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Journal of Retailing 82 (4, 2006) 295–307

Effects of prior brand usage and promotion onconsumer promotional response

Eileen Bridges a,∗, Richard A. Briesch b, Chi Kin (Bennett) Yim c

a Kent State University, Kent, OH 44242, United Statesb College of Business Administration, Southern Methodist University, United States

c School of Business, The University of Hong Kong, Hong Kong

bstract

We examine how prior purchases influence consumer response to promotional activity in brand choice decisions. To improve understandingf the nature of this influence, we separate previous purchases into those on promotion and those not on promotion, and consider their differentialmpact on subsequent brand choices. Impact may be observed at the brand level, category level, or both and we suggest circumstances in whichach might occur. Across four product categories, consumer sensitivity to price, price promotions, and feature advertisements increases for all

rands in the product category following a promotional purchase but also decreases for the most recently purchased brand. The magnitudes ofhe results indicate that prior promotional purchases influence choice more than prior brand usage does. We offer managerial recommendationsegarding promotional activities, for both retailers and manufacturers.

2006 New York University. Published by Elsevier Inc. All rights reserved.

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eywords: Brand usage; Consumer response; Promotion; Price

Introduction

Neslin (2002) has called for research on the related influ-nces of prior purchases and changes in promotional sen-itivities on consumer behavior; we respond to this call byxtending the literature beyond how promotions affect con-umer tendencies to exhibit loyalty by repurchasing the samerand, to examining how various promotions affect consumeresponse to subsequent marketing mix activities. Thus, weddress the question of whether obtaining brand usage isorthwhile when marketers achieve it through promotional

ctivities.In addition to the theoretical ramifications of running pro-

otions, we consider the managerial impact for both retail-rs and brand managers. If promotional activities result in

ncreased consumer price sensitivity for all products in theategory, the market share of lower priced store or regional

∗ Corresponding author. Tel.: +1 330 672 1260; fax: +1 330 672 5006.E-mail addresses: ebridges@kent.edu (E. Bridges),

briesch@Mail.Cox.SMU.edu (R.A. Briesch),im@business.hku.hk (C.K. Yim).

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022-4359/$ – see front matter © 2006 New York University. Published by Elsevieoi:10.1016/j.jretai.2006.08.003

rands could increase following a promotion by a higherriced national brand.1 From a retailer’s point of view, thisutcome may be beneficial, particularly if margins are highern the lower priced brand. However, reductions in the pro-oting national brand’s share would be an undesirable result

rom the brand manager’s point of view.An existing research stream considers the costs and ben-

fits of promotional activities directed at consumers, butmpirical results are mixed (Blattberg et al. 1995). Severaltudies reveal that price promotions may have adverse long-erm effects on consumers’ brand choice behavior by makinghem more sensitive to price, promotions, or both (Bouldingt al. 1994; Dodson et al. 1978; Jedidi et al. 1999; Mela et al.997; Papatla and Krishnamurthi 1996; Strang 1975). Fadernd McAlister (1990) observe that some consumers actively

eek out promotions for preferred brands. These effects areot always undesirable: Shankar and Krisnamurthi (1996)

1 Note that this argument does not necessarily imply that increased priceensitivity will be observed at the aggregate level, as only those consumersho purchase on promotion are expected to exhibit increased sensitivityhen they next purchase.

r Inc. All rights reserved.

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96 E. Bridges et al. / Journal o

nd that feature advertisements and in-store displays canifferentiate a brand, making it more salient and leading toeduced price elasticity. Kopalle et al. (1999) find that priceromotions enhance consumer price sensitivity, but note that,nder some conditions, such promotions can be profitable tooth retailers and manufacturers. Neslin (2002, p.17) con-ludes that non-price promotions also influence price sensi-ivity, stating “there is fairly strong evidence that promotionsffect price or promotion sensitivity.” However, other stud-es fail to find any long-term impact of promotions (Johnson984; Neslin and Shoemaker 1989; Totten and Block 1987).

Most early studies assumed any impact of promotionalctivities to be homogeneous (e.g., Guadagni and Little 1983)nd therefore failed to identify any differential effects. As theesearch stream has developed, cross-sectional variations inonsumer response to different types of promotions have beenbserved across product categories, markets, and householdsBucklin and Gupta 1992; Fader and Lodish 1990; Grover andrinivasan 1992; Inman and McAlister 1993; Kamakura andussell 1989; Narasimhan et al. 1996). In addition, researchn “purchase event feedback” has suggested that consumers’rior purchases might influence their current purchase behav-or and response to promotion. For example, Smith andwinyard (1982, 1983) find that a household’s response toromotional activities can vary across purchase occasionsnd Heilman et al. (2000) further observe that some aspectsf purchase history can influence response to promotionalffers.

Although the brand most recently purchased by a con-umer, or state dependence, has been considered in previ-us research (Chintagunta 1999; Erdem and Sun 2001), fewuthors consider whether the most recent purchase was onromotion. A key exception comes from Gedenk and Neslin1999), who find that the promotional status of the previousurchase can differentially influence brand choice, throughurchase event feedback. In addition, they indicate that thepecific type of promotion (price or non-price) can influencerand preference. Their results suggest that purchase eventeedback following a promotional purchase generally is moreegative than that following a non-promotional purchase.urther, feedback after price promotions is more negative

han that after non-price promotions. Thus, they address theundamental question of how effective different types of pro-otions are at retaining consumers for subsequent brand

urchases.We build on Gedenk and Neslin’s (1999) results by sug-

esting that the promotional status of the most recent pur-hase not only leads to differences in brand loyalty but alsonfluences subsequent response to promotional activities andn turn, switching behavior in the product category. Therefore,e extend their model to address additional issues, such ashy a lower priced store brand’s market share might increase

fter a promotion by a national brand. We apply a modelhat separates household-level effects (cross-sectional het-rogeneity) from purchase-level effects (state dependence orongitudinal heterogeneity) to test empirically our hypotheses

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ling 82 (4, 2006) 295–307

egarding the impact of households’ brand purchase historiesn their response to promotional activities.

Two streams of theoretical and empirical research sup-ort moderating effects of prior brand purchases on consumeresponse to promotions. Usage dominance posits that priorurchases of a brand diminish response to promotions thatollow, because personal usage dominates external informa-ion during purchase decisions. The second stream, which

ight be termed “promotion enhancement,” suggests thatonsumers are more responsive to marketing mix activitiesn a product category after they have made promotional pur-hases in that category, because promotional activities areore salient when consumers are more familiar with the

roduct category. Our research tests for specific effects thatlarify how – whether through usage dominance or promotionnhancement – consumers’ prior purchases influence theiresponse to promotional activities both at the brand and theategory levels.

In the next section, we review some theoretical and empir-cal literature that supports usage dominance and promotionnhancement and describe recent advances that address howrior purchases can influence consumer sensitivity to pro-otions. We then detail our modeling and empirical testing,

nd report our results. Finally, we conclude with managerialmplications, limitations, and directions for further research.

Brand usage and response to promotional activities

Prior theoretical and empirical studies suggest that brandsage history can affect consumer response to temporaryrice reductions and other promotional activities. However,s we noted previously, two streams of research posit oppos-ng influences, which may occur at the brand level, the cat-gory level, or both. Our goal is to enhance understandingf the effects of promotional and non-promotional purchas-ng on consumer response to subsequent price and non-priceromotional activities.

sage dominance

Literature in support of the usage dominance conceptuggests that, after purchase and use of a brand, consumersecome less responsive to promotional activities for thatrand because their direct experience dominates externalnformation (e.g., marketing mix activities) in their purchaseecisions. For example, some researchers find that, if aonsumer has access to multiple sources of information, apecific input is more likely to influence his or her buying

iagnostic information (Alba et al. 1991; Anderson 1971).his idea, that a decision maker having multiple signals avail-ble will weight the signal with less noise more heavily, islso supported by the information economics literature (e.g.,

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E. Bridges et al. / Journal o

anker and Datar 1989).2 Because a consumer’s own usagexperience is more diagnostic than external informationFazio and Zanna 1978; Fazio et al. 1982; Fazio et al. 1989),onsumers who have purchased a brand before are likely toely on their internal information when making a purchaseecision. This proposal is consistent with observations bymith and Swinyard (1982, 1983) that direct experiences

end to be the primary information sources for formingttitudes that increase consumers’ commitment to buy.

As consumers become more aware of their own likesnd dislikes, as well as the performance of various brandsthrough purchase and use), their choices are more likely toe driven by non-price factors (Heilman et al. 2000). Kopallend Lehmann (1995) agree that, after consumers have experi-nced a brand, they tend to rely on their internal informationore heavily than external cues such as advertising to update

heir impressions of the brand, which influences their brandhoice (Kopalle and Lehmann 2006). That is, usage domi-ance suggests that consumers who focus on their personalxperience are less responsive to marketing mix activitiesor the most recently purchased brand and, consequently,ore likely to repurchase the brand after a promotion has

nded.

romotion enhancement

Literature favoring promotion enhancement states that thempact of marketing mix activities increases when the con-umer’s most recent purchase of any brand in the categoryas on promotion. Empirical support for this theory haseen provided by many above-mentioned studies, which indi-ate that promotions reduce subsequent brand loyalty. Pettynd Cacioppo’s (1986) elaboration likelihood model offersheoretical support in suggesting that consumers are more

otivated to process information that has greater personalelevance. Thus, cognitive elaboration is likely to be richer forroduct categories with which consumers have more exten-ive usage histories in different contexts (Cacioppo and Petty985). In turn, promotional activities for such products areore likely to be mentally processed and influence choice.lso, from a search perspective, as a consumer’s experienceith a product category increases, so does his or her ability toistinguish when a brand in the category offers a better deal,hich leads to a greater incentive to buy the brand on pro-otion (Bettman and Park 1980; Johnson and Russo 1984;oorthy et al. 1997). Therefore, due to their increased sensi-

ivity to price and promotional activities, consumers are moreikely to purchase lower priced or promoted brands when they

ake a subsequent purchase.In summary, promotion enhancement suggests that con-

umers are more responsive to price and promotional activity

or all brands in a category after they have made a promotionalurchase of any brand. Thus, while usage dominance suggests

2 We thank an anonymous reviewer for suggesting this connection.

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ling 82 (4, 2006) 295–307 297

hat consumer responsiveness to promotional activities for aecently purchased brand decreases, promotion enhancementimultaneously indicates increases in response to marketingix activities for all brands in the category. We investigate

his conflict further by measuring observed effects.

Model development

The objective of our study is to test empirically for theffects of usage dominance or promotion enhancement, oroth, on consumer response to price and promotional activ-ties. We begin by describing a theoretical model, and theniscuss our model estimation approach.

heoretical model development

Following the discrete choice literature, we define a con-umer’s indirect utility for a brand to be a function of fourarketing mix variables (regular price, deal depth or amount

f temporary price cut, presence of in-store display, and pres-nce of feature advertising), state dependence, time sinceast purchase, and brand-specific intercept terms. We are for-unate to have access to the depth, rather than simply theresence or absence, of any deals, because deal depth isirectly comparable to the price of the item purchased (bothre measured on the same scale). Briesch et al. (2002) find thateal depth provides more accurate choice predictions thanoes deal presence. Further, several studies have observedhat consumers respond differently to deals than to regularrices (Blattberg et al. 1995; Briesch et al. 2002; Van Heerdet al. 2000, 2001). Thus, our model is given by

i,h,t = β0,b,h + β1,s,h + β2,h,tSDb,h,t + β3,h,tSDb,h,tETh,t

+ β4,b,h,tRi,t + β5,b,h,tAi,t + β6,b,h,tDi,t

+ β7,b,h,tFi,t + β8,b,h,tFi,tDi,t + εi,h,t (1)

here Ui,h,t is the utility of item i for household h in period. Note that i (i = 1. . .I) represents a brand-size item, b is anndicator of the brand (b = 1. . .B), and s represents packageize (s = 1. . .S). SDb,h,t is a binary vector set to one if brandwas purchased on the last occasion and ETh,t is the elapsed

ime since household h purchased in the category. The mar-eting mix for item i in period t is described by Ri,t, theegular price, Ai,t, the depth of any promotional price cut,

i,t, a binary vector set to one if there is an in-store display,nd Fi,t, a binary vector set to one if there is a feature adver-isement. For brands that are feature advertised and displayedn the same week, we include an interaction term to controlor any omitted variable bias. The intercepts �0,b,h and �1,s,hfor brand and size, respectively) allow for correlation in con-umer preferences (i.e., all items of the same brand share the

ame brand intercept and all items of the same size sharehe same size intercept). We make the standard assumptionsbout utility and error to obtain a multinomial logit model asur empirical model.

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98 E. Bridges et al. / Journal o

The state dependence term captures the main effect ofsage dominance on brand choice: if positive, consumers areore likely to repurchase the same brand on successive occa-

ions. An interaction term with time since the last purchasellows the impact of usage dominance to decay as the timeetween purchases increases (Chintagunta 1999).

To take into account whether the previous purchase wasromotional, let Ph,t be a binary variable set to one if thelternative that household h selected on the prior occasionas displayed, feature advertised, and/or offered at a promo-

ional price. We hypothesize that, if the household purchasedn promotion, its response to the marketing mix and tendencyo repurchase will be affected. To test this, we use hierarchi-al equations. We modify the variables for state dependencecoefficients β2,h,t and β3,h,t), replacing them with the follow-ng:

k,h,t = γ0,k + γ1,kPh,t + ζk,h,t (2)

here k = 2, 3. Note that ζk,h,t represents a household-specificffect and is the deviation from the mean response in thearket after adjusting for a prior promotional purchase by

ousehold h. We assume that all of the ζk,h,t are normallyistributed (over households and time) with mean zero andtandard deviation σk. Considering the findings of Gedenknd Neslin (1999) and the hypothesized impact of promotionnhancement, we anticipate that γ1,2 will be negative.

Various theories suggest that consumer response to thearketing mix is affected by the brand selected on the prior

ccasion and the promotional status of that brand at the time.hus, we rewrite the coefficients β4,b,h,t through β8,b,h,t using

he following equation:

k,b,h,t = γ0,k + γ1,kPh,t + γ2,kSDb,h,t + ζk,b,h,t (3)

here k = 4, 5, 6, 7, 8. γ1,k represents a category-level effectndicating sensitivity to promotions, γ2,k represents a brand-pecific effect that depends on whether the household is repur-hasing the same brand, and ζk,b,h,t represents a household-,ime-, and brand-specific response. As an example, if γ2,4 isositive, consumers are less price sensitive for the brand they

urchased on the last occasion; if it is negative, they are morerice sensitive for the previously purchased brand. We assumehat ζk,b,h,t has a normal distribution (over time, brands andouseholds) with mean zero and standard deviation σk.

aast

able 1ypothesized directions of effects for promotion enhancement and usage dominanc

Promotion enhancement (sign of γ1,k)

tate dependence Negativeegular price Negative. Consumers are more responsive to price

for all brands in the categoryeal depth Positive. Purchases on promotion increase attention

to price cut promotionseature advertisement Positive/zero. Purchases on promotion may increase

response to non-price promotionsn-store display Positive/zero. Purchases on promotion may increase

response to non-price promotions

ling 82 (4, 2006) 295–307

This functional form is similar to the one used by Jedidit al. (1999) to test for the long-term effects of promo-ions, advertising, and loyalty on consumers’ marketing mixesponse. However, there are several key differences in ourpproaches. First, we use an indicator (Ph,t) to identifyhether the previous brand purchased was on promotion

t the time, whereas Jedidi et al. use the brand’s long-runarketing mix activity, formed as a geometric series of aver-

ge market values. Second, we allow promotions to lead toncreases in marketing mix sensitivities for all brands in theategory; they allow promotions to affect only marketing mixensitivities for the promoted brand. Third, we identify stateependence, whereas they use a loyalty term constructedrom the previous four non-promoted purchases. Althoughoth studies test for the effects of previous promotional activ-ty on current purchases, our study focuses on the short-termmpact of promotions on response to price and promotionalctivities and ties promotional sensitivities to actual purchas-ng behavior rather than market averages.

Separating the effects of a previous purchase on promotionrom those of a previous purchase not on promotion enabless to test for the presence of usage dominance and promotionnhancement effects. As we have described these theories,hey suggest hypothesized directions of effects, given by theigns of the hierarchical coefficients in Table 1.

The directions of the effects described in Table 1 areonsistent with our theoretical discussion of usage domi-ance and promotion enhancement. Specifically, promotionnhancement implies a reduced likelihood to buy the previ-usly purchased brand, simultaneous with an increase in thempact of promotional activities for all brands in the cate-ory. Usage dominance suggests a positive main effect oftate dependence but does not imply any particular inter-ctions with the marketing mix; thus, we do not includeoefficient γ2,k in this model. (However, for the previouslyurchased brand, usage dominance implies that consumersre less sensitive to both price and promotional activities.)oth theoretical effects may act simultaneously and their rel-tive magnitudes then would determine whether consumersre more or less responsive to marketing mix activities for

vailable brands. Because promotion enhancement operatest the category level, we should find that consumers are moreensitive to price and promotional activities for all brands inhe category following a purchase on promotion.

e

Usage dominance (sign of γ2,k)

No coefficient �2,k

Positive. Consumers are less price sensitive for brands they purchaseoff promotionNegative. Consumers are less sensitive to promotions for previouslypurchased brandsNegative. Consumers are less sensitive to promotions for previouslypurchased brandsNegative. Consumers are less sensitive to promotions for previouslypurchased brands

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odel estimation

In our estimation, we account for consumer heterogene-ty by assuming that response coefficients have multivariateormal distributions; thus, if Z is the vector of heterogeneityarameters, then Z MVN(0, Σ). We then estimate the param-ters of this multivariate normal distribution of coefficients.e would prefer to use maximum likelihood estimation, but

ecause of the many dimensions in the parameter space, com-utation using numerical integration methods is not practical.herefore, we employ a quasi-Monte Carlo method to select aample of points in the parameter space and compute the inte-rand at those points. We then apply this simulated maximumikelihood estimation (SMLE) to construct each household’sikelihood function, where each household’s response param-ters are drawn from a multivariate normal distribution withn estimated mean and covariance matrix (Hajivassilios andudd 1994). We provide additional details of the estimationethod in Appendix A.

escription of data

We used the ERIM data from four different product cat-gories (peanut butter, tuna, stick margarine and tissue) toest our hypotheses, as given by the expected directions offfects in Table 1. For each product category, we selectedhe best selling brand sizes to ensure that enough of the cate-

ory activity would be captured by the model. After selectinghe brand sizes, we screened households for inclusion on theasis of two criteria: they must have (1) made at least threeurchases and (2) purchased only the selected brands. The

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able 2escriptive statistics for ERIM category data

Tuna

umber of households 3,081umber of brand sizes 4verage purchases/household 21.3Purchases on promotion 68.8

verage interpurchase time 40.9oyalty (%) 64.7verage % time featured 15.3verage % time displayed 6.4ize metric Oz

rand sizes and shares

Starkist-6.552.7%Chicken of Sea-6.530.5%Control-6.59.5%3 Diamond-6.53.2%

otal share 95.9%

ote: We calculate average percentage of time featured and displayed by weighting

ling 82 (4, 2006) 295–307 299

rst condition is necessary to identify lagged variables (stateependence and whether the previous purchase was on pro-otion for each household) and heterogeneity, and the second

nsures that we model the correct effects. For each productategory, we randomly assigned each observation to eitherhe estimation or the holdout sample, with a probability of0% either way.

We provide descriptive statistics for the observationsssigned to the estimation sample in Table 2, which showshat the total shares of selected brand sizes account for ateast 79.9% of the choices within each product category. Ouroyalty measure indicates that most households purchase theame brand most of the time. In addition, the proportion ofurchases made on promotion varies among the different cat-gories, from 32% in peanut butter to almost 69% in tuna.his variation may be related to the frequency of promotion

hat occurs in each category.As an example of how purchase event feedback may

epend on the marketing mix at the time of the priorurchase, consider observed changes in price sensitivity.romotion enhancement would suggest that, following aromotion by one brand, consumer sensitivities to pricend promotional activities increase for all brands in theategory. We can determine whether consumer priceensitivity meets this expectation by examining consumerwitching behavior in the tuna category. Following are twoabulations that show Period t and Period t + 1 purchases of

ational and local brands. Specifically, in these switchingatrices, “national” brands include Starkist and Chicken

f the Sea and “local” brands include 3 Diamond and theontrol.

Peanut butter Stick margarine Tissue

2,115 1,780 1,3706 4 516.4 24.8 27.232.1 41.4 51.756.1 37.0 29.069.1 72.6 66.33.2 12.4 5.12.4 5.6 4.7Oz Oz Rolls

Peter Pan-18 Parkay-16 Scott-429.8% 47.1% 34.6%Control-18 Control-16 Northern-425.5% 17.3% 28.1%Jif-18 B-B-16 Charmin-412.9% 12.7% 11.4%Skippy-18 Fleischman-16 Northern-67.7% 10.6% 3.2%Peter Pan-28 Scott-67.0% 2.6%Jif-286.4%

89.3% 87.7% 79.9%

the items’ frequency percentages by their relative market shares.

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We begin by considering situations in which no promotions offered during Period t or Period t + 1. We assume that con-umers who buy a national brand at the regular price preferhat brand, and select only those consumers who purchasednational brand during Period t, which enables us to elimi-ate those consumers who tend to switch to national brandsnly during price promotions. Thus, we include all consumersho buy a national brand in Period t, and examine their pur-

hases in Period t + 1, to determine the baseline transitionate for consumers switching to a local brand. The follow-ng tabulation shows the subsequent purchases by consumersho purchased a national brand in Period t, considering allbservations for which there are no promotions in eithereriod.

eriod t + 1 purchases

ational Local Total

Number2,975 266 3,241

Percentage92 8

hus, 8% of the population switches away from nationalrands and to local brands when there are no promotions;his can be considered the baseline transition probability.

To examine price sensitivity, we compare this baselineransition rate to the switching behavior that occurs follow-ng a national brand promotion in Period t + 1. Thus, we retainhose consumers who purchased a national brand for two con-ecutive periods, when there was no promotion in Period t andnational brand promotion in Period t + 1, then examine theirurchases in Period t + 2. These consumers must be at leasts loyal as the consumers in the baseline situation becausehey have not only purchased a national brand when it wasot on promotion but also followed up with another nationalrand purchase in Period t + 1. We assume that when they buyhe national brand on promotion in Period t + 1, their loyaltyo the brand does not diminish.3 Therefore, if the rate ofwitching to a local brand increases among these consumershen there is no promotion in Period t + 2, the change is due

o increased price sensitivity caused by the promotion. Thewitching matrix shows the buying behavior for the retainedonsumers in Period t + 2, when there are no promotions inffect.

eriod t + 2 purchases

ational Local Total

Number

1,157 140 1,297

Percentage89 11

3 This assumption is consistent with the findings of Gedenk and Neslin1999), who observe that consumer loyalty increases when a brand is pur-hased, regardless of whether it is on promotion, although the increase ismaller when the brand is on promotion.

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ling 82 (4, 2006) 295–307

he local brands’ market share among this group of con-umers increased significantly (t = 3.7; p < 0.01), from 8% to1%, after the promotion by a national brand in Period t + 1,s compared to the no-promotion scenario. Therefore, thisxample provides evidence of increased price sensitivity fol-owing a promotion by one of the national brands.

Results

The key questions addressed by this research are as follows. (1)re consumers’ responses to the marketing mix influenced by therand they purchased most recently? (2) Does it matter whether theost recently purchased brand was on promotion at the time of the

urchase? (3) If both usage dominance and promotion enhancementffects occur, what are their relative magnitudes? In this section, wexamine the results of the model estimation and draw conclusionsegarding the relative strengths of each of these hypothesized effects.

odel selection

To provide a comparison of model fit, we estimated a restrictedodel in which we set the hierarchical coefficients (γ1,k and γ2,k)

o zero but still incorporated continuous consumer heterogeneity.his restricted model offers an appropriate benchmark because it

epresents what our model would reduce to if the hypothesizedffects were not observed. Among recent research utilizing simi-ar benchmark models, we note that close variations are employedy Chintagunta and Dube (2005) and by Bell et al. (2005). In Table 3,e provide both the estimation and the holdout sample performance

or both models and all four product categories.In Table 3, we offer four tests that compare the performance of the

estricted and full models: the likelihood ratio (LR), Akaike’s Infor-ation Criterion (AIC), Schwartz’s Information Criterion (SIC),

nd the holdout sample likelihood. For all four product categories,he AIC and holdout sample results recommend the full model; theR test also indicates that the full model is best (p < 0.0001). The LR

est is appropriate in this situation because the models are nested;hat is, the restricted model tests whether or not the key coeffi-ients are zero. The SIC recommends the full model for three of theour product categories, but for tissue, it indicates that the restrictedodel is best. We investigated this puzzling result and found it is an

rtifact that occurs because we included the hierarchical equationor the display × feature ad interaction variable. If we remove theierarchical equation for this interaction (i.e., set γ1,8 and γ2,8 toero), the SIC indicates that the full model is best for all productategories. Although the full model was not initially selected forissue because of the number of insignificant parameters associatedith the hierarchical equation for the interaction term, on the basisf these findings, we conclude that it is appropriate to select the fullodel for all four product categories.

iscussion of estimation results

In Table 4, we provide the coefficient values obtained using theierarchical equations for each product category.4 Our results exhibit

4 Detailed brand, size, and associated heterogeneity parameters are avail-ble from the authors upon request.

E. Bridges et al. / Journal of Retailing 82 (4, 2006) 295–307 301

Table 3Estimation results

Tuna Peanut butter Margarine Tissue

Rstr Full Rstr Full Rstr Full Rstr Full

Estimation sample

-LL 8,173 8,101 9,186 9,131 5,751 5,639 7,220 7,174Families 3,061 2,115 1,780 1,352Choices 18,252 9,254 10,919 9,634Parameters 20 32 22 34 20 32 20 32Likelihood ratio 144 110 226 93Probability (LR) 0.0000 0.0000 0.0000 0.0000AIC 16,386 16,266 18,416 18,330 11,543 11,341 14,481 14,412SIC 16,542 16,516 18,573 18,572 11,689 11,575 14,624 14,641

H-LL 8,138 8,033 9,105 9,058 5,644 5,542 6,833 6,806

2,1048,959

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oldout sample Families 3,081Choices 18,018

otes: Rstr, restricted model; LR, likelihood ratio; AIC, Akaike’s informatiooldface entries indicate best fit model.

ace validity because all four categories have the appropriate signsnd significances for the main effects of the marketing mix variablesprice, deal depth, feature advertising, and display). The coefficientsor state dependence are positive and significant, which indicateshat consumers are inertial in their purchasing behavior, and tend to

epurchase the same brands. Further, the significant negative coef-cient for the interaction between state dependence and time since

ast purchase suggests that this inertial behavior wears off as theime between purchases increases. Thus, our results support a mainffect of usage dominance.

lidsp

able 4arameter estimates for hierarchical equations

Peanut butter

tate dependenceMain (γ0,2) 2.30 (0.20)Promotion (γ1,2) −1.17 (0.24)Heterogeneity distribution (ζ2) 0.11 (0.10)

tate dependence × timeMain (�0,3) −0.15 (0.04)Promotion (�1,3) 0.19 (0.07)Heterogeneity distribution (�3) 0.31 (0.04)

egular price

Main (�0,4) −2.82 (0.17)State dependence (�2,4) −0.12 (0.08)Promotion (�1,4) −0.47 (0.08)Heterogeneity distribution (�4) 1.44 (0.46)

eal depth

Main (�0,5) 5.84 (0.33)State dependence (�2,5) −0.71 (0.49)Promotion (�1,5) 1.60 (0.49)Heterogeneity distribution (�5) 31.20 (8.12)

eature ad

Main (�0,6) 2.32 (0.30)State dependence (�2,6) −0.71 (0.39)Promotion (�1,6) 0.43 (0.38)Heterogeneity distribution (�6) 12.10 (7.57)

isplay

Main (�0,7) 2.13 (0.23)State dependence (�2,7) 0.38 (0.38)Promotion (�1,7) −0.09 (0.36)Heterogeneity distribution (�7) 3.00 (1.48)

eature × display

Main (�0,8) −1.15 (0.99)State dependence (�2,8) 2.72 (2.19)Promotion (�1,8) −0.47 (1.33)Heterogeneity distribution (�8) 1.32 (3.19)

otes: Standard errors are in parentheses. Bolded items are significant (p < 0.05, onp < 0.10, one-tailed test).

1,778 1,37010,703 9,358

on; SIC, Schwartz’s information criterion; and -LL, negative log likelihood.

In all four product categories, our results support the promotionnhancement effect, in that consumers’ tendencies to repurchasehe same brand diminish if the last purchase occurred on promo-ion (although the effect is significant in only three of four cate-ories). This finding is consistent with empirical analyses in the

iterature (e.g., Van Heerde et al. 2003) that indicate that a signif-cant portion of the increased volume for a brand on promotion isue to brand switching. Thus, it is not surprising that many con-umers tend to switch to another brand following a promotionalurchase.

Stick margarine Tissue Tuna

0.56 (0.29) 1.24 (0.23) 1.37 (0.30)−0.90 (0.24) −0.15 (0.22) −0.61 (0.18)10.26 (1.09) 2.94 (0.39) 0.80 (0.29)

−0.11 (0.05) −0.16 (0.05) −0.19 (0.04)0.05 (0.07) −0.09 (0.07) 0.02 (0.06)0.01 (0.01) 0.01 (0.01) 0.03 (0.02)

−5.39 (0.36) −4.06 (0.26) −9.32 (0.49)2.96 (0.33) 0.35 (0.13) 0.49 (0.35)−0.42 (0.25) −0.60 (0.14) −1.89 (0.37)20.94 (3.93) 22.77 (2.41) 14.33 (4.09)

12.56 (0.71) 7.96 (0.38) 12.33 (0.49)−2.26 (0.88) −0.85 (0.42) −1.61 (0.37)1.55 (0.82) 0.84 (0.40) 3.25 (0.51)135.74 (27.32) 58.17 (10.08) 60.80 (11.08)

1.01 (0.18) 0.78 (0.22) 1.73 (0.17)−0.45 (0.24) 0.42 (0.32) −0.36 (0.21)0.85 (0.24) 0.56 (0.28) 0.54 (0.19)0.42 (0.54) 2.87 (2.19) 3.71 (1.26)

1.19 (0.25) 1.31 (0.20) 3.64 (0.27)−0.15 (0.35) 0.02 (0.23) −0.79 (0.28)0.05 (0.34) 0.33 (0.23) 0.43 (0.31)0.01 (0.07) 0.57 (0.96) 3.58 (1.25)

−0.49 (0.41) −1.65 (0.46) −2.13 (0.56)−0.03 (0.63) −0.02 (0.60) 0.21 (0.65)−0.61 (0.62) −0.17 (0.56) −0.75 (0.68)0.10 (0.38) 1.73 (1.63) 0.80 (1.71)

e-tailed test) and bolded, italicized items are significant to a lesser degree

302 E. Bridges et al. / Journal of Retailing 82 (4, 2006) 295–307

Table 5Elasticity decomposition for price and promotional activities

Tuna Peanut butter Margarine Tissue

Regular priceTotal elasticity −3.2 (0.18) −3.2 (0.22) −1.1 (0.10) −3.0 (0.24)UD (%) −3 2 −56 −5PE (%) 13 5 4 7

Deal depthTotal elasticity 1.5 (0.02) 1.1 (0.02) 0.5 (0.02) 1.3 (0.01)UD (%) −6 −6 −10 −5PE (%) 18 12 7 7

Feature adTotal elasticity 0.4 (0.03) 0.8 (0.12) 0.2 (0.06) 0.6 (0.11)UD (%) −6 −13 −66 4PE 9%) 16 3 27 40

DisplayTotal elasticity 1.5 (0.08) 2.7 (0.43) 0.0 (0.01) 0.9 (0.12)UD (%) −13 52 −20 0PE (%) 8 0 −29 17

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otes: To access the actual elasticities obtained in the decomposition, multirice in the margarine category is given by (−56%) × (−1.1) = 0.6. The avero usage dominance is 0.6. Therefore, if usage dominance effects did not oc

Regarding consumer response to regular price following a pro-otional purchase, all four categories exhibit negative coefficients,

upporting promotion enhancement, although the coefficient fortick margarine is only marginally significant (p < 0.06). Only twof the four categories (tissue and stick margarine) provide sup-ort for effects of usage dominance. In terms of response to priceromotions, for deal depth, both usage dominance and promo-ion enhancement are supported. The negative state dependenceariables for all four categories (significant for three of four, andarginally significant for peanut butter) support usage dominance

ffects. Further, consumers become more sensitive to price pro-otions in a product category after purchasing a brand on promo-

ion, as indicated by positive and significant coefficients in all fourategories.

With regard to non-price promotions, we find support for the pro-otion enhancement effect of feature advertisements; coefficients

n all four product categories have the correct sign and three areignificant. We also find some support for the usage dominanceffect through features, in that three of four categories are significantnd have the correct sign. However, neither usage dominance norromotion enhancement effects clearly emerge for displays, whichndicates that consumer response to displays is not affected by aurchase in the previous period (whether promoted or not). This isonsistent with the findings of Seetharaman et al. (1999), who doot find any significant impact of in-store displays.

anagerial implications of results

On the basis of our results, we can make several managerial

ecommendations. However, we first must consider the portion ofhanges in total choice elasticities due to usage dominance and pro-otion enhancement.5 Because we use hierarchical equations, the

lasticity associated with usage dominance is the derivative of the

5 Because feature advertising and displays are indicated by discrete vari-bles, we calculate arc elasticities; these values provide the change in prob-bility when the promotion is present versus absent.

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ercentage by the total elasticity. For example, the UD elasticity for regularrall price elasticity is −1.1, and the average decrease in price elasticity dueaverage price elasticity would be −1.7.

hoice probability w.r.t. γ1,k and the elasticity associated with pro-otion enhancement is the derivative of the choice probability w.r.t.

2,k (k = 4, 5, 6, 7).6 The total choice elasticity is thus the sum of thelasticities w.r.t. γ0,k, γ1,k, and γ2,k. This enables us to calculate theortion of the effects due to each cause as its relative share of theotal elasticity. We provide these estimates in Table 5.

Table 5 reflects the impact of usage dominance (UD%), based onow much the total choice elasticity is modified by the interactionetween state dependence and each of the variables in the model.imilarly, the impact of promotion enhancement (PE%) is given by

he degree to which the total elasticity is altered by the interactionetween a prior promotional purchase and each variable. The resultsn Table 5 suggest that margarine is the least elastic of the four prod-ct categories for all marketing mix elements. Tuna, peanut butter,nd tissue have similar elasticities for regular price, deal depth, andeature ads, as we anticipated, but displays do not engender anyonsistent effects.

Considering regular price elasticity, we find patterns of effectsue to usage dominance and promotion enhancement. Specifically,he impact of usage dominance on price elasticity for margarine is

uch greater than that for the other categories, whereas the magni-ude of the effects of promotion enhancement is greater for the otherategories. Deal depth elasticities suggest that deals have the great-st impact in the tuna category and the least impact for margarine.urther, changes in this type of elasticity due to state dependencere greatest for margarine, but those due to the promotion enhance-ent effect are greater for the other three categories. Thus, regular

rice and price-based promotions have similar state dependence andromotion enhancement effects. Moreover, promotion enhancementnfluences all brands in the product category.

Feature advertisements lead to sales increases in all four productategories and reflect the expected direction of effects in three ofour categories for usage dominance, and in all four categories forromotion enhancement. The results for margarine again indicate

6 The elasticities for feature advertising and display include the interactionffect.

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ery strong effects of state dependence, whereas the strongest impactf promotion enhancement occurs in the tissue category. Thus, byecomposing the contributions to elasticity of usage dominance andromotion enhancement effects, we find that using promotions tobtain purchases may be very effective in the margarine category,ecause state dependence effects consistently outweigh promotionnhancement effects. However, for the other three product cate-ories, marketers must consider what promotions may be expectedo do with regard to other brands in the category, before they makedecision about what promotions to implement.

To summarize, for regular price, price promotions, and featuredvertisements, the effects of promotion enhancement are greaterhan those of usage dominance, in three of four product categories.No such pattern emerges for in-store displays.) We do not have clearesults to explain why margarine behaves differently than the otherhree product categories, and no relationships emerge readily fromhe category data provided in Table 2. We suspect that the differences

ay be related to the shelf life of the product category; margarinetock must be turned over more rapidly than stocks of the other threeategories. Our results also show the importance of including bothsage dominance and promotion enhancement effects in the model.f we were to ignore usage dominance, switching behavior woulde overstated because of the increases in price and promotionalensitivities, and the brand’s ability to retain new consumers fol-owing a promotional trial would be understated. By contrast, weree to neglect promotion enhancement, switching behavior would

e underestimated and the ability of a brand to retain consumersfter a promotion would be overestimated.

Our findings support several promotional strategy implications,or both retailers and manufacturers. Retailers often use national

detd

able 6esults of marketplace simulation

eriod Full model market share

Starkist Chicken of Sea Control 3 Diamond

una product category0 57.2% 24.2% 11.9% 6.7%1 82.3% 10.5% 4.7% 2.5%2 60.5% 19.2% 13.3% 6.9%

ifference1 −4.1% 2.0% 1.3% 0.8%2 −1.8% −2.8% 3.4% 1.2%

ercentage difference1 −5.0% 19.1% 28.3% 30.5%2 −2.9% −14.8% 25.2% 18.1%

eriod Full model market share

Parkay Control B-B Fleischman

argarine product category0 53.7% 19.8% 14.5% 12.0%1 66.8% 14.2% 10.9% 8.1%2 59.2% 19.9% 12.9% 8.0%

ifference1 −9.3% 0.3% 4.4% 4.6%2 −13.4% 2.8% 5.9% 4.7%

ercentage difference1 −13.9% 1.9% 40.1% 57.3%2 −22.6% 14.1% 45.6% 58.6%

ling 82 (4, 2006) 295–307 303

rand promotions to draw consumers to their stores, even thoughuch promotions work against the retailer’s house brands (Dharnd Hoch 1997). However, because promotion enhancement out-eighs usage dominance, national brand promotions could improve

etailers’ profits by sensitizing consumers to price and promotions.ncreased price sensitivity, in particular, might benefit small sharer store brands, or both, for which retailers usually enjoy higherargins, leading to higher profits. As a cautionary note, retailers

hould monitor the competition among and between national andtore brands carefully when developing a product category promo-ion strategy, because increased price competition also may lead toeductions in overall category profits (Raju et al. 1995). Our resultsffer a second explanation as to why retailers successfully can offerrice promotions of national brands to increase their store profits:uch promotions erode consumer loyalty to national brands, whichould make it easier for private labels to gain market share andhus obtain increases in store profits (see also Gedenk and Neslin999).

Unlike retailers, whose primary concern must be the prof-tability of entire product categories, manufacturers are interestedn maintaining or improving only their brands’ market sharesithout sacrificing profitability (Basuroy et al. 2001). Our results

uggest that there may be differences by product category in theelative strengths of usage dominance and promotion enhancementffects. Manufacturers can use our research to develop a clearnderstanding of each brand’s promotional effectiveness, by

ecomposing the contributions of usage dominance and promotionnhancement. This effort would help manufacturers to better useheir resources (e.g., trade incentives) to move retailers towardesirable pricing and promotional strategies for each brand.

Restricted model market share

Starkist Chicken of Sea Control 3 Diamond

57.2% 24.2% 11.9% 6.7%86.4% 8.5% 3.4% 1.8%62.3% 22.1% 10.0% 5.7%

Restricted model market share

Parkay Control B-B Fleischman

53.7% 19.8% 14.5% 12.0%76.1% 13.9% 6.5% 3.5%72.5% 17.1% 7.0% 3.3%

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04 E. Bridges et al. / Journal o

To assess how market share estimates and the resulting manage-ial implications might be different if we had not included the effectsf promotion enhancement and usage dominance in our model,e performed a simulation. Two product categories were selected,argarine because it exhibits strong effects of usage dominance

nd tuna because it shows greater impact from promotion enhance-ent. We simulated a promotion of the leading brand in Period 1

nd tracked the resulting market shares in subsequent periods. Inddition, we calculated the simulated shares by market segment,hich we assigned based on whether a consumer had purchased

he brand in the previous period. We then obtained brand shares byumming across the segments. We also gathered simulated sharesor the restricted model, for comparison. We present the results inable 6.

The results show that the restricted model severely under-redicts market shares for smaller and house brands, compared tohe full model. Thus, if we do not consider state dependence andromotional effects, the market shares of national brands are over-tated, which will affect promotional strategy recommendations.pecifically, the share of the promoting brand might be overstatedecause the effects of state dependence on consumer response haveot been included. (This possibility is reflected in the negative val-es for the leading brand and positive values for other brands, ineriod 1.) Kopalle et al. (1999) report a similar phenomenon, inhich brand managers failed to account for the dynamic effects ofiscounting on sales. We also note that the shares of the smallest tworands are dramatically understated in both Periods 1 and 2. Thus,uring a promotion, if store brands are more profitable to a retailerhan national brands, this profitability may be understated, partic-larly in categories that exhibit stronger promotion enhancementffects.

Conclusion

The primary purpose of this research has been to observeny differences in consumer response to marketing mix activ-ties for brands in a product category after regular or promo-ional purchases in the category. Further, we hope to improvenderstanding of whether recent consumer purchases tendo lead to usage dominance, promotion enhancement, or aombination of these effects. The unique features of our esti-ated model enable us to tease out each of these theoretical

rivers of promotional response, as well as their combinedffects.

Prior usage of a brand and prior promotional activitiesan both play roles in driving consumer promotional sensi-ivities. In general, households that previously purchased aon-promoted brand are more likely to buy it again, whilehose that bought on promotion are less likely to repeat buy.

ore specifically, we observe both usage dominance and pro-otion enhancement effects in all four product categories

hat we investigate. The influence of promotion enhancementutweighs that of usage dominance in three categories (cf.

argarine), so the typical aggregate-level observed behav-

or appears more consistent with promotion enhancement.etween-category differences may explain this result; forxample tuna, peanut butter, and tissue manufacturers may

stak

ling 82 (4, 2006) 295–307

ffer more innovations than margarine makers, or differ-nces in product shelf lives may explain differences in buyingehavior.

We note that the effects of promotion enhancement andsage dominance typically coexist and influence brand choiceointly. Further, when the effects of promotion enhancementre greater than the effects of usage dominance, nationalrand managers should avoid promoting too often, becausehe resulting increased price sensitivity can drive consumerso lower priced local and regional brands. However, retail-rs may prefer frequent promotions of national brands,ecause the increases in consumers’ price sensitivities leado increased sales of local and regional brands. When theffects of usage dominance are greater than those of pro-otion enhancement, promotions are a very effective tool

or building market share, though we qualify this conclu-ion in two respects: (1) the brand manager should perform

profitability analysis prior to offering a promotion and2) extensive promotion could lead to a situation in whichhe profitability for all brands is diminished. As an exam-le, consider the tuna and tissue categories, both of whichave greater promotion enhancement than usage dominanceffects. For both categories, more than 50% of all purchasesccur on promotion, which implies that a category can be overromoted, harming the national brands. Therefore, furtheresearch should determine specifically which factors driveromotion enhancement and usage dominance, and identifyhe “tipping point” at which one effect begins to overtake thether.

Our results help explain the reasons for mixed findingsn previous literature. If a researcher focuses only on theffect of promotions on the promoted brand, the relativeagnitudes of promotion enhancement and usage dominance

etermine the sign and duration of the observed effects. How-ver, researchers and managers might overlook the largermpact of increasing consumer sensitivity to prices and pro-

otions of all brands in the category, when the focus is at therand level. Our results take a broader viewpoint and sug-est a potential negative impact of promotions that enhanceonsumer sensitivity for all brands after a promotion for anyrand in the category.

Several challenging opportunities remain for futureesearch. Although we are confident that our findings areeneralizable because they are theory driven and show gener-lly consistent results across four product categories, furtheresting in other, more varied product categories would beppropriate. The present methodology also could be tested inther choice contexts in which purchase behavior may changever time; for example, the situational need for a shopping tripe.g., quick versus planned trip) may affect consumer buying.he consumer’s reason for buying also could influence his orer response to a promotion; if a consumer is purchasing for

omeone else, whether as a gift giver or a service provider,his position could impact his or her buying behavior as wells the relationship between preferences and response to mar-eting mix variables. As we have noted, additional research

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hat clarifies the conditions under which we might expect pro-otion enhancement to overtake usage dominance, and vice

ersa, would be useful.One limitation of the current study is that we do not

ake into account the possibility of asymmetric effects acrossrands. This may prove to be a fruitful area for futureesearch. Despite this limitation and the ongoing need forore research, we believe our research increases understand-

ng of the factors that drive differences in consumer pricend promotion sensitivities across different circumstances,nd thus provides valuable theoretical knowledge. Further,e believe this topic is managerially relevant and that the

ncreased understanding is an important step toward refiningtrategies for temporary price reductions and other promo-ional activities.

Acknowledgements

We wish to express our appreciation to Editors Dhruvrewal and Michael Levy, an anonymous Associate Edi-

or, three anonymous reviewers, Ed Fox, Roger Kerin, andmna Kirmani for helpful comments and suggestions. We

lso thank the organizations providing data, including Infor-ation Resources, Inc., A.C. Nielsen Co., and the Marketingcience Institute Library of Single-Source Data.

Appendix A. Details of estimation method

In this appendix, we provide more details of the estimationethodology used in this research. First, we define the likeli-

ood function for all of household h’s purchases (also calledts purchase string), conditional on household h’s parameterector θh, as:

h〈Yh|θh.〉 =Th∏t=1

N∑i=1

Pr(yhti = 1; θh)yhti (A.1)

here Ys is the vector of item choices, Th is the number ofhoice occasions, and N is the number of items available.7

ather than estimating a separate parameter vector for eachousehold, we assume that the distribution of parameter vec-ors across households is multivariate normal; that is, θh hasmultivariate normal distribution with mean θ0 and variance. We wish to estimate the parameters of this multivari-

te normal distribution. Under the distributional assumptions

escribed, we can designate the likelihood function for all

7 For expository ease, we assume that N is not time-varying, although theethod extends to this situation. We further assume that all of the parameters

f θ contain unobserved consumer heterogeneity, although the algorithmllows non-heterogeneous parameters similar to those in the hierarchicalquations. The non-heterogeneous parameters are incorporated by settingheir rows in Σ to zero.

A

B

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ling 82 (4, 2006) 295–307 305

ouseholds as:

(Y, θ, Σ) =H∏

h=1

∫Lh(Yh; θ)f (θ; Σ) dθ (A.2)

here f(θ; Σ) is the distribution of the parameter vector θ con-itional on the covariance matrix Σ. In our case, we assumehe distribution to be multivariate normal. The integral inq. (A.2) can then be numerically integrated using simu-

ated maximum likelihood estimation (SMLE), as describedy Hajivassilios and Rudd (1994).

Rather than drawing numbers from a normal distribu-ion, we use quasi-random Halton sequences to perform theumerical integration, which enables us to use considerablyewer draws. For example, Bhat (2001) finds that 100 drawsrom a Halton sequence are equivalent to approximately,000–1,500 draws from a normal sequence. In our appli-ation, we use 200 draws from a Halton sequence.8

We further assume that Σ is a diagonal matrix or thathe unobserved heterogeneity is uncorrelated among the pre-ictors (which implies an additional K parameters, where= number of elements in θ). The additional K parameters

re the variance terms of the unobserved heterogeneity. Notehat we allow correlation in the predictors through the rela-ionships with prior period promotions and state dependence.lthough SMLE can be used to estimate the vector of param-

ters and the covariance matrix, it is difficult to estimate suchlarge multivariate normal distribution empirically; the num-er of parameters is o(n2) (Chintagunta 1999).

To perform the numerical integration, we calculate eachousehold’s likelihood separately (using Eq. (A.1)), thenombine the households’ likelihoods (as in Eq. (A.2))o obtain the total likelihood. We define R as a matrixND = number of draws, by K) of random draws and C as aower triangular K by K matrix of covariance terms (in ourase, C is a diagonal matrix), such that CC′ = Σ.9 Thus,0 + RC′ provides ND instances of the parameter vector.or the numerical integration, we take the average house-old likelihood across the ND instances of the parameterector.

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