An Empirical Investigation of the Spillover Effects of Advertising and Sales Promotions in
Umbrella Branding
Author(s): Tülin Erdem and Baohong Sun
Source: Journal of Marketing Research, Vol. 39, No. 4 (Nov., 2002), pp. 408-420
Published by: American Marketing Association
Stable URL: http://www.jstor.org/stable/1558554
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TULINERDEMand BAOHONGSUN*
The authorsinvestigateand findevidencefor advertisingand sales
promotion spillover effects for umbrella brands in frequently purchased
packaged product categories. The authors also capture the impact of
advertising (as well as use experience) on both utilitymean and variance
across two categories. They show that variance of the random component of utility declines over time on the basis of advertising (and use
experience) in either category. This constitutes the first empirical evidence for the uncertainty-reducing role of advertising across categories
for umbrella brands.
An
of the
Spillover
Empirical Investigation
Sales
of Advertising and
Effects
Promotions in Umbrella
Branding
may be insufficientfor umbrellabrandingto generate savings in marketingcosts and enhance marketingproductivity
over time. To increasesavings in marketingcosts over time,
the marketingmix, such as advertising and sales promotions, must be more effective for umbrellabrandsthan for
other brands. For example, Tauber (1981) suggests that
umbrellabrandingcan create advertisingefficiencies. Leigh
(1984) finds some experimentalevidence for better recall
and recognition performance for umbrella branding than
other branding strategies. Yet there is surprisingly little
empirical work on advertisingand/or any other marketingmix synergies for umbrellabrands.
The objectives of this study are twofold. First, we test for
marketing-mixstrategyspillover effects of umbrellabrands
in frequently purchased packaged product categories. We
are especially interestedin testing for own- and cross-effects
of advertising,that is, whether advertising in one product
category affects sales in another product category for
umbrella brands. However, we investigate the crosscategory effects of all marketing-mix effects, including
price, coupon availability, display, and feature. Another
relatedobjective is to shed some light on the magnitudeof
any effects that may exist. We seek to answer the following
questions: (1) What is the impact of the cross-effects on
sales? (2) How large are the cross-effects of the marketing
mix elements in relationto the own-effects? and (3) Which
marketing-mix elements seem to reveal higher crosseffects?
Second, we explore the dynamics behind the advertising
spillover effects. Similar to use experience, advertising
spillovereffects occur for variousreasons,but they may also
transpirebecause of the uncertaintyreduction.More specifically, we examine whetherthere is a reductionin consumer
uncertainty in one category due to advertising of the
Many companies widely practice umbrella branding.
Also, a fair amount of managerial research argues that
umbrellabrandinggeneratessavings in branddevelopment
and marketing costs over time (e.g., Lane and Jacobson
1995; Tauber 1981, 1988) and enhances marketingproductivity (e.g., Rangaswamy,Burke, and Oliver 1993).
Wernerfelt(1988) has shown analytically that a multiproductfirm can use its brandname as a bond for quality
when it introduces a new-experience product. Umbrella
brandingis posited to both increaseexpected quality(Wernerfelt 1988) and reduce consumer risk (Montgomery and
Wernerfelt1992). Consumers'use experience in one product category needs to affect their perceptionsof quality in
anotherfor umbrellabrandingto serve as a credible signal
of a new-experienceproduct'squality,becausea false signal
would be costly if the quality of the extension turnedout to
be poor. Experimentalresearch has shown some evidence
that the parent brand'sperceived quality affects the extension evaluations (Aaker and Keller 1990) and vice versa,
which indicatesthatbrandequity dilutionmay occur (Loken
and RoedderJohn 1993).
Although the cross-categorylearningeffects for umbrella
brandsare a necessary condition for umbrellabrandingto
functionas a signal (Erdem 1998), the learningeffects alone
*TulinErdem is an associate professor,Universityof California,Berkeley (e-mail: erdem@haas.berkeley.edu).Baohong Sun is an assistant professor, Kenan-Flagler Business School, University of North Carolina,
Chapel Hill (e-mail: sunb@bschool.unc.edu).The authorsthankthe three
anonymous JMR reviewers for their constructive feedback. The authors
also thank Andrew Ainslie and Michael Keane for their suggestions in
regardto the updating mechanism of the stochastic component of utility
employed in this article. This researchwas supportedby NationalScience
FoundationgrantSBR-9812()67 grantedto Tiilin Erdelm.
Joi)rltl
o1!f Marketing Research
Vol. XXXIX (Novcelbhcr 200(2), 4()8-420
40()
409
Advertising and Sales Promotions in Umbrella Branding
umbrellabrandin anothercategoryover time. If uncertainty
exists and advertisingreduces consumer uncertaintyabout
products,advertisingis expected to shift both utility mean
and variance.To model and test for both utility mean- and
utility variance-shiftingcross-effects of use experience and
advertising,we develop a novel and parsimoniousapproach.
Our approachenables us to test whether there is any evidence for the uncertainty-reducing
role of advertisingacross
categories.
We use ACNielsen scanner panel data on two categories-toothpaste and toothbrushes-to test for marketingmix synergies for umbrellabrands.For the first time in the
literature,on the basis of our analysis of revealedpreference
data, we find evidence for advertisingspillover effects for
umbrellabrands.Furthermore,we find that advertising(as
well as use experience) in one category serves as a mechanism of reducing uncertainty in the other category for
umbrellabrands.In addition, our results indicate that there
are spillover effects of price, coupon availability,and display. Feature cross-effects exist only in the toothbrush
category.
The rest of the article is organizedas follows: In the first
section, we briefly discuss the relevantliterature.In the second section, we outline the proposedmodel and the estimation method. In the third section, we briefly describe the
data. We then outline the results and conclude the article
with implicationsand a discussion of furtherresearch.
LITERATURE
REVIEW
Previous researchin marketinghas studied the effects of
sales promotionsacross categories for complementaryand
substitute products (e.g., Mulhern and Leone 1991) and
multicategory choices (e.g., Bell and Lattin 1998; Manchanda,Ansari,and Gupta 1999). A separatestreamof literature focuses on cross-categorytraitsof consumerbehavior
and finds that consumer price sensitivities (Ainslie and
Rossi 1998; Erdem 1998) or sensitivities to state dependence (Erdem 1998; Seetharaman,Ainslie, and Chintagunta
1999) can be correlatedacross categories.
However, there is surprisingly little published research
with respect to cross-categoryeffects in choice for umbrella
brands. Erdem and Winer (1999) have shown that brand
preferencescan be correlatedacross categoriesfor umbrella
brands.Erdem (1998) has studied how consumers'experience in one category may affect theirquality perceptionsin
another category for umbrella brands. In a two-category
context, Erdem has shown that consumer mean quality
beliefs, as well as the varianceof these quality beliefs, may
be updated through experience in either category for
umbrella brands.Thus, Erdem studied only cross-category
use experience effects.
Specifically, Erdem(1998) does not study any promotion
or advertising effects, neither own-category nor crosscategory, which is the topic of this article. Therefore,she
does not study and answerany questionsaboutthe existence
and magnitude of cross-advertisingand promotioneffects
for umbrellabrands.Furthermore,her model does not incorporatepurchaseincidenceand coincidence in choice, thatis,
the possibility that the stochastic component of the utility
function could be correlatedacross categories. In addition,
Erdem'sapproachonly allows for use experienceeffects due
to learning, whereas the approach we take in this article
allows for both learning effects and other sources through
which use experience may affect currentchoices. Furthermore, the approachErdemtakesto incorporatethe impactof
use experienceon utility varianceis difficult to apply to settings in which cross-categoryvariancereductioneffects can
be due to other variablesas well (such as advertising).The
approachtaken in this articleprovidesa more parsimonious
and novel way to incorporatesuch effects. Finally,we allow
for more complex heterogeneity structuresin this article
than Erdemdoes.
Manchanda,Ansari,and Gupta(2000) also find some preliminaryevidence for spillover effects in store promotional
activity for umbrella brands;however, they have not analyzed advertising and they have not allowed for choice
dynamics.
THEMODEL
UtilitySpecification
Considera set of consumersI = {ili = 1, 2, ..., I that,on
each purchaseoccasion, makes purchasesfrom none, one, or
both of two distinct productcategories m = 1, 2, where I is
the numberof consumers.A nonemptysubset, but not necessarily all, of these consumersmakes purchasesfrom both
of the categories. Suppose that for both productcategories,
the purchasesof the consumersare observedover the period
T = tit = 1, 2, ..., T}, whereT is the time span of the period
and t denotes the week. Let J = (jlj = 1, 2, ... J} be the set
of brand options available, where j = 1 ..., J brands are
available in at least one of the two categories and J is the
numberof brands.Note that we set j = 0 to indicatethe nopurchaseoption (choice).
Let Knj,m = 1, 2, be an indicatorvariablesuch thatK,-j =
I if brandj is available in category m, and K,n.= 0 otherwise. Also let Dmijt,m = 1, 2, be an indicatorvariablesuch
that if consumeri purchasesbrandj at time t in categorym,
D,iji = 1, and D,,jt = 0 otherwise.Finally,denote the utility
consumeri derives from purchasingbrandj in categorym at
time (week) t by Uijt, which is postulatedto be
(1)
Umijt
= (tmij +
,miPRmiit
+ ymiADmiit + XmiDlSPmijt
+ TmiFEATmijt + OmiCAVmijt+
KmiUEmijt
+ K3-m,j(miPR3-
m,i,j,t + tmiAD3-m,ij,t
+ lmiDISP3 m,i,j,t + VmiFEAT3,- ,i,j,t
+ ,nmiCAV3
- m,i,j,t +(On, i UE3 m,i,j,t) + ?Emijt,
where PR,,ijtis the price paid for brandj in category m by
consumer i at t, AD,n,jtis the advertisingstock variablefor
consumer i with respect to brand j in category m at t,
DISP,nijtis the display dummy, FEAT,nitis the in-store ad
dummy, CAV,ijt is the coupon availability variable, and
UE,nijtis the use experience of brandj in category m consumer i has at t. Equation 1 suggests that if brandj is available in both categories, the utility of purchasingbrandj in
one of the categories will also depend on the price, coupon
availability,advertising,display,feature,and use experience
associated with the same brandname in the othercategory.
INote that in Equation I, cross-effects are specified for umbrellabrands
only. In the empirical analysis, to ensure that the effects exist for only
umbrella,we also tested whethersuch effects exist for otherbrandsas well,
and we did not find evidence for such effects.
410
JOURNAL OF MARKETINGRESEARCH, NOVEMBER 2002
The parametersin Equation I are as follows: a,ij is the
consumer- and brand-specificconstant (brand preference),
and imi is the consumer-specific price coefficient. Moreover, Ymi,Xni, tmi, Omi, and Kmi are the consumer-specific
advertising, display, feature, coupon, and use experience
coefficients in category m (we label these own-effects
response coefficients), whereas, ini, tmi,
mi, Vmi, cmi, and
are the price, advertising,display,feature,coupon availability, and use experience coefficients of the price, advertising, display, feature, and use experience variablesin the
other category, respectively (we label these cross-effects
response coefficients). Similar to Erdemand Keane (1996),
we specify i's utility from not purchasingany brandin category m at t as2
()mi
(2)
Umi0t = (mo + TRENDmt + emot,
where TRENDMis the time trendcoefficient, and for identification purposeswe set
(3)
(mO
= 0,
m = 1,2.
The use experience variable, UE,ijt, in Equation I is
defined as the exponentiallysmoothed weighted averageof
past purchases, as in Guadagni and Little's (1983) brand
loyalty variable. Its decay parameteris denoted by DUm.
Similarly,AD,nijtis defined as the exponentially smoothed
weighted average of past aggregate gross rating points
(GRPs).3 Its decay parameter is denoted by DAm. The
AD,,,ijtis updatedon a weekly basis. Note thaty,i (andr1mi),
the coefficient on the advertising stock variable, embeds
both a consumer'stelevision commercialviewing habitsand
responsivenessto the advertisements,conditionalon having
seen them. Because advertisingeffectiveness in the context
of television advertisementsdependson television and commercial viewing habitsof consumers,we can justify thaty,,
(and tl,) representsadvertisingresponsivenessat the individual level.4
We define the DISP variablesuch that DISP,nij,= I if a
display is availablefor brandj in categorym in the store that
consumer i visits at t, whereas DISP,,ijt= 0 otherwise.Similarly,we define the FEATvariablesuch thatFEAT,ijt= 1 if
a feature is available for brandj in category m in the store
= 0 otherwise.
that consumer i visits at t, whereasFEAT,nijt
of
is
the
value
the
store's
and manaverage
Finally, CAV,,ijt
2Similarto Erdem and Keane (1996), our purposeis to control for purchase incidence ratherthanexplicitly model purchaseincidence decisions.
3An alternativeto GRP data is television exposure data;however,television exposure data are not available because ACNielsen discontinuedthe
collection of such data in the early 1990s. Televisionexposuredatahave the
advantageof havingthe estimatedadvertisingcoefficient reflectadvertising
responsivenessonly because the advertisingvariableitself will capturethe
television/commercial viewing habits. However, the television exposure
data are noisy as well, because we only knew that the television was tuned
to that channel duringthe airingof a particularcommercial.One advantage
of the GRP data, in contrast,is that GRP is underthe direct control of the
firm whereas television exposure is not. Therefore, firms are more interested in models that use GRP data (Abe 1997). Finally, Tellis and Weiss
(1995) find that advertising effectiveness results are not sensitive to the
types of advertisingmeasures.
4Wealso have GRP data for different"dayparts"(e.g., primetime versus
late morning).When we used these dataandestimatedEquationI with separate coefficients for each daypart,we obtained largeradvertisingcoefficients for certain daypartsthan others, as we would expect, but the main
results are the salmewhetherthe daypall data are used or not.
ufacturer'scoupons availablefor brandj in categorym in the
store thatconsumeri visits at t. It includes the zero value for
nonavailable coupons (Keane 1997b). We introduce this
variableto avoid the endogeneityproblemcaused by including coupons redeemedin the utility specification.Last, Emijt
in Equation 1 denotes the time-varyingstochastic component of utility that is knownby the consumerbutnot observable by the analyst.
We should note that the cross-effects specified in Equation 1 may exist for several reasons. First, price cuts,
coupons, advertising,displays, and featuresin one category
may remind the consumer about the umbrella brand and
thereforeincreasethe likelihoodof theirchoosing thatbrand
in anothercategory, conditional on productcategory decision. In the case of promotionaltools such as displays, the
promotionof the umbrellabrandin one category may help
promote the umbrella brand in consumers' consideration
sets in the second category, in which the brandis not promoted, as well. Advertisingcan be expectedto increasegeneral brandawareness of individualproductsthat share the
same brand name. Cross-category use experience effects
may be due to habit formation.
Second, the effects of use experience and advertisingon
consumeruncertaintymay cause use experienceand advertising spillover effects. If use experience affects quality
beliefs, both the mean and variance of these beliefs may
change on the basis of use experience. Therefore, if use
experienceaffects consumeruncertainty,we shouldexpect it
to affect both the utility mean and variance in a reducedform model. The same rationale holds for advertising.
Erdemand Keane (1996) have shown that in the context of
one category, both use experience and advertising affect
both mean and varianceof qualityperceptions.Therefore,if
advertisingspillover effects are partly due to the effect of
advertisingon consumer uncertainty,we should expect utility variance(that is, the varianceof the randomcomponent
of utility) to decline over time on the basis o' advertising.
We capture these utility variance effects by adopting a
novel approach.Specifically,we allow for not only the "random" components of utility to be correlatedacross categories but also the variancesof the randomcomponentsfor
umbrellabrandsto be updatedon the basis of past purchases
and advertising associated with umbrella brands in either
category.We cover this issue in more detail subsequently.It
may suffice to state that we attemptto capturethe effect of
use experience and advertisingas a shifter of both utility
mean and variance; the latter effect should occur mainly
because the consumer uncertaintydecreases over time on
the basis of past purchasesand advertising.
Heterogeneity Specification
It is well established that not accounting for unobserved
heterogeneitycan bias parameterestimates(Heckman 1981;
Rossi and Allenby 1993). In this article, we account for
unobservedheterogeneityas well. To do so, we assume that
brand preferences for each category A,mi= (a,n,i, aii2, ..
a1ij)T; own-effects response coefficients B,,i =(P:- i, ni,
, lmi, K,lni)T;and cross-effects response coefficients
mi'
mti
t li,
Cmi = (i,
lli, Vm,I,
i O,,1i)T, m = i, 2, where the
superscriptT denotes the transpose,are normallydistributed
with the following mean vectorand covariancematrix:
411
Advertising and Sales Promotions in Umbrella Branding
-A7 ii
-Al-
Bli
B,
Ci
C,i
(4)
A2i
to be more advertisingsensitive than the averageconsumer
in the toothpaste category. The corresponding crosscategorycorrelationscan be calculatedfrom the covariance
matrixn. We denotethese cross-categorycorrelationsby A.6
- Nc
A2
B2i
B2
-C2i
-C2.
Choice Probabilities
Let us rewriteEquation1 for each categorym, wherem =
1, 2, as
-'
-E;A
"A1B,
YBB
]EBI
"AlC,
nBC
B11C,
?C|
!AIB2
HAB
IIAIC,
nAC2
BBIA2
!CiA2
nClB
!BiB,
nBC
-CC2
nAA2
nBB2
I"AB2
nciA2
?A2
"CIB
A,B2
mmijt
Vmijt
+
?mijt'
A,C2
nBiA,
nA2C2
(5)
i"A2B2
EB2
A 2C2
nB,C2
nCC2
r[
A2C2
A2C2
ZC2
The vectors Al, B , Ci are the means in Category I and
the vectors A2, B2, C2 are the means in Category 2. The
block diagonal matrices EAI, E?B,ICI, IA2, ?B2, and ?C2
are the covariancematricesof A l, Bij, Cli, A2i,B2i,and C2i,
respectively.The diagonal elements ofA AI, B 1,ECi, IA2,
ZB2, and ZC2themselves containthe variancesof the corresponding heterogeneity distributions. Finally, F! are the
appropriatecovariancematrices.
Within-CategoryCorrelations
Given the large numberof covariancesto be estimatedas
shown in Equation4, we allow only a subsetof these covariances to be nonzero for parsimony.5We allow heterogeneity
in all brand-specific constants and allow own and cross
marketing-mix response and use experience coefficients;
however,we estimate and reportin the "Results"section of
the article only the following covariances (correlations)
among the heterogeneity distributions: (I) correlations
among the brand specific constants and price (as well as
advertising)sensitivities, (2) pairwise correlationsbetween
own price (and advertising) sensitivities and all other
marketing-mixsensitivities (e.g., display), and (3) pairwise
correlationsbetween own price (and advertising)sensitivities and use experience.
Cross-CategoryCorrelations
Ainslie and Rossi (1998) suggest that consumersensitivities to marketing variables (own-effects of the marketing
mix in our model) may be correlated across categories.
Therefore, a person who is more price sensitive than the
average consumer in one category can be also more price
sensitive than the average consumer in another category.
Therefore,we also estimate nBI B2 (the covariancematrixof
Bl and B2). This means that we permit consumer price,
advertising, display, feature, coupon, and use experience
sensitivities to covary across the two categories. For example, a consumer who is more advertisingsensitive than the
averageconsumer in the toothbrushcategory may also tend
sHowever, in the empirical analysis, we tested the sensitivity of our
results to the assumptions made about covariance structureby allowing
more covariances(correlations)to be nonzero. For example, we estimated
the covariancesbetween brand-specificconstants,a few covariancesamong
the cross-responsecoefficients, and so forth,but these did not improvethe
model fit significantly and did not change the parameterestimiates.
whereVmijtis the "deterministic"partof the utility.
Let 0 denote the vector of parameters(Al, A2, BI, B2, Cl,
C2, d(ZAI), d(EA2), d(ZBI), d(IB2), d(ECI), d(IC2),
w(n))T. The vectors d(EAI), d(AA2), d(ECI), and d(IC2)
containthe diagonalelements of the correspondingmatrices
(i.e., the variances of the heterogeneity distributions of
brandpreferencesand cross-effects response coefficients).
The vectors d(EBi) and d(EB2) contain the diagonal elements(variancesof own-effects responsecoefficients)of the
correspondingmatrices, as well as the off-diagonal elements, or covariances, that we allowed to be nonzero.
Finally,the vector w(n) contains the covariancesamong the
brand-specificconstantsand advertisingand price sensitivities, as well as cross-categorycovariancesto be estimated.
Note that though we used the covariancematricesin defining the parametervectorfor notationalconvenience,we estimate the correspondingstandarddeviationsand correlations
introduced previously, instead of the variances and
covariances.
Recall thatall coefficients in EquationI are allowed to be
heterogeneousacross consumers,and their distributionsare
given in Equation4. Let (pidenote the multivariatestandard
normalvectorthatgeneratesthese coefficients.To be able to
write down the choice probabilities,we need to specify a
distribution for Emijt
We assume that ?,mijtj e J is given by
(6)
?miit
= Ymiit
-Et[Ymiit],
where y,ijt is the generator of ?,ijt, whose prior distribution
is
(7)
Y,io
Y2ij0_
N([o]P p I1
,
Note that Et[.] indicates expectation conditional on the
informationat time t. Equations 6 and 7 indicate that the
means of the randomcomponentsare always zero, whereas
the assumption is that the initial variancesare one and the
initial covariancebetween the randomcomponentsis p.
A parsimonious and new way of capturing the utility
variance-shiftingrole of use experienceandadvertisingis to
allow the initial utility variance,which we set to unity,to be
updatedconditionalon past purchases.Neitheradvertising's
6Finally,note that we adopt the recent classical inferencetechniquesto
model and estimate unobserved heterogeneity. An alternative approach
would have been Bayesiantechniques(e.g., Rossi, McCulloch,andAllenby
1996). However, the main advantageof such techniques is the ability to
providehousehold-levelparameterestimates, which is particularlyuseful if
the research objective includes micromarketingimplications, which is
beyond the scope of this article.
412
JOURNAL OF MARKETINGRESEARCH, NOVEMBER 2002
impact on the consumer utility mean nor its impact on the
consumer utility variance across categories for umbrella
brandshas been empiricallyverified in previouswork.
To capturethe effects of both use experience and advertising on the varianceof the randomcomponent,thatis, utility variance,let the informationobtainedby consumeri by
purchasingbrandj before time t fromcategorym be denoted
by xmijt.This informationcan be about attributes,including
quality,or match informationbetween consumertastes and
productofferings and the like, which are not observedby the
analyst. Let x,ijt be given by
(8)
Xmijt
=
Ymijt
+
6mijt'
where 6i.jt is an i.i.d. random error term that reflects the
level of noise in the informationobtained.We suppose that
(9)
-
,mi.t
N(O, o62).
Note that additionalinformationcomes from weekly advertisements. Denoting this informationwith z,ijt , we let
(10)
mijt
=
Ymijt
+ 1
Given these, we then assume that the randomcomponent
of utility is updated according to Bayesian updatingrules
and invoke the Kalmanfilter for updatingthe distributionof
Y,nijtand therefore of ?,j1.t There is no updatingfor j = 0,
which correspondsto no purchase.To derivethe Kalmanfilter, we regress
Ymiit- Et- I[Ymiit]
(14)
b2mijt
am,,
m,i,j,t-
b3mijt
2
b
12
_ 4mijt
i,3-m
- Et(
,mi,2 = E{[Ymijt
12i.jt=
_
12,i,j,t-I
m = 1,2
t)]2}
E {[Yijt- Et (ylijt)][Y2ijt - Et(Y2ijt)]}
Note that the precedingequationssuggest that the higher
the precisionof informationfromuse experience(l/(' n)and
advertising (I/'2 n) are (or the lower the use experience
variability, 'n,, and advertising variability,oG2, are), the
more consumerswill update.
Given Equations 12 to 14, the distributionof ?1ijt, m = I,
2 at time t is given by
2 0
-.
O _101
(T 12ijt
ijt
j=
1,2,...,J.
11t2
02i 12ijt
We then can write the choice probabilitiesconditional on 0
and (pias
(16)
Probjt(D!ijt
=
1;p)=
= 1,Dt
)
tjk(0,(Pi),
where q)tjk(-)is the appropriatecumulativedistributionfunction whose probabilitydistributionfunction is determined
from Equation 15. Note that this is a 2J + 2 dimensional
integral.Therefore,the probabilityof consumer(household)
i makingthe sequenceof purchasesgiven by D,,,jt, m = 1, 2,
j = 0, , ..., J, t = 1, ..., Ti, conditionalon 0 and (pi,is
T
(17)
J
J
Prob (0, i) = n
DlijtD2Ijtt ( pi).
t=l j =O k=0
m = 1, 2
Xmijt- Et_ I[xmijt]and Zmijt- Et_ i[Zmijt],
by suppressing the intercept,and we obtain the following
updatingformulasfor the expectations:
=
Et[Ymiit] Et _[Ymijt] + blmijt{xmijt Et,- [mijt]}
+
- Et
+ b4mijt{z3 -m,i,j,t
[nmi,j,t ])
Et[Z3
m,i,j, t]'
where bk,,lit, k = 1, 2 and m = 1, 2 are solved from
2
2
, t- I + 6m
-
Dm,i,jt-j
D3- m,i,j,t - 1o12,i,j,t -I
2
DDm i jt - I(Y
m,i,j,t - I
I
Dm,i,j,t 11(12,i,j,t
(13)
D
- Im,,j,
-
CY- 11,i,,-- II..
IniJDi.,t- I 12I,i,j,t
2 ++ 03
G
i,,t
D3 1- m i.j.t - lI312,i,,jt
D3 -m.i.,l t -
I<3-12,i,
-D0II
,3 - n
12Y
2
2
m,i,j,t - I
ni, j,t - I
-
0 2, i,j.t -
D
D3-m,i,t
j,i
2
j,- 2 I2 ++ I
j.t -I
Integratingover(Qi,we obtain
(18)
Probi(0) =
I Prob(0,(p )f/(pi 0)d(pi,
n
E - I[Y3 m,i,j,t 1)
b2mijt {x3- m,i,j,t
+b3mijt{Zm,ij,t
D
Dm,i,j,t-
and with Equation 12, the updatingformulas for the variances are obtainedfrom
on
(12)
2+
im,i,j,t - I
3-m,i,j,t - 112,i,j,t- I
"bimijt
-1
2
03-m,i,j,t-
mijt'
N(O, m 2)
rmiit
D3-m,i,j,t - (3-m,i,j,t
012,i,j,t - I
-c2ij'tL[2ijt
where rijtm is an i.i.d. randomerror term that reflects the
level of noise in the informationfrom the advertisement.It
is given by
(11)
m,i,j,t - 1 12,i,j,t - I
m
I
where Q2is the domain of the integration,whereasf((p10) is
the multinomialprobabilitydistributionfunctionfor pi,conditional on 0.
Thus, we estimate a multivariate multinomial probit
model, which has been adopted in some recent articles in
marketing as well (e.g., Manchanda,Ansari, and Gupta
1999). This model allows randomcomponents to be correlated across the m categories, which Manchanda,Ansari,
and Gupta label as "co-incidence."Thus, factors that are
unobservedby the analystbut knownby the consumercould
be correlatedacrosscategories.However,for the first time in
the economics and marketingliterature,we also incorporate
into this model a process by which the varianceof the random component (as well as the covariance of this component across the m categories) is updatedover time, on the
basis of past purchasesof the consumersand past advertisement. The extent of the updatingis determinedby l/o'1n in
the case of use experienceand by 1/o', in the case of adver-
413
Advertising and Sales Promotions in Umbrella Branding
tising; that is, the more precise the informationsource is, the
more updatingthere will be.
Given Equation18, the log-likelihoodfunctionto be maximized is
I
(19)
LogL(O)= Xln[Probi(0)],
i=l
Note that the calculationof Probi(O)in Equation19 requires
the evaluation of multiple integrals,which in turn requires
Monte Carlo simulation techniques. We use a probability
simulatoras the Monte Carlo method (Geweke 1991; Hajivassiliou, McFadden,and Ruud 1994; Keane 1990, 1994).
As our estimation method, we adopt the method of simulated moments (MSM) first developed by McFadden(1989)
and extended by Keane (1990).
EMPIRICALANALYSIS
Data
The models are estimatedon scannerpanel data provided
by ACNielsen for toothpasteand toothbrushes.Given new
productfeatureintroductionsand the existence of long-term
experience attributes(e.g., cavity-fightingpower of a toothpaste), quality uncertaintymay exist even in the relatively
matureproductcategoriestypically studiedin scannerpanel
research (Erdem and Keane 1996). Also note that because
most frequentlypurchasedproductcategories are not highinvolvementgoods, consumerstypicallyrely more on brand
names and advertisingthan on active search to resolve any
quality uncertainty.
The panels cover 157 weeks from the end of 1991 to the
end of 1994. The data sets includehouseholdsfrom two test
markets in Chicago and Atlanta.We use the Chicago panel
for model calibration.We use the Atlanta panel to assess
out-of-sample fit (i.e., the predictivevalidityof the models).
The analysis includes four brandsin each category.In both
the calibration(Chicago) and the prediction(Atlanta)panel,
we randomlydraw the panel members from the sample of
households that do not buy multiple brandsin a given category on the same purchaseoccasion.
The first two brands(Brand 1 and Brand2) are available
in both categories. Table I providessummarystatistics.The
weekly GRPs are plotted in Figure 1. The price used in the
study is the price paid without any coupons. Using a price
value net of coupons introducesa seriousendogeneityproblem; therefore, we model coupon effects by using the
coupon availabilitymeasurewe have defined previously.
ParameterEstimatesand Goodnessof Fit
We estimatedthe model describedpreviouslyand various
nested models. Table 2 reports the results for five nested
models and the full model. The sixth nested model has the
multivariateprobit specification with correlatederrorsand
allows for updatingerrorson the basis of past purchasesbut
does not allow the errorsto be updatedon the basis of past
advertising.The fourth nested model (NM4) has the multivariate multinomial probit specification with correlated
errorsacross the categories but does not allow for updating
errorson the basis of purchasesand advertising.The third
nested model (NM3) allows for neitherupdatingnor correlated errors across the two categories. The second nested
model (NM2) is similar to NM3 but does not allow for use
experience, price, coupon availability,advertising,display,
and featurein one category to affect the utility of the other
category.Finally,the first nested model (NM ) is similarto
NM2 except that heterogeneitydistributionsare assumedto
be independentacross the two categories. Both in-sample
and out-of-sample, the full model fits the data statistically
betterthan all other nested models. Therefore,we focus on
the parameterestimates obtained by estimating the full
model.
In a given category,the results indicatethatcoupon availability,advertising,displays, features,and use experienceall
have significant, positive effects on consumer utility,
whereasprice has the expected negativeeffect. Thereis significant heterogeneityin consumer tastes and responses to
use experienceand marketing-mixvariables.The time trend
in the no-purchasespecification is statisticallyinsignificant.
Given the objectives of this article, the importantparameter estimates are associated with the cross-effects of
marketing-mixvariables(and use experience),as well as the
cross-categoryparametersthat govern the process of updating the utility error term variance over time through use
experience and advertising.These parameterestimates are
markedin bold in Table 3.
We find thatuse experience in one categoryhas a positive
impact on brandchoice probabilitiesin the other category
Table 1
SUMMARYSTATISTICS
Brand Name
MarketShare
AveragePrice"
Average
GRP
Frequencyof Display
Frequencyof Feature
AverageCouponti
Toothpaste
Brand 1
Brand2
Brand3
Brand4
31.3%
20.0%
10.6%
9.5% (71.4%)
$1.81
$1.87
$1.81
$2.67
13.54
27.07
0
0
2.0%
1.6%
1.4%
1.2%
2.9%
2.8%
3.2%
1.8%
$.15
$18
$.23
$.23
Toothbrush
Brand 1
Brand2
Brand5
Brand6
10.2%
21.8%
19.4%
17.3%(68.7%)
$2.36
$1.99
$2.36
$1.96
12.62
19.75
22.84
0
1.2%
1.1%
.6%
.7%
2.6%
3.1%
3.2%
3.3%
$23
$19
$.27
$19
t'Weightedaverage price per 50)ounces for toothpaste.Weightedaverageprice per unit for toothhrush.
'Average nonzero coupon value of toothpaslenormalizedat 50()ounces. Averagenolnero coupon value of toothbrushper unil.
:
JOURNAL OF MARKETINGRESEARCH, NOVEMBER 2002
414
Figure 1
WEEKLY
GRPs
Plot of Weekly GRP for Toothpaste
100
'I
Xl
,
Week
Brand 1
------
Brand 2 [
Plot of Weekly GRP for Toothbrush
100
a-
c:
50
o .
o
1
1
,iI' . .
9
.,
*
/ '
,...
, i,,f T i (, ? 1nr
1
,,,
^'.a,.r, A 3,
.
17
25
33
41
49
57
-
65
81 89
73
r ir
97
1 11 11I i T
tTIIT
1 1l
105 113 121 129 137 145 153
Week
Brand 1 ------
Brand2
Brand5
Table 2
MODELSELECTION
In-Sample(Chicago)
-LL
AIC
NMI
NM2
NM3
NM4
NM5
FM
21323.4
21417.4
20731.6
20831.6
20212.5
20336.5
20101.2
20226.2
19510.4
19637.4
18925.3
19054.3
21839.0
13260.5
21280.1
12936.2
20892.3
12712.2
122179.6
20786.9
12367.5 2592.7
20208.0
19633.9
Out-of-Sample (Atlanta)h^
BIC
-LL
"Numberof observations= 59,032 (includingno purchases),numberof households(hhs) = 376; 167 hhs made621 purchasesof toothbrush;345 hhs made
2880 purchasesof toothpaste; 136 hhs purchasedboth toothbrushand toothpaste.
hNumberof observations= 30,301 (includingno purchases),numberof hhs = 193; 92 hhs made 198 purchasesof toothbrush;186 hhs made912 purchases
of toothpaste;85 hhs purchasedboth toothbrushand toothpaste.
Notes: LL = log likelihood;AIC = Akaike informationcriterion;BIC = Bayesian infornation criterion.
for umbrella brands. We also find statistically significant
cross-categoryeffects of advertisingin both categories.Furthermore,we find evidence thatcoupon availabilityand display (significantat the p <. 10 level) in the toothbrushcategory affects consumer utility in toothpaste positively for
umbrellabrands.Coupon availability,display,and featurein
the toothpastecategory affect consumerutility in the toothbrushcategory positively as well. Finally,prices have negative cross-category effects in both categories. Therefore, a
price cut in one category for an umbrellabrand increases
consumer utilities and thus the choice probabilitiesfor that
brandin the other category.
Turningour attention to the random component of the
consumerutility and the process of utility varianceupdating
based on use experience and advertising,we find the following results: Random components are correlatedacross
the two categories.Recall that we estimatedan initialcorrelation betweenthe randomcomponents,which we find to be
positive and significant. However, our results indicate that
both osa and oln are statisticallysignificant in both categories. Therefore,dependingon the degree of the precision
of information (i.e., 1/o,Smor I/oG,n), the variance and
covarianceo' randomcomponentsacross the two categories
are updatedwith use experienceand advertising.This result
415
Advertisingand Sales Promotionsin UmbrellaBranding
Table3
ESTIMATION
MODEL
Parameters
Brand I
Brand2
Brand3 (5)a
Brand4 (6)
No purchaseh
Brand I
Brand2
Brand3 (5)
Brand4 (6)
No purchase
Brandspecific constantsan,j
Standarddeviationof brandspecific constant nctmj
Trend,,
Mean price coefficient P1,
Standarddeviationof the price randomeffect 7p,.
Mean coupon coefficient on
Standarddeviationof the coupon randomeffect (o,m
Mean ad coefficient y,,
Standarddeviationof the ad randomeffect (9ml
Mean display coefficient kn
Standarddeviationof the display randomeffect o(,,
Mean featurecoefficient x,,
Standarddeviationof the featurerandomeffect on
Mean use experience coefficient Km
Standarddeviationof the use experience randomeffect (oc
Mean cross-price coefficient 3 - m
Standard deviation of the cross-price random effect ag_ m
Mean cross-coupon coefficient 3 - m
Standard deviation of the cross-coupon random effect 3 _ m
Mean cross-ad coefficient 13 m
Standard deviation of the cross-ad random effect a3 - m
Mean cross-display coefficient 13 _ m
Standard deviation of the cross-display random effect 3-_m
Mean cross-feature coefficient V3_ m
Standard deviation of the cross-feature random effect av3 - n
Mean cross-use experience coefficient (03_ m
Standard deviation of the cross-use experience random effect o
Use experience smoothing parameterDUM
Media smoothing parameterDA,
Correlationbetween constanttelrmsand price pa,i,p,,
_-m
Correlationbetween constantterms and advertisingPamrn,
Correlationbetween price and ad coefficients pi,,,ny
Correlationbetween price and display coefficients pp,,lmX
Correlationbetween price and featurecoefficients Ppi,Tm
Correlationbetween price and coupon coefficient Ppnmom
Correlationbetween price and use experience coefficients P,c;m
Correlationbetween ad and display coefficients Pyv.m
Correlationbetween ad and featurecoefficients Ppini
Correlationbetween ad and coupon coefficients P,nl,9
Correlationbetween ad and use experience coefficients Py,,n,
Cross-categorycorrelationbetween price coefficients AIP2
Cross-categorycorrelationbetween ad coefficients AyI.2
Cross-categorycorrelationbetween display coefficients AX.i2
Cross-categorycorrelationbetween featurecoefficients At,t2
Cross-categorycorrelationbetween coupon coefficients A0102
Cross-categorycorrelationbetween use experience coefficients A,i,e2
Cross-category correlation between error terms p (in the initial period)
Advertising variability o%for toothbrush
Advertising variability a6 for toothpaste
for toothbrush
Experience variability CYn
Experience variability Oc for toothpaste
Brand I
Brand2
Brand3 (5)
Brand4 (6)
Brand I
Brand2
Brand3 (5)
Brand4 (6)
Toothpaste(m = I)
Toothbrush(m = 2)
-2.07(.19)
-2.54(.28)
-3.29(.22)
-3.31(.17)
0
1.51(.34)
1.35(.20)
1.00(.22)
.58(. Il)
0
.015(2.53)
.010(2.57)
-1.21(.18)
.56(.16)
.82(.12)
.55(.16)
.12(.05)
.05(.02)
1.49(.26)
1.20(.25)
1.09(.40)
.82(.43)
2.74(.33)
1.86(.30)
-.11(.04)
.08(.13)
.45(.18)
.23(.09)
.011(.005)
.008(.005)
.097(.055)
.091(2.58)
.132(.083)
.085(.044)
.82(.25)
.45(.45)
.78(.12)
.28(.10)
.15(.09)
.19(.19)
.07(.11)
-.29(.05)
.12(.04)
.09(.05)
.06(.03)
.07(.05)
-.17(.08)
-.25(.10)
-.17(.07)
-.18(.07)
-.21(.09)
.11(.06)
.08(.04)
.16(.07)
.27(.12)
-3.77(.32)
-.26(.10)
-.86(.21)
-1.03(.26)
0
1.02(.18)
.47(.19)
.23(. 11)
.11(.07)
0
.002(1.10)
.001(1.73)
-.95(.13)
.45(.12)
.95(.17)
.46(.20)
.15(.05)
.07(.04)
1.36(.33)
.75(.23)
1.29(.20)
.43(.59)
2.10(.23)
1.10(.23)
-.18(.08)
.08(.08)
.47(.17)
.20(.08)
.013(.005)
.011(.004)
.147(.077)
.100(1.15)
.170(.037)
.095(.144)
.69(.14)
.20(.09)
.88(.25)
.56(.17)
.13(.08)
.17(.06)
-.08(.09)
-.25(.1 )
.14(.05)
.15(.07)
.08(.06)
.07(.05)
.16(.06)
-.23(. 11)
-.22(.10)
-.13(.07)
-.20(.1 1)
-.18(.()8)
.13(.()05)
.10(.09)
.23(.13)
.63(.13)
.44(.10)
.16(.10)
.13(.08)
.14(.10)
.42(.18)
.19(.08)
.22(.1 1)
.74(.31)
.16(.06)
.27(. 1 )
aToothpastebrandsare I, 2, 3, and 4, and toothbrushbrandsare 1, 2, 5, and 6.
hNo-purchaseoption constantis set to zero for identificationpurposes.
Notes: Figuresin boldface indicateparameterestimates associatedwith the cross-effectsof marketing-mixvariablesand the cross-categoryparamleters
that
govern the process of updatingthe utility errorterm varianceover time.
416
JOURNAL OF MARKETINGRESEARCH, NOVEMBER 2002
confirms the finding in the literaturethat use experience
may reduce uncertainty across categories for umbrella
brands (Erdem 1998) and establishes the new result that
advertisingdecreases consumer uncertaintyacross product
categories for umbrellabrandsas well.
Note also thatthe precisionof use experienceinformation
is found to be higher than that of advertisingin both categories, which is an intuitive result because use experience
should provide less noisy informationthan advertisingdoes
in most cases. Also, the ratioof advertisingvariabilityto use
experience variability is higher for toothpaste than toothbrushes; that is, although advertisingprovides more noisy
informationthandoes use experiencein both categories,the
relative noisiness of advertising informationis greater for
toothpastethan toothbrushes.
Finally, we estimated several within-categoryas well as
across-category correlations, as discussed in the previous
section. Not all the correlationsbetweenbrandconstantsand
price (advertising) responses are statistically significant.
The statistically significant estimates suggest that in the
toothpaste(toothbrush)category,consumerswho like Brand
I (Brand2) more than the averageconsumertend to be less
price sensitive than the average consumer.The reverse is
true for Brand 4. Therefore,the correlationbetween brand
specific constants and price sensitivity is positive for some
brandsand negative for others. The estimates also suggest
thatconsumerswho like Brands 1, 2, and 3 in toothpasteand
Brands 1 and 2 in toothbrushesmore than the averageconsumertend to be more advertisingsensitivethanthe average
consumer. Finally, in both categories, there are statistically
significantcorrelationsof price sensitivity with advertising,
display, feature, and use experience sensitivities. Advertising and featuresensitivities and advertisingand display sensitivities are significantly correlated in both categories as
well. Advertising and coupon availability sensitivities, as
well as advertisingand use experiencesensitivities, are significantlycorrelatedonly in the toothpastecategory.
Given the signs of the parameterestimates, these combined results indicate that in the two categories we study,
there seems to be an individual-specific trait we call
"marketing-mixsensitivity" (i.e., some consumers are in
general more sensitive to marketing-mixstrategies).Indeed,
similar results have been found in otherrecent work as well
(Arora2000). The only result that seems somewhatsurprising is the negative sign of the correlationbetween use experience and price sensitivities. Although these correlation
terms do not necessarily imply any causal links, we might
still have expected the reverseresultto hold. This is because
high use experience involves positive purchase feedback,
and consumers who are sensitive to this may be expected to
be less price sensitive (Seetharaman,Ainslie, and Chintagunta 1999). However, note that the estimate we obtain is
not necessarily counterintuitive.This is because more use
experience-sensitive people may have utility fartherout in
the tail of the distributionand may need a largerprice coefficient to have the same price elasticity.This would be consistent with high sensitivity to use experience being associated with high price sensitivity.Anotherexplanationfor the
result is that consumers with higher sensitivity to use experience may have smaller considerationsets (Rajendranand
Tellis 1994); this may increasethe abilityof use experiencesensitive consumers to make price comparisons,which may
result in higher price sensitivities in these two categories,
ceteris paribus.Finally, we also find that consumer price,
advertising,and use experience sensitivities are correlated
across the two categories we study.
Policy Simulations
To evaluatethe impactof temporarypolicy changes (i.e.,
a change in one of the marketing-mixelements in the second
week) in each category, we ran the following simulations
separatelyfor Brand I and Brand2: a 20% increase in (1)
coupon availability,(2) advertising,(3) displays, and(4) features and (5) a 20% decrease in price. We reportonly the
impact of the policy change on the cumulative purchases.
The impactof the temporarychangeon cumulativesales can
be conceptualizedas the long-termeffect of these temporary
policy changes. In regardto the short-termeffects of a temporarypolicy change (i.e., the impactof a policy change in
the beginningof the second week on sales in that week), it
may suffice to indicatethat the short-termeffects are larger.
It is also worthwhile to note that we found the long-term
effects to be largerthanthe short-termeffects in advertising.
Table 4 reports the results for the respective temporary
policy changes adoptedby Brand I in toothpasteand toothbrushesseparately.Table5 reportsthe correspondingresults
for Brand2. In bothTables4 and 5, the first half of the table
compares the baseline figures with the cumulative "sales"
figures when a temporarypolicy change was adopted in
Week 2 (orjust beforeWeek 2 in the case of advertisingand
coupon availability).In particular,the first row indicatesthe
baseline figures.The next five rows indicate the impacts of
a change in pricing, couponing, advertising, display, and
featurepolicy, respectively,in toothbrushon toothpasteand
toothbrushsales. The following five rows revealthe impacts
of a change in pricing, couponing, advertising,display, and
featurepolicy, respectively,in toothpasteon toothpasteand
toothbrushsales. The lower half of the table reportsthe percentage change in sales due to the respectivepolicy changes,
again separatelyfor toothbrushesand toothpaste.The tables
do not include the featureeffects in toothpaste,because the
cross-effects of features were found to be statistically
insignificantfor toothpaste.
To assess the impact of a 20% increase in Brand I's
advertising frequency in toothbrushes on the toothpaste
sales of Brand I, for example, comparethe baseline figure
of 1326, which reflects the baseline cumulative sales of
Brand I, with the correspondingpostintervention(a temporary increasein advertisingfrequencyby Brand I in toothbrushes at PurchaseOccasion 2) figure of 1431. This comparison reveals an 8% increase in toothpastesales due to a
20% increase of advertising frequency of Brand I in the
toothbrushcategory.
The resultson spillovereffects (cross-effects)can be summarized as follows: Overall, coupons and then advertising
have the largest cross-effects on sales in both categories
except for Brand2 in the toothpastecategory,for which features (more so than any other marketing-mixelement in the
toothpastecategory)affect sales the most in the toothbrush
category, followed by coupons and advertising. In most
cases, the smallest cross-effects are the price cross-effects.
The cross-effectsof coupon availabilityon sales rangefrom
5% to 13%acrossproductcategoriesand across Brand I and
Brand2. The ~
cross-effects
advertising
~ ~
~ ~ rangefi-om40%to 8%.
?
I
417
Advertising and Sales Promotions in Umbrella Branding
Table 4
SIMULATION
RESULTSFOR POLICYCHANGESBY BRAND1
Sales of Toothbrush
Sales of Toothpaste
Brand I
Brand2
Brand 3
Brand4
Brand I
Brand 2
Brand5
Brand6
Baseline
1326
777
424
353
99
256
160
106
Toothbrush
Price cut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincrease by 20%
1405
1464
1431
1419
734
713
734
730
402
384
388
397
339
319
327
334
121
118
125
124
116
245
245
141
243
246
154
155
153
152
155
101
103
102
102
104
Toothpaste
Pricecut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincrease by 20%
1748
1763
1665
1659
1641
519
571
594
600
604
334
299
338
340
343
279
255
283
281
292
105
112
107
105
104
252
248
252
253
255
159
158
157
159
158
105
103
105
104
104
Toothbrush
Toothpaste
BrtandI
Brand2
Brand3
B-rand4
Brand I
Brand2
B-rand5
B-rand6
Toothbrush
Price cut by 20%
Coupon increase by 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincrease by 20%
.0596
.1041
.0792
.0701
-.055
-.082
-.055
-.06
-.052
-.094
-.085
-.064
-.04
-.096
-.074
-.054
.2222
.19/9
.2626
.2525
.17/7
-.043
-.043
-.449
-.051
-.039
-.038
-.031
-.044
-.05
-.031
-.047
-.028
-.038
-.038
-.019
Toothpaste
Price cut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincrease by 20%
.3183
.3296
.2557
.2511
.2376
-.332
-.265
-.236
-.228
-.223
-.212
-.295
-.203
-.198
-.191
-.21
-.278
-.198
-.204
-.173
.0606
.1313
.0808
.0606
.0505
-.016
-.031
-.016
-.012
-.004
-.006
-.013
-.019
-.006
-.013
-.009
-.028
-.009
-.019
-.019
Notes: The cross-effects are indicatedin bold and the own-effects are indicatedin italics. The figures in bold and italics refer to the impactsof the policy
changes on the sales of the umbrellabrandimplementingthe policy change.
The display cross-effects range from 3% to 7%. Similarly,
the feature cross-effects in toothbrush (the impact of a
change in the display activity in toothpasteon toothbrush
sales) are 5% for both Brands I and 2. Finally,price crosseffects range from 3% to 6% as well in both categories.
The simulation results for a temporarypolicy change by
Brand I (Brand2) in the toothbrushcategorysuggest thatas
a percentageof the own-effects, cross-effects on Brand I's
(Brand 2's) toothpaste sales are 27%, 54%, 30%, and 28%
(23%, 61%, 43%, and 64%) for price, coupon availability,
advertising, and display, respectively. Thus, in the toothpastector
couponavailabilityandthen advertisinghave
category,
the largest cross-effects as a percentageof own-effects for
Brand I, and displays and coupons have the largest crosseffects as a percentageof own-effects for Brand2. The simulation results for a temporarypolicy change by Brand I
(Brand 2) in the toothpastecategory suggest that as a percentage of the own-effects, cross-effects on Brand I's
(Brand2's) toothbrushsales are 19%,40%, 32%, 24%, and
21% (16%, 61%, 43%, 64%, and 29%) for price, coupon
availability, advertising, display, and feature, respectively.
Thus, in the toothbrushcategory, as in the toothpastecategory, cross-effects as a percentageof own-effects are largest
for coupons and advertisingfor Brand 1. For Brand2, in the
toothbrushcategory, cross-effects as a percentageof owneffects are largestfor features,because of a policy change in
the toothpastecategory, followed by coupons and advertis-
ing. In contrast,in the toothpastecategory these effects are
largestfor display and coupons, followed by advertising.As
these numbersshow, as a percentageof own-effects, crosseffects are substantialfor all marketing-mixelements.
MANAGERIAL
IMPLICATIONS,
CONCLUSIONS,AND
FURTHERRESEARCH
We tested for the spillovereffects of advertisingand other
marketing-mix/salespromotion strategies (price, coupon,
display,and feature)in umbrellabrandingon scannerpanel
data.We found that advertisingspillover effects, along with
the use experiencespillovereffects, affect both utility mean
and variance. The latter effect provides evidence for the
uncertainty-reducingrole of advertising (along with use
experience) across categories for umbrellabrands.To capture the varianceeffect in a parsimoniousway, for the first
time in the literature,we estimated a multivariatemultinomialprobitmodel, in which the variancesandcovariances
of random components of utilities across categories were
updatedon the basis of use experience and advertising.
Although there were some differences across categories
and brands,marketing-mixspillover effects were largestfor
coupon availability,generally followed by advertising.The
relativelylargecoupon effects, as well as advertisingeffects,
can be explained by various behavioralphenomenarelated
to brandequity; for example,couponingor advertisingby an
umbrellabrandmay increaseconsumer awarenessof prod-
JOURNAL OF MARKETINGRESEARCH, NOVEMBER 2002
418
Table 5
SIMULATION
RESULTSFORPOLICYCHANGESBY BRAND2
Sales of Toothbrush
Sales of Toothpaste
Brand I
Brand2
Brand3
Brand4
Brand I
Brand2
Brand5
Brand6
Baseline
1326
177
424
353
99
256
160
106
Toothbrush
Price cut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increaseby 20%
Featureincreaseby 20%
1294
1270
1306
1311
813
867
829
814
419
403
404
410
354
340
341
345
85
84
85
95
96
307
305
296
275
271
140
142
146
152
153
89
90
94
99
101
Toothpaste
Price cut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincreaseby 20%
1206
1207
1226
1237
1246
966
963
950
924
915
382
383
385
392
390
326
327
319
327
329
95
94
94
95
93
266
269
267
264
269
157
156
158
158
157
103
102
102
104
102
Br-andI
Brand2
Brand3
Brand4
Brand I
Brand2
Brand5
Brand6
Toothbrush
Price cut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincrease by 20%
-.024
-.042
-.015
-.011
.0463
.1158
.0669
.0476
-.012
-.05
-.047
-.033
.0028
-.037
-.034
-.023
-.141
-.152
-.141
-.04
-.03
. 992
.1914
.1563
.0742
.0586
-.125
-.113
-.088
-.05
-.044
-.16
-.151
-.113
-.066
-.047
Toothpaste
Price cut by 20%
Coupon increaseby 20%
Advertisingincrease by 20%
Display increase by 20%
Featureincrease by 20%
-.09
-.09
-.075
-.067
-.06
.2432
.2394
.2227
.1892
.1776
-.099
-.097
-.092
-.075
-.08
-.076
-.074
-.096
-.074
-.068
-.04
-.051
-.051
-.04
-.061
.0391
.0508
.043
.0313
.0508
-.019
-.025
-.013
-.013
-.019
-.028
-.038
-.038
-.019
-.038
Toothbrush
Toothpaste
Notes: The cross-effects are indicatedin bold and the own-effects are indicatedin italics. The figures in bold and italics refer to the impactsof the policy
changes on the sales of the umbrellabrandimplementingthe policy change.
ucts that share the same umbrellabrand(Aaker 1991) and
may strengthenthe brandimage (Keller 1993). Given that
clipping the coupons and using them requiresmore cognitive elaborationon the partof consumers,such cross-effects
may even be largerfor coupons than for advertising,as our
results indicate.
We also found thatadvertising(as well as use experience)
shifts both utility mean and variance across categories.
Thus, we found that the varianceof the randomcomponent
of utility in each categorydeclined as a resultof advertising
(and use experience) in eithercategoryover time. The crosscategory effects of advertisingon the utility mean may be
due to the factors discussed previously,such as awareness,
as well as advertising'seffect of increasingquality beliefs
underuncertainty.However,the effect of advertisingon utility varianceis evidence for learningdue to advertisingunder
uncertainty (the variance of quality beliefs is decreasing
over time because of advertising).Thus, we find evidence
for the uncertainty-reducingrole of advertisingacross categories, which gives rise to advertising spillover effects,
though the effects on mean utility may be a combinationof
different impacts (relatedor not relatedto uncertainty).
Our empirical results obtained from the analysis of
revealed preference data have several other managerial
implications. First, the results confirm the expectation that
umbrella brandinggenerates savings in branddevelopment
and marketingcosts over time and enhances marketingpro-
ductivity.These results call for integratedmarketingcommunications across products that share the same brand
name. Work on brandequity suggests this for advertising,
butour resultssuggest thatthe need for integrationexists for
all the marketing-mixelements, includingsales promotions
and especially couponing strategies, given the relatively
large cross-couponingeffects we found.
Second, our results imply that there are some asymmetries across productcategoriesand across brands.Forexample, for Brand 2, featuringtoothpastewill have the largest
cross-effects on toothbrushsales, whereas displaying the
toothbrushproduct(or giving a coupon for it) will have the
largestcross-effectson toothpastesales, ceteris paribus.Furthermore,as a percentageof own-effects, cross-effects of a
policy change in toothbrushseem to affect toothpastesales
more than the other way around in the case of Brand 2,
whereas the magnitudesof cross-effects as a percentageof
own-effects are fairly similar across the two categories for
Brand 1. Therefore,it would be importantfor brandmanagers to know how specific cross-effects work across categories for their brands.
Although it was not the focus of this article,we also estimated and found evidence for several within- and crosscategory correlations in response coefficients. First, we
Iound that marketing-mixsensitivities are generally positively correlated within a category. We also found that
depending on the brandin question, some consumers who
419
Advertising and Sales Promotions in Umbrella Branding
like certain brands may be more or less price sensitive than
the average consumer. These results indicate that in these
two categories, there are segments that, overall, are
marketing-mix sensitive, as well as segments that are
marketing-mix insensitive. Second, the finding that consumers' marketing-mix sensitivities tend to be correlated
across categories suggests that there is a strong individualspecific component to marketing-mix sensitivity.
There are a few avenues for further research. We showed
evidence for both utility mean- and utility variance-shifting
effects of advertising and use experience, and the the variance effects indicate evidence for the uncertainty-reducing
role that advertising and use experience play across categories for umbrella brands. The processes by which these
spillover effects occur are worth exploring in more detail.
However, as discussed by Keane (1997a), because of identification issues that arise with respect to models estimated on
panel data, it is often not feasible to differentiate among all
possible behavioral processes using scanner panel data. Survey or experimental data would be needed to provide a
detailed account of all the underlying behavioral processes.
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FOR
PAPERS
CALL
APPLIED PROBABILITYMODELS AND MEASUREMENT
IN MARKETING
Due to an increasedavailabilityof dataandalso to an increasedinterestamongstthe practitioners,
one can notice more and more applicationsof statisticaltechniquesand stochasticmodels in
marketing.It is not uncommon that marketingdecisions are made based on the outcomes of
sophisticatedmodels for empiricaldata.As developmentsin this areaproceedwith rapidpace, it
is importantto once in a while establishthe currentstate of the art concerningthese empirical
models. It is for this reason that the journalAppliedStochastic
Modelsin Businessand Industry
is
a
issue
on
(Wiley) publishing special
Applied Probability Models and Measurement in Marketing.
All empiricalworkrelatedto the probabilisticmodellingof choice,preference,and/orbehaviouris
welcome.Analysisof largedatasets is also encouraged.Paperscan be submittedelectronicallyto
the Guest Editors:
AlbertBemmaor
ESSEC
BP 105
95021 Cergy Pontoise Cedex
France
bemmaor@essec.fr
or
PhilipHansFranses
ErasmusUniversity
P.O.Box1738
NL-3000DR Rotterdam
The Netherlands
franses@few.eur.nl
The deadlinefor submissionis June 30, 2003. All paperswill be refereed,and there is no a priori
guarantee of ultimate acceptance.