reduction in trade spend. In theory, this process aligns the incentives of the retailer and manufacturer. It is worth noting that trade spend budgets are generally set by the manufacturer and not negotiated with the retailer. This was confirmed by interviews with a sample 8 See generally Besanko et al. (2005); Srinivasan et al. (2004); Tyagi (1999). 9 Cf. Dreze and Bell (2003). 10 Messinger and Narasimhan (1995); Ailawadi et al. (1995).

3 The practice of price promotion

of buyers from fifteen supermarket companies that indicated, in general, budget decisions are not jointly determined by manufacturer and retailer. In fact, ten of the buyers explained “that the manufacturer determines the budget first and then negotiates with the retailer on its allocation.” (Gómez et al., 2007, p. 412). The allocation process often occurs via annual joint business planning (JBP). Nearly every manufacturer has a major annual meeting with large retail accounts to plan 12 months of trade and marketing spend. There are often quarterly reviews of these annual plans to make adjustments during the year. As we will document later in this chapter, promotions are planned months in advance of their execution and for academics, this is an important institutional fact. In sum, the process we describe suggests that price promotions may be modeled as a sequential game where manufacturers first determine the budget and then negotiate with a retailer on how to spend the budget over the course of a year.

3.2 Empirical example of trade rates While trade rates are common in practice, there is a lack of empirical information regarding trade rates in the academic literature. Manufacturers make offers to retailers and the terms of these trade deals vary across time, retail account, and product. To our knowledge, trade rates have not been studied in previous academic papers. Trade rate data is difficult to obtain and hence we are unable to provide a broad set of empirical generalizations about trade rates. But, we hope that our unique example illustrates that trade rates exist and vary among retail chains, products, and time. Our data is from an anonymous, mid-size manufacturer that sells two different brands of cheese. Each observation in the data is a promotion event that has a start and end date for a specific retail account and a specific product. For each promotion event, there is a trade rate that indicates how the retailer accrues trade funds. For example, if the trade rate is 7% then $100 in purchases by the retailer yields $7 in trade funds. In Fig. 2, panel A, we plot a histogram of the raw data for product 1 and we see that the average trade rate is 12%, which is again consistent with the Acosta report. Notably, there is substantial variation and the trade rate ranges from near zero to more than twenty percent. What is important for academic researchers to notice is that the trade rate varies by both retail chain and promotion event – it is not a constant percentage. In Fig. 2, panel B, we plot the average trade rate by retail chain, which collapses the temporal dimension of the data, and observe a bimodal distribution. While somewhat speculative, this is consistent with some chains executing a hi-lo strategy that is funded by a trade rate of 14.5%. For comparison, in Fig. 3 we also plot the same information for another product offered by the same manufacturer (product 2). Here we see much lower trade rates (2.2%) and no longer see a bi-modal distribution (see Fig. 3, panel B). Empirically, we observe that product 2 is not as reliant on trade funding as product 1. We look at two facts to explain this difference in reliance on trade promotions between products 1 and 2. First, while product 1 has a larger trade rate, the number

515

516

CHAPTER 9 How price promotions work: A review

FIGURE 2 Panel A shows a histogram of all trade promotion events for product 1. Panel B shows a histogram of the average trade rate by retail chain.

FIGURE 3 Panel A shows a histogram of all trade promotion events for product 2. Panel B shows a histogram of the average trade rate by retail chain.

of promotion events is 4,000 versus nearly 25,000 for product 2. Thus, product 2 has higher frequency of events but a lower funding rate for each event. Second, an online search reveals that product 2 is heavily advertised while product 1 has virtually no advertising. Hence, it appears that marketing dollars are directed towards the trade for product 1 and towards consumer advertising for product 2, consistent with Allender and Richards (2012). Another challenge with point-of-sale (POS) data is that researchers do not directly observe the duration of a promotion event. In Fig. 4, we plot a histogram of the promotion duration, which is recorded in our data. The duration of trade promotion events are roughly equivalent for both products 1 and 2. 88% of promotion events are less than 30 days in duration more than 96% are less than 60 days. The modal number of days is 12 for both products, and the mean durations are 16.9 and 18.47 days for products 1 and 2, respectively. Keep in mind that this characterizes the supply side of

3 The practice of price promotion

FIGURE 4 Panel A shows a histogram of the duration in days of each promotion event for product 1. Panel B shows the same information for product 2.

FIGURE 5 Panel A shows a histogram of the number of promotion events each month for product 1. Panel B shows the average trade rate each month for product 1.

the market—the trade deal (i.e., trade funding) is available for an average of 16-18 days. The data do not describe the price promotion offered by a retailer as a result of this trade deal. In Fig. 5, we look at the number of promotion events each month and the average trade rate by month for product 1; Fig. 6 contains similar information for product 2. Panel A of Fig. 5 shows that promotions are concentrated in the first six months of the year and there are almost no promotions from September through December, which suggests that product 1 is seasonal. Panel B of Fig. 5 shows that the trade rate is also not constant over time and peaks in April, May, and January. For product 2 (Fig. 6), there are promotions in every month with a spike in promotions during January. The average trade rate is relatively constant across months and varies between 1.9% and 2.6%.

517

518

CHAPTER 9 How price promotions work: A review

FIGURE 6 Panel A shows a histogram of the number of promotion events each month for product 2. Panel B shows the average trade rate each month for product 2.

Again, our goal for this analysis is to briefly illustrate the variability of trade rates across retailers, products, and time. For academic researchers, it is important to be aware of the existence of trade rates as a funding mechanism for trade promotion budgets. Empirically, there is a need for a deeper analysis of this type of data that could ultimately link promotion offers from manufacturers to their realization at the point of sale.

3.3 Forms of trade spend Trade rates typically lead to the creation of a manufacturer promotion budget for a retail account. One might think of this as the creation of a pool of dollars that can be spent on retail marketing activities. These manufacturer dollars are then allocated, or transferred, to retailers in different ways. This is often referred to as the form of trade spend. These transfers can vary in at least three important ways: (1) retailer performance requirements, (2) which retailer costs are defrayed, and (3) the form and timing of payment. We discuss each form of trade spend in more detail below. Note that the discussion below includes forms of trade spend that are not trade promotions per se, but rather incentives to make the manufacturers’ transportation, warehousing, and cash flow more efficient.

3.3.1 Off-invoice allowances Off-invoice allowances are discounts from the list price, either a percentage off list price or a fixed amount off per case or unit. While it is difficult to determine when off-invoice allowances originated, evidence points to the Nixon administration’s implementation of a retail price freeze to stem inflation in August 1971 (Acosta, 2012b). To create financial flexibility in advance of the impending price freeze, many manufacturers raised wholesale prices, then immediately offered off-invoice allowances to retailers to offset part or all of those price increases. Perhaps unknowingly, this led

3 The practice of price promotion

the CPG industry down a path of what ultimately became complex financial transfers between manufacturers and retailers. One characteristic of off-invoice allowances is that the manufacturer does not actually pay them—the price is reduced before the retailer is ever invoiced (Bell and Dreze, 2002). A second characteristic of off-invoice allowances is that they do not depend on the retailer’s sales performance. This raises obvious moral hazard problems where retailers can accept the off-invoice allowances but may not lower the price to increase sales. A third characteristic is that the retailer is only required to buy the product to get the off-invoice allowance. When off-invoice allowances are offered, some retailers buy larger quantities than they expect to sell and either warehouse the product to sell after the promotion at regular retail prices (“forward buying”) or resell the excess product to other retailers that did not have access to the off-invoice allowances (“diverting”).

3.3.2 Bill backs Bill backs are payments made to retailers based on the number of units they purchase from the manufacturer during a specified period.11 One primary difference between an off-invoice allowance and a bill back is the timing of the payment—off-invoice allowances are deducted from the invoice before it is paid; bill backs are rebated to the retailer after the specified period (Blattberg and Neslin, 1990). Bill backs are often used by small manufacturers who may rely on wholesalers and brokers. The bill back documents that the retailer purchased a manufacturer’s product and so enables a direct financial transfer from the manufacturer to the retailer. This financial transfer can bypass intermediaries like wholesalers, which avoids double marginalization in the channel.

3.3.3 Scan backs In scan back promotions, the manufacturer typically pays a fixed dollar amount for each unit that the retailer sells during a specified promotion period, not for products that the retailer purchases as with bill backs and off-invoice allowances. And, like bill backs, scan back payments are made after the retailer has purchased and paid for the manufacturer’s products (Dreze and Bell, 2003). The retailer documents its sales by sending scanner data to the manufacturer, which then pays the retailer based on the number of units sold during the promotion period.12 Much of trade spend that was previously been offered as off-invoice allowances is now offered as scan backs, which enables manufacturers to limit forward buying and diverting (Bell and Dreze, 2002). Scan backs also allow manufacturers to monitor retailer performance for their trade spend dollars by observing the prices at which their products are sold.

11 The term “bill back” may be used for other rebates, including payments made for retailer performance. 12 In some cases, scan back payments are not made to the retailer but rather deducted from future invoices.

519

520

CHAPTER 9 How price promotions work: A review

3.3.4 Advertising and display allowances The greatest differential benefit that retailers provide to manufacturers, in addition to retail price discounts, is by advertising or displaying the manufacturers’ products (Waller et al., 2010; Blattberg and Neslin, 1990). As a result, manufacturers offer allowances for retailers to allocate their limited advertising pages or display space on end-of-aisle, free-standing or other in-store displays to the manufacturers’ products (Blattberg and Neslin, 1990). Advertising allowances, sometimes called co-op funds, often take the form of lump sum payments, although they can also be funded via other mechanisms like scan backs. Ad allowances are used to defray the costs of creating, printing, and distributing retailers’ feature advertisements. Retailers often advertise families of products rather than individual items, typically by manufacturer by pack type. Allocating a lump sum allowance for advertising a family of products to each individual product in that family is typically not done in practice. Display allowances, which are paid for temporary placement in-store in locations such as end-of-aisle, can also take the form of lump sum payments or scan backs. Scan backs are sometimes offered for a combination of retailer price discounts, advertising, and display.

3.3.5 Markdown funds Markdown funds are offered to mitigate retailers’ costs to mark products down at the end of a season (or for discontinued items). Demand for many products is highly seasonal. Retailers mark down the prices of such products at the end of the high demand season in order to avoid carrying unsold inventory into low demand seasons. Markdown funds encourage retailers to buy seasonal products in larger quantities, because the risk of overstocking is shared by the manufacturer. In CPG, markdown funds may be negotiated after there is joint realization that a product did not sell. In the apparel industry, markdown funds are often negotiated at the time an order is placed. For example, an apparel item may have a suggested retail price of $99 and wholesale price of $50. If the retailer discounts the retail price to $79, the manufacturer may make $10 of markdown funds available, maintaining the retailer’s gross margin at 50%. In apparel, the schedule of markdown dollars and prices is often known when an order is placed. This practice raises agency issues and has led to fraud. For example, in 2007, Saks Incorporated (which owns the retail chain Saks Fifth Avenue) faced a lawsuit from the Securities Exchange Commission for fraudulently claiming vendor allowances and illegally accounting for markdown funds from vendors, a practice referred to as the rolling of markdowns. The New York Times (Barbaro, 2007) reported that “At Saks, according to the S.E.C. documents, buyers routinely misled suppliers by overstating the number of products sold at a deep discount to collect greater payments.” The improper treatment of markdown dollars was alleged to have overstated annual net income by six to nine million dollars per year. Ultimately, Saks settled the lawsuit, fired several senior employees, and changed internal processes to address the issue.

3 The practice of price promotion

3.3.6 Bracket pricing, or volume discounts Bracket pricing is an incentive for retailers to purchase in volume. Truckload purchases generally earn retailers the highest bracket discounts. By purchasing and taking delivery of larger quantities, however, retailers incur additional inventory holding costs. Thus, bracket pricing effectively shifts inventory holding costs from the manufacturer to the retailer. Note that the bracket price is reflected on the invoice as a discount from the list price.

3.3.7 Payment terms Also known as prompt payment discounts, payment terms are manufacturer incentives for retailers to pay their invoices quickly. Payment terms typically give a deadline for retailers to earn a discount from the invoiced amount as well as a date by which the entire invoiced amount must be paid. For example, payment terms of “2/10 net 30” offer a 2% discount from the invoice price if payment is made within ten days; if not, the entire invoiced amount is due in 30 days.

3.3.8 Unsaleables allowance This allowance, alternatively known as waste, spoils, or swell allowance, is offered by manufacturers to pay for products that are delivered in an unsaleable condition. Unsaleables can be contentious because there may be uncertainty about how the product became unsaleable and whether the manufacturer, retailer, or another party is responsible. Processing unsaleables and determining responsibility can add administrative costs. To avoid such costs, manufacturers often simply offer a percentage of the invoice price to the retailer to offset the cost of unsaleable products.

3.3.9 Efficiency programs Separate from bracket pricing, many manufacturers offer incentive programs for retailers to increase the manufacturer’s supply chain efficiency. Criteria to receive efficiency funds include large order sizes (full truckloads and pallets) and low order cancellation rates. By taking delivery in larger quantities, the retailer again incurs additional inventory holding costs.

3.3.10 Slotting allowances Slotting allowances are one-time payments that manufacturers make to retailers in return for putting a new product in distribution. “Although these fees [allowances] help to defray the costs of adding (and deleting) an item from the system, they also cover the retailer’s opportunity costs for allocating shelf space to one item over another” (Dreze et al., 1994).

3.3.11 Rack share Rack share is effectively a rental payment to the retailer for shelf space on the racks in the checkout area at the front of a store. The payment is typically charged per linear inch of rack space, although it may be a fixed fee instead.

521

522

CHAPTER 9 How price promotions work: A review

3.3.12 Price protection When manufacturers raise their prices, they typically offer their retail customers funds to cover the difference between old and new prices for a period of time corresponding to merchandising commitments made before the price increase was announced—e.g., eight or twelve weeks. Price protection is paid to retailers in one lump sum after the protection period or at the end of shorter windows during the protection period.

3.4 Some implications of trade promotions Nearly every type of trade promotion is offered with an expectation of retailer performance. Scan backs and bill backs are offered for short-term promotions; markdown allowances are used to run through excess inventory of seasonal items; price protection extends pricing and merchandising agreements made before a manufacturer price increase. And even though most types of trade promotion lower the retailer’s cost of goods, the retailer often incurs a cost to implement retail promotions. Such costs include changing prices at the shelf and in point-of-sale systems, putting up and then taking down shelf tags, building in-store displays, laying out feature ads, etc. In a typical week, the vast majority of prices in a retail store are regular shelf prices, not promotional prices (McShane et al., 2016). Retailers generally set regular shelf prices to meet gross margin targets, consumer expectations, or competitor prices. This varies by retailer, and sometimes by category and by the perceived importance of an item to the retailer’s price image. Categories that are more important in store choice decisions are called destination categories (Briesch et al., 2013); individual items perceived to be more important in store choice decisions are often called key value items. By definition, gross margin targets for retailer prices are based on manufacturers’ gross, or list, prices—not on manufacturers’ net prices. In fact, most retailers never calculate manufacturers’ net prices. Thus, retailers’ regular shelf prices, the vast majority of prices in their stores, are not influenced at all by manufacturer trade spend. Bracket pricing, payment terms, unsaleables allowances, efficiency programs, slotting allowances, and rack share are all similarly ignored for the purposes of retail shelf prices or regular prices. The existence of an annual manufacturer trade budget, which funds negotiated price promotions, casts doubt on the notion that price promotions and trade deals represent “call options with unlimited quantities.” Quantities are clearly bounded by the trade budget. While the implied constraint of a trade budget is rarely captured in empirical studies, it does exist and our discussions with managers illustrate its relevance.

3.5 Trade promotion trends A study by the Point of Purchase Advertising Institute famously concluded that “PO-P is significant as the ‘last three feet’ of a brand’s marketing campaign, and serves as the ‘closer’ for in-store purchasing decisions as well as an influencer for impulse

3 The practice of price promotion

purchases” (Consumer Buying Habits Study, 1995, p. 3). Because the retailer has exclusive control of the ‘last three feet,’ it is not surprising that CPG manufacturers have reconfigured their marketing budgets to provide more incentives for retailers to perform activities at point-of-purchase that benefit their brands. In fact, many CPG manufacturers shifted their marketing budgets away from media advertising and toward trade promotions. The allocation of trade spend between different types of promotions has changed as well. Two decades ago, the vast majority of trade spend was allocated to off-invoice allowances. Industry experts reported that, for CPG manufacturers, off invoice allowances fell from 90% of all trade spend in the mid-1990s to about 35% by 2003 (Gómez et al., 2007). This drop in off-invoice allowances as a percentage of trade spend was driven by manufacturers’ desire to prevent forward buying and diverting (Bell and Dreze, 2002), and so that manufacturers could monitor retailer performance. A recent study by Boston Consulting Group (2012) highlighted that trade spend growth exceeded revenue growth between 2008 and 2010. Given the emergence of online shopping, one may speculate as to whether this trend will continue. As of the writing of this chapter, we see no signs that trade spending is declining with the growth of online shopping.

3.6 Planning and tracking: Trade promotion management systems In the academic literature, we often conceptualize the financial transfer from a manufacturer to a retailer as simple wholesale discount. In practice, the financial transfers between manufacturers and retailers are much more complex. In the previous sections, we have documented how trade rates vary across retailers and products. Due to this complexity, manufacturers typically need tools to assist with promotion planning, tracking financial flows, and evaluating promotion performance. For many years, this information was often planned manually, tracked in a spreadsheet, and often not evaluated in terms of ROI. Today, nearly all large manufacturers use trade promotion management (TPM) systems to assist with these tasks, though the adoption rate among small manufacturers is much lower. Gartner (2015) conducted a comprehensive review of all TPM vendors, which includes large IT providers like Accenture, SAP, and Oracle. The Gartner report notes that TPM systems should provide five broad functions for manufacturers: (1) promotion planning and budgeting at various levels, (2) abbreviated P&L statements prior to the promotion, (3) promotion execution and encumbering funds, including accruals, (4) settlement of funds with retailers, wholesalers, brokers, and (5) post promotion analysis. A typical TPM system has a web-based dashboard that allows managers to visualize planned promotions for the year as well as track planned vs. actual promotion volume by retail account, product, and time. TPM systems also help managers keep track of planned vs. actual trade budgets and provide financial summaries at different levels of granularity.

523

524

CHAPTER 9 How price promotions work: A review

FIGURE 7 Example of a deal sheet from a TPM system.

The exact details of each promotion event are stored in the TPM system as what one manager we spoke with referred to as a “deal sheet.” We were provided with numerous examples of deal sheets and, in Fig. 7, we provide a mock-up of one example. While the details are disguised, it is intended to provide academics with a better understanding of how promotions are planned. At the top of Fig. 7, we see the start and end date of the promotion event, which is December 1 to December 31. The communication between the manufacturer and retailer is stored in the TPM system. We see that the deal is created by copying a previous deal (from the same time period in the previous year), and interviews with practitioners confirmed that this is a very common practice—the promotion from a previous year is very likely to be run in a subsequent year. For academics, this suggests state dependence among promotion events. Next we see that the final negotiation process consists of a single text message. In this case, the manufacturer and retailer had planned for a specific scan back promo-

3 The practice of price promotion

tion as part of joint business planning or the annual promotion process. In August, four months before the promotion, there is a last-minute adjustment in the magnitude of the scan back. Notice that the negotiation is in increments of ten cents, which a manager we spoke with referred to as “dimes.” Managers often anticipate that retail price changes will be in increments of dimes and may negotiate in multiples of dimes. For academics, this suggests that retail price adjustments are lumpy (e.g., dimes) rather than continuous (e.g., pennies). While there is consistency between this empirical fact and the menu cost literature (Dotsey et al., 1999; Golosov and Lucas, 2007), we are not aware of any menu costs associated with this practice. Instead, the practice of pricing in dimes appears to be grounded in managerial norms or habits. The TPM system provides detailed financial information about each of the three UPCs that are part of the promotion. The deal sheet contains information about the retail regular price and the gross margin at that price, which is 38%. Notice that one of the UPCs has a slightly lower regular price, $3.99, than the other two UPCs, $4.19. During the promotion, however, the manufacturer would like the retailer to lower the price to $2.99 for all three UPCs. Offering the same price on all UPCs, called line pricing, is a common practice. To achieve this discount of between $1.00 and $1.20, the retailer accepts a scan back of between $0.57 and $0.68. The scan back keeps the retailers percentage gross margin at roughly the same level with and without the promotion (38% vs. 36%). Presumably, the retailer is willing to accept a lower dollar margin and make this up via increased volume. For this deal, the pass through rate (price change/cost change) is significantly greater than 100%. As we prepared this chapter, a manager shared six other scan back deal sheets with us that included sixty UPCs. The pass through rate on all sixty UPCs exceeded 100%. Among these examples, we found no evidence that the gross margin percentage increased during a promotion, which must occur if pass through is less than 100%. McShane et al. (2016) showed that managers pay close attention to gross margin percentage when adjusting the regular price and that there may a similar emphasis on gross margin percentage when determining the promoted price. Our discussion thus far has focused on TPM systems. Many vendors also offer trade promotion optimization (TPO) systems, which focus on using data to design promotions. In 2015, Gartner reported that TPO systems were not as widely adopted as TPM systems but were growing in popularity. The underlying tools and methods used in TPO systems are often grounded in demand models which have their origins in academic research. TPO is an area where academics have a considerable amount of expertise to share with practitioners. For example, a TPO system that uses historical data will likely suffer from endogeneity concerns, a well-known issue in the academic literature. Improving the quality of data via A/B testing, quasi-experimental methods, or valid instrumental variables are approaches that academics have used to overcome these challenges. TPO systems represent an opportunity for academic research to advance managerial practice.

525

526

CHAPTER 9 How price promotions work: A review

4 Empirical literature on price promotions 4.1 Empirical research – an update In 1995, Blattberg, Briesch, and Fox (hereafter BBF) compiled an extensive set of empirical generalizations, uncertain results, and insufficiently researched topics related to promotions. In this section, we update their results with more recent findings, with special emphasis where previous research was limited or inconclusive.

4.1.1 Promotional pass-through Promotional pass-through captures the proportion of the manufacturer’s trade spend that is passed through to consumers in the form of retail price reductions.13 BBF found that, in general, retailer pass-through was less than 100%. Yet recent studies suggest that this generalization requires refinement. Notably, Ailawadi and Harlam (2009) found that “. . . the retailer actually spends a little more on price promotions across all products than the total funding it receives from all manufacturers.” Moorthy (2005) developed a theoretical model of price promotion pass through that accounts for retail concerns of category management and store competition. In theory, a price promotion on one brand can affect the price of all other brands in the category, which has been referred to as cross-brand pass-through. Empirically, there is a lack of consensus as to whether cross-brand pass through exists (McAlister, 2007; Dubé and Gupta, 2008). Besanko et al. (2005) evaluated pass-through rates for 78 brands across 11 categories, finding that pass-through was systematically higher for high-share brands than for low-share brands.14 Pauwels (2007) evaluated pass-through rates for 75 brands across 25 categories, finding an average pass-through rate of 65% though it was higher for larger brands and more expensive categories. Nijs et al. (2010) analyzed product shipments in a single category to more than 1,000 retailers, and found the mean pass-through rate to be 69% for retailers that purchase directly from manufacturers, but 106% for retailers that purchase from wholesalers. Trade promotion spend, and hence pass-through rates, can be difficult to calculate for specific products. Promotional incentives sometimes apply to multiple products; for example, a single lump-sum allowance may be paid to advertise or display all flavors of a particular brand-size of yogurt. While previous analyses included only the incentives reflected in the manufacturer’s price, Ailawadi and Harlam (2009) incorporated the full array of manufacturer incentive payments. In aggregate, they found that pass-though rates exceeded 100%, varying by department from 65% to more than 200%. Their findings imply that failing to incorporate all manufacturer incentives may substantially underestimate pass-through. 13 Pass-through is the ratio of manufacturer trade spend for a given promotion to the total retail price

discount (= average discount per unit × number of units sold). Similarly, pass-through has also been calculated as the change in retail price for a given change in manufacturer (or wholesaler) price. 14 McAlister (2007) noted the consequences of this paper incorporating variation across different price zones.

4 Empirical literature on price promotions

Ailawadi and Harlam found a great deal of variation in pass-through rates across manufacturers. Using data from a major US retailer over two years, they found the median pass-through rate to be 20%, but aggregate pass-through exceeded 100% and 14% of manufacturers offered trade promotion rates exceeding 250%. Interestingly they found that 34% of manufacturers offered no trade promotions, though retailers sometimes contributed funds to the promotion of these manufacturers’ products. Nijs et al. (2010) focused on a single category at various manufacturers, wholesalers, and retailers around the US. They found the mean pass-through rate was higher for wholesalers, 71%, than for retailers, 59%. Meza and Sudhir (2006) evaluated variation in pass-through over time using a structural model. Not surprisingly, they found that pass-through was higher for lossleader products compared to other regular products. More interesting, they found this difference to be larger during periods of high demand. They concluded that retailers augment manufacturers’ trade spend for loss-leader products during periods of high demand, compared to regular products and periods of lower demand. In a study of automobile purchasing, Busse et al. (2006) assessed the simultaneous use of trade and consumer promotions. They found pass-through rates for trade promotions (dealer discounts) to be only 30-40%, less than half the pass-through rates of consumer promotions (customer rebates). Our field would benefit from further studies of the simultaneous use of consumer and trade promotions by different types of retailers.

4.1.2 Long-term effects of promotion Do price promotions decrease brand differentiation? Do they increase consumers’ price sensitivity? In their review, BBF found conflicting evidence, concluding that the “jury is still out” (p. G127). However, they determined that promotional sales decrease with the frequency of price promotions. Although the objectives of price promotions are short-term, the growth of trade promotion budgets has led to an increased interest in long-term effects. Mela et al. (1997) analyzed 8¼ years of panel data to determine how promotions affect consumer response over time. They found that price promotions make consumers more price sensitive in the medium- and long-term. This increase in price sensitivity is much greater for non-loyal customers than for loyal customers. In contrast, non-price promotions cause loyal customers to become less price sensitive. Analyzing the same data, Mela et al. (1998) determined that brands had become less differentiated over time as manufacturers shifted dollars from media advertising to trade promotion, which increased retailer price promotion. This loss of differentiation negatively affected premium brands. Based on the same data, Jedidi et al. (1999) conducted an extensive investigation of the long-term effects of price promotions and advertising. In general, they found that price promotions had a negative long-term effect on brand equity, while advertising had a significant positive effect. In the long term, price promotions were also found to make consumers more price sensitive yet less responsive to discounts. These results suggest that price promotions become less effective over time, even though they cause consumers to become more price sensitive.

527

528

CHAPTER 9 How price promotions work: A review

Using a much shorter panel dataset of detergent purchases, Papatla and Krishnamurthi (1996) found that purchasing products that were displayed or feature advertised, in particular if they were also discounted, caused consumers to be more responsive to subsequent promotions. More recently, Sriram and Kalwani (2007) used eight years of data to study competition between two brands of orange juice. They found that the positive short-term effects of trade promotions are partially offset by the negative long-term effects on brand equity. Sriram and Kalwani determined that an optimized sequence of trade promotions would have a net positive long-term effect, yet they found that the retailer they studied spent too much money on trade promotions with adverse long-term consequences. Taken together, these investigations suggest that current trade promotion spending increases consumer price sensitivity and decreases brand loyalty in the long-term. The result is a negative long-term effect on brand differentiation. Fok et al. (2006) assessed both the immediate and dynamic effects of price (promotional and non-promotional) on brand sales. Estimating hierarchical Bayesian error correction models on data for 100 brands across 25 categories, they determined that long-term cumulative effects are significant, though smaller in magnitude than immediate effects. Of relevance here, they found that the cumulative effect of promotional pricing is to increase price sensitivity. This negative long-term effect of price promotion is mitigated somewhat by brand differentiation. In a related study, Ataman et al. (2008) investigated the drivers of success for newly launched brands. Using five years of retail sales and advertising data for 225 new brands, they found that trade promotion investments were less effective drivers of long-term brand performance than other marketing investments (in particular investments in product line and retail distribution). Interestingly, promotional discounting was found to have a negative long-term effect on sales for new brands, as well. Sahni et al. (2017) analyze seventy field experiments from a company that sells tickets to events. They document that price promotions not only influence immediate demand, as expected, but have a spillover effect and influence demand for several weeks after the promotion.

4.1.3 Asymmetric cross-promotional effects In their literature review, BBF observed that cross-promotional effects are asymmetric, with higher quality brands disproportionately impacting lower quality brands. More recent studies show that this generalization is subject to boundary conditions. Bronnenberg and Wathieu (1996) decomposed brand positioning into two orthogonal components, “positioning advantage” and “brand distance.” Using these two components for estimation, their results varied by category. For orange juice, lower quality/lower price brand promotions were more effective than higher quality/higher price brand promotions; for peanut butter, promotional effectiveness results were reversed. They concluded that the prevailing promotional asymmetry result (favoring higher quality/higher price brands) can be offset lower quality/lower price brands enjoy a positioning advantage.

4 Empirical literature on price promotions

Based on a meta-analysis of 1,060 estimated cross-price effects, Sethuraman et al. (1999) found evidence for a “neighborhood” effect. Specifically, they found that a brand’s sales are most affected by promotional discounts of the immediately higherpriced brand, and affected almost as much by discounts of the immediately lowerpriced brand. Similarly, store brand promotions were found to affect sales of national brands priced near the store brand. Conversely, promotions on those low-priced national brands were found to have the largest impact on store brands. Sethuraman et al. measured both absolute cross-price effects and cross-price elasticities, arguing that absolute effects are more relevant for profit maximization. Sethuraman and Srinivasan (2002) compared these same two metrics in a study of cross-effects for brands with differing market shares. Analysis of elasticities led to the conclusion that promotion of higher-share brands has a larger effect on lower-share brands than the reverse, a conclusion consistent with the view of market share as reflective of brand power. Analysis of absolute cross-price effects, their preferred measure, led to the opposite conclusion—that promotion of lower-share brands has a larger effect on higher-share brands than the reverse. This result suggests that higher share brands may not be able to exploit their market power with price promotions. Lemon and Nowlis (2002) used panel data and experimental evidence to assess the effects of synergies between price, feature, and display promotions on brands in different price-quality tiers. They found that high-tier brands benefit more from price, feature, and display promotions than low-tier brands do (consistent with Blattberg and Wisniewski, 1989). However, the benefit that high-tier brands enjoy from price promotions was found to disappear when (1) price promotions are used in combination with feature or display, and (2) in settings where comparison is difficult, such as on end-of-aisle displays.

4.1.4 Decomposition of promotional sales The incremental sales that result from promotions are usually been partitioned into a few categories: brand switching, category expansion, and stockpiling/purchase acceleration. Category expansion represents a true increase in primary category demand; brand switching does not; stockpiling/purchase acceleration may generate some primary demand. BBF reported conflicting evidence about the proportion of promotional sales attributable to brand switching—some studies found brand switching to represent for the majority of promotional sales; other studies did not. Further, they were unable to generalize about incremental sales due to category expansion, or to compare the incremental sales attributable to stockpiling vs. purchase acceleration. Fortunately, a good deal of additional work has been published since that article. Bell et al. (1999) decomposed promotional price elasticities for 173 brands across 13 product categories into choice, incidence, and quantity components. They found that the majority of incremental promotional sales—75% on average—were a result of brand switching (choice), with incidence and quantity responsible for the remainder. Van Heerde et al. (2003) took a different approach to the promotional decomposition, focusing on unit sales of the promoted brand rather than its promotional price elasticity. Using the results of Bell et al., they calculated that the 75%

529

530

CHAPTER 9 How price promotions work: A review

average of promotional price elasticity attributable to brand switching corresponded to a 33% average of unit sales switching to the promoted brand. While this approach did not invalidate previous analyses, it highlighted the limits of interpreting the elasticity composition. Van Heerde et al. (2004) applied a similar unit sales approach to aggregate store data, decomposing promotional sales for brands in four different categories. While the decomposition varied depending on support for the price promotion (feature, display, or feature and display), they found brand switching to be lower than 50% in almost every case. Category expansion was found to be larger than in previous studies, averaging roughly 35%. Leeflang et al. (2008) extended this approach to a multi-category framework in order to quantify the effects of cross-category complementarity and substitution resulting from price promotions. Using data from a Spanish supermarket, they found these effects to be modest, with category complementary usually exceeding substitution (for example, a beer price promotion would probably generate more sales increases in complementary categories such as salty snacks than sales decreases in substitute categories like wine). Within category, incorporating substitution and complementarity led to smaller cross-item effects (22% on average). Interestingly, category expansion (72% on average) was found to be larger than in previous studies. Chan et al. (2008) incorporated consumer heterogeneity (in brand preference and usage rate) in their promotional sales decomposition. Applying a dynamic structural model to canned tuna and paper towel data, they determined that brand switching was not the primary driver of promotional sales. They found that brand loyal shoppers’ primary response to price promotions was to stockpile, while brand switchers did not stockpile at all. Heavy users stockpiled more than light users did; light users’ primary response to price promotions was increasing their consumption rate.

4.1.5 Advertised promotions result in increased store traffic In their review, BBF generalized from the extant literature that advertised promotions increase store traffic. Since their review, panel data has become widely available, enabling household-level studies of store choice. The general picture painted by these studies is that the traffic impact of price promotions is primarily the result of shoppers making additional store visits to purchase promoted products, not switching stores. Bell and Lattin (1998) used data from 1042 households in two geographic markets to investigate the relationship between store format (Everyday Low Price (EDLP) vs. Promotional Pricing (HiLo)) and store choice, controlling for factors such as feature advertised promotions.15 They found that large basket (infrequent) shoppers are less responsive to price promotions than small basket (frequent) shoppers, who can postpone category purchases to take advantage of promotional price variation over time. Note that their feature ad control variable had a strong positive impact on store choice 15 EDLP is an acronym for Everyday Low Price and refers to retailers who tend to who do not typically

offer discounts and maintain a constant price. HiLo refers to retailers who offer periodic, temporary price discounts (low prices) that contrast with regular prices (high prices).

4 Empirical literature on price promotions

probability across basket size segments. Ho et al. (1998) modeled price variability, induced by promotions, as offering consumers “option value” which can be exploited to pay lower prices. Using data from 513 households making 66,694 store visits, they found that shoppers acted like cost minimizers, visiting stores more often and buying smaller quantities in response to price variability. In a study focused on supermarkets, Rhee and Bell (2002) used data from 548 households shopping at five stores to investigate the causes of store switching. They determined that shoppers did not switch from their main store to a secondary store to take advantage of price promotions on a common basket of items. On the other hand, Rhee and Bell noted that shoppers did cherry-pick their secondary stores for price deals. Fox and Hoch (2005) focused on cherry-picking (shopping at two stores on the same day). Using household-level data for 9,562 cherry-picking trips, they found that shoppers bought 25% more price promoted items and 33% more feature advertised items when cherry-picking. Interestingly, shoppers who cherry-picked often bought more price promoted and more feature advertised items than those who did not, even when they did not cherry-pick. Fox and Hoch concluded that the propensity to shop at multiple stores is an individual characteristic, observing that it was correlated with a lower opportunity cost of time. In a more recent study, Breugelmans and Campo (2016) addressed the effects of price promotions on multi-channel grocery retail. They investigated multi-channel shopping behavior using 78 weeks of U.K. panel data with both online and in store shopping trips. They found that price promotions in one channel can negatively effect contemporaneous category purchases in the other channel. These effect was asymmetric, with in-store promotions hurting online purchases more that the converse. Breugelmans and Campo also found that promotional frequency can hurt the effectiveness of future promotions in the other channel.

4.1.6 Trough after the deal Researchers have long expected that, because price promotions cause purchase acceleration and stockpiling, a sales dip, or “trough,” should follow a promotion. BFF observed that store sales data seldom reveal these troughs, which “is surprising and needs to be better understood” (p. G127). Van Heerde et al. (2000) investigated troughs before and after price promotions by estimating three different distributedlag dynamic models. Using two multi-store time series datasets, they found prepromotion and/or post-promotion effects for nearly all brands in the data. The magnitude of these troughs ranges from 4% to 25% of current brand sales, and so can be managerially significant. Interestingly, this appears to be the only empirical investigation to address this topic explicitly.

4.2 Empirical research – newer topics Since BBF reviewed the empirical research on promotions, several new topics have become more prominent in the marketing literature. These topics are discussed below.

531

532

CHAPTER 9 How price promotions work: A review

4.2.1 Price promotions and category-demand Related to the long-term effects of promotions, a few recent studies have used time series models to investigate the effects of price promotions on category demand over time. Nijs et al. (2001) applied such models to data from 560 product categories over a 4-year period in Dutch supermarkets in order to assess the impact of price promotions on category demand. They found category demand to be stationary, either around a constant mean or a trend line. Although they found short-term elasticities to be of customary magnitudes, the effects of promotions almost always dissipated within a 10-week period. In general, perishable categories were found to be more responsive to price promotions. Interestingly, non-price advertising was found to decrease the effectiveness of price promotions. Lim et al. (2005) used similar methods to study heterogeneity in demand due to price promotions across consumer segments. Analysis of 138 weeks of data in four product categories showed that promotional effects lasted longer for light users in perishable product categories and for other-brand loyals. In non-perishable categories, heavy users had a negative adjustment effect, generally reducing the effectiveness of promotions in these categories. Lim et al. found no permanent effects of price promotions in any segment studied, confirming the finding of Nijs et al. In a related study, Ailawadi et al. (2007) identified four stockpiling effects on promotional purchasing: (1) increased consumption rate, (2) purchase acceleration by brand loyals, (3) preemption of future brand switching, and (4) changing subsequent brand purchase probabilities. The later effect could be positive or negative for a brand, depending upon how a brand’s purchase probability is affected. Estimating promotional decomposition models using data for brands in two categories, the authors determined that all four effects contributed meaningfully to the promotional sales via stockpiling. Increased consumption rate, however, was the most important effect. This study offers more nuanced understanding of how the timing and depth of price promotions affect stockpiling. Bell et al. (2002) noted that price promotions can have an endogenous impact on consumption. If a consumer responds to a price promotion and increases their inventory, then this can affect a consumer’s consumption rate. They show that this can then lead to increased frequency of depth of price promotions and more intense price competition.

4.2.2 Cross-category effects and market baskets Related to price promotions is the question of how prices in one category affect sales in other categories. Blattberg and Neslin (1990) concluded that cross-category effects are small, implying that studying these effects should not have a high priority. Nevertheless, more recent work has generated useful insights. Manchanda et al. (1999) partitioned cross-category price effects into category complementarity, coincidence (due to similar purchase cycles), and other household factors. Using data from intentionally complementary category pairs, they found that (1) cross-price and cross-promotion effects are smaller than own-price and own promotion effects, and (2) cross-effects are asymmetric across complementary category

4 Empirical literature on price promotions

pairs, even after controlling for coincidence and other factors. This study therefore supported extant empirical generalizations, eliminating alternative explanations. Erdem and Sun (2002) developed a model to account for marketing spillover effects from marketing activities, including price promotions. They study categories that are connected via an umbrella brand and show that price promotions for a brand in one category affect purchase probabilities in other categories. Song and Chintagunta (2007) proposed a structural multi-category model that allows consumers to purchase in multiple categories, subject to a budget constraint, while also controlling for coincidence and complementarity/substitution. Using data from four categories (including an intentionally complementary category pair), they also found that cross-price effects for brands in different categories are small. Interestingly, they found that these cross-price effects are due to coincidence, rather than category complementarity. These two papers provide support for the generally accepted notion that cross-category promotional and price effects are small, but differ in their findings about the role of category complementarity in cross-category effects. Another more recent topic of empirical research is the shopper’s market basket. The market basket is of particular importance to retailers, who are not only interested in where consumers choose to shop, but also on what they choose to buy at that store. Moreover, the market basket represents a disaggregate look at multi-category purchases. Mulhern and Padgett (1995) used an in-store survey along with purchase data at a home improvement store to determine how promotional purchases affected by purchases at regular price. Noting that purchases in this store are much sparser than in grocery stores, they found that over ¾ of shoppers who visited the store for a promotion also bought items at regular price. Of particular importance, shoppers who visited the store for the promotion spent more on regular priced items than on promoted items. Arora and Henderson (2007) documented spillovers both within category and across category in the context of embedded premium promotions that are linked with social causes. For example, if a consumer purchases a product then there is a donation to charity. In addition to documenting cross-category spillovers, they showed that traditional price promotions are less effective than embedded premium promotions.

4.2.3 Effectiveness of price promotion with display Narasimhan et al. (1996) estimated price promotion elasticities, including those which are advertised, and/or displayed, in order to investigate heterogeneity in promotional response across categories. Based on data from 108 categories, they found that categories that are more responsive to price promotions have the following characteristics: (1) higher penetration, (2) shorter interpurchase times, and (3) are easily stockpiled. Interestingly, they found a negative relationship between the actual price level and the elasticity of promotions, when those promotions are accompanied by display. This relationship implies that displayed promotions are more effective in categories with lower prices than in categories with higher prices. In a related study, Swait and Erdem (2002) evaluated how the temporal consistency of retail promotions affects consumer utility and choice, with implications for

533

534

CHAPTER 9 How price promotions work: A review

brand equity. Based on analysis of panel data for a single category, they found that consistency in feature advertising affects consumer utility and choice positively. In contrast, pricing consistency (i.e., fewer promotions) has a more nuanced effect, with the increased utility of price changes somewhat offset. Taken together, “these results suggest that sales promotion mix. . . inconsistency have an overall negative impact on consumers’ utilities and thus their choices” (p. 318).

4.2.4 Coupon promotions Dhar and Hoch (1996) compared the effectiveness of retailers’ in-store coupon promotions to shelf price discounts. Based on data from five field tests, they found that in-store coupons generated a greater increase in the promoted brand’s sales (35% greater) compared to shelf price discounts. Using the observed in-store coupon redemption rate of 55% together with product cost data, they found that in-store coupons actually generated much greater profit increases (108% greater) compared to shelf price discounts. Sales in the rest of the category were not affected by promotion type. The authors further demonstrated that an in-store coupon with an optimal discount would generate higher category unit sales, dollar profits, and higher passthrough of promotional funds to consumers compared to an optimized shelf price promotion. Anderson and Song (2004) also addressed coupon promotions, but incorporated contemporaneous shelf price reduction as an additional managerial variable. They used data from over 400 coupon promotions (not in-store, but in free-standing inserts) across six CPG categories to test predictions of their economic model about the relationships between coupon face value, coupon redemption rate, and shelf price. They found that lower coupon face values are associated with greater contemporaneous discounts from non-coupon prices and with lower prices. They also found that reducing the retail price and/or increasing the coupon face value increases the efficiency of a coupon promotion. Ramanathan and Dhar (2010) applied regulatory focus theory to determine how promotional cues affect purchases in other, non-promoted brands. Using data from two experiments and a field study, they found that compatibility of regulatory orientation (promotion vs. preventative) induced by a coupon promotion with preexisting orientation influences people to buy not just the promoted brand, but other nonpromoted brands as well. Generalizing from this finding is difficult, but it suggests that retailers should design coupon promotions to be consistent with the regulatory orientation induced by the rest of the retail mix.

4.2.5 Stockpiling and the timing of promotions Pesendorfer (2002) used supermarket data in a single category to investigate the timing of promotions. He found evidence that retailers appear to be timing their price promotions in response to consumer stockpiling. After controlling for retailer competition, the incidence of price promotions is well explained by the accumulation of demand since the most recent promotion.

4 Empirical literature on price promotions

Hendel and Nevo (2006) analyzed an inventory model of the timing of price promotions using supermarket using data from nine supermarkets and about 1,000 households. They found (1) that promotional sales volume increases in the time since the most recent promotion, (2) that the timing of promoted and non-promoted purchases are fundamentally different (consistent with the authors’ inventory theory), and (3) that differences the timing of promotional purchases across categories is consistent with differences in storage costs between those categories. Together, these studies suggest that retailers time their price promotions to accommodate consumer stockpiling.

4.2.6 Search and price promotions Banks and Moorthy (1999) developed a theoretical model of consumer search for price promotions. They assumed that firms first select a regular price and then choose to periodically offer price promotions. Consumers are assumed to have full information about regular prices but need to search to learn about promoted prices. A key finding from this paper is that frequency and depth of promotion increase with consumer search cost. Seiler (2013) developed a model of consumer search that was estimated using consumer panel data for laundry detergent. A core intuition in Seiler’s model is that consumers may have limited information for products that are infrequently purchased and hence may not be aware of a price promotion. For example, if a product is offered on deep discount and a consumer does not purchase (after controlling for consumer inventory), this is an indication that the consumer did not know about the deep discount. Seiler estimated that consumers are unaware of prices on 70% of their shopping trips and that price promotions can be used to increase consumer search.

4.2.7 Targeted price promotions A review article by Grewal et al. (2011) provides an excellent overview of emerging opportunities in price promotion. In particular, they explain how various technologies and databases enable targeted price promotions. For example, mobile technologies now allow firms to customize offers to specific individuals in different locations. The topic of targeting individual consumers was explored theoretically by Shaffer and Zhang (2002) and empirically by Zhang and Krishnamurthi (2004) for an online grocery store. More recently, mobile technology allows targeting to a specific consumer in a specific geographic location. Chen et al. (2017) developed a theoretical model that illustrates how geo-targeted price promotions (or geo-conquesting) can increase firm profits. Fong et al. (2015) conducted one of the first randomized field experiments to investigate the impact of geo-conquesting in practice. Importantly, they showed that deep discounts in competitor’s local market can lead to incremental profits but result in cannibalization in the focal market.

535

536

CHAPTER 9 How price promotions work: A review

4.3 Macroeconomics and price promotions As previously discussed, the macroeconomics literature has focused on understanding which models can explain typical pricing patterns observed for fast moving consumer goods. This includes regular prices, with low frequency price movements, and promoted prices, with high frequency price movements. A conventional view among macroeconomists was confirmed by Kashyap (1995) who analyzed a single product category from one retailer and showed that nominal prices are typically fixed for more than one year. In contrast, Bils and Klenow (2004) showed that price adjustments are quite frequent, roughly every 4 months, which challenged the conventional view that prices are relatively sticky. However, subsequent research by Nakamura and Steinsson (2008) showed that much of this variation was due to price promotions (i.e., temporary sales). They showed that, after excluding price promotions, the average duration of a price is eight to eleven months. They further noted that “some type of sales may be orthogonal to macroeconomic conditions.” Klenow and Malin (2010) provided a comprehensive review of the empirical macroeconomics literature on price setting, which covers several global markets and includes many categories (i.e., beyond just grocery stores). They concluded with ten empirical facts regarding price setting behavior of firms. Among these facts is that price promotions are critical to price flexibility, and that this effect is more pronounced in the United States. The authors also noted that many price changes have memory in the sense that the price reverts to the previous regular price after a price promotion is offered (Nakamura and Steinsson, 2008). This empirical observation is consistent with views held by marketing academics for decades (Blattberg and Neslin, 1990). The resulting “sawtooth” pricing pattern is also consistent with regular prices and promotional prices arising from two distinct processes (McShane et al., 2016; Anderson et al., 2017). Klenow and Malin also observed that, on average, price changes are large; again, consistent with the marketing literature (McShane et al., 2016) and our sample deal sheets. In a recent paper, Anderson et al. (2017) showed that regular prices are largely responsive to changes in demand and supply but that price promotions are not. For macroeconomists, this finding is critical because, while price promotions are common, they do not appear to be responsive to large economic forces. Instead, price promotions are “sticky plans” that are determined well in advance and respond sluggishly to unanticipated economic forces. Via simulation, Anderson et al. (2017) found that “while the use of sale prices to price discriminate is crucially important, varying the extent of price discrimination in response to a cost shock is not.” Recently, Chevalier and Kashyap (2019) showed that frequent price promotions introduce complexities for accurately constructing a price index. One solution is to obtain detailed data on both prices and quantities, such as in IRI or Nielsen scanner data, though this may not be a practical solution for many reasons. The authors propose an elegant alternative that is both intuitive and practical to implement and mimics an approach used in non-food categories such as airline pricing. Their proposed price index uses a weighted average of normally sampled prices for the CPI

5 Getting practical

along with the lowest price in a category of similar goods. The approach is practical and addresses an ongoing concern that price promotions may distort the CPI.

4.4 Promotion profitability Anecdotally, many pundits believe that price promotions are unprofitable investments by retailers and manufacturers. However, measuring the return on investment of promotions requires one to take a stand empirically on how to measure return on investment. For example, one view is to evaluate the optimal price promotion strategy conditional on offering a promotion during a specific period. By conditioning on offering a promotion, say in a given year, this may influence the regular price. A retailer may choose to set a higher regular price in anticipation of periodic price promotions. Under this view, the question is not whether a price promotion is profitable; rather, it is which price promotion strategy is most profitable. An alternative view is to compare the optimal price promotion strategy (conditional on offering a price promotion) with a strategy of not offering price promotions. For example, one might compare an every-day-low-price (EDLP) strategy with the optimal price promotion strategy. Empirically, it can be extremely difficult to determine return-on-investment due to limited variation in prices. To illustrate this problem we note many manufacturers want their brands promoted during peak periods such as 4th of July in the United States. As a result, the counterfactual of “What would prices and demand have been had we not promoted?” is not observed. Inferring what would happen if a price promotion is removed on these peak weeks is difficult to impossible. As a result, it can be extremely difficult to evaluate the return on investment of offering a price promotion on 4th of July. If it is difficult to evaluate a single promotion event due to limited price variation, it is even more difficult to evaluate a price promotion strategy (i.e., a vector of prices) in a competitive, dynamic market.

5 Getting practical While trade spend continues to be an enormous marketing expenditure, incentivizing a variety of retailer activities, the topic does not receive commensurate attention among academics. For example, nearly all MBA programs offer a course on advertising but issues like trade funding are no more than a small part of a typical MBA pricing course. At Kellogg School of Management, for example, students are more likely to learn about trade funds, trade budgets, and price promotions through their job internship or work experience than from the classroom. In reviewing the academic literature for this chapter, we determined that there was far more practical research on price promotions between 1995 and 2013 than in the last five years (i.e., 2013 to 2018). For example, work by Bucklin and Gupta (1999) summarized the ways in which managers used scanner data and found that price promotions was at the top of the list. Cooper et al. (1999) demonstrated the im-

537

538

CHAPTER 9 How price promotions work: A review

plementation of an academic model (PromoCast) to forecast volumes for promotion events. Similarly, Natter et al. (2007) showed how a better approach to pricing and promotion planning led to increased profits and sales for an Austrian retailer. These types of papers are important, as they help bridge the gap between academic and practitioner research. The operations management literature has recently tackled the challenge of optimizing prices for a grocery store (Cohen et al., 2017) and for an online retailer (Ferreira et al., 2016). In both papers, the authors’ core contribution involved solving a complex, high dimensional, constrained optimization problem. The objective function is realistic from the perspective of incorporating managerial constraints in the optimization algorithm. While such research is impressive, the authors did not address a core question raised in empirical studies in marketing and economics— empirical identification. The authors demonstrated that their models fit well in a predictive sense, but did not address the question of whether and how their models could recover causal estimates. We believe that truly making progress on these types of difficult problems requires insights from multiple academic disciplines, including marketing, economics, and operations management, along with practitioner integration. This is clearly a high bar. A further limitation of the price promotions literature is that it is heavily focused on CPG. Price promotions occur in many markets, yet most researchers focus on CPG because the data is available and abundant. This narrow focus limits the impact of academic work on practice. Examples of other product types that have been studied include automobiles (Bruce et al., 2005; Busse et al., 2006; Pauwels et al., 2004; Silva-Risso and Ionova, 2008), magazines (Esteban-Bravo et al., 2005, 2009), pharmaceuticals (Gonul et al., 2001), and produce (Richards, 1999). To help bridge this gap, we conducted interviews with more than twenty managers who would prefer to remain anonymous. We hope that insights from these interviews can help academics identify new, impactful problems that can help advance the theory and practice of price promotions. The main insights and themes from those interviews are summarized below.

5.1 Budgets and trade promotion adjustments There was consensus among the managers we spoke with that best practice in price promotions was typically the result of joint annual planning among retailer, manufacturer, and agency (e.g., digital marketing agency). The expectation for all brands is to have a 52-week plan. Developing this plan can take as long as six months. Thus, planning typically begins in Q3 and is finalized in Q4 with a strategic plan for the next 12 months as output. This plan sets joint expectations for the year and represents what economists now refer to as “sticky plans.” While adjustments in execution can be made during the year, there is typically little change in overall expectations over that period. Blattberg and Neslin (1990) highlighted the importance of long-term promotion planning nearly thirty years ago, but few empirical or theoretical models have tackled

5 Getting practical

the challenge of developing a long-term budget and strategic plan. Managers struggle with this issue and, in lieu of generating a new optimal plan each year, it is common practice to take last year’s plan and make small adjustments. The result is considerable state dependence in price promotions from year to year. Managers indicated that adjustments to the promotion plan were based on a mix of top-down and bottom-up assessments and included questions such as: Did a promotion work as expected nationally? Did a promotion work as expected at a specific chain and region? Based on answers to these types of questions regarding past performance, the promotion plan is adjusted. One difficulty in the planning process is risk management; some retailers act as if promotion planning guarantees performance and carries no risk. Effective planning requires a shared understanding of the promotion plan and its associated risks. One manager we spoke with indicated that some retailers view trade spend as an entitlement (i.e., a guaranteed financial transfer), which they bank on when creating their own financial plans. Because of demand uncertainty, preserving flexibility is critical in trade promotion planning and budgeting. One manager we spoke with explained that, if a manufacturer commits to $10 million dollars in trade funds for a retail account but volume subsequently fails to meet plan (i.e., a “soft” market), then the manufacturer finds itself in trouble. This can lead to a long-term financial spiral in which the manufacturer embarks on cost-cutting to meet financial expectations—both internally and with retailers. Cost-cutting may lead to short term success, but result in long-term negative consequences for brand health. To manage these risks, a manufacturer may “hold back” trade funds. For example, rather than committing to $10 million, the manufacturer may commit to only $8 million (80% of allocated funds). If the market is soft, then the trade spend can be aligned with market volume. If volume forecasts are met, however, then the additional $2 million can be allocated strategically. Such an approach avoids the pre-commitment problem and allows trade funds to be allocated more profitably. Research opportunities: There is very little academic research focused on how to create and manage promotion budgets, long-term planning, and risk sharing. These involve the creation of an annual budget, optimal adjustments as uncertainty is resolved throughout the year, and management of the negotiation process. There is also a lack of agreement among academics on how the budgeting process affects the true economic, marginal costs faced by sellers. While wholesale prices may exist, there is also the practical reality of a budget constraint that is rarely observed by academics but cannot be ignored.

5.2 Retailer vs. manufacturer goals and issues As we have noted, price discrimination is the most common rationale for price promotions offered by academics. It was illuminating for us is that this topic never surfaced in our interviews with managers. That does not mean that price discrimination is not a rationale for price promotions in practice, but managers are clearly not voicing this as a key, strategic goal.

539

540

CHAPTER 9 How price promotions work: A review

Among retailers that we interviewed, motivations for price promotions include driving store traffic and maintaining retail brand awareness. Weekly price promotions offer retailers an opportunity to engage the consumer in store, and to support a low price image to maintain shopper loyalty. Yet one manager told us that, if store operations were not prepared to handle a major price promotion, then the event could extremely disruptive and costly. Successful execution of promotional events is more likely with careful planning. Retail managers expressed a desire to have a unique promotion each week that allowed them to differentiate themselves from a competing retailer. Managers were quick to note that they did not demand exclusivity from a manufacturer on a promotion, but that they did pay close attention to competing promotions at other retailers. In periods of peak seasonal demand, retailers often end up promoting the same items (e.g., turkey and cranberries before Thanksgiving). In all periods, retailers “keep score” by noting whether they had the only product on price promotion and, if not, whether they had the lowest price point for that product. Managers on the manufacturer side that we spoke with indicated that price promotions are part of their overall business planning process. Their broad goal was to hit targets on key metrics such as share, unit volume, margin, and profit. Winning share from competing brands was highlighted as a primary motivation for price promotions. There was also a shared belief that price promotions result in the “big weeks” that enable brand managers to hit their numbers. Academic theories of stockpiling and forward buying resonated with managers. Loading the channel with product was recognized to be inefficient, but was nevertheless viewed favorably from a competitive standpoint as it helped keep other brands off the shelf. In our conversations, we found substantial differences in the relative emphasis on trade spend vs. consumer spend by size of manufacturer and by brand. Among small CPG firms, there was a heavy emphasis on trade spend with little to no consumer spend. This was based on three beliefs. First, managers believe that trade spend are a prerequisite to working with a retailer. Most retailers are unlikely to work with a small CPG firm that offered insufficient trade funds. Second, we found a widely held belief that trade spend is more certain in terms of demand generation than consumer spend (e.g., media advertising). In other words, increased trade promotion budgets are due to beliefs about the relative riskiness of these two approaches. Third, once money is allocated for trade spend, there is typically nothing left over for consumer promotions. Many small CPG firms would love to fund consumer marketing programs, but they simply cannot afford them. Both retailers and manufacturers indicated that it is important to time promotions so that they coincide with times when consumers are in “shopping mode.” This implicitly means that many promotions are coordinated with peak demand periods such as holidays, back to school, grilling season or hunting season. One manager referred to these periods as “power windows,” and noted that they can be quite specific to a brand. To managers, it is perfectly logical to offer price promotions during peak demand, yet academics have struggled to rationalize this practice (Chevalier et al., 2003).

5 Getting practical

Finally, managers indicated that price promotions have historically been aligned around manufacturer goals. There was a perception that, while the retailer may benefit, the manufacturer’s goals are paramount. This perspective has evolved and become more balanced with the role of “category captain,” which often requires a broader view of both retailer and manufacturer objectives. Despite this trend, retail managers indicated that they want to become more independent, more strategic, and have greater control over price promotions. Research opportunities: Retailers’ competitive concerns offer a great opportunity to apply game theory to promotion planning and timing. Interestingly, managers don’t think of this in terms of an equilibrium. If promotions are timed with peak periods, this raises questions of causal inference. In other words, how does one separate the incremental impact of a price promotion from the impact of a seasonal shock? Finally, there is an opportunity to extend recent models of category management (Nijs et al., 2013; Alan et al., 2017) that address how the joint concerns of manufacturers and retailers may affect price promotions. Within the industrial organization literature, it has been common to abstract from vertical channel relationships. For example, Nevo (2000) studied competition among brands, but retail goals such as driving store traffic and retail brand awareness are not considered. Explicitly modeling the goals of retailers and manufacturers represents an opportunity for future research.

5.3 When decisions happen: Promotion timing and adjustments Based on our review of the academic literature, few researchers have given serious consideration to the practical issues that affect when promotions occur and whether adjustments can be made. For example, the theoretical idea that adjustments can be made and executed each week simply has little connection to practice. Models that make such assumptions are divorced from reality. Among all managers we spoke with, there was a clear indication that decisions are made well in advance and are very hard to change at the last minute. In CPG, plans occur annually and the actual execution starts twelve to sixteen weeks in advance of a price promotion. The deal sheet in Fig. 6 notes that a December 1 price promotion was finalized on August 22—100 days or 14 weeks in advance. When price promotions are combined with advertising in a retail flyer, the planning process typically starts months in advance. These flyers are commonly used by many types of retailers, such as department stores (e.g. Macy’s, JCPenney), sporting goods retailers (e.g., Dick’s, Cabela’s), mass merchants (e.g., Target, Walmart), and grocery stores (e.g., Safeway, Kroger). As the execution date nears, details such as the promoted price point, ad copy, images, etc. are finalized. To allow time for printing and distribution of flyers, all details are typically finalized four weeks in advance of a promotion. One manager commented that this constraint is relaxing as they move from print to digital flyers. Many retailers are looking to eliminate print flyers altogether. As this happens, there will be increased flexibility in the timing of advertised price promotions.

541

542

CHAPTER 9 How price promotions work: A review

Within CPG, the ability to execute last minute price promotions varied by product category. For example, meat and produce are highly perishable, and both retailers and manufacturers are set up to run last-minute price promotions to liquidate excess inventory. These price promotions are more likely to be in-store only and not have advertising support outside the store. Manufacturers noted that the ability to make last minute adjustments often depends on the retailer’s flexibility. Larger retailers are generally viewed as being less flexible and more process-driven, which limited their ability to execute a last minute promotion. This is understandable, since a last-minute promotion may create unintended negative spillovers such as stealing demand from other products. Managing the spider-web of price promotion spillovers among products is perhaps easier for small retailers. Research opportunities: Many theoretical and structural models assume that prices are set on a weekly basis. In reality, price promotions arise from sticky plans, making adjustments difficult. The shift from print to digital flyers will reduce adjustment costs and the subsequent impact on price promotions is an interesting topic for future research.

5.4 Promoted price: Pass-through The stylized view among academics is that manufacturers provide off-invoice wholesale price reductions, and then a retailer decides how much of the discount to pass through to consumers. Our interviews suggested that this may not be the best characterization of how price promotion decisions are made. First, the broad plan of when to promote, the desired price point, the associated trade funding, and other marketing activities are laid out in the annual plan. Second, multiple marketing activities often coincide with a price promotion, the in-store portion of which is paid for by the same vendor allowances that fund price promotions. Managers indicated that it was often difficult to separate how these funds were allocated to specific activities. Third, manufacturers have promotion guidelines for different types of retailers, depending upon their preferred promotional pricing strategy (Hi-Lo, EDLP, BOGO [buy one get one], etc.). The actual pass-through on a given price promotion is the result of considerable joint planning, and should be viewed through a broad lens that may involve multiple marketing activities that depend on the retailer’s strategic positioning. When we asked managers whether retailers would pocket trade dollars and not pass through discounts to consumers, there were mixed responses. Yes, we were told that this happens. Yet we were surprised to learn that running a “hot price,” a very deep discount, is of greater concern among brand managers. A retailer may offer an extremely low price to drive store traffic that may benefit a retailer, but could also disrupt the marketplace, anger competing retailers, and damage brand equity. Research opportunities: Causal inference for price promotions in the presence of coordinated marketing activities demands more attention from academic research. In addition, there are opportunities, both theoretical and empirical, to improve our

5 Getting practical

understanding of price promotions by recognizing that observed prices are the result of long-term plans, involve multiple marketing activities, and are influenced by the retailer’s overall pricing strategy.

5.5 Durable goods price promotion While most research on price promotion has been in CPG, price promotions are common in many other industries. Minimum advertised price (MAP) is a common practice among the durable goods manufacturers that we interviewed. Within the MAP framework, a manufacturer may offer PMAP, or promotional MAP. Our small sample of interviews suggested that, while PMAP is possible, it is not common. Instead, if a price promotion is required, then MAP is often removed from the product. One rationale for price promotion in durable goods is to drive volume of noncurrent merchandise (e.g., last year’s model). A major manufacturer in of sporting goods products stated that roughly one-third of price promotions were used to push obsolete inventory out of the channel. There is a similar motivation in the apparel industry, where price promotions are frequently used to manage excess inventory. Managers distinguish between in-season markdowns (discounts), which are typically part of the marketing plan, and end-of-season markdowns, which are the result of excess inventory. One manager of a national brand noted that price promotions are localized geographically to take advantage of regional opportunities. For example, a regional sporting event that generates consumer buzz may be combined with a price promotion. Local events create an opportunity for brands to establish a call to action among consumers and encourage them to purchase. Durable goods do not depend on repeat purchasing in the same way that frequently purchased consumer goods do. Once a consumer has bought a tennis racket, she may have little or no demand for that item for a long period of time. As a result, the timing of price promotions for durable goods is a critical concern. On the one hand, there is a temptation to offer a price promotion early in the season to be first to promote, getting all consumers to buy your tennis racket and locking out the competition. But managers warned that price promotions are ineffective if they are too early and not coordinated with consumers’ normal shopping patterns. For example, demand for swimsuits starts in Spring and ends in Summer; trying to offer price promotions too early, say in January, may beat the competition to market but won’t succeed because consumer interest is low. Demand uncertainty and inventory considerations also affect a retailer’s willingness to promote a durable good multiple times in a narrow window. A manufacturer, for example, may want to encourage trial for a product and ask a retailer to offer consecutive price promotions on the first two weeks of a month. A manager called our attention to the paradox that success in week 1 can create a stockout problem in week 2. If all inventory is sold in week 1, it is often impossible to get additional product for week 2. If an item is sourced domestically, additional supply can be ob-

543

544

CHAPTER 9 How price promotions work: A review

tained in four weeks. However, if an item is sourced internationally, the minimum lead time is 12-14 weeks. Recalling that flyers and advertising also have long lead times, there is no way to remove a product from the weekly circular if it is sold out. Retailers never want a prominently advertised item to drive traffic to the store but then disappoint consumers when they arrive at the store and find a stock-out. As a result, inventory considerations must be carefully integrated with price promotion timing. A senior manager noted that it is common to offer incentives to the channel (e.g., wholesale price discounts, cooperative marketing funds) to encourage price promotions in the store. However, it is often entirely up to the retailer whether and how a promotion is implemented. Hence, what academics refer to as agency concerns are very real for durable goods manufacturers. Related to this point, many durable goods require extensive sales support, instore education, and post-sales support. Manufacturers indicated that they are more likely to support retailers whose goals are aligned with their brand, but discussion of whether the manufacturer favors some retailers over others is very sensitive. Joint training programs funded by a manufacturer can help retailers with best practices in merchandising, marketing, and sales. However, the reality is that some retailers are simply better business partners. Manufacturers must strike a balance between rewarding committed partners and treating all partners fairly and equitably. Research opportunities: In general, price promotions for durable goods are a wide open area for researchers. The process by which decisions are made is often driven by heuristics, so there is an opportunity to add rigor to these decisions. Agency theory is clearly applicable, but we have little empirical evidence to explain how these concerns affect price promotions.

5.6 Private label price promotions Private label and store brands are growing parts of nearly every retail product assortment. In academic research, private label and store brands have been modeled as vertical integration decisions where the retailer is assumed to own and control the product. In reality, the retailer has considerable control of the brand name, product design, etc., but manufacturing of private label products is often done by third-party manufacturers. This has important practical implications for price promotions. A retail manager we spoke with indicated that private label manufacturers offer trade funds (e.g., cooperative advertising funds) to support their products in-store. When pressed about why this happens, it was noted that volume declines dramatically when products are not promoted (e.g., not in the weekly flyer, no in-store merchandising). Research opportunities: Theoretically, there is an opportunity to improve models of retail private label. Empirically, the impact and pass through of trade funds for private label vs. national brand manufacturers has not been studied.

6 Summary

5.7 Price pass through Economists and marketers have studied the topic of price pass through from various lenses. In marketing, the literature has tackled pass through of promoted prices as well a pass through of regular prices. In contrast, the macroeconomic literature has addressed exchange rate pass through. In a global economy, it is important to understand how cost shocks flow across geographies (i.e., countries) and impact both promoted and regular prices. Research opportunities: There is a near term opportunity to compare exchange rate pass through with price pass through and a longer term opportunity to understand when and why cost changes impact prices.

6 Summary In this chapter, we have provided an overview of the academic literature on price promotions in marketing and economics. We also have provided extensive discussion of how price promotions work in practice. We have concluded the chapter by pointing out opportunities for future academic research. We hope that our summary of how price promotions work in practice is of interest to academics. In particular, we found that price promotions are coordinated, planned activities among retailers and manufacturers. For economists, we hope that this chapter spurs greater interest in understanding the data generating process for promoted prices which commonly appear in the BLS and/or CPI and are more widely available in syndicated data sets from IRI and Nielsen. In any empirical study, it is critical to understand not only what prices are offered but the underlying data generating process. We believe the insights from this chapter have direct implications for work on price stickiness, promotion pass through, and demand estimation. This chapter also suggests that one thinks carefully about structural models that involve short-term price competition among sellers, such as weekly brand competition in a grocery store. Such models may ignore the coordinated, planned data generating process. We believe that there is considerable opportunity for collaboration with and impact on practice. Trade spending represents hundreds of billions of dollars and is clearly allocated inefficiently. Academic theories need to explain not only what happens (descriptive) but take a normative position and explain how price promotions can become more profitable. We hope that this chapter spurs researchers to look at the major issues facing practitioners today.

References Acosta, 2012a. The trend behind the spend: client spending study. AMG Strategic Advisors presentation. Acosta, 2012b. Trade Promotion: A Shift in the Lift. White Paper. Acosta, 2016. Reversing the Diminishing Returns of Trade Promotion. White Paper.

545

546

CHAPTER 9 How price promotions work: A review

Ailawadi, Kusim L., Borin, Norm, Farris, Paul W., 1995. Market power and performance: a cross-industry analysis of manufacturers and retailers. Journal of Retailing 71 (3), 211–248. Ailawadi, Kusum, Gedenk, Karen, Lutzky, Christian, Neslin, Scott, 2007. Decomposition of the sales impact of promotion-induced stockpiling. Journal of Marketing Research 44 (3), 450–467. Ailawadi, Kusum L., Harlam, Bari A., 2009. Retailer promotion pass-through: a measure, its magnitude, and its determinants. Marketing Science 28 (4), 782–791. Alan, Yasin, Kurtulus, Mumin, Wang, Chunlin, 2017. The Role of Store Brand Spillover in a Retailer’s Category Management Strategy. Vanderbilt Owen Graduate School of Management Research Paper 3042004. Allender, William J., Richards, Timothy, 2012. Brand loyalty and price promotion strategies: an empirical analysis. Journal of Retailing 88 (3), 323–342. Anderson, Eric T., Dana, James D., 2009. When is Price Discrimination Profitable? Northeastern University College of Business Administration Research Paper No. 08-003. Anderson, E.T., Kumar, N., 2007. Price competition with repeat, loyal buyers. Quantitative Marketing and Economics 5 (4), 333–359. Anderson, Eric T., Kumar, Nanda, Rajiv, Surendra, 2004. A comment on: “Revisiting dynamic duopoly with consumer switching costs”. Journal of Economic Theory 116 (1), 177–186. Anderson, E., Malin, B.A., Nakamura, E., Simester, D., Steinsson, J., 2017. Informational rigidities and the stickiness of temporary sales. Journal of Monetary Economics 90, 64–83. Anderson, Eric T., Simester, Duncan I., 2004. Long-run effects of promotion depth on new versus established customers: three field studies. Marketing Science 23 (1), 4–20. Anderson, Eric T., Simester, Duncan I., 2008. Does demand fall when customers perceive that prices are unfair? The case of premium pricing for large sizes. Marketing Science 27 (3), 492–500. Anderson, E.T., Simester, D.I., 2010. Price stickiness and customer antagonism. The Quarterly Journal of Economics 125 (2), 729–765. Anderson, Eric T., Song, Inseong, 2004. Coordinating price reductions and coupon events. Journal of Marketing Research 41 (4), 411–422. Arora, Neeraj, Henderson, Ty, 2007. Embedded premium promotion: why it works and how to make it more effective. Marketing Science 26 (4), 514–531. Assuncao, Joao L., Meyer, Robert J., 1993. The rational effect of price promotions on sales and consumption. Management Science 39 (5), 517–535. Ataman, M. Berk, Mela, Carl F., van Heerde, Harald J., 2008. Building brands. Marketing Science 27 (6), 1036–1054. Banks, Jeffrey, Moorthy, Sridhar, 1999. A model of price promotions with consumer search. International Journal of Industrial Organization 17 (3), 371–398. Barbaro, Michael, 2007. Saks settles with S.E.C. on overpayments. The New York Times. 9/6/2007:C12. Bell, David R., Chiang, Jeongwen, Padmanabhan, V., 1999. The decomposition of promotional response: an empirical generalization. Marketing Science 18 (4), 504–526. Bell, David R., Dreze, Xavier, 2002. Changing the channel: a better way to do trade promotions. MIT Sloan Management Review 43 (2), 42–49. Bell, David R., Iyer, Ganesh, Padmanabhan, V., 2002. Price competition under stockpiling and flexible consumption. Journal of Marketing Research 39 (3), 292–303. Bell, David R., Lattin, James M., 1998. Shopping behavior and consumer preference for store price format: why “large basket” shoppers prefer EDLP. Marketing Science 17 (1), 66–88. Besanko, David, Dubé, Jean-Pierre, Gupta, Sachin, 2005. Own-brand and cross-brand retail pass-through. Marketing Science 24 (1), 123–137. Bils, Mark, Klenow, Peter J., 2004. Some evidence on the importance of sticky prices. Journal of Political Economy 112 (5), 947–985. Blattberg, Robert C., Briesch, Richard, Fox, Edward J., 1995. How promotions work. Marketing Science 14 (3), G122–G132. Blattberg, Robert C., Eppen, Gary D., Lieberman, Joshua, 1981. A theoretical and empirical evaluation of price deals for consumer nondurables. Journal of Marketing 45 (1), 16–29.

References

Blattberg, Robert C., Neslin, Scott A., 1990. Sales Promotion: Concepts, Methods, and Strategies. Prentice Hall, Englewood Cliffs, NJ. Blattberg, Robert C., Wisniewski, Kenneth J., 1989. Price-induced patterns of competition. Marketing Science 8 (4), 291–309. Boston Consulting Group, 2012. Consumer products trade spend benchmark. Braido, Luis H.B., 2009. Multiproduct price competition with heterogeneous consumers and nonconvex costs. Journal of Mathematical Economics 45 (9–10), 526–534. Breugelmans, Els, Campo, Katia, 2016. Cross-channel effects of price promotions: an empirical analysis of the multi-channel grocery retail sector. Journal of Retailing 92 (3), 333–351. Briesch, Richard A., Dillon, William R., Fox, Edward J., 2013. Category positioning and store choice: the role of destination categories. Marketing Science 32 (3), 488–509. Bronnenberg, Bart J., Wathieu, Luc, 1996. Asymmetric promotion effects and brand positioning. Marketing Science 15 (4), 379–394. Bruce, Norris, Desai, Preyas S., Staelin, Richard, 2005. The better they are, the more they give: trade promotions of consumer durables. Journal of Marketing Research 42 (1), 54–66. Bucklin, Randolph E., Gupta, Sunil, 1999. Commercial use of UPC scanner data: industry and academic perspectives. Marketing Science 18 (3), 247–273. Burstein, A., Hellwig, C., 2007. Prices and Market Shares in a Menu Cost Model (No. w13455). National Bureau of Economic Research. Busse, Meghan, Silva-Risso, Jorge, Zettelmeyer, Florian, 2006. $1,000 cash back: the pass-through of auto manufacturer promotions. The American Economic Review 96 (4), 1253–1270. Calvo, Guillermo A., 1983. Staggered prices in a utility-maximizing framework. Journal of Monetary Economics 12 (3), 383–398. Cao, Wen, 2011. Another look at price promotion. Journal of Industrial Economics 59 (2), 282–295. Chan, Tat, Narasimhan, Chakravarthi, Zhang, Qin, 2008. Decomposing promotional effects with a dynamic structural model of flexible consumption. Journal of Marketing Research 45 (4), 487–498. Chandon, Pierre, Wansink, Brian, Laurent, Gilles, 2000. A benefit congruency framework of sales promotion effectiveness. Journal of Marketing 64 (4), 65–81. Chen, Yuxin, Li, Xinxin, Sun, Monic, 2017. Competitive mobile geo targeting. Marketing Science 36 (5), 666–682. Chevalier, Judith A., Kashyap, Anil K., 2019. Best prices: implications for price measurement. American Economic Journal: Economic Policy 11 (1), 126–159. Chevalier, Judith A., Kashyap, Anil K., Rossi, Peter E., 2003. Why don’t prices rise during periods of peak demand? Evidence from scanner data. The American Economic Review 93 (1), 15–37. Cohen, M.C., Leung, N.H.Z., Panchamgam, K., Perakis, G., Smith, A., 2017. The impact of linear optimization on promotion planning. Operations Research 65 (2), 446–468. Conlisk, John, Gerstner, Eitan, Sobel, Joel, 1984. Cyclic pricing by a durable goods monopolist. The Quarterly Journal of Economics 99 (3), 489–505. Consumer Buying Habits Study, 1995. Point-of-Purchase Advertising Institute and Meyers Research Center, Englewood, NJ. Cooper, Lee G., Baron, Penny, Levy, Wayne, Swisher, Michael, Gogos, Paris, 1999. PromoCast: a new forecasting method for promotion planning. Marketing Science 18 (3), 301–316. Corstjens, Judith, Corstjens, Marcel, 1995. Store Wars: The Battle for Mindspace and Shelfspace. Wiley, Chichester. Cui, Tony H., Raju, Jagmohan S., Zhang, Z. John, 2008. A price discrimination model of trade promotions. Marketing Science 27 (5), 779–795. Dhar, Sanjay K., Hoch, Stephen J., 1996. Price discrimination using in-store merchandising. Journal of Marketing 60 (1), 17–30. Díaz, Antón García, González, Roberto Hernán, Kujal, Praveen, 2009. List pricing and discounting in a Bertrand-Edgeworth duopoly. International Journal of Industrial Organization 27 (6), 719–727. Dolan, Robert J., Simon, Hermann, 1996. Power Pricing: How Managing Price Transforms the Bottom Line. Free Press, NY.

547

548

CHAPTER 9 How price promotions work: A review

Dotsey, Michael, King, Robert G., Wolman, Alexander L., 1999. State-dependent pricing and the general equilibrium dynamics of money and output. The Quarterly Journal of Economics 114 (2), 655–690. Dreze, Xavier, Bell, David R., 2003. Creating win-win trade promotions: theory and empirical analysis of scan-back trade deals. Marketing Science 22 (1), 16–39. Dreze, Xavier, Hoch, Stephen J., Purk, Mary E., 1994. Shelf management and space elasticity. Journal of Retailing 70 (4), 301–326. Dubé, Jean-Pierre, Gupta, Sachin, 2008. Cross-brand pass-through in supermarket pricing. Marketing Science 27 (3), 324–333. Eichenbaum, Martin, Jaimovich, Nir, Rebelo, Sergio, 2011. Reference prices, costs, and nominal rigidities. American Economic Review 101 (1), 234–262. Erdem, Tulin, Keane, Michael P., 1996. Decision-making under uncertainty: capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Science 15 (1), 1–20. Erdem, Tulin, Sun, Baohong, 2002. An empirical investigation of the spillover effects of advertising and sales promotions in umbrella branding. Journal of Marketing Research 39 (4), 408–420. Esteban-Bravo, Mercedes, Múgica, José M., Vidal-Sanz, Jose M., 2005. Optimal duration of magazine promotions. Marketing Letters 16 (2), 99–114. Esteban-Bravo, Mercedes, Múgica, José M., Vidal-Sanz, Jose M., 2009. Magazine sales promotion. Journal of Advertising 38 (1), 137–146. Felgate, Melanie, Fearne, Andrew, DiFalco, Salvatore, Garcia Martinez, Marian, 2012. Using supermarket loyalty card data to analyse the impact of promotions. International Journal of Market Research 54 (2), 221–240. Ferreira, Kris J., Lee, Bin H.A., Simchi-Levi, David, 2016. Analytics for an online retailer: demand forecasting and price optimization. Manufacturing & Service Operations Management 18 (1), 69–88. Fok, D., et al., 2006. A hierarchical Bayes error correction model to explain dynamic effects of price changes. Journal of Marketing Research 43 (3), 443–461. Fong, Nathan M., Fang, Zheng, Luo, Xueming, 2015. Geo-conquesting: competitive locational targeting of mobile promotions? Journal of Marketing Research 52 (5), 726–735. Fox, Edward J., Hoch, Stephen J., 2005. Cherry-picking. Journal of Marketing 69 (1), 46–62. Gartner, 2015. Market guide for trade promotion management and optimization. Gedenk, Karen, Neslin, Scott A., Ailawadi, Kusum L., 2006. Sales promotion. In: Retailing in the 21st Century. Springer, Berlin, pp. 345–359. Golosov, Mikhail, Lucas Jr., Robert E., 2007. Menu costs and Phillips curves. Journal of Political Economy 115 (2), 171–199. Gómez, Miguel I., Rao, Vithala R., McLaughlin, Edward W., 2007. Empirical analysis of budget and allocation of trade promotions in the U.S. supermarket industry. Journal of Marketing Research 44 (3), 410–424. Gonul, Fusun F., Carter, Franklin, Petrova, Elina, Srinivasan, Kannan, 2001. Promotion of prescription drugs and its impact on physicians’ choice behavior. Journal of Marketing 65 (3), 79–90. Gönül, Füsun, Srinivasan, Kannan, 1996. Estimating the impact of consumer expectations of coupons on purchase behavior: a dynamic structural model. Marketing Science 15 (3), 262–279. Grewal, Dhruv, Ailawadi, Kusum L., Gauri, Dinesh, Hall, Kevin, Kopalle, Praveen, Robertson, Jane R., 2011. Innovations in retail pricing and promotions. Journal of Retailing 87 (Suppl. 1), S43–S52. Guo, Liang, Villas-Boas, J. Miguel, 2007. Consumer stockpiling and price competition in differentiated markets. Journal of Economics & Management Strategy 16 (4), 827–858. Gupta, Sunil, 1988. Impact of sales promotion on when, what and how much to buy. Journal of Marketing Research 25 (4), 342–355. Hendel, Igal, Nevo, Aviv, 2006. Sales and consumer inventory. The Rand Journal of Economics 37 (3), 543–561. Hendel, Igal, Nevo, Aviv, 2013. Intertemporal price discrimination in storable goods markets. American Economic Review 103 (7), 2722–2751. Hess, James D., Gerstner, Eitan, 1987. Loss leader pricing and rain check policy. Marketing Science 6 (4), 358–374.

References

Ho, Teck-Hua, Tang, Christopher S., Bell, David R., 1998. Rational shopping behavior and the option value of variable pricing. Management Science 44 (12-part-2), S145–S160. Hoch, Stephan J., Dreze, Xavier, Purk, Mary E., 1994. EDLP, Hi-Lo, and margin arithmetic. The Journal of Marketing, 16–27. Hong, Pilky, Preston McAfee, R., Nayyar, Ashish, 2002. Equilibrium price dispersion with consumer inventories. Journal of Economic Theory 105 (2), 503–517. Inman, Jeffrey J., McAlister, Leigh, Hoyer, Wayne D., 1990. Promotion signal: proxy for a price cut? Journal of Consumer Research 17 (1), 74–81. Jedidi, Kamel, Mela, Carl F., Gupta, Sunil, 1999. Managing advertising and promotion for long-run profitability. Marketing Science 18 (1), 1–22. Jeuland, Abel P., Narasimhan, Chakravarthi, 1985. Dealing-temporary price cuts-by seller as a buyer discrimination mechanism. Journal of Business, 295–308. Kashyap, A.K., 1995. Sticky prices: new evidence from retail catalogs. The Quarterly Journal of Economics 110 (1), 245–274. Kehoe, Patrick J., Midrigan, Virgiliu, 2007. Sales and the Real Effects of Monetary Policy. Federal Reserve Bank of Minneapolis, Research Department. Klenow, Peter J., Kryvtsov, Oleksiy, 2008. State-dependent or time-dependent pricing: does it matter for recent U.S. inflation? Quarterly Journal of Economics 123, 863–904. Klenow, Peter J., Malin, Benjamin A., 2010. Microeconomic evidence on price-setting. In: Handbook of Monetary Economics, vol. 3. Elsevier, pp. 231–284. Kollat, David T., Willett, Ronald P., 1967. Consumer impulse purchasing behavior. Journal of Marketing Research 4 (1), 21–31. Lal, Rajiv, Little, John D.C., Villas-Boas, J. Miguel, 1996. A theory of forward buying, merchandising, and trade deals. Marketing Science 15 (1), 21–37. Lal, Rajiv, Matutes, Carmen, 1994. Retail pricing and advertising strategies. Journal of Business, 345–370. Lal, Rajiv, Villas-Boas, J. Miguel, 1998. Price promotions and trade deals with multiproduct retailers. Management Science 44 (7), 935–949. Lazear, Edward P., 1986. Retail pricing and clearance sales. The American Economic Review 76 (1), 14–32. Leeflang, Peter S.H., Parreño Selva, Josefa, Van Dijk, Albert, Wittink, Dick R., 2008. Decomposing the sales promotion bump accounting for cross-category effects. International Journal of Research in Marketing 25 (3), 201–214. Lemon, Katherine N., Nowlis, Stephen M., 2002. Developing synergies between promotions and brands in different price-quality tiers. Journal of Marketing Research 39 (2), 171–185. Levy, Daniel, Bergen, Mark, Dutta, Shantanu, Venable, Robert, 1997. The magnitude of menu costs: direct evidence from large US supermarket chains. The Quarterly Journal of Economics 112, 791–824. Lim, Jooseop, Currim, Imran S., Andrews, Rick L., 2005. Consumer heterogeneity in the longer-term effects of price promotions. International Journal of Research in Marketing 22 (4), 441–457. Manchanda, Puneet, Ansari, Asim, Gupta, Sunil, 1999. The ‘shopping basket’: a model for multicategory purchase incidence decisions. Marketing Science 18 (2), 95–114. McAlister, Leigh, 2007. Cross-brand pass-through: fact or artifact? Marketing Science 26 (6), 876–898. McShane, Blakeley B., Chen, Chaoqun, Anderson, Eric T., Simester, Duncan I., 2016. Decision stages and asymmetries in regular retail price pass-through. Marketing Science 35 (4), 619–639. Mela, Carl F., Gupta, Sunil, Jedidi, Kamel, 1998. Assessing long-term promotional influences on market structure. International Journal of Research in Marketing 15 (2), 89–107. Mela, Carl F., Gupta, Sunil, Lehmann, Donald R., 1997. The long-term impact of promotion and advertising on consumer brand choice. Journal of Marketing Research 34 (2), 248–261. Messinger, Paul R., Narasimhan, Chakravarthi, 1995. Has power shifted in the grocery channel? Marketing Science 14 (2), 189–223. Meza, Sergio, Sudhir, K., 2006. Pass-through timing. Quantitative Marketing and Economics 4 (4), 351–382. Midrigan, Virgiliu, 2011. Menu costs, multiproduct firms, and aggregate fluctuations. Econometrica 79 (4), 1139–1180.

549

550

CHAPTER 9 How price promotions work: A review

Misra, Kanishka, Schwartz, Eric M., Abernethy, Jacob, 2019. Dynamic online pricing with incomplete information using multi-armed bandit experiments. Marketing Science. Moorthy, Sridhar, 2005. A general theory of pass-through in channels with category management and retail competition. Marketing Science 24 (1), 110–122. Mourdoukoutas, Panos, 2017. A strategic mistake that still haunts JC Penney. www.forbes.com. (Accessed 24 December 2018). Mulhern, Francis J., Padgett, Daniel T., 1995. The relationship between retail price promotions and regular price purchases. Journal of Marketing 59 (4), 83–90. Nakamura, Emi, Steinsson, Jón, 2008. Five facts about prices: a reevaluation of menu cost models. The Quarterly Journal of Economics 123 (4), 1415–1464. Narasimhan, Chakravarthi, 1984. A price discrimination theory of coupons. Marketing Science 3 (2), 128–147. Narasimhan, Chakravarthi, Neslin, Scott A., Sen, Subrata K., 1996. Promotional elasticities and category characteristics. Journal of Marketing 60 (2), 17–30. Natter, Martin, Reutterer, Thomas, Mild, Andreas, Taudes, Alfred, 2007. An assortment wide decisionsupport system for dynamic pricing and promotion planning in DIY retailing. Marketing Science 26 (4), 576–583. Neslin, Scott A., 2002. Sales Promotion. Marketing Science Institute, Cambridge, MA. Nevo, Aviv, 2000. Mergers with differentiated products: the case of the ready-to-eat cereal industry. The Rand Journal of Economics, 395–421. Nijs, Vincent R., Dekimpe, Marnik G., Steenkamp, Jan-Benedict E.M., Hanssens, Dominque H., 2001. The category-demand effects of price promotions. Marketing Science 20 (1), 1–22. Nijs, Vincent, Misra, Kanishka, Anderson, Eric T., Hansen, Karsten, Krishnamurthi, Lakshman, 2010. Channel pass-through of trade promotions. Marketing Science 29 (2), 250–267. Nijs, Vincent R., Misra, Kanishka, Hansen, Karsten, 2013. Outsourcing retail pricing to a category captain: the role of information firewalls. Marketing Science 33, 66–81. Papatla, Purushottam, Krishnamurthi, Lakshman, 1996. Measuring the dynamic effects of promotions on brand choice. Journal of Marketing Research 33 (1), 20–35. Pashigian, B. Peter, 1988. Demand uncertainty and sales: a study of fashion and markdown pricing. The American Economic Review, 936–953. Pashigian, B. Peter, Bowen, Brian, 1991. Why are products sold on sale? Explanations of pricing regularities. The Quarterly Journal of Economics 106 (4), 1015–1038. Pauwels, Koen, 2007. How retailer and competitor decisions drive the long-term effectiveness of manufacturer promotions for fast moving consumer goods. Journal of Retailing 83 (3), 297–308. Pauwels, Koen, Silva-Risso, Jorge, Srinivasan, Shuba, Hanssens, Dominique M., 2004. New products, sales promotions, and firm value: the case of the automobile industry. Journal of Marketing 68 (4), 142–156. Pesendorfer, Martin, 2002. Retail sales: a study of pricing behavior in supermarkets. The Journal of Business 75 (1), 33–66. Raju, Jagmohan S., Srinivasan, Venkatesh, Lal, Rajiv, 1990. The effects of brand loyalty on competitive price promotional strategies. Management Science 36 (3), 276–304. Rakesh, Arvind Rajan, Steinberg, Richard, 1992. Dynamic pricing and ordering decisions by a monopolist. Management Science 38 (2), 240–262. Ramanathan, Suresh, Dhar, Sanjay K., 2010. The effect of sales promotions on the size and composition of the shopping basket: regulatory compatibility from framing and temporal restrictions. Journal of Marketing Research 47 (3), 542–552. Rao, Ram C., 1991. Pricing and promotions in asymmetric duopolies. Marketing Science 10 (2), 131–144. Rao, Ram C., Arjunji, Ramesh V., Murthi, B.P.S., 1995. Game theory and empirical generalizations concerning competitive promotions. Marketing Science 14 (3_Suppl.), G89–G100. Rhee, Hongjai, Bell, David R., 2002. The inter-store mobility of supermarket shoppers. Journal of Retailing 78 (4), 225–237. Rhodes, Andrew, 2014. Multiproduct retailing. The Review of Economic Studies 82 (1), 360–390.

References

Richards, Timothy J., 1999. Dynamic model of fresh fruit promotion: a household production approach. American Journal of Agricultural Economics 81 (1), 195–211. Richards, Timothy J., 2006. Sales by multi-product retailers. Managerial and Decision Economics 27 (4), 261–277. Rozen, Kareen, 2010. Foundations of intrinsic habit formation. Econometrica 78 (4), 1341–1373. Sahni, Navdeep S., Zou, Dan, Chintagunta, Pradeep K., 2017. Do targeted discount offers serve as advertising? Evidence from 70 field experiments. Management Science 63 (8), 2688–2705. Salant, Stephen W., 1989. When is inducing self-selection suboptimal for a monopolist? The Quarterly Journal of Economics 104 (2), 391–397. Schindler, Robert M., 1992. A coupon is more than a low price: evidence from a shopping-simulation study. Psychology & Marketing 9 (6), 431–451. Seiler, Stephan, 2013. The impact of search costs on consumer behavior: a dynamic approach. Quantitative Marketing and Economics 11 (2), 155–203. Sethuraman, R., Srinivasan, V., 2002. The asymmetric share effect: an empirical generalization on crossprice effects. Journal of Marketing Research 39 (3), 379–386. Sethuraman, Raj, Srinivasan, V., Kim, Doyle, 1999. Asymmetric and neighborhood cross-price effects: some empirical generalizations. Marketing Science 18 (1), 23–41. Shaffer, Greg, Zhang, Z. John, 2002. Competitive one-to-one promotions. Management Science 48 (9), 1143–1160. Shelegia, Sandro, 2012. Multiproduct pricing in oligopoly. International Journal of Industrial Organization 30 (2), 231–242. Silva-Risso, Jorge, Ionova, Irina, 2008. A nested logit model of product and transaction-type choice for planning automakers’ pricing and promotions. Marketing Science 27 (4), 545–566. Simester, Duncan, 1997. Note. Optimal promotion strategies: a demand-sided characterization. Management Science 43 (2), 251–256. Simester, Duncan, Hu, Yu, Brynjolfsson, Erik, Anderson, Eric T., 2009. Dynamics of retail advertising: evidence from a field experiment. Economic Inquiry 47 (3), 482–499. Sinitsyn, Maxim, 2008. Price promotions in asymmetric duopolies with heterogeneous consumers. Management Science 54 (12), 2081–2087. Sinitsyn, Maxim, 2012. Coordination of price promotions in complementary categories. Management Science 58 (11), 2076–2094. Slade, Margaret E., 1998. Optimal pricing with costly adjustment: evidence from retail-grocery prices. The Review of Economic Studies 65 (1), 87–107. Sobel, Joel, 1984. Non-linear prices and price-taking behavior. Journal of Economic Behavior & Organization 5 (3–4), 387–396. Song, Inseong, Chintagunta, Pradeep K., 2007. A discrete-continuous model for multicategory purchase behavior of households. Journal of Marketing Research 44 (4), 595–612. Srinivasan, Shuba, Pauwels, Koen, Hanssens, Dominique, Dikempe, Marnik, 2004. Do promotions benefit, manufacturers, retailers, or both? Management Science 50 (5), 617–629. Sriram, Srinivasaraghavan, Kalwani, Manohar U., 2007. Optimal advertising and promotion budgets in dynamic markets with brand equity as a mediating variable. Management Science 53 (1), 46–60. Stokey, Nancy L., 1979. Intertemporal price discrimination. The Quarterly Journal of Economics, 355–371. Swait, Joffre, Erdem, Tülin, 2002. The effects of temporal consistency of sales promotions and availability on consumer choice behavior. Journal of Marketing Research 39 (3), 304–320. Taylor, John B., 1980. Aggregated dynamics and staggered contracts. Journal of Political Economy 88 (February), 1–24. Tyagi, Rajeev K., 1999. A characterization of retailer response to manufacturer trade deals. Journal of Marketing Research, 510–516. Van Heerde, Harald J., Gupta, Sachin, Wittink, Dick R., 2003. Is 75% of the sales promotion bump due to brand switching? No, only 33% is. Journal of Marketing Research 40 (4), 481–491. Van Heerde, Harald J., Leeflang, Peter S.H., Wittink, Dick R., 2000. The estimation of pre- and postpromotion dips with store-level scanner data. Journal of Marketing Research 37 (3), 383–395.

551

552

CHAPTER 9 How price promotions work: A review

Van Heerde, Harald J., Leeflang, Peter S.H., Wittink, Dick R., 2004. Decomposing the sales promotion bump with store data. Marketing Science 23 (3), 317–334. Varian, Hal R., 1980. A model of sales. The American Economic Review 70 (4), 651–659. Waller, Matthew, Williams, Brent, Tangari, Andrea, Burton, Scot, 2010. Marketing at the retail shelf: an examination of moderating effects of logistics on SKU market share. Journal of Academy of Marketing Science 38 (Spring), 105–117. Walters, Rocknie G., 1991. Assessing the impact of retail price promotions on product substitution, complementary purchase, and interstore sales displacement. Journal of Marketing 55 (April), 17–28. Zhang, Jie, Krishnamurthi, Lakshman, 2004. Customizing promotions in online stores. Marketing Science 23 (4), 561–578.