Winners and Losers in a Major Price War Author(s): Harald J. van Heerde, Els Gijsbrechts and Koen Pauwels Source: Journal of Marketing Research, Vol. 45, No. 5 (Oct., 2008), pp. 499-518 Published by: American Marketing Association Stable URL: http://www.jstor.org/stable/20618839 . Accessed: 26/10/2014 04:38

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This content downloaded from 88.255.245.252 on Sun, 26 Oct 2014 04:38:26 AM All use subject to JSTOR Terms and Conditions HARALD J.VAN HEERDE, ELS GIJSBRECHTS, and KOEN PAUWELS*

Although retail price wars have received much business press and some research attention, it is unclear how they affect consumer purchase behavior. This article studies an unprecedented price war in Dutch grocery retailing that started in fall 2003, initiated by the market leader to halt its sliding market share. The authors investigate the short- and long term effects of the price war on store visits, on spending, and on the sensitivity of these decisions to weekly prices and price image. They use a unique data set with consumer hand-scan and perceptual data for a national panel of 1821 households, covering two years before and two years after the price war started. Although the price war initiallyentailed more shopping around and increased spending, spending per visit ultimately dropped because consumers redistributed their purchases across stores. The price war made consumers more sensitive to weekly prices and price image, which helped both the chain that showed an improvement in price image (the price war initiator) and the chains that already had a favorable price image (hard discounters). The price war initiatormanaged to halt the slide in itsmarket share, and its stock price improved. The losers were the rivalmid-level and high-end chains. Unlike the initiator, their price image did not improve, and they suffered from increased price image sensitivity. The authors provide managerial implications for firms that are (or about to be) involved ina price war.

Keywords: price war, multivariate Tobit IImodel, store visits, spending, price image

Winners and Losers in a Major Price War

A price battle between large retailers is not uncommon. In the early 2000s, the leading Dutch supermarket chain But the war that now is different. price rages entirely Albert Heijn suffered from an unfavorable and deteriorating The cuts a much assortment, price encompass larger price image, which was especially troublesome in light of and the percentage price reductions are spectacular. the rise of hard discounters (Aldi and Lidl) and worsening More is going on here. (Sch?ndorff2003, p. 1) economic conditions. Despite their continued belief in the retailer's quality and service, fewer and fewer shoppers could justify paying such high prices. After several years of a market on October Albert *Harald van Heerde is Professor of Marketing, Waikato Management sliding share, 20, 2003, Heijn School, University ofWaikato, and Extramural Fellow at CentER, Tilburg decided to slash its prices formore than 1000 products. the University, Netherlands (e-mail: [email protected]). Els Gijs Using the headline "From now on, your daily groceries are brechts is Professor of Marketing, Tilburg University, the Netherlands much less expensive," its double-page color advertisements (e-mail: Koen Pauwels is Professor of [email protected]). Marketing, in all national and local made clear that the Ozyegin University (Istanbul), and Associate Professor of Business newspapers chain was to Administration, Tuck School of Business, Dartmouth College (e-mail: committed decrease its prices systematically [email protected]). The authors gratefully acknowledge the and permanently.1 support of Aimark and Publi-info, which provided the data for this study. They especially acknowledge the assistance of Alfred Dijs, Dick Valstar, Peter Gouw, Ton Luyten, and Vincent van Witteloostuyn (Europanel). For their constructive comments and feedback, the authors thank Marnik additional factors may have contributed toAlbert Heijn's decision to Dekimpe; Scott Neslin; Laurens Sloot; and seminar participants at the initiate a major policy change. Its holding company, Ahold, was involved Marketing Dynamics Conference, theMarketing Science Conference, and in a major accounting scandal in 2002, which seriously affected its reputa theNorth-East Marketing Consortium. Research funding was provided by tion as a reliable firm. Furthermore, in theweeks preceding the price war, theMarketing Science Institute and theNetherlands Organization for Sci themedia and the general public had been stirred up by a payment bonus entific Research (to the first author). Michel Wedel served as associate edi forAlbert Heijn's chief executive officer, which many considered exces tor for this article. sive in a time of economic decline. Several customers even decided to par ticipate in a boycott of Albert Heijn to express their disagreement.

? 2008, American Marketing Association Journal of Marketing Research ISSN: 0022-2437 (print), 1547-7193 (electronic) 499Vol. XLV (October 2008), 499-518

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Figure 1 PRICES OF A 1.5-LITERBOTTLE OF COCA-COLA AT FOUR LEADING CHAINS OVER TIME

Albert Heijn 130.00

S 120.00 -J ^ -^ 110.00 IS c 100.00 ?? ? s g 3 90.00 o0) 80.00 ? 70.00 i-r 2002-01 2002-27 2003-01 2003-27 2004-01 2004-27 2004-53 2005-26 2005-52 Year-Weak

C1000 120.00

110.00

100.00

- 90.00

- O m 80.00 ? w O O - 70.00

- 60.00 -1-1-1 -1-1 T "T T T" 2002-01 2002-27 2003-01 2003-27 2004-01 2004-27 2004-53 2005-26 2005-52 Year-Weak

Edah 130.00 Start price war 120.00

110.00 COc 100.00

(bS 90.00 O Ml - 80.00 O - 70.00

60.00 -1 T T -1-1 T T ~r 2002-01 2002-27 2003-01 2003-27 2004-01 2004-27 2004-53 2005-26 2005-52 Year-Weak

Super de Boer 140.00

130.00

Jj-ST 120.00 ?? 110.00 g 100.00 O MJ? ~ - < > 90.00 '= ?. 80.00 H 70.00 i-r 2002-01 2002-27 2003-01 2003-27 2004-01 2004-27 2004-53 2005-26 2005-52 Year-Weak

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The price reduction applied to many national brands 2006). In theNetherlands, more than 52% of households from a wide variety of categories. For example, Figure 1 frequently shopped at hard discounters Aldi or Lidl in fall shows how the regular price for a 1.5-liter bottle of Coca 2003, up from 30% in 2001 (GfK 2003). The reaction of Cola went down from 1.23 to 1.12 (-9%). Although traditional retailers has varied from focusing on quality and Albert Heijn's operation to decrease prices was undertaken service to engaging the challengers with substantial price in complete secrecy, within two days all major competitors reductions (Rogers 2001). However, these price reductions carrying this (Coca-Cola) stockkeeping unit (SKU) (C1000, may trigger price wars, as in the case of Dutch supermar Edah, and Super de Boer) matched or even exceeded the kets, which can last for a long time and strongly affect all price reductions. market players (Rao, Bergen, and Davis 2000). A week later,Albert Heijn decreased prices for another The literature is inconclusive about the consequences of 550 products. The price war that followed is unprecedented price wars. Although, in general, price wars are believed to in Dutch retailing. As Table 1 shows, many more price hurt revenues and long-term prospects for themarket play cutting rounds occurred over the next years and lasted until ers (Brandenburger and Nalebuff 1996), other studies sug October 31, 2005. These subsequent rounds involved differ gest that the impact depends on each player's price position ent brands (national versus private label) and categories, and role in the price war (Busse 2002; Elzinga and Mills resulting in negative retailmargins for hundreds of products 1999; Rao, Bergen, and Davis 2000). Although the (Holla and Koreman 2006; Van Aalst et al. 2005). As for antecedents of price wars have been well documented (see scope and depth, this national price war dwarfs both docu our subsequent literature review), empirical research on mented incidents in the grocery industry that Heil and their consequences is sparse. As a recent review concludes, Helsen (2001) mention: the price cuts on private labels "It is unclear what the overall effects of price wars are. among theU.K. retailers Tesco and Asda and the 2% price Price wars are often assumed to lead to losses for the firms drop in theHouston retailingmarket. In our case, the price involved in the battle.... It is, therefore, important to war was nationwide, entailing an 8.2% reduction in food research how price wars affect firms in the industry, prices (Baltesen 2006a) and resulting in the lowest inflation whether these effects are uniformly distributed, and how level in 15 years (Consumer Reports 2004). The loss in such effects persist in the long run through lower reference added value for theDutch retailing industry is estimated to prices" (Heil and Helsen 2001, p. 96). be 900 million in one year, and more than 30,000 employ To fill this gap in the literature, we study the conse ees in the grocery industry lost their jobs (Van Aalst et al. quences of theDutch supermarket price war on consumer 2005). purchase behavior. We analyze how the price war affected This Dutch supermarket price war fits inwith the trend two major components of purchase behavior (Singh, that retail price competition has become increasingly vivid Hansen, and Blattberg 2006): store visits and spending in recent years, reducing retailer profitability (Ailawadi (money spent per store per week). In particular, we investi 2001). Discounters such as Wal-Mart, Aldi, and Lidl are gate whether the price war led tomore shopping around in challenging traditional retail formats on both sides of the the short run and to decreased spending in the long run. Atlantic (BusinessWeek 2003). In almost all Western mar Furthermore, we test the hypothesis that the price war made kets, grocery discounters have captured market share from store visit and spending decisions more sensitive to weekly traditional supermarkets and now occupy a prominent posi prices and price image. To examine these issues, we use a tion (Cleeren et al. 2007). In theUnited States, Wal-Mart unique data set that combines consumer hand-scan and per controls a large part of the retailmarket and is driving down ceptual data for a national panel of 1821 households, cover prices at other retailers (Singh, Hansen, and Blattberg ing a period of 90 weeks before and 114 weeks after the

Table 1 OVERVIEW OF PRICE WAR ROUNDS

Number of Products Date Initiator (Approximately) Emphasis on

October 20, 2003 Albert Heijn 1000 A-brands October 27, 2003 Albert Heijn 550 A-brands November 10, 2003 Albert Heijn 300 A-brands and dairy January 19, 2004 Albert Heijn 500 A-brands and produce March 8, 2004 Albert Heijn 100 Meat May 10, 2004 Albert Heijn 100 Cheese September 20, 2004 Albert Heijn 1000 Private labels November 13, 2004 Albert Heijn 2000 A-brands January 30, 2005 Albert Heijn 1000 A-brands, cleaning, and personal care February 21, 2005 Albert Heijn 100 Prepared meat/cheese March 7, 2005 Edah 250 A-brands and private labels April4, 2005 Edah 250 A-brands and private labels July28, 2005 Vomar 1000 Not available August 23, 2005 Super de Boer 600 A-brands September 12, 2005 Albert Heijn 100 Cleaning and personal care October 31, 2005 Albert Heijn 1000 A-brands

Sources: Van Aalst et al. (2005); Holla and Koreman (2006).

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war we price started. For the six-largest national chains, Price wars have become a part of life in a wide range of estimate a multivariate heterogeneous Tobit II model that industries (Rao, Bergen, and Davis 2000). Business press includes the short- and long-term effects of the price war on and academic research have reported on price wars in store visits, spending, and the sensitivity of these decisions industries including electricity (Fabra and Toro 2005), oil to weekly prices and price image. To complement our (Slade 1992), telecommunications (Young 2004), automo we not analyses, only estimate competitive reaction func biles (Breshnahan 1987), airlines (Busse 2002), fast food tions but also assess the effects of the price war on stock (Gayatri 2004), and groceries (Barnes 2004). Price wars prices. erupt at various levels in the distribution channel and with Although the price war initially entailed more shopping growing frequency and intensity (Heil and Helsen 2001). around and increased spending, spending per visit ulti As Rao, Bergen, and Davis (2000, p. 116) conclude, "If mately dropped because consumers redistributed their pur you're not in a battle currently, you probably will be fairly across war consumers chases stores. The price made more soon." sensitive to weekly prices and price image, which helped Literature on Price Wars both the player that showed an improvement in price image war (the price initiator) and the players that already had a Academic literature on price wars can be classified into favorable The war ini streams. price image (hard discounters). price three research A first stream comprises game to tiatormanaged halt the slide in itsmarket share, and its theoretic contributions, with a strong focus on price war are stock price improved. The losers the rival mid- and antecedents. An important price war trigger revealed in this high-end chains: Unlike the initiator, their price image did steam is competitive entry (Elzinga and Mills 1999; Mil not improve, and they suffered from the increased price grom and Roberts 1982). Other factors deemed to be induc image sensitivity.We expect these results to be generaliz tive to price wars are declining economic conditions (Eilon able because theDutch grocery retail industry is representa 1993; Slade 1990) and, often related to this, consumers' low on tive of many Western markets several key indicators (and/or declining) brand loyalty and high (and/or increas a (Steenkamp et al. 2005, p. 40). Moreover, recent meta ing) price sensitivity (Klemperer 1989; Sairamesh and analysis has concluded that price elasticities do not differ Kephart 2000). countries Van more significantly among developed (Bijmolt, A second stream includes managerial research. Heerde, and Pieters 2005). Thus, the consequences of the This work reflects on the link between price wars and firm war Dutch price may hold lessons for retailers in other strategies and characteristics. Companies with high exit a countries facing similar situation. barriers (Heil and Helsen 2001) and high stakes in themar We organize the remainder of this article as follows: In ket or a worsened financial situation (Busse 2002) are more we war the next section, discuss the price literature, focus inclined to initiate a price war or to enter an ongoing battle. on we we ing the gaps aim to address. Then, discuss the In doing so, these firms hope to bring about a market clear to war on store model used quantify the price effects visits out and to increase theirprofit from reduced competition in and spending. The subsequent section describes the empiri the long run (Fudenberg and Tir?le 1986; Klemperer 1989), our sets. cal setting and details data We then present the or at least to halt the loss of customers and maybe even a estimation outcomes and conclude by providing discus reattract clientele (Elzinga and Mills 1999; Klemperer sion and limitations. 1989). A widely advertised price cut may also establish a RESEARCHBACKGROUND AND HYPOTHESES more favorable price image (Busse 2002; Rao, Bergen, and Davis 2000). Price War: Definition and Importance The third stream consists of empirical research docu Price wars are characterized by competing firms strug menting price war consequences. Unfortunately, despite the gling to undercut one another's prices (Assael 1990). importance of price wars, such empirical contributions are Urbany and Dickson (1991) refer to a "price-cutting extremely scarce and suffer from some limitations. momentum," or the downward price pressure that drives Although the studies by Green and Porter (1984), Breshna other competitors to follow the initialmove. Price is a logi han (1987), Rotemberg and Saloner (1986) and Levenstein cal weapon of choice because it is easy to change fast (1997) provide a glimpse of the nature and impact of price (Kalra, Raju, and Srinivasan 1998). Unlike typical, intense wars, the data set limitations of these studies do not allow price competition, price wars lead to prices that are not sus the research to go beyond a rough empirical assessment. On tainable in the long run (Schunk 1999). After an extensive the basis of 15 case studies in a diverse range of industries, review of business press articles and academic literature, Heil and Helsen (2001) provide some preliminary evidence Heil and Helsen (2001) define a price war as requiring one on overall price war effects, including dwindling prices, or more of the following conditions: (1) There is a strong declining image and revenues, and profit erosion for the focus on competitors rather than on consumers, (2) the pric parties involved. They also provide initial indications of ing interaction as a whole is undesirable to firms, (3) the increased shelf price elasticities for incumbent brands of a competitors neither intend nor expect to ignite a price war, personal care product following a price war. They conclude (4) the competitive interaction violates industry norms, (5) (p. 86) that though their "descriptive statistics illustrate the occurs a nor war more the pricing interaction at much faster rate than importance and scope of price phenomena,... rig mal, (6) the direction of pricing is downward, and (7) the orous empirical research is needed." We help fill in this gap pricing interplay is not sustainable. Subsequently, we verify by testing hypotheses on price war consequences with an that the Dutch price war meets most (if not all) of these empirical model, which we estimate using a unique and conditions. rich data set.

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Price War Effects on Store Visits, Spending, and Price Figure 2 Sensitivity:Hypotheses THE EFFECTS OF A PRICE WAR ON (1) STORE VISIT AND SPENDING AND SENSITIVITIES TO WEEKLY PRICE AND Henderson (1997) suggests that in the absence of a (2) wars are PRICE IMAGE strong and sustainable cost advantage, price "good for absolutely nothing" and may lead to dramatic losses for themarket players involved. In this section, we develop a Store Visits: Hypotheses and Empirical Results more refined picture of how price wars affect consumer spending, leading to a negative impact of the price war on some market and a for others. Weekly price players positive impact ~~ Given our focus on a retail we this "3a" setting, decompose ST: -.058 effect into its two store visits spending major components: LT: n.s. and spending, after a consumer decides to buy in the store. Moreover, we distinguish between the price war's main effect on these performance measures and itsmoderating on to store impact consumers' sensitivity weekly prices and H^STh to overall store we substantial ST: .020 price image. Finally, expect Price war Store visits differences in the price war's performance effects in the (LT:-.011) short run versus the long run. The latter is important from a managerial perspective because great initial results may encourage retailers to cut prices further, even when the + long-term effects of competitive escalation are disastrous H4a: ST: n.s. and Hanssens 1999; Ghemawat 1991). 2 (Dekimpe Figure LT: .005 displays our conceptual framework and hypotheses.

Price image Main Effects of thePrice War on Store Visits and Spending Short-term effects. By definition, price wars constitute market disruptions. Market players announce major strat and formulate claims on egy changes unprecedented Spending: Hypotheses and Empirical Results reduced prices. For example, the two major high-service/ high-price Dutch retailers stated that shopping in their chain allows for "dramatic on savings" grocery spending Weekly price and that benefits are to be (Albert Heijn) "gigantic" reaped H3b:~ from permanent price reductions (Super de Boer). Such ST: -.084 + ifspending is widely publicized claims may shake up consumers' former LT: -.026 price inelastic beliefs about themarket and lead them to reconsider their ? ifspending is established purchase patterns, in terms of both store visits price elastic and spending. - H2: LT In the short run (i.e., right after the startof a price war), (ST: .008) consumers face increased about which stores Price war uncertainty LT.-.004 Spending offer the best value for the money. As a result, they are likely to adopt risk-reducing strategies (Blattberg and Nes lin 1989), engaging in comparison shopping to update pre vious information and Fournier In other (Mick 1998). + visit more at to out H4b: words, they chains, least check the ST: n.s. (new) prices in these stores. Thus: LT: .004

H^ The price war leads to an overall increase in store visits in Price image the short run.

At the same time, the price war's influence on spending is to three forces. First, the war leads to lower = = = subject price Notes: ST short term, and LT long term. n.s. not significant. prices, and as a result, spending is reduced even when same. our we quantities remain the In approach, focus on the impact of the price war on spending and control for these price-driven changes. This impact may be negative "psychological income" or "windfall" effect. For example, because of the second force; consistent with the argument a field experiment found that when given a monetary on uncertainty, consumers may redistribute theirpurchases reward before entering a store, shoppers spentmore in the across stores, thus reducing the probability of systemati store, in excess of the monetary reward (Heilman, cally getting the worst deal (Fox and Hoch 2005). Con Nakamoto, and Rao 2002). In a similar vein, the price war's versely, the short-term impact of a price war on spending sudden promise of "dramatic savings" may induce con may be positive because of the third force; the sudden and sumers to "burn a hole in their pockets"?that is, to heavily publicized price drop may create an unexpected increase their spending disproportionally?because the sav

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ings enable them to afford better-quality brands and to price is positive in the case of price-inelastic demand and enjoy the transactional utility of getting a great deal (Chan negative in the case of price-elastic demand (see Figure 2). don, Wansink, and Laurent 2000). Given these opposing Second, consumers also hold subjective summary views we forces, investigate the price war's short-term effects on of the stores' overall price appeal. As M?gi and Yulander spending in an exploratory way. (2005) show, these subjective price images constitute a sep Long-term effects.Compared with the short run, there is arate price dimension that, at best, ismoderately associated little reason for the price war to increase store visits in the with actual objective prices and is more stable over time. long run. Indeed, consumers in mature markets tend to Price image differentiates stores on the basis of their per develop stable purchase patterns, which are only temporar ceived price positioning. This perceived price positioning ily disrupted by marketing activities (Ehrenberg 1988). has been found to exert an important influence on store Although specific stores may benefit from increased visits selection (Arnold, Oum, and Tigert 1983; Severin, Lou consumers are in the long run, unlikely to increase the over vi?re, and Finn 2001), beyond objective weekly store all frequency of store visits permanently. prices. We define the store visit sensitivity to price image In contrast, the price war is likely to decrease spending in (spending sensitivity to price image) as the response the long run, even afterwe control for the changes driven parameter of price image in themodel for store visit proba by price reductions. Analogous to our argument for the bility (spending probability), and we expect both sensitivi short-term effect, we expect that a shopping environment ties to be positive (see Figure 2). characterized by an escalating price war induces consumers Consistent with this dual retail price construct, increased to redistribute their total grocery spending across the stores sensitivity to weekly prices and price image triggered by a they visit. In contrast, the opposing force of a windfall price war may materialize in two ways (Bell and Lattin effect is most likely only short lived because families are 1998; Galata, Bucklin, and Hanssens 1999; Lai and Rao unlikely to consume much more food overall, even when 1997). First, the price war may stimulate more opportunis prices drop substantially. An analogous result holds at the tic buying behavior, with consumers shopping around more category level; that is, although weekly price promotions to benefit from weekly deals on prices (Bell and Lattin may expand the category substantially, they do so only 1998; Fox and Hoch 2005). Thus, consumers will be more temporarily (Pauwels, Hanssens, and Siddarth 2002; Van responsive to stores' actual weekly prices (Dr?ze, Nisol, Heerde, Leeflang, and Wittink 2004). Because we believe and Vilcassim 2004; Fox and Hoch 2005): that the negative force is present (splitting the grocery bill H3: The price war increases (a) the sensitivity of store visits to across and that the effect is stores) positive (windfall) weekly prices and (b) the sensitivityof spending toweekly absent in the run, we that the war will long expect price prices (i.e., the price war makes the corresponding response reduce spending. parameter more negative).

The war leads to an overall decrease in in H2: price spending Second, responding more strongly to weekly prices the run. long requires increased effort from consumers. They may also engage in other, more general impression-based forms of A consumer's enhanced focus on Moderating Effects of a Price War: Consumer Sensitivity to price-oriented shopping. then translates into out stores Weekly Prices and Price Image price systematically seeking with a favorable overall price image (Bell, Ho, and Tang A feature of a war is that inter unique price pricing 1998; Galata, Bucklin, and Hanssens 1999; Rhee and Bell actions occur at a much faster rate than previously (Heil 2002) and allocating larger shares of wallet to these stores. and Helsen 2001). Intensive interactionsmake a price price This leads to additional moderating price war influences: more as a easily accessible attribute, which, result, The war increases the of store visits to increases its importance as a purchase criterion (Wanke. H4: price (a) sensitivity and (b) the of to Bohner, and Jurkowitsch 1997). Lab experiments by price image sensitivity spending price image (i.e., the price war makes the corresponding response Wathieu, Muthukrshnan, and Bronnenberg (2004) show parameter more positive). strong evidence for this effect in a brand setting; specifi cally, offering and retracting discounts decreases the subse Because it is an empirical question whether H3 and H4 quent choice share for high-priced brands but increases the imply sensitivity changes in the short run and/or long run, choice share of low-priced brands. our tests allow for both possibilities. Note that the hypothe war a A price between stores may enhance consumer's sized increase in price image sensitivitywould entail a dif reliance on two types of price information. First, a con ferential impact of the price war on differentmarket play sumer is confronted with the actual, objective prices the ers. This would be especially troublesome for high-end stores charge, which may vary weekly as a result of regular chains, but itmight actually help low-end competitors in price changes or promotional deals. These weekly prices the long run (Boulding, Lee, and Staelin 1994). As such, determine how much the consumer actually pays for a spe the price war may make the price differences between cific product basket in a specific store and week. We define storesmore salient, causing stores with worse price images the store visit sensitivity to price as the response parameter to suffer. of weekly store price in themodel for store visit probability Because price wars are different from a period of intense and the spending sensitivity to price as the response price promotions (Heil and Helsen 2001), we test the price parameter of weekly store price in themodel for spending war hypotheses and control for price promotion-intensive (formore details, see the "Model" section). Consistent with weeks (we provide more details in the subsection "Indepen a we that store visit sen our no preference for lower prices, expect dent Variables"). To the best of knowledge, empiri sitivity to price is negative and that spending sensitivity to cal study has systematically distinguished the impact of a

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= war on store and we assume that the error vectors ..., price consumers' visits, spending, weekly specifically, uht (uhlt, = ..., follow a multivariate price and price image sensitivity. This is an important uhSt)' and eht (ehlt, ehSt)' joint a matrix: gap because the net outcome for firms involved in a price normal distribution,with full variance-covariance ~ store for visits war hinges on these (possibly countervailing) effects. (eht>uht) MVN(0, Z). Intrinsic preferences Researchers used to lack the necessary data on consumer and spending may also be correlated, leading to a jointmul error and behavior before and during the price war. tivariate normal distribution for the terms inEquations perceptions ~ Our data set on the recentDutch war enables 4 and 5 as well: MVN(0, V). We also allow for retailing price (& T^)' us to overcome this hurdle. Before we provide details on unobserved heterogeneity in response coefficients. Specifi the data set, however, we outline themodel. cally, we assume that the coefficients from the store visit are and spending equations jointly distributed multivariate MODEL ~ We estimate thismul normal: (??, ?h)' N[(?, %')', SI], To study the consequences of the price war for national tivariate heterogeneous Tobit II model using Markov chain a retail chains, we model the purchase behavior of national Monte Carlo procedures. Technical details appear in the war panel of Dutch households before and after the price Web Appendix, Part A (http://wwwmarketingpower.com/ started.A household faces choices along two dimensions: jmroct08). which of the stores to visit more than one in a (possibly THE DUTCH PRICE WAR INGROCERY RETAILING: how much to at each store.We given week) and spend SETTINGAND DATA develop a model for the store visit decision and In spending = level of every household h (h 1, ..., H), for every chain i Empirical Setting = ..., and in week t = ..., Given that (i 1, S), every (t 1, T). Previously, we described the Dutch supermarket price a household visit stores in one week and may multiple war in detail. How does it compare with the definitional the left-censored nature of household we given spending, conditions of a price war in Heil and Helsen's (2001) a multivariate Tobit II model Mont specify (e.g., Fox, study? First, as for the strong focus on competitors rather and Lodish and gomery, 2004; Singh, Hansen, Blattberg than on consumers, the rival chains Super de Boer, Edah, A store visit of household h for store i inweek t 2006). (zhit) and C1000 reacted within two days toAlbert Heijn's initial is described a multivariate model: assess con by probit move, which does not allow enough time to sumer responses fully.This fast competitive reaction might (1) have been provoked by the goal Albert Heijn began at Zh,.=?1?fZ??i,>0ifotherwise [O the start of the price war: "to become less expensive than themarket average" (Baltesen 2006b, p. 1). To verify that In a week t, household h visit stores. given may multiple competitive interactions intensified because of the price 1 for those stores. The latent variable, Thus, zhitequals z*hit, war, we estimate competitive reaction functions (Leeflang ismodeled a linearmodel: through and Wittink 1996) before and after the price war started. = + The results reveal more (significant) reactions after the start (2) zhit lhi+xhitCh uhit. of the price war for every retailer than before (for details, = see theWeb Appendix, Part B, at http://www.marketing Conditional on a store visit (zhit 1), we model yhit,the In power.com/jmroct08). of spending (in euro cents) by household h in store i in interaction as a whole is undesirable to week t as follows: Second, pricing firms because it places a lot of pressure on already tight (3) yhit= ?hi+ vhit?h+^it margins (Van Aalst et al. 2005). Third, although we cannot peer intomanagers' minds to assess whether the competi Consistent with the extant literature thatuses Tobit mod tors neither intended nor expected to ignite a price war, store els for visits and spending (Fox, Montgomery, and there is no evidence of such intent (Baarsma and De Nooij Lodish and we 2004; Singh, Hansen, Blattberg 2006), 2005). Fourth, the claim that the competitive interaction of on a store model the logarithm spending (conditional violates industrynorms is evident from lawsuits brought by its to normal than the visit) because distribution is closer large national brand suppliers against the price war initiator The in the distribution of spending. independent variables for selling far below the recommended price. In addition, store visit equations (xhit) and spending equations (vhit) smaller suppliers and grocery stores are facing bankruptcy not same. need be the We specify the independent variables (Van Aalst et al. 2005). As a result, theDutch Ministry of we more after give details about the data. The intercepts in Commerce opened an investigation to consider outlawing store Equations 2 and 3 capture individual-specific prefer below-cost pricing (Baarsma and De Nooij 2005).2 Fifth, ences. assume are We that these intercepts randomly dis the pricing interaction occurs at a much faster rate than nor means: tributedaround store mal (i.e., days instead of weeks/months) and the direction = of pricing is downward, as Figure 1 illustrates. Finally, the (4) lhi \|/i+ xhi, and

= (5) ahi ?i + ?hi. 2In Belgium, France, Greece, Ireland, Italy, Luxemburg, Portugal, and Spain, law prohibits retailers from selling at a price below cost. Other The stores visited and the amounts spent depend on con European countries, such as Denmark, Finland, Austria, Swe sumers' time and constraints and are Germany, budget interdependent den, and the United Kingdom, are similar to the Netherlands in that they between stores. Our model allows for this by embedding do not explicitly impose that selling prices must exceed costs (Baarsma Equations 2 and 3 in a multivariate framework. More and De Nooij 2005).

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pricing interplay is not sustainable because hundreds of profit (Baltesen 2006a). In a similar vein, Figure 3, which items are now sold below cost inDutch supermarkets (Van displays market share for the six leading supermarkets in Aalst et al. 2005). the 2002-2005 period, indicates a strong post-price war sources Although most agree that the price war appears decline forEdah, whereas the slide inAlbert Heijn 'smarket detrimental to grocery retailers on average, there are mixed share before the price war is halted. A key question signals when it comes to individual players, especially by remains: What explains the difference in price war conse the time the price war seems to have taken its full effect.By quences for these key market players? By disentangling the the end of 2005, aftermore than two years of price warfare, price war impact from that of other drivers of chain revenue Laurus (the holding company of Edah and Super de Boer) and by unraveling its effect on separate revenue compo was on the edge of bankruptcy, but Albert Heijn claims to nents, our model and empirical results shed light on these have achieved its goals, reporting a revival of revenues and issues.

Figure3 QUARTERLY MARKET SHARES OF THE SIX NATIONALCHAINS (WITHINTHE SUBMARKET OF THE SIX CHAINS)

.40 H Start price war

.30 H

'v _? ?' <0 C .20 H (0 2

.10 H v._^

.oo H

i-1-1-i-1-r i i i i i i i r & & & & & & ?> & oN & S> & oN & & ? f# f# f# r# ^Q

Year-Quarter

? Mean share Albert Heijn -Mean share Aldi ? -Mean share C1000 -Mean share Edah ? - Mean share Lidl

-Mean share Super de Boer

Notes: These market shares are based on the representative sample of 1821 households used in the analyses.

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Data Sources vations: purchases of 1821 households at six retail chains over 204 weeks. Our data set combines several sources. First, we use pur We model store visit and at the six chase records from the Dutch GfK consumer hand-scan spending largest chains with national coverage, which jointly comprise 70% across a of four and a half years (July 1, 2001 panel period share of the 2002 market. To illustrate the of December 31, 2005). Panel members scan at home all their positioning these chains before the price war, Figure 4 summarizes the purchases at all Dutch grocery retailers, and the data are store perception data in twomain dimensions (according to sent electronically toGfK Benelux. This GfK panel con GfK): service and value for themoney. Albert Heijn is the sists of 4400 households, which represent a stratified market leader that initiated the price war. As illustrated by national sample. We use this source to operationalize our its scores on price image and produce quality (see Table 2), dependent variables (store visits and spending) and the Albert Heijn is a high-price, high-service chain, which also household- and store-specific weekly prices. A unique applies to Super de Boer. The middle segment comprises advantage of consumer hand-scan data (over in-store two chains: C1000, with good scores for service and value, scanned data obtained through household identification and Edah, with low ratings on both dimensions. The two cards) is that themarket research agency does not need the hard discounters (low price, low service) are Aldi and Lidl. permission for data collection from the retail chains. Such Notably, the price war led to a strongly improved price permission is increasingly problematic in both Europe image forAlbert Heijn, as Figure 5 shows. (especially for the hard discounters) and theUnited States Actual weekly prices hardly decreased across the four (Wal-Mart). and a half years of data (Table 2), which may be surprising GfK also provided household perceptions of grocery given themagnitude of the price war. Two comments are retailing chains. Every sixmonths, some of the panelists are relevant here. First, the prices we report inTable 2 are nom surveyed on their perceptions of price image and produce inal price indexes. As Baltesen (2006a) points out, the cor quality. On the basis of these surveys,GfK prepares Christ responding decline in real prices was much stronger: In the mas and summer reports for theDutch grocery industry. In absence of the price war, Dutch food prices would have addition to these reports,GfK conducted a survey biyearly been 8.2% higher than they actually were. Second, although a few weeks after the war started.We obtained the price many items were reduced in price, the majority of the store data at the individual household level for the image stores' SKUs were not (and some prices of heavily featured same and for each week t,we the percep period, assigned SKUs increased again after an initial advertised price drop), tions from themeasurement moment that is closest toweek implying that price drops for the entire basket remained were a t. For the households that not surveyed for specific modest. Christmas or summer report,we imputed image data using a two-way linear model?a typical and commonly used Independent Variables best-fit Little and Rubin imputation approach (see 1987, Store selection and spending depend on a trade-off Chap. 2). between shopping benefits and costs (Bell, Ho, and Tang We obtained data from Information Resources Inc. and 1998; Tang, Bell, and Ho 2001), and Table 3 summarizes Publi Info in the on feature and (both Netherlands) weekly the corresponding independent variables. As store benefits for all items sold inDutch chains display grocery retailing variables, we include store price image, produce quality (an across the same We used these variables to period. opera indicator of general quality), store surface (an indicator of tionalize household- and feature and store-specific display assortment size), and feature and display variables (Bell, variables. Reed Business the sizes Finally, provided (in Ho, and Tang 1998; Fox, Montgomery, and Lodish 2004; and the locations for all Dutch square meters) (zip codes) Sirohi, McLaughlin, andWittink 1998; Tang, Bell, and Ho stores and each in our data set. store grocery year The size 2001).3 Store familiarity or spending habits affect store vis are a data useful proxy for assortment size of each chain's its and spending as well (Bell, Ho, and Tang 1998; Rhee store nearest to the household. store We combined the zip and Bell 2002). Such state dependence can be captured codes with theGfK household panelists' zip codes to com with lagged purchase indicators (Ailawadi, Gedenk, and pute the Euclidean distance between a household and the Neslin 1999; Seetharaman 2003). To capture a variety of store closest from each chain. shopping visit and spending patterns, we use four lagged variables that represent prior store visits and spending, one Data Selection for each of the four preceding weeks. We include two variables for store costs: Because the panel composition changes over time, we independent (1) decided to select the 1821 households that remained in the store distance, representing fixed costs, and (2) weekly to a basket of panel across the four-and-a-half-year period. We use the prices paid acquire products, representing variable costs and Ghosh and first 30 weeks (Week 27 of 2001-Week 4 of 2002) as the (Bawa 1999; Bell, Ho, Tang initialization period for determining households' spending across categories and for the lagged store visit and spend variables. We used the 204 weeks 5 of ing remaining (Week 3We include feature in the store visit model but omit it from the spend 2002-Week 52 of 2005) formodel calibration. The price ing equation because feature promotions represent out-of-store communi cation intended to store we war started inWeek 43 of 2003, and thuswe have 90 weeks enhance visits. Similarly, include display in the spending model but exclude it from the store visit model because thismar before the start of the price war and 114 weeks afterward. keting instrument is observed only by shoppers inside the store.We veri This seems sufficient tomeasure effects because long-term fied both restrictions and found that posterior interval for the display by the end of 2005, the price war was in its aftermath (Van parameter includes zero in the store visit model, and the same applies for the Aalst 2006). The full data set consists of 2,228,904 obser feature parameter in the spending model.

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Figure 4 POSITIONING OF THE SIX MAJOR DUTCH RETAIL CHAINS INSUMMER 2002

Source: GFK (2003).

Table 2 DESCRIPTIVE STATISTICS OF THE SIX CHAINS BEFORE (PRE) AND AFTER (POST) THE START OF THE PRICE WAR

Albert Heijn Super de Boer C1000 Edah Aldi Lidl (Service) (Service) (Middle) (Middle) (Discount) (Discount) Post Pre Post Pre Post Pre- or post-price war perioda Pre Post Pre Post Pre Post Pre Market share 32% 31% 14% 13% 24% 24% 10% 8% 16% 18% 4% 7% .14 .11 .24 .25 .08 .12 Weekly store visits .36 .35 .16 .15 .27 .27 Weekly spending (given 24.07 22.17 20.66 21.70 15.96 17.14 spending > 0) 28.92 27.12 27.74 26.03 28.03 27.57 = Price image (1 "lowest," = 5.8 6.9 6.7 6.7 6.7 and 7 "highest") 5.1 5.5 5.4 5.4 6.0 6.1 5.8 = Produce quality (1 "lowest," = 5.5 4.3 4.5 4.7 5.0 and 7 "highest") 6.4 6.5 6.2 6.1 6.2 6.2 5.5 5.2 3.2 7.0 5.3 Distance to panelists (km) 2.3 2.3 4.0 4.0 3.1 3.0 5.1 3.2 Store surface (m2) 1326 1385 867 979 838 927 1008 1024 421 428 613 622 Price (index) 1.19 1.20 1.11 1.12 .98 .96 1.01 1.01 .60 .58 .59 .59 Featureb 3.09 2.67 3.45 2.97 1.46 1.45 4.05 2.40 .31 3.5 1.68 3.45 1.41 3.20 2.82 .31 3.5 1.68 3.45 Displayb 2.37 2.69 2.81 2.56 1.40 war runs from October to the end of 2005. aThe pre-price war period runs from January 2002 toOctober 19, 2003; the post-price period 20, 2003, of that are It varies from 0 bThis variable is the product of the percentage of stores that carry the promotion times the percentage products promoted. (no are activity whatsoever) to 10,000 (100% of the products in 100% of the stores promoted).

1998; Popkowski-Leszczyc, Sinha, and Sahgal 2004).4 nal variation only, whereas the (untransformed) price image we for variable both cross-sectional and var Importantly, because mean-center weekly prices captures longitudinal each store-household combination, they capture longitudi iation. Furthermore, we include seasonal dummies (Weeks 1, 51, 52, and Easter). To test the we include war 4As we discussed in the "Research Background and Hypotheses" sec hypotheses, price variables, tion, we need to include weekly price as an independent variable in the based on the price war rounds outlined in Table 1.We the war models for store incidence and spending. This enables price define the step variable PWRound as the cumulative num of variable (which we discuss subsequently) to capture the impact the price ber of items thatwere reduced in since the startof the war while for mere reductions. To avoid price controlling price endogeneity war. Its coefficient in themodel for store visit and issues, we use purchases from the initialization sample to define each price war's household's basket of products rather than the current week's basket. spending represents the price long-term (permanent)

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Figure5 This operationalization identifies promotion-intensive peri PRICE IMAGEFOR THE SIX CHAINS OVER TIME ods thatmake intuitive sense because they largely corre spond to the periods when households are on tighterbudg ets (beginning of the year and end of summer).We include themain effect of week and its interactionwith 7.00 promotion x and V-r?h, weekly prices (Promweek InPrice) price image x A_,_ (Promweek Pricelmage) in themodels for store visits and spending. Our results are robust to alternative definitions of 6.50 4 Promweek on a that is 2% or 3% lower than Start price war (based price o average). CO Table 2 shows that themeans of several store activities ? r-?.-1 between the before and after the war 6.00 ,-4U?f-i___r change periods price _I O started. For example, the average distance to a Lidl store decreases from 7.0 to 5.3 kilometers, reflecting Lidl's the 5.50 increase in the number of outlets. In addition, average store surface areas tend to increase over time of 0= (because either remodeling or new stores).Moreover, the feature and display activities increase forAldi and Lidl and decrease 5.00 for some other players. Our model includes control (inde pendent) variables for each of these changes to obtain unbi oN fl> 0N ?> oN oN ?> for the war # & # & ?T ?K# ^ ased estimates price effects. RESULTS Year-Week Store Visits We the store visit results in the left-hand of Chain present part Table 4. All benefit ? ? variables (Pricelmage, ProduceQuality, Aldi Edah ? - - ? StoreSurface, Feature, and LagVisitl-4) have positive Lidl Super de Boer ? effects on store visit their 95% C1000 ?Albert Heijn probabilities (and posterior interval excludes zero). The positive impact of InStore Surface (.155) is consistent with store size being a proxy for assortment size. The coefficients of lagged visit (.235, .298, .283, and .264) indicate the expected positive state As for we find that a distance effect.We also use its first difference, the pulse variable dependence. costs, greater between a household and a store more travel time and Pulse_PWRound, which represents the extra number of (i.e., has the effect on store visit items reduced in price in a particular week. Its response costs) expected negative In the effect of is coefficient represents the short-termeffect of the price war probability (-.502). addition, price as we The seasonal effect esti on store visits and incidence. The use of step and pulse negative (-.097), expected. mates indicate a decreased to visit stores variables, combined with lagged endogenous variables, propensity grocery in the Christmas week 52: and in the first captures a wide variety of dynamic effects (Hanssens, Par (Week -.107) sons, and Schultz 2001, pp. 295-96); at the same time, this week of the year (Week 1: -.458), possibly because stores limit their hours stores are closed on specification is still parsimonious and tractable.5 In both the opening (grocery December 25 and 26 and on and consumers store visit and the spending equations, we also testwhether January 1), pre fer to at home with and friends. On the the price war affects consumers' sensitivity toweekly prices stay family Easter, store visit because con and price image, in both the short and the long run. To that propensity goes up (.073), plausibly end, we use the interactions between these variables and the sumers want to shop for holiday meals, and the longer hours to to do so. pulse and step price war variables: Pulse_PWRound x opening (relative Christmas) enable them We find that consumers InPrice, Pulse_PWRound x Pricelmage, PWRound x during promotion-intensive weeks, more often to stores In in InPrice, and PWRound x Pricelmage. go (Promweek: .24). addition, these their store visit decision ismore sensitive to Finally, consumers may become more price and price weeks, x InPrice: Both effects image sensitive not only in the course of the price war but weekly prices (Promweek -.350). make intuitive sense. also in other periods of intensified price promotions in on the of the war we which supermarkets tend to engage. To identify these peri Focusing impact price variables, note several also Consistent with ods, we define a new dummy, Promweek, which is 1 in pro findings (see Figure 2). the overall store visit increases motion intensive weeks (average price index across stores Hj, propensity temporarily because of the the coefficient for is 2.5% or more below the yearly average) and 0 otherwise. price war; Pulse_ PWRound is positive (.020). However, in line with expecta tions, this traffic increase does not persist. In the long run, the price war even reduces visits for the average store; the or 5It is unlikely that retailers set basket prices decide on the number of coefficient of PWRound is This result items to reduce in as a function of same-week levels of negative (-.011). price spending must be the that the war individual households, especially in a competitor-centered price war set interpreted against finding price makes the store more to ting. This justifies our choice of treating weekly price and price war visit decision sensitive weekly variables as exogenous. prices and price image, consistent with Heil and Helsen's

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Table 3 OVERVIEW OF INDEPENDENT VARIABLES INTHE STORE VISIT AND SPENDING MODELS

Variable Ope rationalization Variable Operationalization

Store Benefits Seasonalities Pricelmagehit Price image of store i for household h inweek t Weeklt,Week51t, Dummy variables forWeek 1,Week 51, Week 52, = measured on a ten-point scale (1 "worst," and Week52t, Easter and Easter, respectively. = 10 "best").3 Price War Variables

ProduceQualityhit Produce-quality image of store i for household h in PWRoundt Cumulative price war round variable for permanent on = week tmeasured a ten-point scale (1 "worst," effects: 0 before start of price war and equal to the = and 10 "best").a This is an important indicator of cumulative number of items reduced in price up to perceived chain quality. time t (see Table 1); scaled by dividing by 1000.

lnStoreSurfacehit In floor surface of closest store of chain i to Pulse_PWRoundt Pulse price war round variable for temporary household h inweek t.aThis variable is an effects: 0 before start of price war and equal to the important indicator of assortment size. number of items reduced in price at time t (see Table 1); scaled by dividing by 1000.

Featurehit Feature activity of store i inweek t for household h: of store i's feature activities in weighted average PWRoundt x Interaction between cumulative price war round c inweek twith household h's category category lnPricehit variable and In price. shares as weightsa'b (only in store visit model). war PWRoundt x Interaction between cumulative price round variable Displayhit Display activity of store i inweek t for household Pricelmagehit and price image. h: weighted average of store i's display activities in c inweek twith household h's war category category Pulse_PWRoundt x Interaction between pulse price round shares as in model). weightsa'b'c (only spending lnPricehit variable and Inweekly price.

war LagVisit/hit Indicator for store visit (store i) by household h in Pulse_PWRoundt x Interaction between pulse price round t- /= 4. week /,where 1, 2, 3, Pricelmagehit variable and price image.

- LaglnExpend/hit In spending for household h in store i inweek t /, Promotion Week Variables where/= 1,2,3,4. Promweekt Dummy for price promotion intensive week: 1 if average price across chains is 2.5% or more below Store Costs average and 0 if otherwise.

lnDistancehit In distance (km) between household h and store i inweek t.a Promweekt x Interaction between promotion week and Inweekly lnPricehit price. lnPricehit Inweekly price of store i for household h inweek t: a of store i's in weighted average price category Promweekt x Interaction between promotion week and price c inweek t with household h's (p?it), category Pricelmage image. shares as weights.b'd'e

aObtained from themeasurement moment that is closest toweek t. bThis variable ismean-centered for each household-store combination to use longitudinal information only to assess its effect. cThis variable is the product of the percentage of stores carrying the promotion times the percentage of products that are promoted. It varies from 0 (no activity) to 10,000 (100% of the products in 100% of the stores are promoted). = dA benefit of mean-centering described in note "b" is that InPrice is only weakly correlated with Pricelmage: p -.015. across with different units into a store are eTo allow for meaningful aggregation categories (e.g., ounces, liters) weekly price, category prices (p?h) expressed as an index by dividing them by the across-store average unit price for the category in the initialization period.

(2001) prediction. Specifically, we find support forH3a in Moreover, it increases with InStoreSurface (.098), consis the short run (but not in the long run); the sensitivity of tentwith the notion that larger assortments allow for the store visits to weekly prices increases temporarily at each fulfillment of more consumer needs, and with lagged new price war round (Pulse_PwRound x InPrice: -.058). spending (.002, .009, .010, and .009), consistent with posi For H4a, we find support only in the long run (PWRound x tive state dependence. On the cost side, a longer distance to Pricelmage: .005), implying that price image becomes a the store leads to less spending (-.116). This may be true more important criterion for store visit as the cumulative either because transportation from the store to home by foot number of items reduced in price increases. or bike (which is common in the Netherlands) becomes increasingly difficultwhen there are more groceries to carry Spending or because consumers visit these far-away stores for fill-in The estimates for the In spending equation appear in the tripson theirway home fromwork. The elasticity of spend right-hand part of Table 4. All the benefit variables have the ing toweekly prices is positive (.282) but lower than 1. This expected positive effects. Spending increases with Price implies that before the price war, the elasticity of quantity Image (.008), ProduceQuality (.010) and Display (.003). to price was negative but inelastic. As for seasonalities, the

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Table 4 POSTERIOR DISTRIBUTIONS OF RESPONSE PARAMETERS

Model for Store Visit Model for In Spending

Standard Standard Deviation Deviation Across Across Percentiles of^ Households Percentiles of (0 Households

2.5 50 97.5 (Based on il) 2.5 50 97.5 (Based on Qj

Pricelmage .000 .009* .018 .078 .002 .008* .014 .069 ProduceQuality .003 .011* .017 .033 .006 .010* .014 .040 InStoreSurface .139 .155* .171 .108 .089 .098* .105 .102 Feature .001 .002* .003 .006

Display .002 .003* .004 .006 Lag Visit 1 .220 .235* .250 .269 LagVisit2 .287 .298* .312 .168 LagVisit3 .273 .283* .292 .148 LagVisit4 .251 .264* .273 .139 LaglnExpendl .001 .002* .003 .018 LaglnExpend2 .008 .009* .010 .013 LaglnExpend3 .009 .010* .011 .012 LaglnExpend4 .008 .009* .010 .010 InDistance -.526 -.502* -.476 .321 -.142 -.116* -.100 .175 InPrice -.107 -.097* -.082 .075 .269 .282* .323 .092 Weekl -.478 -.458* -.443 .133 -.227 -.217* -.205 .070 Week 51 .019 .040* .060 .065 .115 .127* .140 .046 Week 52 -.122 -.107* -.089 .065 .014 .025* .037 .060 Easter .062 .073* .083 .060 .119 .128* .134 .049 PWRound -.013 -.011* -.009 .034 -.005 -.004* -.002 .029 Pulse_PWRound .008 .020* .030 .030 .000 .008* .014 .021 PWRound x InPrice -.005 .009 .026 .045 -.035 -.026* -.018 .047

PWRound x Pricelmage .003 .005* .007 .029 .002 .004* .005 .019 Pulse_PWRound x InPrice -.086 -.058* -.021 .108 -.099 -.084* -.062 .036 PulseJPWRound x Pricelmage -.009 .004 .016 .024 -.008 -.002 .005 .019 Promweek .013 .024* .032 .029 -.013 -.007* -.002 .026 Promweek x InPrice -.364 -.350* -.338 .055 -.034 -.026* -.016 .067

Promweek x Pricelmage -.001 .009 .016 .040 -.004 .002 .009 .023

*The 95% posterior interval excludes 0. Notes: To preserve space, we do not report store-specific moderators (intercepts) of the random household effects.

effects of the pre-Christmas week (Week 51: .127), the makes spending more sensitive toweekly prices both in the Christmas week (Week 52: .025), and the Easter week short run (Pulse_PwRound x InPrice: -.084) and in the long (.128) on In spending are positive, whereas the effect of the run (PWRound x InPrice: -.026). Similar to the store visit year's firstweek on spending is negative (Week 1: -.217), results, for H4b, we find support only in the long run possibly because of consumers' use of excessive stocks (PWRound x Pricelmage: .004), implying that price image from the preceding holiday week or their economizing or becomes a more important criterion for spending as the dieting. During promotion-intensive weeks, the reduced cumulative number of items reduced in price increases. prices enable consumers to spend less (Promweek: -.07), Decomposing theNet Impact of Price War on Store Visits and their store spending decisions are more sensitive to and Spending weekly prices (Promweek x InPrice: -.026); these effects make intuitive sense. The price war affects the models for store visit and Again, the price war variables reveal some notable spending in multiple ways. First, the price war has an results (see also Figure 2). Consistent with H2, the price impact on independent variables that capture price aspects war causes decreases in In spending in the long run (e.g., weekly price, price image). Second, there is a direct (PWRound: -.004). However, the coefficient of Pulse_ effect of the price war on intercepts and response coeffi PWRound indicates that after the startof the price war, con cients for the store visit and spending models. The intercept sumers initially spend more per shopping trip (.008). This effect is captured by the cumulative price war variable, short-term phenomenon is consistent with a temporary PWRound, whereas themoderating effect of response coef income or windfall effect; that is, consumers initially per ficients ismanifested in the terms PWRound x InPrice and ceive the announced price reductions as a gain that triggers PWRound x Pricelmage. Because we want to focus on we them to buy more, but then they adjust spending downward long-term changes, exclude the temporary change cap again. Consistent with H3b, we find that the price war tured by the pulse variable Pulse_PWRound.

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To decompose the effect of the price war on store visit results for each of the six chains. For Albert Heijn, average and spending, we proceed as follows: Because we calculate spending decreases by 1.09, which is a reduction of a ceteris paribus effect, we vary only the price-related 10.3%. However, because the six chains together also lose variables (price image, basket price, and the PWRound 10.3%, Albert Heijn's market share is preserved (consistent variables), keeping the other variables (e.g., distance to with Table 2 and Figure 3). Albert Heijn's spending loss is store, store surface, feature, display) constant. This avoids primarily due to a strong decrease in current customers' confounding these variables and the price war variables. conditional spending (-.72), which is largely due to the Specifically, we consider the quarter before the price war effect of the price war rounds on the intercept (-.61). On started the pre-price war period (2003, Weeks 30-42). The the positive side, Albert Heijn, as the price war pioneer, vectors vhi0 (the expenditure equation) and xhi0 (the store enjoys an improvement in overall price image (see Figure visit equation) include the price and price image values in 5), which somewhat enhances conditional spending (+.01). war to store the pre-price period for household h and store i. They However, consumers' increased sensitivity price also include other independent variables, such as distance image, combined with the notion thatAlbert Heijn's rela to a store,which are kept at theirmeans across the pre- and tive price image in themarket remains unfavorable, more post-price war periods to isolate the price war effect. The than offsets this effect (-.16).8 Albert Heijn also experi ences a net corresponding response coefficients are (D^ (the expendi decrease in store patronage (-.38), caused pri ture equation) and ?h0 (the store visit equation). For the marily by an interceptdriven down by the price war rounds post-price war period, we take the last quarter of the data (-.41). (fourth quarter of 2005), and the variables and parameters Ironically, the two hard discounters, Aldi and Lidl, are vhi and xhil (again, all non-price war variables are kept remain largely unaffected. Although the price war some means across war at their pre- and post-price periods), co^, what reduces the intercept part of store visit probability and ?hl.6 The price war-induced change in a household's (-.23 and -.14 forAldi and Lidl, respectively), consumers' = - expenditure at a store, AE(Rhi) E(Rhil) E(Rhi0), can be increased sensitivity to their still-favorable price image decomposed into five (a-c) components (see theWeb (Figure 5) enhances store visits (+.25 and +.11, respec Appendix, Part C, at http://www.marketingpower.com/ tively). The other three chains (C1000, Edah, and Super de jmroct08): Boer) all experience net losses in average spending (-.85, -.32, and -.56, respectively). Table 5 shows that the (6) AE(Rhi)= Pt(z*hi0=l)AE(y*hilAvhi,(oh0) increased sensitivity of spending and store visits toClOOO's (a) Expenditure change due to changed independentvariables favorable price image (+.10 and +.09, respectively) is not to for losses for + = enough compensate major intercept (-.51 Pr(z*hi0 l)AE(y^lAoh,vhi,) both store visits and spending). Edah faces an array of due to coefficients (b) Expenditure change changed problems: Both spending (-.16) and visits (-.16) are down, + = in each case driven by price war-induced intercept losses Pr(z*hi0 l)o(n2) and an increased sensitivity to an unfavorable price image. (c) Expenditure approximationerror Finally, Super de Boer's loss in spending is driven by inter reductions and reduced of the cus + APr(z*hi=l|Axhi,Ch0)E(y;?1) cept spending existing tomer base due the chain's increased to its (d) Incidence change due to change independentvariables vulnerability weak price image (-.11). + . Aft(z^=l|xh|1.ACh)E(y^1) on (e) Incidence change due to changed coefficients The Impact of thePrice War Profitability and Share Values Because parts a and b capture expenditure changes multi Our core analysis pertains to changes in purchase behav plied by pre-price war store visit probabilities, these parts ior due to the price war. Itmight be argued that purchase can be interpreted as changes in spending at the existing behavior and the associated revenue implications are medi store visit we as "the propensity (which interpret existing as ators for ultimate performance measures, such profitabil customer base"). Conversely, because parts d and e capture ity and stock market performance. Although detailed and store visit changes multiplied by post-price war spending, reliable figures on national chain-specific margins are lack they represent the effect of the changed store visit propen ing (in particular, forAldi and Lidl), the retailers' annual sity at the new expenditure level. Part c is an approximation reports unveil some important insights. Ahold (2004, p. term that is due to a Taylor series expansion (for details, see 64)?the holding company of Albert Heijn?indicates that the Web Appendix, Part C, at http://www.marketing "Albert Heijn's ongoing price repositioning strategy power.com/jmroct08). We find this term to be negligible in resulted in fierce price competition in theDutch food retail all the subsequent calculations. market. This made itmore difficult tomaintain gross profit We calculate decomposition (Equation 6) at the house margins, and this pressure on gross profit margins is hold level (using the households-specific parameters) and then take the average across households.7 Table 5 shows the

8As price image enters the interaction term after mean centering, it 6We also tested a few alternative post-price war periods and found that reflects the chain's price position compared with themarket average. Thus, the substantive outcomes remain the same. it takes on negative values for stores with a worse-than-average price 7In these calculations, we use all parameters regardless of whether their image. As a result, an increased price image coefficient leads to negative store for such stores. posterior intervals exclude zero. visit and spending effects

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expected to continue in 2005. Albert Heijn was able to image (independent variables in our model) but also by compensate for part of the impact of lower prices by reduc changes in household shopping behavior (model coeffi cost as a ing the of goods, largely result of negotiations cients). As hypothesized, we find that the price war has with vendors as well as increased vendor allowances. The induced consumers to a shop around more, entailing tempo cost at on reduction program Albert Heijn is focused lower rary increase in store visits across the board. Moreover, and distribution Albert ing logistic expenses." Heijn's domi although the price war initially created a windfall effect that nant retailmarket enabled it to much of the position recoup triggered temporarily increased spending, spending levels at the of whose price drop expense manufacturers, profit have shrunk in the long run as consumers have redistributed to some have margins, according industry sources, their purchases across the stores they visit. At the same decreased 80% after three of theDutch war by years price time, the price war has enhanced consumers' sensitivity to fare with a massive effort to (Baltesen 2006a). Together both weekly store prices and chain price image, confirming the of its this to have improve efficiency operations, appears predictions in the literature (Heil and Helsen 2001) and lab downturns inAlbert prevented huge Heijn's profitability. experiment findings (Wathieu, Muthukrshnan, and Bron In contrast, forLaurus owns the chains Edah and (which nenberg 2004). Thus, consistent with theLucas critique, we de Boer), net sales (revenues times Super gross margin) find that the initiator'smajor policy change affects response decline in three successive years (Laurus 2003, sharply parameters (Van Heerde, Dekimpe, and Putsis 2005). 2004, 2005) to -26% in 2002-2003, -14% in 2003-2004, Importantly,we distill these price war-based effects while and -10% in 2004-2005, to the state leading following controlling for price promotion-intensive weeks. Our ment: "As a result of the price war,... hundreds of products decomposition of the spending change reveals differential are sold below cost. Combined with a lower sales volume, consequences for key retail chains depending on (1) their this has had a significant negative impact on our bottom overall (perceived) price position and (2) their ability to line. Given the financial position of Laurus, therewas no improve price image through the price war. other option but to sell Edah" (Laurus 2005, p. 3). Lacking the and market of Albert deep pockets power Heijn/Ahold, Price War, What Is It Good For? the Edah chain is a primary casualty of the price war. For Our answer to the "What is a war C1000, net sales initially keep increasing (+8.1% in 2002 question price good for?" has five theDutch 2003; +3.2% in 2003-2004), but they begin to tumble as aspects. First, supermarket price war has been for the of its that the price war lingers on (-1.6 % in 2004-2005) (Schuitema good price image initiator; Albert succeeded in its 2003-2005). is, Heijn improving price image without harm to its and service We observe similar patterns in the share values (see the doing significant quality other which followed Albert Web Appendix, Part B, at http://www.marketingpower.com/ images.9 However, retailers, move within did not obtain such jmroct08). Structural break analyses of the retail compa Heijn's days, price image This first-mover in a war nies' weekly stock price indexes (own stock price divided gains. advantage price (Busse and Mills by the total market price) on the Amsterdam Stock 2002; Elzinga 1999; Rao, Bergen, and Davis et Exchange reveal that the price war, though not significantly 2000) confirms several business anecdotes (Pauwels al. war altering the share notation of Schuitema (C1000), goes 2004; Simon 1997). If the price initiator specifically along with a downward slope shift forLaurus (Edah, Super wants to improve its price image, the price war appears to de Boer, Konmar) but an upward slope shift forAhold that be successful. almost nullifies the pre-price war downward trend. It Second, the price war has been good for the initiator's appears that the improved price image of Albert Heijn, market share; specifically, the slide in itsmarket share came togetherwith its no-longer-declining market share and pur to a screeching halt (Figure 3). However, our analysis suit of massive efficiency improvement operations, has out shows that this holds because the price war decreased weighed the harmful store visit and spending implications spending at Albert Heijn at the same rate as at themarket of the price war, thus restoring shareholders' faith. average (-10.3%). In that sense, Albert Heijn's "price war victory," as reported in the business press (Baltesen 2006a), DISCUSSION is somewhat bittersweet. Still, investors have rewarded the price war initiatorby neutralizing the downward trend in its Summary stock price, consistent with the halt in its market share This article examines the of a war on impact major price decline and its improved price image among consumers. consumer behavior. We find that the war that purchase price Third, with regard to competitors, the price war has been has been Dutch retailers since Octo raging among grocery bad for the high-service follower (Super de Boer) and the ber 2003 has affected both store visit and probabilities middle-service followers (C1000 and Edah) because they On the basis of a national household that spending. panel were also affected by smaller spending. Moreover, they hand-scan data and measures of store provides perceptual have not enjoyed an improved price image, as the pioneer across a we estimate a mul image nearly four-year period, has. Their lower prices appear simply to subsidize existing tivariate Tobit II model for store visits and The spending. customers. Ironically, however, the price war has been good model allows for household and a full covari heterogeneity for the hard discounters. Although consumer spending has ance structure (across store visits and spending) for the errors, for the random store intercepts, and for response coefficients. Decomposing the price war-induced changes in we show that these are spending patterns, changes 9We fail to reject the null hypothesis of no change in these components induced not only by a shift in weekly prices and price after the start of the price war.

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hardly been affected, these discounters' market shares have Third, discounters may actually benefit from a price war. revenues con can increased because their competitors' have They advertise their low price levels, forwhich there tracted. The hard discounters have benefited from an may be increased consumer attention and sensitivity, lead a increase in store visit propensity triggered by consumers' ing tomore store visits and expenditures. If there remains enhanced sensitivity to price image. substantial price gap with themiddle- and high-end players was case Fourth, from a broader perspective, the upside of the (as the forAldi and Lidl in theDutch price war; see Table seem to have reason to price war for consumers is lower prices. Downsides are less 2), low-end players little a war. obvious, but the lower manufacturer and retailer margins reduce prices furtherduring price should not be too if a harm the industry as a whole. One likely consequence is a Fourth, managers encouraged price war more visitors to their stores or to reduction in resources for research and development, initially brings buyers their brands. A move customers which, in the long run, harms product quality and thus price may reengage (Chen andMcMillan to in the short but could hurt consumers. Another downside is that the price 1992) compare prices run, in the run, are to return to their usual war may reduce the focus on importantmarketing variables, long they expected such as service and assortment. shopping frequencies. In this study,we find that storeswith an unfavorable tend to lose store visitors in the Fifth, firmsmay go bankrupt, which reduces consumer price image run. choice. For example, theDutch price war forced the Edah long Fifth, to a war escalation, it be a supermarket chain to go out of business. prevent price may good idea first to analyze consumer responses when one market External Validity player begins to cut prices. If purchase behavior changes or itmay be better to focus on Although our findings are based on a unique data set and only modestly temporarily, instruments other than to win back a new methodology, they are consistent with trends reported marketing-mix price customers. If the are there is little resort by external sources. The increased consumer sensitivity to changes strong, other than to reductions as well, weekly prices is reflected in the nationwide Consumer respond by offering price possibly spiraling down to a war. Trends survey by EFMI (2002, 2004, 2005), which reports price Sixth, channel power is a major asset when a retailer is that low prices became themajor consumer decision crite involved in a price war. In theDutch price war, Albert Heijn rion in store choice (46% of respondents named itwithin initiated the price war when it still had market leadership their top three criteria, up from 35%). This high sensitivity and was widely regarded as offering superior service and to price/promotions appears to be maintained in 2005. quality. As such, this situation is consistent with the power Because our findings are related to theweekly household transition paradigm (Organski 1968); that is, themarket level for a representative sample, theymust be projected to leader launches a preemptive strikewhile it is still powerful aggregate levels (whole population for a full year) to grasp (i.e., before most shoppers have lost interest). Because the their relevance to the retailer's overall performance. For chain had and has the highest market share, it represents a example, the total per-household spending change of major outlet formany manufacturers, which they cannot -1.09 forAlbert Heijn (Table 5) translates into an annual afford to lose. As result,Albert could divert a 369 million revenue loss across the 6.5 million Dutch Heijn major part of its loss inmargin (due to reduced consumer prices) households (www.cbs.nl [accessed November 7, 2007]). to its suppliers. Edah, a price war casualty, probably suf Thus, themagnitude of the price war effects ismanageri fered from both low channel power and a lack of cost lead ally substantial compared with the 5.6 billion in revenue ership (Rao, Bergen, and Davis 2000). ofAlbert Heijn in 2003 (Ahold 2003, p. 61). Our estimated Seventh, it seems particularly unwise to provoke com total loss among the six largestDutch retailers amounts to petitors with a competitor-focused goal, such as becoming 972 million, which is close to industry estimates that put less expensive than themarket average, as Albert Heijn did the loss at 900 million (Van Aalst et al. 2005). at the startof the price war. Instead, it seems better to focus on the for consumers. Managerial Implications savings Finally, retailers should consider themarket characteris our are related to the conse Although findings specific tics thatmay moderate the consequences of the supermar quences of this war, we can on particular price speculate ket price war we found in this study.The negative market recommendations for retail and brand who either managers level consequences of the price war may be related to the intend to start a war or are price perhaps unintentionally grocery category, whose primary demand appeared to be involved in one. First, if the situation is such competitive price inelastic. Overall spending in the category may that a war is based on the price likely anyway (e.g., early increase when the price war brings the product within reach thatHeil and Helsen it is warning signals [2001] identify), of large new consumer segments (e.g., when air travel, then desirable to make the first strike because it a may bring computers, and finally printers became inexpensive enough first-mover advantage in price image improvement. This formost Westerners). price war benefit is especially relevant formarket players with a price image problem, as was the case for theDutch Policy Implications war initiator. price The scope of price war consequences differentiates them we caution market Second, high-end players about the from periods of intense price promotions (Heil and Helsen risk of as a using price competitive weapon because itmay 2001). The Dutch supermarket price war has incited a increase price (image) sensitivity.This could backfire if the nationwide discussion on setting minimum prices and as a high-end player's price remains relatively high result of competitive regulations, right up to theDutch parliament competitive reactions. (Baarsma and De Nooij 2005). Such a public debate is

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