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CARLF.MELA,KAMELEDIDI,and DOUGLASBOWMAN*

Although brands have increased their promotional spending substan-

tiallyin many categories over the past decade, panel-based research intoconsumer stockpiling behavior typically has assumed that consumers'

decisions regarding whether and how much to purchase have remainedinvariant to this increase. The authors develop a varying parameter modelof purchase incidence and purchase quantity to ascertain whether thisincrease in promotions has affected households' stockpiling decisions inthe long term. The authors estimate the model on the basis of more than

eight years of panel data for a frequently purchased, nonfood, consumer

packaged-goods product. The results suggest that consumers' stockpil-ing behavior has changed over the years. The increased long-term expo-sure of households to promotions has reduced their likelihood of makingcategory purchases on subsequent shopping trips. However, whenhouseholds do decide to buy, they tend to buy more of a good. Suchbehavior is indicative of an increasing tendency to "lie in wait"for espe-cially good promotions. This change appears to have some deleterious

ramifications for category profitability.

The Long-Term Impact of Promotions on

Consumer Stockpiling Behavior

Consumer packaged-goods firms spend approximately$70 billion annuallyon tradepromotions ProgressiveGro-

cer 1995). Moreover,many of these firms have been in-creasingthe proportionof their marketingbudget spent ontrade promotions; more than 70% of manufacturers n-creasedpromotionalexpendituresbetween 1990 and 1995.Consumer and tradepromotionsnow account for 50% of

many manufacturers'marketing budgets. This increasecomes in spite of efforts by some majorconsumerpack-aged-goods companiesto reducetheirpromotional xpendi-tures(Forbes 1991) and/orcut theircouponingefforts (TheWall Street Journal 1996). The reason for increasing pro-motionalspending is clear: Promotionshave a large, mea-

surable,immediateeffect on a brand's sales (BlattbergandNeslin 1989).1

'By promotions,we refer to features,displays, coupons, and temporaryprice reductions. Here, we use promoiotn interchangeablywith dealsbecause these promotionsoften areaccompaniedby pricereductions.

*CarlF. Mela is Assistant Professorof Marketing,College of Business

Administration,Universityof Notre Dame (e-mail:carl.f.mela. @nd.edu).Kamel Jedidi is Associate Professor of Marketing,Graduate School ofBusiness, Columbia University (e-mail: [email protected]). DouglasBowmanis Assistant Professorof Marketing,KrannertGraduateSchool of

Management, Purdue University (e-mail: [email protected]).The authors thankthe editor and the formereditor, fouranonymousJMRreviewers, Rebecca Mela, Scott Neslin, and Joe Urbanyfor their helpfulcomments and InformationResources Inc. and an anonymousconsumer

packaged-goodscompanyfortheir financial assistanceanddiscussions.

.ournaitl of Marketing ResearchVol. XXXV(May 1998), 250-262

In spite of the substantial,temporary ncrease in house-hold purchasesattributable o the use of promotions,there

are concerns that such promotionsmight have a differinglong-termeffect. For example, in categories in which pro-motions have become frequent,consumersmight learn to

anticipate uturedeals.This particularcenariosuggeststhat

(1) a particularpromotionalevent induces a household to

stockpile2on a given purchaseoccasion (short-term ffect),followed by (2) the promotion's long-term, negativeeffect,which is manifested as an increased probabilitythat thehousehold waits for another promotionbefore buying on

subsequentpurchaseoccasions.Consistentwith the spirit of the foregoing example, we

define a short-term promotional effect as an immediate re-

sponse to a promotionon a particularhoppingvisit (single

pointin time). Incontrast,a long-termeffectrefers to the cu-

mulative effect of previous promotional exposures (overquartersor years) on a consumer's current,or short-term,decision of whetherand how muchto buy.The effect of pastexposureson currentpurchasesalso suggestsa carryover f-

fect; a promotion n the currentperiodwill affect behaviorin subsequentperiods.Because the durationof thesepromo-

2Stockpiling s defined as buying largerquantitiesof a productand/or

shifting purchasetimes to buy before the expected time of next purchase(Blattberg and Neslin 1990). Ailawadi and Neslin (1998) suggest that

stockpiling is distinct from category expalnsion because, with stockpiling,consumerscompensateforbuyingmoreby making ewerpurchasesorpur-chasing smallerquantitiesin the future.Althoughour focus is on the for-

iier, we also offer insightsinto the latter.

250

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The Long-Term Impact of Promotions

tional effects can be substantial quarters r years), they areconsideredto be long term(BlattbergandNeslin 1989;Fad-er et al. 1992).3

Althoughthererecentlyhave been a few studiesthathaveexamined the long-term effect of promotionson choice,share,or sales (Boulding, Lee, and Staelin 1994; Dekimpeand Hanssens 1995a,b; Lal and Padmanabhan 995; Mela,Gupta,and Jedidi 1998; Mela, Gupta,and Lehmann 1997;

Papatlaand Krishnamurthi 996), few, if any, studies haveexamined the long-term impactof promotionson stockpil-ing. Doing so is particularly mportantbecause Blattberg,Briesch,and Fox (1995) arguethat,in some categories,theeffects of promotionson incidence and quantityare even

greaterthanthey are on choice. Therefore, he primarygoalof this article is to examinethe long-term mpactof promo-tions on consumers' stockpilingdecisions. Specifically, weseek to answer the following arrayof questions:Are con-sumers more or less likely to make a category purchase nthe absence of promotions n responseto an increasein theuse of promotions by manufacturers?Given a decision to

buy, are they more or less likely to buy larger quantities?

Whatare these effect sizes, if any? Forexample, would thenegative long-term effect of a promotionoutweigh the in-centive to buy thatit providesin the currentperiod?

The article is organizedas follows. We begin by develop-ing hypotheses.We thendevelop a varying parametermod-el of categoryincidenceandpurchasequantity,as well as anestimationprocedurethat enables us to assess how house-hold stockpiling behaviorchanges over time. Next, we de-scribe our data, variableoperationalization,and the model

specificationused to testourhypotheses.The data section isfollowed by a presentationof the results and a discussionoftheirimplications.We conclude with a summaryof our con-tributionsand directionsfor furtherresearch.

TheEffect of

Promotions on ConsumerStockpiling

Both reference pricingtheories (Bell and Bucklin 1996;Jacobson and Obermiller 1990; Kalyanaramand Winer

1995) and inventory managementmodels (Assuncao and

Meyer 1993; Helsen and Schmittlein 1992; Krishna1992,1994) make predictionsregardingthe long-termeffect of

promotionson stockpiling.However,these models often areestimatedor predicatedon shorterperiodsof data or fewer

purchasecycles thanwe seek to analyze.Furthermore,hesemodelsdo not partial he short-andlong-termeffects of pro-motions explicitly, nor do they disentangle purchaseinci-dence and quantityeffects directly.Concurrentlymodelingthe decisions of whetherand how muchto buy (conditionedon incidence) offers additional insights over modeling

expectedpurchasequantitiesalone (theproductof incidenceprobabilitiesand conditionalpurchasequantities).

For example, consumers might reduce their incidence

probabilitiesand increase theirconditionalpurchasequanti-ties (i.e., they might lie in wait for especially good deals).Such a strategy could yield constant expected purchasequantitieseven in the face of radicallydifferentstockpilingbehavior. Solely modeling expected purchase quantitiescannotcapturethischangebecauseexpectedpurchasequan-

3Not all long-termeffects (Blattbergand Neslin 1989;Faderet al. 1992)can be considered persistent (Dekimpe and Hanssens 1995a, b). Our defin-

ition of long-term effects includes effects that decay over time (albeit over

quarters or years). Persistent effects are permanent and enduring.

tities would remain constant over time. The foregoing sce-nario thereforesuggests that it is importanto model the in-cidence andquantitydecisionsjointly and to captureany po-tentialdependencethatmightexist betweenthese decisions.The example also implicitlysuggests why it is importanto

disentangle promotion's short- and long-term effects, as

they mightbe very different.In the shortterm,a promotionincreasessales in the week it is offered. In the long term,it

mighttrainconsumers to lie in wait for a good deal.Purchase incidence decision. Reduced incidence proba-

bilities are one component of a lying-in-wait heuristic

(along with higher conditional purchase quantities), andthere are several theories that suggest that a household's

long-term exposure to promotionscould reduce incidence

probabilities.Referencepricemodels suggest that increased

discountingon previous purchaseoccasions resultsin lowerreferencepriceson the currentpurchaseoccasion. Using this

theory, Bell and Bucklin (1996) develop a "sticker shock"model of category purchaseincidence. In their model, in-creasedpromotionalexposureon priorstorevisits increasesthe reference"value" or the productcategoryon the current

store visit. Consequently,the difference between the cate-gory value andthereference valuediminishes,which resultsin a reduced likelihood of a category purchaseincidence.

Therefore, increased long-term exposure to promotionsmightlead to a lowercategory purchase ncidenceprobabil-ity on subsequentpurchaseoccasions.

Futurepriceexpectations(as opposedto referenceprice)also affect stockpiling.Gonil and Srinivasan(1996) showthat consumers develop expectations about coupon avail-

ability and further suggest that these expectations of

coupons in futureperiods lead consumersto deferpurchas-es on subsequent occasions. Jacobson and Obermiller

(1990) also argue that past prices affect expectations re-

gardingfutureprices. Analyzing five categories, they find

that currentpurchase s discouragedby expectationsof low-er prices in the future. Consequently,we hypothesize the

following:

H1: ncreasedong-termxposureo promotionseads o loweroff-dealpurchasencidence robabilities.

Normative household inventory management models

(Assuncao and Meyer 1993; Helsen and Schmittlein 1992;Krishna 1992, 1994) make several further predictionsregardingthe long-termeffect of promotionson incidencebehavior.These modelssuggest thatconsumers radeoff the

savings from purchasingon promotionwith the addedcostof stockpilingextra inventory.The use of promotionsis acriticalcomponent n how this trade-off s made. Helsen and

Schmittlein(1992) arguethatconsumers' reservationpricesfor makinga categorypurchasedropas they areexposed tomore promotions.Hence, as promotionsbecome endemic,increasinglybig promotionsare needed to stimulatea pur-chase. Therefore,consumersforego many of the discountsand appearto be less promotionsensitive. Assunqaoand

Meyer (1993) echo this reasoning.They hypothesize that

increasingexpectations of a betterdeal in the near futureincreases the likelihood of foregoing a deal in the current

period (resultingin lower promotionsensitivity).We there-fore hypothesizethe following:

H2: ncreasedong-termxposureo promotionseads o lower(lesspositive)purchasencidence romotionensitivity.

251





JOURNAL OF MARKETINGRESEARCH, MAY 1998

Kalyanaramand Winer (1995) reason that, over time,

promotionserode purchaseprobabilitiesby lowering refer-ence prices and thereby increasing price sensitivity. As a

result, consumers might be more reluctant to pay regularpricesor tolerateprice increases.Such argumentsare con-sistent with a lie-in-wait strategy,in which consumers areless likely to buy at high prices as they learn to buy when

pricesareespecially low. Therefore,

H3: ncreasedong-termxposureopromotionseads o higher(morenegative) urchasencidence rice ensitivity.

The normative nventory managementmodels also offer

predictions regarding how increased promotions affect

inventory sensitivity in the purchase incidence decision.Krishna(1992) suggests that, especially when promotionsarecommonandinventory s high,householdsmightbe less

likely to make a purchase.Gonul and Srinivasan(1996)show that householdswith excess inventoryaremore likelyto defer purchasesif the expectationof future promotionavailability s high. We thereforehypothesizethe following:

H4: ncreasedong-term xposure o promotionsmakescon-sumersmore ensitiveo inventoryevels in theirpurchaseincidence ecision i.e.,inventoryensitivity ecomesmore

negative).

Purchasequantitydecision. There are few priorresearchstudies that lend direct theoreticalor empiricalinsight intohow promotionsmight affect categorypurchase quantitieswhen conditioned on incidence (conversely, most priorresearch reviews the effect of increased promotionalfre-

quency on expected quantity,that is, the productof inci-dence and quantityconditioned on incidence). Helsen andSchmittlein 1992) andAssunqaoandMeyer(1993) contendthatconsumerswho areexposed to frequentpromotionsare

only likely to buy in a category (purchase ncidence)whenthere is a deep discount. Similarly, Krishna(1994) showsthat consumercertaintyand learningregarding uturepro-motions results in consumers who wait for promotionsto

buy and then buy all the quantitynecessaryon those pur-chase occasions. Such reductions n incidenceprobabilities(see also Hi), coupled with increases in mean purchasequantities,would be indicativeof a lying-in-waitheuristic.This strategysuggeststhatconsumers earnto anticipateandreact to good promotions.On finding one, they are more

likely to purchasein the category and in largeramounts.Such patternsare likely to be especially common in cate-

gories in which consumption is relatively constant and

goods are nonperishable-if consumers are buying less

often, theymustbuygreaterquantitieswhenthey makepur-chases. Accordingly,

H5: ncreasedong-termxposureopromotionseads ohigherpurchaseuantitiesonditionednpurchasencidence.

Our expectations regarding the anticipated long-termeffect of promotionson conditionalpurchasequantitypriceand promotionsensitivities are more speculative. Becauseincreases in long-term promotional exposure lead con-sumers to lie in wait for promotionsand then buy largeramounts,we expect them to become increasingly priceand

promotionsensitive in their conditionalquantitydecision.Consistent with this assertion is Assuncao and Meyer's

(1993, p. 528) observation that, when promotional fre-

quency increases, "greaterpurchases at deal prices andfewer purchases at regular prices ... inflate the apparentshort-term elationshipbetweenprice and purchase."Anal-

ogous reasoningholds for promotionsensitivity.Therefore,

H6: ncreasedong-termxposureopromotionseads o higher(morepositive)conditional urchasequantitypromotion

sensitivity.H7: ncreasedong-termxposureo promotionseads o higher(more egative)onditionalurchaseuantity rice ensitivity.

We expect little difference between the quantityand inci-dence decisions regarding the importance of inventory.Should promotionsbecome increasinglycommon, house-holds might become increasinglydisinclined to buy largequantitieswhen the product s alreadyin inventory.There-

fore, similar to H4,we expect that

H8: ncreasedong-term xposure o promotionsmakes con-sumersmore ensitive o inventoryevelsin theirpurchasequantity ecision i.e., inventory ensitivitybecomesmore

negative).

The central hesis of these hypothesesis thatpastpromo-tions affect currentbehaviorand that consumers adapt to

changes in theirpromotional nvironment.Yet econometricmodels of stockpilingbehaviortypicallyhave assumed thathousehold response parametersare invariant to marketingpolicy. Erdem and Keane (1996) denote such models"reducedform"and argue that they can lead to erroneousinferencesregardingconsumerbehavior. We now outline a

modeling approach hat relaxes the assumptionsof invarianthousehold response parametersand enables us to assesswhether stockpiling behavior indeed is affected by long-termexposureto promotions.

METHODOLOGYOverview

To test ourhypotheses,we develop a joint model of inci-dence andpurchasequantity hathas the following features:

*Categoryncidences modeled s a function f marketingari-ables e.g.,price) ndhousehold-specificariablese.g., nven-

tory),usinga probitramework.*The incidence esponseparameterse.g., priceresponse) remodeled s a function f long-termxposureo marketingc-

tivity e.g.,pastexposureopromotions).*Purchaseuantitys modeled s a function f marketingnd

household-specificariables,singa regressionramework.*Theresponse arametersnthequantitymodelvarywith ong-

term xposureo marketingctivity.*Thepurchasencidence ndquantity ecisionsaredependent.

Methodologically, we extend the switching regressionmodel in econometrics(cf. Maddala 1983, p. 223; Nelson

1977) by reparameterizinghe model's coefficients as afunction of long-termmarketingactivity. This generaliza-tion enables us to capture the dynamics of consumer

responsesto marketingactivityover time.

Purchase Incidence Model

Nonpurchasesresult from a decision not to buy, not nec-

essarily from zero desired purchase quantity (Maddala1985, 1991). We assumea latentutilityvalue, Uit,underly-

252






ing a household i's category purchasedecision at time t. The

utilityvalue is specifiedas a function of observablemarket-

ing actions and household characteristics.Algebraically,householdi decides to buy if theutilityfrombuyingexceedsa threshold.That is,

M

(1) Uit =P I+ smitX + Et > it,

m=

where superscriptI is used to denote purchaseincidence,

xmit is the value of explanatoryvariable m for household iat time t, P3it is the effect of the mthexplanatoryvariablefor household i at time t on the utilityvalue, and Eit is anerror term N [0 (c O)2 k, represents a latent purchase

utility threshold above which a household will decide to

buy. Note thatkit is not estimablebecauseit can be absorbedinto the intercept Poit. Hereafter, we set kit = 0 without a

loss of generality.Next, we reparameterize ach of the it (includingthe

intercept)as a function of J moderatingvariablesto capturethe long-term impact of marketingactivities (e.g., past ex-

posureto promotions).That is,

(2) 1=Y + I z!. +VIt mj i t mit

j=i

wherez t is the value of moderating ariable for householdi attime t, Ymj s themoderatingmpactof thejth variableonthe mth sensitivityparameter includingm = 0 or the inter-

cept), and vi is an errortermthatreflectsthe inabilityofthe moderatingvariablesto fully explainthe responsepara-meters.4

SubstitutingEquation2 in 1, we thereforeobserve a pur-chase if

/ h

(3) Ut= Y ]YzIt X1IYoo ii Olt

xi +=! >

M ( J

YmO 7 Zijt x mit+ it

m=lk j=

where

(4)

M

it - VLit Xmitx itm = 0

is the overall errorterm for the purchaseincidence modeland xOit = 1 (intercept). The first bracketed expression in

Equation3 reflects the varyingintercept alteratively, the

4Equation2 (andsubsequently,Equation6) controls for heterogeneity nthe manner suggested by Papatla and Krishnamurthi 1996). In their

approach,the y,O, are assumed to have a deterministiccomponent(ym()and an errorcomponent(o4,, ) thatcapturesheterogeneityin the popula-tion. That is, y,o = Y + i. The error n Equation2 thereforecan be

interpretedas v it = ejit + (o, where )ojt capturesheterogeneityande l representserrorarising from othersources. The intercept n Equation2 can be interpreted s ylO. Papatlaand Krishnamurthi1996) set the vari-ance of e it to unity.u touiy

main effect of the z jt), and the second represents he vary-ing slopes. Given our distributional ssumptions, it is nor-

mallydistributedwith zero mean and variance

M M M

C5!\2

= I . I I t2/ i \2( I t)2 =

E (X Xrit)apr+

(Xmit) m),

p=Or=O m=O

p-r

where(o is thecovariance between the rth andpth varyingincidence model parameters.

PurchaseQuantityModel

We model purchasequantityas a function of a set of

explanatoryvariables.Algebraically,

(5) Q =,Bq + Lq

X4 +q;l

ltIt

li

ild N 0,(oq)2idt

wheresuperscriptq is used to distinguishthe variables and

parameters n the purchasequantitymodel from the pur-chase incidencemodel, Qitis the quantityhouseholdi pur-chasedat timet, x t is the value of explanatoryvariable1forhouseholdi at time t, Pqt is the effect of the lthexplanatoryvariablefor households i at time t, andeq is an error erm.

As in the incidence model, we reparameterizehe P9it asa function of the moderating actorshypothesizedto affectthese sensitivity parameters.Specifically,

(6)

K

P=iIo+XYoZo+vkI

vi - N[O,(i)2 1

wherezqk is the value of moderatingvariable k for house-hold i at time t, yq is the moderatingmpactof variablezqton pqt,andvq is an error ermthatcaptures he inabilityofthe z4k to fully explain the P3q

SubstitutingEquation6 in 5, thepurchasequantity hen is

expressedas

K

(7) Q,= Y4 + CY ZqXzQit= qo+Eyqok zqktxit

< k=l y

L K

+ Yo+ x=z ,-i,!=1 k=l

I

where

(8)

L

=it= 0 Vt X +,t1=0

is an overallerror ermcombiningerror n coefficients,errorin specification, and x0it = l(intercept).

253





JOURNAL OF MARKETINGRESEARCH, MAY1998

Ourdistributionalassumptions ead to a q9 with a meanof zero and variance

L L L

- ? oqq q qt ()2

p=O r=O 1=0

p r

where(oq is the covariancebetween the rthandpth varyingparametersn the quantitymodel.

Joint Model

Combining Equations 3 and 7, we can write the jointmodel as

, \L K

Qit = Ek ikt xt + if

!=0 k=0

M J

Uit =mjz ijt x mt + it >0,

m =O j=0

I

where the joint vector{(q, it

I follows a bivariate normal

distributionwith zero meanvector and covariancematrix

(10)

2q

( it) qtLo1l (Y2t)

where

L

1 =0

M

q I qlX lit X mit(l1m

m = 0

is thecovarianceof it and iq thatcaptures hedependencebetweenpurchase ncidence andpurchasequantity.Theo Irepresents he covariance between the Ithquantityparame-ter errorand the mth incidenceparameter rror.

Severalimportant eaturesof theerror tructure hould benoted.First,the error erms of the varying parametersn theincidence andpurchasequantitymodelsarecorrelated.Sec-

ond, as suggested by Lee and Trost (1978) and Krishna-murthi and Raj (1988), the error terms in the incidencemodel are correlated with those in the quantitymodel to

capturethe dependence between the two decisions. Third,the

parametererrors induce

heteroscedasticityn both the

incidenceandquantitymodels.Fourth,we assume the errorsto be serially independent n orderto enhancethe tractabil-

ity of the model.This assumption s commonly employedin

varyingparametermodels (e.g., Papatlaand Krishnamurthi

1996). (The estimation procedurefor the joint model in

Equation9 is available from the firstauthor.)

DATA

The data used in this study are from an InformationResources Inc. static panel of 1590 households during a

period of eight years. The households were located in asmall midwesterncity. The data,which were purchasedbya large consumer packaged-goodsfirm, contain shopping

(9)

trip and store data. The productis a frequentlypurchased,nonfood product.We cannot reveal the specific categorybecause of confidentialityagreementswith the firm,but the

product s a nonperishable taple,such as cleaningproducts.Eight brandsin the category account for 90% of the pur-chases. More than 92% of the householdspurchasedmulti-

ple brandsover the durationof the data. Consistent with

previousstudies (Chintagunta1993;Gupta 1988), we sam-

pled approximately 100 households for analysis (by ran-domly selecting approximatelyone-fifteenth of the totalhouseholds yielding a sample of exactly 109 households).Ourunit of analysis is a shoppingtrip by a household.Weobserve whetherthey makea purchase n the categoryand,if so, the quantitypurchased.The model is calibratedon

105,515 storevisits and 3866 purchasesmadeby these 109households. The purchase requencyof thiscategoryis sim-ilar to thatof othercategoriesused in recentresearch Pap-atla and Krishnamurthi 996).

Operationalization of Variables

Price, promotion, and inventory. Category price (PRICE)

was formed frombrand-levelshelf prices5througha brand-shareweightedaverageof brandprices. Category-level pro-motion (PROM) similarly was formed from brand-level

promotions through a brand-shareweighted average ofbrandpromotions.These brand-shareweights were calcu-lated at the household level (Dillon andGupta 1996;Gupta1991; Jain and Vilcassim 1991) during the entire sampleperiod.Similarto Boulding,Lee, and Staelin (1994), Buck-lin and Lattin(1991), Bucklin and Gupta(1992), and Sid-

darth, Bucklin, and Morrison (1995), we created a

composite measureof a brand'spromotionalstatus. Brand-level promotions were formed by averaging a brand's

coupon,6 feature, display, and temporary price reduction

dummy variables.Using a composite measure for promo-

tions offers several advantagesover separatingthe promo-tion variables.First, our substantiveconcern lies with the

impactof promotionson stockpiling,andaccordingto cate-

gory managers, all of the promotionalactivities tend toinduce stockpiling.7Second, the composite measure sub-

stantiallyreduces the numberof parameterso be estimated

(by as manyas 48 parameters, ssumingno cross-parametercorrelations), herebyenhancingtheparsimonyof the modeland the reliabilityof the results.Third,the composite mea-sure reduces the multicollinearity,resultingin more robustestimates(thecorrelationnducedby splittingthepromotionvariablein our data was substantial; he determinantof the

split promotioncorrelationmatrixwas .009). This collinear-

ity is due to the tendencyof manufacturerso increasetheir

use of differentpromotionalvehiclesconcurrentlyover time(The Marketer 1990). The remainingvariable, household

5Prices are in nominalterms. The categoryaverageprices in 1992 were

only 3%higherthanthose in the firstquarterof 1984.

6Couponavailabilityfor a brandwas inferred rom the coupon redemp-tion information.When redemptionsof a brand'scoupons exceeded onestandarddeviation above the mean weekly redemptionsfor that brand,a

coupon was said to be available (Mela, Gupta, and Lehmann 1997). Abrandwas said to be on feature,display,ortemporarypricereduction f anyof its brandsizes were on promotion.

7Thepossibilitythat differenttypes of promotionshave different long-term effects is discussed in greater detail in the "ManagerialIssues"section.

254






inventorylevel (INV), was inferredby the procedureout-lined by Bucklin andGupta(1992).

Long-termexposureto promotions.Our measureof long-term exposure to promotions(LTPROM)follows PapatlaandKrishnamurthi's1996). Specifically,we createda geo-metrically agged seriesusing thehistoryof households'ex-

posureto promotionson each shopping trip.The measure salso similar to Jacobson and Obermiller's

(1990) adaptivemodel of expectationsand therefore uggeststhatLTPROMalso can be interpretedas a household's expectationof adeal arising in the subsequent period. Consequently,long-termexposure to promotionsfor householdi at time t was

operationalizedas

(11) LTPROMit= LTPROM,t- +( - X PROMi,t i;

0 <<1,

where PROMj,t l is the category-levelpromotion n shop-ping trip t - 1 and k is the carryovereffect, or lag coeffi-

cient, to be estimated. As X approachesone, all prior

promotionsaredefinedto

carry equal weight,andthe influ-

ence of a promotiondoes not decay over time. Smallerval-ues for the carryover effect attenuate the long-terminfluence of promotions;as k approacheszero, promotionsoffered priorto t - 1 are specified to have negligible influ-ence. Note that the formulationin Equation 11, with the

propererrorassumptions, s equivalent o thedistributedagor Koyck model. We use the first yearof data to initializethe long-termvariable,because we have no informationonhouseholds' exposure to promotions priorto theirenteringthe panel. We use the remaining seven-and-one-quarteryearsof data to calibratethe model.

Purchase incidence and conditionalpurchase quantity.Each shopping trip recordcontainsdata on whethera pur-chase was made in the

categoryand the numberandsize of

the unitspurchased, f applicable.Fromthis information,anindicatorvariable was created for whether the householdmadeany purchase n the categoryduring he shoppingtrip.This variable served as our incidence measure.Conditional

purchasequantitywas obtained from the numberof ounces

bought by the household.

ModelSpecification

To assess how a household's price and promotionresponsesare affected by its long-termexposureto promo-tions, we first model the incidence utility for the categoryduring a particularshopping trip as a function of price(PRICE), short-term promotion (PROM), and inventory

(INV). Mean household purchaserate (PRATE) also wasincluded as a control for cross-householdheterogeneity.8The utility function for category purchase incidence in

EquationI is specified as

U it= Pit + } itPROM it+ itPRICEit

+ 03 INVit + P'PRATEi + eit

8Time-varyingand fixed householdpurchaseratevariablesyielded vir-

tually identicalresults in the incidence model;however,the model fit was

slightlydecreased for the time-varyingpurchaserate variable.:__5I ?~ ??- ?J ?b U??UV UY?U?IVY

where the varying parameters,Pmit are reparameterizedfurtheras a function of long-term exposure to promotions.Thatis,

(13) mit Ym+m7 I LTPROM it+ V

Note that 43 is fixed over time because PRATE is includedto capturecross-householdheterogeneity.

As in the incidence model, we model conditional pur-chase quantityas a function of price,short-termpromotion,and inventory.To accommodatethe possibility that loyalsbehave differently in their purchase quantity decisions

(Krishnamurthi,Mazumdar,and Raj 1992; KrishnamurthiandRaj 1991), we specify theconditionalquantitymodel atthe brand evel9andincludeloyalty(as in Guadagniand Lit-tle 1983)10as a controlvariable.1 FollowingKrishnamurthiand Raj (1988), we also include household mean purchasequantity(MQTY) as a control variable for cross-household

heterogeneity.The expression for the conditionalquantity

purchased or brandb therefore s given by

(14) Qibt = Ipibt+ P3 it PROM ibt+ PqitPRICEibt

+13it INV it+MQTY + LOYibt+ (l W t+it 4 5 it it

whereWitis a selectivity bias termthatcaptures he depen-dency between the incidence and conditional purchasequantitydecisions (Maddala1983, p. 223).12We thenrepa-rameterize he varyingparametersn Equation14to capturethe effect of a household's long-term exposure to promo-tions on its currentpurchasequantitydecision,

"Ofhiscategory'spurchases,99%aresingle-brandpurchases.Thus,Qit= Qibt,andthe brandquantitygiven in Equation14 also can be interpretedto reflect the categorypurchasequantity.We are gratefulto the editor fornoting this congruency,and we use this interpretationn subsequentdis-cussions.

'(Following Fader,Lattin,and Little(1992), we useda lag of .83 to cre-ate the loyaltyvariable.This lag is also in the middleof the rangeof valuesused by Guadagni and Little (1983), Gupta (1988), and Papatla andKrishnamurthi1996). As in Gupta's (1988) work, results were robusttovariations n the lag.

IWe thank an anonymousJMR reviewer for this suggestion and itsresultingmodel specification.We also ran a conditionalcategotl quantitymodel (I) withoutloyaltyor brand nterceptsand (2) using categorylevelPROMand PRICE in lieu of brand evel variables and obtainedvirtuallyidentical results.

[2Thegeneralexpression for Wit is given by (the completederivation savailable from the firstauthor)

M J

YmjZijI Xmit

WIit

M J

i: Y jZ1j Xmit >

m=() j=0

!1

255

(12)





256 JOURNAL OF MARKETINGRESEARCH, MAY 1998

Table 1

SUMMARY ABLEOF HYPOTHESES

Hypothesis Parameter ExpectedSign

IncidenceHypotheses

Hi: Maineffect of long-termpromotionson incidence Y)I Negative

H,: Long-termeffect of promotionson incidencepromotionsensitivity Y| Negative

H3: Long-termeffect of promotionson incidencepricesensitivity Y71 Negativea

H4: Long-termeffect of promotionson incidenceinventorysensitivity y3 Negativea

QuantitvHypotheses

H5: Maineffect of long-termpromotionson quantity Positive

H6: Long-termeffect of promotionson quantitypromotionsensitivity yql Positive

H7:Long-termeffect of promotionson quantitypricesensitivity ?2 Negativea

Hg: Long-termeffect of promotionson quantity nventorysensitivity yli Negativea'

aA negative long-termeffect of promotionson these parametersmplies that households become increasinglypriceand inventorysensitive.

Table 2

VARIABLE EANS

First Second

Variable 4?%Years 47? Years

Promotion (percent of store

visits with promotion) .085 .141

Price ($/ounce) .046 .049

Inventory (ounces) -7.44a 12.52

Long-term promotions .08 .14

Mean expected household quantityb(ounces/visit) 1.09 1.08

aInventory is mean centered.

hExpected quantity is the product of the incidence likelihood and the

conditional purchase quantity. A graph of mean household incidence like-

lihoods and mean household conditional purchase quantities over time is

provided in Figures I and 2.

(15) P)oibt YqOOb +)+ 1 LTPROMit+vqi

and

Figure 1

MEANLIKELIHOODF INCIDENCE ND PROMOTIONALFREQUENCY VERTIME

5.0%

4.5% -

4.0% -

3.5% J

3.0% -

2.5% -

2.0% -

1.5% -

1.C0%

.5% -

.0%

20%

18%

16%

. 4%

- 12%

10%

-8%

6%/

- 41

- 2%

I I I I I I I I I I I I II I I I I I I I I I I I ;

1985 1986 1987 1988 1989 199(0 1991

Year

= Mean Incidence Likelihood -- Frequencyof Promotions

pqt=

Yq0 i LTPROM +vqt

wherey0ol is set to zero to ensureEquation15 is identified.

p3 and 3q are fixed over time, because MQTY and LOYare control variables included to capturecross-household

heterogeneity.Before discussing the results, it is helpful to restate our

hypothesesin terms of our

expectations regardinghe

y pa-rameters n the incidence andquantitymodels. In Table 1,we presenta summaryof our hypotheses regarding he ex-

pectedsigns of these coefficients.The main effect of long-termpromotions s equivalentto

the effect of long-termpromotionson the intercept n Equa-tions 12 and 14. We standardizedall of the independentvariables to mean zero and varianceone to facilitate com-

parisonof effect sizes.

EMPIRICAL NALYSIS ND RESULTS

In Table 2, we reportthe mean values for the key vari-ables (unstandardized) f concernfor both the first and sec-ond half of the data.Comparingmarketingactivity in each

half of thedataprovidesa concise overviewof any potentialchanges in marketingpolicy. A clearshift in strategy s evi-

dent, as the frequencyof promotionshas increasednotablyover the durationof the data.

Figure 1 portraysmean promotions(percentageof storevisits with a promotion)and meanpurchase ncidencerates

(numberof

purchasesover number of

shopping trips) byquarter.A trendregressionof the meanquarterlypromotionseries indicatesthatpromotionshave increasedsignificantlyover time (p < .001). A trendregressionof the incidence da-ta suggeststhat meanquarterlypurchaseratesacross house-holds have diminishedover time (p < .001). Therefore,pro-motionshave increasedover time, whereasthe likelihood of

purchase ncidencehas diminished.Figure 2 graphs mean promotionsand mean purchase

quantitiesconditionedon incidence over time. A trend re-

gressionof the quantityseries suggests thatmeanquarterlypurchasequantitieshave increasedover time (p < .01).

Coupled with the decrease in incidence likelihoods, theincreasein conditionalpurchasequantitiesmightbe indica-

I

...............................................

........................

...... .. . ... ...........

... ---------- ---- ------------

...............................................

ci

ti

c,C

.u

:iL

-j

z






Figure 2

MEAN CONDITIONALQUANTITIESAND PROMOTIONAL

FREQUENCY OVER TIME

Z.

c-

r

'T.

35

30 -

25 -

20-

15-

10-

5-

0

20%

18%

16%

14%12%10%

8%

6%

4%2%

0%

o

o

>

o

I

s-

;0-S

L,._

p'

I1

1985 1986 1987 1988 1989 1990 1991

Year

--- Frequencyof Promotions - Mean ConditionalQuantity

tive of the lying-in-wait heuristic discussed previously.Moreover,in the first half of the data,the mean pricepaidwhen a purchase incidence occurred was .03 cents/ounceless than when a purchasewas not made. Inthe latterhalf ofthe data,as the frequencyof promotions ncreased,the dif-ferenceincreasedsevenfold to .21 cents/ounce.The tenden-

cy to seek out lower pricesandbuy largerpurchasequanti-ties offers further vidence of the lying-in-waitheuristic.Al-so consistent with this heuristic are the larger inventoriesthat households carried in the second half of the data (seeTable 2).

Note thathouseholds' meanexpectedpurchasequantities(the

productof incidence likelihood andconditional

quanti-ty) remain constant over time, even though incidence ratesand conditionalquantitieshave changed (see Table 2). Byanalyzing consumers' incidence and conditional quantitydecisions separately, we are able to uncover underlyingchanges obscuredby theirproduct i.e., incidenceprobabil-ities decrease whereasconditionalquantities ncrease).Theconstant level of expected purchasequantities s consistentwith a patternof constantconsumption.Increasingexpectedquantities would be more consistent with a patternof in-creasedconsumption.

Model Selection

We calibratedandcomparedseveralalternativencidenceandquantitymodel specificationsto assess whether(1) the

long-termeffect of promotingand (2) the parameter rrorstructureadd significantlyto the model. Assessing whether

long-termeffects are significantis one of our core researchconcerns. The second issue is also importantbecause no

stockpilingstudies (as opposed to choice; e.g., Gonul andSrinivasan1993) to our knowledge have tested for the sig-nificance of stochasticparameters. n all models tested, wecontrol for selectivity bias (i.e., the correlationbetweentheincidence and conditional quantity decisions). We alsoallow the varyingparametererrors to covary within13andacross14 he incidence and quantity equations.Because allthe alternativespecificationsare nested, we use the likeli-hood ratio test to assess the best-fittingmodel. In Table 3,we presenta more detaileddescriptionof the variousmod-els tested,as well as their fit.

The results in Table 3 show that both the long-termef-fects and the parametererror structurecontributesignifi-cantly to model fit in the incidence and quantitymodels.

Table 3 also indicates that the gain in fit for the incidencemodel arising from long-termeffects is similar to the gainfor accommodatingparameter rror.

Short-TermEffects

In Table 4, we presentthe estimated coefficients for the

best-fittingmodel (the "short-and long-termeffects with

parametererrors" model in Table 3). As is expected,increasedprice significantly decreases both the propensityto purchaseand the quantitypurchased.The negativeshort-termeffect of priceincreaseson categoryincidence andpur-chase quantity(see Table 4) has been well documented nsome research, houghotherstudies have foundno effect of

priceon purchase imingor acceleration Gupta1991). Pro-

motionshave a significantpositive effect on incidence andno effect on quantity.The strongereffect of promotionon

13We llow parameter rrors o covarywithin the incidenceandquantitymodels,because other researchershave found them to covary (Gniil andSrinivasan1993).

14Parameterrrorcorrelationsacross the incidenceandquantitymodelswere constrained to common parameters e.g., incidence price parametererrorwith quantityprice parameter rror).A nestedtest confirmsthe sus-

picion that cross-equation, cross-parametererrors do not improve themodel fit significantly.

Table 3SUMMARYSTATISTICS FOR MODELSELECTION

Incidence(n = 105,515) Quantity'(n = 3866)

Degrees of Degrees ofModell Freedom InL L. R. Test Freedom/ InL L. R. Test

Short-term ffects with no parameter rrors 5 -16081.3 86.6* 14 -13758.8 259.2*Short-term ffects with parameter rrors 11 -16054.1 32.2* 24 -13638.9 19.4*Short-and long-termeffects with no parameter rrors 9 -16058.4 40.8* 18 -13749.0 239.6*Short- and long-termeffects with parameter rrors 15 -16038.0 -28 -13629.2

*p < .001.

aSummary tatisticsfor quantitymodelsareconditional on selected incidencemodel.

bTheshort-term ffects modelsconstrain Y ... y = 0, y ... y = 0). The no-parameter-errors odels constrain v ... v31 = 0, V1q ... V1 =0,q?T a o2t,p = (oa 2,and i = o ).t ( 0) I 0) n oqit 0

I

I I I I I I I I I I I I I I I I I I I I I I I I I I I

257

--------

- - -- - - - - - --- - - - - - - - -

W - - -'5Ze- v - - - -- - - - - -

- Z - - - -

-

----

- ;






the incidence decision is similar to Gupta's (1988) andKrishnamurthi nd Raj's (1991) studies. Increased inven-

torysignificantlyreducespurchase ncidences.The negativeeffect of inventoryon purchase ncidencehas been well doc-umented n some research BucklinandGupta1992; Gupta1988), whereas other studies have found no effect (Chinta-gunta 1993).The effect of inventoryon purchasequantity s

negativeand strongly significant. Again, though seeminglyintuitive,some studies have found little effect of inventoryon purchasequantity(Gupta1988). Finally,two of the con-trol variables (mean purchase quantityand purchaserate)arestronglysignificantandcorrectlysigned.The remainingcontrol variable, loyalty, is insignificant.This finding isconsistent with Bodapati,Lal, and Padmanabhan's1997).The strengthand consistencyof the short-term esultshelplend validityto our model findings.

Long-TermEffects

Inplied duration of promotions. We performeda gridsearch to obtainthe best-fittingvalue for the lag parameter,k. The likelihood maximizingestimatefor the promotionallag is .96. To assess the durationof the long-termeffect ofpromotions,we calculatedtheimplied90% duration nterval

(Clarke1976) for this value of lag andcompared t to stud-

ies that assess the long-termeffect of promotionson choice.Witha decay parameter f .96, the implied90% duration n-terval(Clarke 1976) is approximately56 store visits, or 21weeks. This result s similarto the duration ntervalestimat-ed by Mela,Gupta,andLehmann 1997), who find the 90%

decay interval of both long-termpromotionaland advertis-

ing effects on brandchoice to be approximately2.58 quar-

terlyperiods,or 33 weeks. Our

decayestimateis also simi-

larto, but less than,the advertisingdecay found in Clarke's

(1976) study, of approximately39 weeks, which suggeststhatpromotionshavea slightly less enduringeffect.

The long-term impact of promotions on category pur-chase incidence.H1suggeststhat the main effect of a house-hold's long-termexposureto promotionson its likelihood of

purchase ncidence is negative.The coefficient for the maineffect of long-termpromotions s negativeandstrongly sig-nificant(see Table4).

H2suggests that,in the long term,promotionswill makehouseholdsless promotionsensitive, andas is expected, weobserve a negative and significant resultfor the long-termeffect of promotionson promotionsensitivity. This result

confirmsourpremisethatconsumers seem to be more will-ing to foregopromotions,presumablybecausetheyexpect abetterdeal to be just around he corer. H3arguesthatpro-

Table 4

MAXIMUMLIKELIHOODESTIMATES

Coefficient

InterceptPricePromotion

Inventory

LoyaltyMeanQuantityPurchaseRate

Long-TermPromotionMain effectEffecton promotionsensitivityEffect on price sensitivityEffect on inventorysensitivity

ParameterVariance

aprice

(prolno

Oinv

Within Model Covariance

(pricepromo

(priceinv

(promoinv

Across Model Covariance

'qiO

Cqlpriceprice

Oql promopromo

Oqlinvinv

Log-likelihood

n

Purchase Incidence

(Stacndard rror)

-18.69 (.086)**-.26 (.081)**

.68 (.080)**-1.05 (.076)**

PurchaseQuantity(StandardError)

30.53 (1.496)**-1.01 (.186)**

0.00 (.147)-.74 (.146)**

.53 (.453)7.55 (.164)**

1.78 (.074)**

-.39 (.090)**-.15 (.071)**-.12 (.074)*-.07 (.071)

.011.641.47

-1.66

-.55-.42

.57 (.156)**-.02 (.088)-.33 (.189)**-.13 (.131)

4.951.111.03

-1.42

1.89.41

-.30.11

0.00-.03

-13629.2516038.50

105515 3866

Note:All independentvariablesare standardizedo meanzero and variance 1. Bold indicatessignificantcoefficient.

To conserve space, we do not reportbrand-specific onstants n the quantitymodel.

*p < .05 one-tailed test.

**p < .025 one-tailedtest.

258






Table 5

SIMULATIONESULTS

Elasticities

TotalEffect IncidenceEffect QuantityEffect

Increaseprice 1%short-term ffect only -.64 -.38 -.26Increasepromotions1%short-term ffect only .12 .12 0.00

Increasepromotions1% ong-termeffect only -.12 -.16 .04Increasepromotions1% otal effect 0.00 -.04 .04

motions will make households more price sensitive in theincidence decision, and the result is nearlysignificantin aone-tailed test. The finding implies that it is becomingincreasinglydifficult to maintain or raise pricesas promo-tions become morecommon.

Althoughthe sign of the long-termeffect of promotionson inventorysensitivity is as predicted,the relationship snot significant.Therefore,H4is not supported.

The long-term impact of promotions on conditional pur-

chase quantity.As is hypothesizedin H5,the long-termef-fect of promotionson average quantitybought, given a pur-chase incidence, was significantly positive. This result,combined with the finding that the long-termeffect of pro-motionson purchase ncidence is negative,providesfurtherevidence for the lying-in-waitheuristic.

Our speculative hypothesis, H6, regardingthe effect of

promotionson promotion sensitivity is not supported.This

finding, coupled with the insignificantshort-termeffect of

promotionson quantity,is consistent with Gupta's (1988)findingthatpromotionsplay a greaterrole in the incidencedecision than they do in the quantitydecision. H7 is sup-ported,which suggests thathouseholds are becomingmore

price sensitive in the conditionalquantitydecision as pro-

motionsbecome morecommon.Inventory sensitivity increases as promotions become

more commonplace, which weakly supportsH8 (p < .16).This result indicates that households burdenedwith largeinventories are likely to purchase lower quantities in re-

sponse to increased ong-termexposureto promotions.Thisresult might account, to some degree, for the insignificantresult regardingthe effect of inventory in studies such as

Gupta's (1988). In categories in which promotionsare in-

frequent, inventory might not play a significant role in

stockpilingbehavior.

Managerial Issues

Assessing long-and short-term

incidence and quantityprice and promotion elasticities. Our empirical results sug-gest that promotions have a positive short-termeffect on

stockpiling. They also indicate that the long-termeffect of

promotions s negativeon incidenceandpositiveon quanti-ty. It is not clear, however, if the short-termgains frompro-motions outweigh the long-term losses and whether inci-dence accounts for more of the promotions' effects than

quantity.We addressthese issues by runninga simulation o

decompose the total impactof a 1%increase in price and

promotionsinto short- and long-termeffects and into inci-dence andquantityeffects.

To obtain the price decomposition, we employ the fol-lowing procedure:Let Q0 be the simulatedpurchasequan-

tity using actual data. Let Qi be the simulated purchasequantity,given a 1%price increasein the incidencemodelandno priceincrease in the conditionalquantitymodel. Let

Q2 be the simulatedpurchase quantity, given a 1% priceincrease n boththe incidenceandpurchasequantitymodels

(Guadagniand Little 1983). The total price elasticity,mlt,s(Q2- Qo)/1lSimilarly,the incidenceprice elasticity, rl, is

(QI - Qo)/l. The quantity price elasticity is then the differ-ence between the totalelasticityand the incidenceelasticity,

rlt- mrl.We follow the same procedure o decompose pro-motionalresponseinto an incidence and a quantitypromo-tional elasticity. In this simulation,the promotionvariablewas increasedby 1%,and the long-termpromotionalvari-able was updatedaccordingly.The incidence and quantityeffects were separatedas outlinedpreviously.To partial heshort- and long-termeffects of promotion,we constrainedthe long-termpromotionvariablesto be unchangedwhile

increasingthe short-termpromotionalvariableby 1%.Thissimulationenabled us to calculate the short-term ffects of

promotionson incidence and expected quantity.The long-termeffect of promotions hen is obtainedby takingthedif-ference between the total effect of promotions and theshort-term ffect of promotions.The resultsof these simula-

tions arepresented n Table 5.The overall stockpiling price elasticity is -.64, which is

lower thanTellis's (1988) meta-analyticmeanpriceelastic-

ity of-1.76. We suspectour lowermeanarises frommodel-

ing categoryincidenceandquantitypriceelasticities,as op-posed to brandswitching elasticities (Gupta 1988), whichoften are found to be greater n magnitude.Consistentwith

Gupta's (1988) work, the incidence elasticity (-.38) is

greater han the quantityelasticity(-.26).The estimated short-termpromotionalelasticity is .12.

The low elasticityarises fromtherelativelymodestincrease

representedby a 1%change in the frequencyof promotions.We furtherobserve that the combinedlong- and short-term

elasticityof promotions s zero. Inthiscategory,stockpilinginducedby promotions n the shorttermis essentiallyoffset

by reduced demandin the long term.In essence, increasedsales are more the result of borrowedfuturesales than in-creasedconsumption,andthere s no categoryexpansionef-fect. The long-termeffects areconsistentwith our hypothe-ses; in responseto increasing promotions n the long term,households are buying moreproduct, ess often. This result

suggests an increasedsophisticationon the partof house-holds in their ability to buy on promotion(i.e., lying inwait).

Managerial implications. The insights arising from bothour simulationand model have several importantmanageri-

259






al implicationsregarding he profitabilityof promotions.15The simulationsuggests that, in the long term,promotionsdo not increasecategorydemand. Because promotionscosta greatdeal, it seems apparent hatpromotionsreducecate-

gory profitability.As a caveat,we note that this observation

might not be surprising.In matureproductcategories, pro-motions are designed to induce stockpiling and brand

switchingrather han increasecategorydemand.However,

the long-termeffect of promotionson primarydemandhasimplications orthiscompetitivegame.Thefindingthatpro-motions do not tend to increasecategorydemandsuggeststhatpromotionsmight leadto more of a prisoner'sdilemma

problemthan was previouslysuspected.Our consumer model also explicitly suggests some

behavioralmechanisms by which promotionscan under-mine profitability in the long term. First, promotionsincreaseprice sensitivity.The increase constrainscategoryprofitabilitybecause it becomes more difficult to raise

prices. Second, promotions lead to greater inventories,

thereby suppressing demand in subsequent,nonpromotedperiods. As a result, the likelihood of nonpromotedpur-chases

decreases,which furtherreduces

categoryprofitabil-ity. Third, promotionsbecome increasinglyless effective,makingit necessaryto offer morecostly promotions n thefuture.Fourth,the patternof lying in wait for promotions(buyingmore, less often) leads to greatervolatilityin sales.This volatilityexacerbates he task of managing nventories,

thereby ncreasingcosts and furtherreducingcategoryprof-itability.Each of the behavioralchanges broughtabout bypromotions has conspired to induce potentially serious,albeitunintended,consequencesfor managers.

In summary,our analysishas several important amifica-tions for managersregardinghow promotionsaffectcatego-ry profitability n the long term. Furthermore,manyof thebehavioral indingsregarding onsumerstockpiling provide

novel insights into how promotionsaffect sales. Many ofthese long-term implicationshave been theorized widely,yet our analysis is one of the first to offer empiricalsub-stantiationof these theories.

Comparing the long-term effects of price and nonprice

promotions.An additional ssue of managerial nterestper-tains to whetherdifferenttypesof promotionshavedifferent

long-termeffects on stockpiling.Forexample, manufactur-ers mightwant to know whetherprice-orientedpromotionshavea greater endencyto induceconsumers'lie-in-wait be-havior over the long term.Were price-orientedpromotionstheprimarydriversof suchbehaviors,managersmightwantto alter theirpromotionalmix. Because of the near multi-

collinearityof long-termpromotionsin our data (determi-

nant of split promotionquantitymodel regressors= .0006),we were unableto test directlyfor these differentialeffects

by including price and nonprice promotions in the samemodel.Consequently,we reestimatedourmodelby redefin-

ing promotionas price promotions.We then repeatedthe

analysis by redefining promotionsas nonpricepromotions.Although subject to variableomission bias, the results ofboth models were nearlyidenticalto the resultsof the com-

posite model. Using a pairedcomparisont-test, we foundnoneof the parameterso be significantlydifferent(p < .05)

15Weare grateful to an anonymous JMR reviewer for motivating this dis-

cussion and providing many of these implications.

across models. However, as the long-term promotionalstrategieswere too highly correlatedin our data to assess

confidently how such effects differ, we recommendthatmanufacturersonsider"decoupling" heirdifferingpromo-tional activities in certaintest markets o exploremorefullywhetherpriceandnonpricepromotionshavediffering long-term effects on stockpiling.

CONCLUSIONS

Contributions

The goal of this studyis to assess the long-termeffect of

promotions on consumer stockpiling behavior. Such an

analysis complementsrecent researchinto how increasingpromotionshave affected consumerbrandchoice. To ana-

lyze how suchincreasesaffectstockpilingbehavior,we gen-erate several hypotheses regardinghow increases in the

frequency of promotions affect households' decisions

regardingwhetherand how much to buy. These hypotheseslead us to develop a joint model of purchase ncidenceand

purchase quantityin which households' responsivenessto

price and promotionare allowed to vary with changes in

households' exposure to promotionsover long periods oftime.Given the longitudinalnatureof the researchquestion,we calibratethis model on an eight-and-one-quarter-yearscannerpanel.

We hypothesizethat households develop price expecta-tions on the basis of theirpriorexposureto promotionsovera longperiodof time,suchas monthsoryears.Weargue hatthese expectations,coupled with the costs of inventorying

product, ffectconsumerpurchase imingandpurchasequan-

tity decisions. We assertthat increasingexpectationsof fu-turepromotions eadto (1) a reduced ikelihoodof purchaseincidence on a given shoppingtripand(2) an increase n the

quantitybought when a purchase s made. This strategyis

consistentwith a consumerlearning

to wait forespeciallygood deals and then stockpiling when those deals occur.

Overall,ourresultsareconsistentwith thesehypotheses.The resultthatconsumersadaptbuyingstrategieshas an

importantanalytical implicationpreviously raised by As-

suncaoandMeyer(1993), ErdemandKeane 1996), andoth-

ers. Traditional ptimizationmethodsuse marketingmix re-

sponse functionsto determineoptimalpricingand promo-tion. However, after a policy is developed, the consumers

adapt,whichpossiblyleadstonew optima.Erdemand Keane

(1996) suggest thatthe failureto accommodatedynamicsin

behavior eadsto biasin theestimatesof policyoutcomes.By

accommodatingchanging behavior, structuralmodel ap-

proaches uchas ourstake animportantteptowardenabling

estimatesof optimal evels of marketing ctivity.Finally,we offer severalinsightsregarding he long-term

effect of promotionson category profitability.Promotions

lead to higherprice sensitivities, reducedpromotionaleffi-

cacy, greater inventories, and higher demand volatility.These effects all conspireto hurtcategory profits.Although

many prior studies have theorized such effects, ours is

amongthe first to documentthem.

Further Research

Our resultsandanalysessuggest severalextensions.First,our model is calibratedon one categoryin one market.As

additionaldatasets with a sufficienthistoryof promotional

260






activity and purchasing behavior become available, general-

izability of our results across categories will become feasible.

Second, our theory assumes that households develop a

knowledge of prices on the basis of prior exposure to promo-tional activity. Such an assumption might be reasonable

(Krishna 1991), but it could benefit from more thorough test-

ing. Third, long-term exposure to promotions also mightaffect consumption and purchase rates. Some early short-

term work has been done in this area (Ailawadi and Neslin

1998; Assuncao and Meyer 1993; Wansink and Deshpande1994) and could benefit from taking a varying parameter

approach.The recent interest in research into the long-term effects

of promotions and the opportunities that remain for addi-

tional research suggest that this research stream will provefruitful in years to come. We hope that other studies will

continue to use the long streams of scanner panel data that

are increasingly available to explore further the longitudinaldimension of consumer behavior. The limited amount of re-

search regarding the long-term effects of marketing activity,when compared to the substantial body of work regardingshort-term

effects, suggests that such exploration can con-tinue to yield high dividends to researchers and managers.

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