Determinants of Store-Level Price Elasticity
Author(s): Stephen J. Hoch, Byung-Do Kim, Alan L. Montgomery, Peter E. Rossi
Source: Journal of Marketing Research, Vol. 32, No. 1, (Feb., 1995), pp. 17-29
Published by: American Marketing Association
Stable URL: http://www.jstor.org/stable/3152107
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STEPHENJ. HOCH,BYUNG-DO
KIM,
and PETERE. ROSSI*
ALANL.MONTGOMERY,
Using weekly scanner data representing 18 product categories, the
authors estimated store-specific price elasticities for a chain of 83
supermarkets.They related these price sensitivities to a comprehensive
set of demographicand competitorvariables that described the trading
areas of each of the stores. Despite the inabilityof previous research to
find much of a relationshipbetween consumer characteristicsand price
sensitivity,11 demographicand competitivevariablesexplainon average
67% of the variationin price response. Moreover,the authorsfound that
the consumer demographic variables are much more influentialthan
competitivevariables.Theirfindingsopen the possibilityfor more effective
everyday and promotional pricing strategies that exploit store-level
differences in price sensitivity.
Determinantsof
Store-Level
Price
Elasticity
design of promotions, choice of regular shelf prices, and
pricing of privatelabel brands.
Using store-level scanner data from Dominick's Finer
Foods (DFF), a major chain in the Chicago metropolitan
area,we develop price elasticity measuresfor each store.We
obtain store-level price elasticity estimates in each of 18
productcategories.These elasticities exhibit a great deal of
variation across the diverse set of DFF store locations,
demonstratingthat individual stores have distinct price response profiles. These distinct store price profiles for each
store provide the necessary condition for successful
micromarketing.
To provide insight into the determinantsof price sensitivity and for predictivepurposes,we then relateour price elasticity estimates to consumerdemographiccharacteristicsas
well as to measuresof the natureand extent of local market
area competition.Our findings can be used to predict price
sensitivityon the basis of readilyavailabledemographicand
competitiveinformationfor undevelopedstore locations, or
to help retailers who are unwilling or unable to conduct
time- and computer-intensiveeconometricanalyses of store
scanner data. A small set of demographicand competitive
environmentalvariablesexplains two thirdsof the variation
in the price elasticities across stores. For the most part,there
areconsistentpatternsin the sign and size of the relationship
between the explanatoryvariablesand the price elasticities
across the 18 categories. We use pooling methods to summarize the informationfrom individual category analyses
and provide furtherevidence on the commonality between
categories.
The approachof the paper is as follows. First, we develop a conceptual frameworkthat guides the selection of demographic and competitive variables and review work on
the correlatesof price sensitivity that uses either household
Micromarketingseeks to customize retailing policies to
exploit differences across stores in consumercharacteristics
and the competitive environment. This store-level customization presupposes the existence of significant and
identifiable differences in consumer response to retailing
policies across stores. To implementa micromarketingstrategy, the retailermust develop quantitativemeasuresof relevant aspects of consumer response to differentmerchandising policies and then adopt a method for computing these
measureson a store-by-storebasis. The retailercan then implement customized pricing on the store level. In addition,
retailersand manufacturerscan work togetherand lever existing promotional expendituresthrough more precise micromarkettargetingof stores. The premise is simple: Spend
disproportionatelymore in stores that are most sensitive to
changes in promotionand price.
We focus on consumerresponse to price changes, as capturedby price elasticity measures.Price sensitivityis fundamental to many importantaspects of retail policy, including
*Stephen J. Hoch is Robert P. Gwinn Professor of Marketingand BehavioralScience at the Universityof Chicago GraduateSchool of Business.
Byung-Do Kim is Assistant Professor of Marketing at Carnegie-Mellon
University GraduateSchool of IndustrialAdministration.Alan L. Montgomery is Assistant Professorof Marketingat the Universityof Pennsylvania WhartonSchool of Business. Peter E. Rossi is Professorof Marketing
and Statistics at the University of Chicago GraduateSchool of Business.
The authors thank Dominick's Finer Foods, InformationResources Inc.,
and MarketingMetrics for their assistance and the provision of data, and
Xavier Dreze and Mary Purkfor their indispensablehelp throughout They
acknowledge useful comments from Greg Allenby, Bob Blattberg, Ed
George, Rob McCulloch, and Carol Simon. Fundingfor this work was provided by the Micro-MarketingProjectat the GraduateSchool of Business,
Universityof Chicago.
17
Journal of MarketingResearch
Vol. XOXII(February 1995), 17-29
18
JOURNALOF MARKETING
RESEARCH,FEBRUARY1995
or store-level data. Sources of data are then discussed. Second, we presentthe item-level sales responsemodels used to
estimate store price elasticities, which are related to variables thatmeasurethe store areademographicand competitive environment. The results of the individual category
analyses are provided, followed by material on pooling
acrosscategories.Finally,we presentthe implicationsof our
findings for everyday and promotionalpricing policies.
OF
CONSUMERAND COMPEIl lVE CORRELATES
PRICESENSITIVITY
Household-LevelResearch
We view shopping activity as a part of an overall household productionprocess (Becker 1965). Returns to household shopping,which allow the consumerto take advantage
of price reductions, are balanced against various costs, including inventory, transportation,opportunity (time), and
searchcosts. This viewpoint provides some discipline in selecting and constructing variables from the rather large
menu of availabledemographicdata.
Inventoryor storage costs influence price sensitivity by
affecting a household's ability to take advantageof deals.
Households with largerstorage facilities have the ability to
buy larger quantities.Transportationcosts also may influence price sensitivity; vehicle ownership increases one's
ability to take advantageof deal prices and to stockpile.
Sensitivity to price requiresan awarenessof the distribution of prices, which requiresconsiderabletime and psychological effort. Therefore,factors that influence the opportunity cost of time should in turndrive price sensitivity.From
an economic perspective, consumers will make trade-offs
between expendingtime on shoppingversus otheractivities,
dependingon the returnsfrom each activity.
The prediction is that consumers with high opportunity
costs (in particular,wealthy consumerswith a high marginal
value of time) will be less price sensitive. This implies a
negative relationshipbetween price sensitivity and level of
education, though some have argued that education might
contemporaneouslyreduce the psychological costs of price
comparisons. It also implies that all else being equal, we
would expect elderly consumersto be more price sensitive,
because of greaterleisure time. Moreover,we expect large
households to be more price sensitive; because of the large
amountof disposableincome spent on groceries,the greater
the potentialreturns(savings) from shopping.
Standard economic demand theory suggests that the
household allocates disposable income over various expenditure categories, taking into account the relative price indices of each category. Measuredhousehold income, however, may play only a small role in determiningthis price response, because the observedprice response of a household
to changes in the price of items in any productcategory depends on many factors.
Blattbergand colleagues (1978) adoptedBecker's (1965)
point of view in their study of diary data. Using data from
the Chicago TribunePanel for five productcategories collected over the years between 1958 and 1966, they discovered threedemographiccharacteristicsthatwere mildly predictive of deal proneness:car ownership,home ownership,
andhouseholdswith unemployedwives. However,theirdata
were collected more than 30 years ago, and the radical
changes in the work force and in family structurethat have
occurredsince then may have alteredthe empiricalregularities. Many women work because they have to (e.g., single
motherfamilies) and thereforethey may be more price sensitive. Income had no effect on deal proneness when controlling for other relevantfactors, despite theory suggesting
a negativerelationship.
Most otherattemptsto identify demographiccorrelatesof
price sensitivity and deal proneness using household panel
dataconclude thatthereis a weak to nonexistentrelationship
(Frank,Massy, andWind 1972; Montgomery1971; Webster
1965). In theirevaluationof differentaggregationapproaches to developing marketsegments, Elrod and Winer (1982)
relatedobserveddifferencesin price sensitivityto socioeconomic and demographic characteristics and found only
weak associations. Price response increasedwith age, educationallevel, and householdsheadedby females. Rossi and
Allenby (1993) found little relation between household
characteristicsand household-level estimates of logit price
coefficients. Narasimhan (1984) found relationships between coupon usage and household demographiccharacteristics. Under a price discriminationtheory of couponing,
this would suggest indirectlythat there is a relationshipbetween price sensitivity and the demographicvariablesconsidered by Narasimhan.In related work, Bawa and Shoemaker(1987, 1989) found thatpropensityto use an off-price
coupon increased with education, income (weakly), and
household size.
Thereis also substantialliteraturethatinvestigatesthe determinantsof price search behavior (e.g., number of store
visits and prices paid). The theory is well developed, but
there is a paucity of supportingempirical findings. Carlson
and Gieseke (1983) found that price search behavior increased with age and was single-peakedwith respect to income. Marvel (1976) and Maurizi and Kelley (1978) found
an inverse relation between income and search. Goldman
and Johansson (1978) related a combination of attitudinal
and economic variables to self-reported search behavior.
Marmorstein,Grewal, and Fishe (1992) found that the subjective value of price comparisonshopping time decreases
with wage rates and increases with enjoyment of the shopping process. In all cases, the percentage of variance explained was small (< 15%).
To summarize,previous research at the household level
has not been able to explain a very large fractionof variation
in price sensitivity on the basis of observed consumercharacteristics.There are severalpossible reasons for lack of explanatorypower.First,most of the researchhas utilized consumerpanel data;such dataoften containsmanyhouseholds
with very short or sparse purchase histories. Second, the
purchasehistories usually involve only one or two product
categories and do not capturebehavior across the multiple
categoriesthat make up consumers'weekly marketbaskets.
Third, in-store and competitive promotionalactivities may
overwhelmany demographic-basedconsumerprice proclivities that might exist, that is, short-termenvironmentalfactors swamp individualdifferences, much as has been found
in the field of personality psychology (Mischel 1984). Finally, the variation in demographicattributesis often less
Store-LevelPrice Elasticity
among scannerpanel membersthan in the store populations
found in a majormetropolitanarea.1
Store-LevelResearch
We expect observedprice sensitivityto be relatednot only
to consumercharacteristicsbut also to the competitiveenvironment. Price sensitivity can be reflected in switching
among brandsof the same productcategory,between product categories in the same store, and across differentstores.
As the number of alternativeretail outlets increases, the
household is given more substitution possibilities and
should be more price sensitive unless the prices are perfectly correlatedacross stores.
The competitive environmentis determinednot only by
the numberof shopping alternativesor substitutionpossibilities but also by the cost of searchand the formatof the competition.The costs of searchshould increaseas a functionof
the physical distance and driving time between stores. The
greaterthe dispersion of prices in the market,the higher is
the expected savings from search.Therefore,we would expect tradingareas with greaterretail density (more different
outlets in a given area) to display greaterprice dispersion,
which in turn drives greaterprice sensitivity on the part of
the consumer.
Most consumers shop at a numberof stores, both within
and across weeks. Depending on whethera shoppingtrip is
primary(buying groceries to last 1 to 2 weeks or more) or
secondary (filling a immediate need resulting from an outof-stock condition at home), consumers may place more or
less emphasis on convenience versus price. Larger stores
will tend to attractmore primaryshoppers(because of wider
assortments),whereas smaller stores will attractmore secondaryshopperswho are interestedin convenience.
We might expect that, relative to the competition, larger
volume stores would be more price sensitive than smaller
stores. To stay in business, larger stores have to draw from
largertradingareas;consumersmust be willing to drive farther. Consumers who frequent smaller, more proximate
stores may self-select on the basis of location and the ability to get in and out of the store quickly ratherthan on selection/assortmentor price.
Store-level scannerdata has been used to study this competition between stores. In general, the research suggests
that within-store price effects are much greater than those
observed between stores. Kumarand Leone (1988) studied
the disposable diaper category and found that though there
is significant promotion-inducedstore substitutionbetween
proximatestores, within-storesubstitutionrates were two to
three times greater.Walters (1991) examined both withinstore and between-store substitutionand complementarity.
Store substitutionrates were very low compared to brand
switching within a store.
Bolton (1989) used store-levelscannerdatato relateprice
elasticities to a varietyof variablesthatcharacterizethe promotion and performanceof each brand.She consideredthe
IFor example, the standarddeviation of our ETHNIC variable across
stores is .34, whereas the median standarddeviation among the census
tracts that make up each store is .10. Similarly, the standarddeviation of
HOUSE_VALacross stores is .33, with a median of .26 within store market areas.
19
extent of couponing, market share, price activity, and display/feature activity and found that these were correlated
with price elasticities. All the variables considered by
Bolton arejointly determinedby the marketingstrategyand
not fundamentallyexplanatoryin nature.The characteristics
of consumersand the competitiveenvironmentstudiedhere
are better seen as the primitives that determine price
sensitivity.
To summarize,the store-levelresearchhas focused on the
patternsof substitutionbetween and across stores. In the
empiricalwork discussed subsequently,we examine the relationshipbetween store price elasticity and detaileddataon
the competitiveenvironmentsurroundingeach store.
SOURCESOF DATA
There were three main sources of data:Dominick's Finer
Foods (DFF); InformationResources, Inc. (IRI); and Marketing Metrics.Dominick's has approximately90 stores and
a marketshare of approximately20%.
Store-LevelSales Data
Dominick's provided the University of Chicago with
weekly store-level scanner data by UPC, including unit
sales, retail price, profit margin,and a deal code indicating
shelf-tag price reductions(bonus buys) or in-store coupons.
The categories are displayed in Table 1. There is an average
of 270 UPCs in a productcategory,from a low of 57 in bath
tissue to a high of 637 in cookies. The time span for DFF
scannerdata is 160 weeks, the first 80 of which are used for
elasticity estimationand the next 80 are reservedfor model
validation.Because some of the stores had limited historical
data, we limited our attentionto a sample of 83 stores.
Chain and Market-LevelPromotionalActivity
InformationResources, Inc. provided us with InfoScan
data for the Chicago market that was broken out for Dominick's and the remainingcompetitorsas an aggregate;for
our purposes,we focus only on Dominick's promotionalactivity. The IRI data is based on a representativesample of
approximately60 stores drawn from the population of all
major Chicago-areachains and independents.We utilized
this data to obtain informationon promotionalactivity. By
week we had available the percentage of All Commodity
Volume(ACV) of a particularUPC that(1) receivedin-store
display along with a price reduction,(2) was featureadvertised along with a price reduction,or (3) was both displayed
and featureadvertised.
Because feature advertisingis a chainwide corporateactivity, an individualUPC will either be featuredor not featuredin all stores duringa particularweek. Display activity
is more discretionary,and except for largerpromotionsthe
decision is left up to the store managers.Except for the extremecases where no stores or all stores display an item, we
do not know which stores actually displayed the item. The
percent ACV data from IRI provide probabilisticinformation about the likelihood that an item has received display
support. Given the measurementerror that is likely to be
presentin any display variable,we used only informationon
chainwide featureactivity in the demandmodel.
20
JOURNALOF MARKETING
RESEARCH,FEBRUARY1995
Table 1
ESTIMATES
ANDELASTICITY
CATEGORY
AGGREGATES
Basis of Aggregation
Average
Category
Elasticity
Standard
Deviation
Average
Own
Elasticity
10
12
18
13
10
15
12
12
17
14
21
21
4 manufacturersand 2 size aggregates
4 brandsby water/oil,4 price tier aggregates
13 Campbell'sflavor/size aggregates,5 misc. aggregates
8 brands(all sizes), 5 manufactureraggregates
5 brands(all flavors) by 2 types
11 brands(all flavors, sizes), 4 manufactureraggregates
11 brands(all flavors), 1 miscellaneous aggregate
5 orangejuice brandsby 1-2 sizes, 4 flavor aggregates
5 brandsby 2-5 types, 1 miscellaneous aggregate
9 orangejuice brands,5 flavor aggregates
19 brands(all sizes), 5 type aggregates
I10brandsby 1-4 sizes, 1 miscellaneous aggregate
-3.18
-1.79
-1.62
-1.60
-1.01
-.86
-.77
-.74
-.72
-.55
-.20
-.09
.39
.47
.22
.25
.57
.36
.46
.51
.35
.32
.22
.26
-2.59
-.96
-1.66
-.90
-1.46
-.79
-1.65
-2.24
-1.44
-1.95
-1.14
-1.49
9
10
8
13
11
12
8 brands,1 aggregate
4 brands(all sizes) by 2 forms, 2 form aggregates
3 brands(all sizes) by 2 forms, 2 form aggregates
6 brandsby sizes, 2 size aggregates
4 brands(all flavors) by 2 sizes, 2 size aggregates,kids
7 brandsby 2 sizes, 2 size aggregates
-2.42
-1.58
-.79
-.74
-.45
.05
.19
.21
.06
.29
.37
.52
-2.28
-1.99
-1.77
-1.64
-2.00
-1.21
Number
of UPCs
Number
of Items
Food Items
Soft Drinks
CannedSeafood
CannedSoup
Cookies
Grahams/Saltines
Snack Crackers
Frozen Entrees
RefrigeratedJuice
Dairy Cheese
Frozen Juice
Cereal
Bottled Juice
619
180
89
637
230
197
500
108
367
105
298
242
Nonfood Items
Bath Tissue
LaundryDetergent
FabricSoftener
Liquid Dish Detergent
Toothpaste
PaperTowels
57
303
140
178
296
90
Category
StoreTradingAreaData
Metricsprovidedus withtradingareadatafor
Marketing
each of the 83 stores.Theydeterminetradingareasfor all
stores with yearly sales volume greaterthan $2 million,
modelsthattakeinto accountpopulation
using proprietary
density (e.g., urbanversus suburban),competition,road
conditions,and various regional differences.Marketing
Metricsdefines a tradingarea by expandinga polygon
aroundeach storelocationto enclosean arealargeenough
to supporttheACVof the store.
functionsbasedon the Bureauof
Householdexpenditure
LaborStatisticsConsumerExpenditure
SurveyandMediamarkResearchInstitute's1990Doublebasesurveyarecombinedwithcensusblockdatato estimatethesize of thepopulationneededto supporttheACV.Thesetradingareasalso
who areshartakeintoaccountthe presenceof competitors
totalfor a givenareaas well as
ing the groceryexpenditure
geographicalbarrierssuch as expresswaysand riversand
times.
differencesin estimatedtransportation
EventhoughDFFoperatesin a generallyhighlycompetthereis stilla greatdealof variationin the
itiveenvironment,
natureand extent of competition.In some locations,the
DFF andcompetingstoresare locatedside by side andin
otherlocationsthe DFF storeis not nearany majorcompetitor.Tomeasuretheextentandnatureof thecompetition,
aboutthetoptencompetiwe reliedon detailedinformation
tors in the tradingarea,includingdistancefrom the Dominick'sstore (in miles and estimateddrivingtime minandwarehouse
superstore
utes),retailformat(supermarket,
outlets,andfood/drugcombos),andestimatedweeklysales
volume.Thislevel of detailon thecompetitiveenvironment
is uniquein studiesof competitivebehavior.
Censusblockdatafrom1990provideveryextensivedeon householdsize andcomposition,
mographicinformation
income, housing, educationalattainment,ethnicity,and
manyothervariables.The marketareadefinitionsareused
to formweightedaveragesof theblockdatawithinthetrading area.The weightsdecayat differentratesfor everydirectionfromthe storelocation.
DATAANDAGGREGATION
ISSUES
STORE-LEVEL
Becauseourempiricalworkis basedon store-leveldata,
it is importantto recognizethe advantagesand disadvanover
On the positiveside,aggregation
tagesof aggregation.
individualconsumersto the storelevelmaytendto average
householdout individualstochasticbehavior.Temporary
factors(e.g.,existinginventorylevelsafspecificsituational
fectingresponseto a deal)will tendto be equalizedacross
consumers,leadingto a reductionin systemwidenoise.Alternatively,store-levelanalysiswouldseem to be counterproductiveon the independentvariableside, becausethe
couldwashout interestingdifferhighlevel of aggregation
encesbetweenindividualconsumers.
In our analysisof storemarketareas,we mustrely on
thedistribution
of keyexsummarystatisticsto characterize
in
area.
market
over
the
consumers
the
variables
planatory
As in manymajormetropolitan
areas,Chicagohas always
been a city of manydistinctand homogenousneighborto the suburbshas inhoods;in recentyears,out-migration
creasedthe patternof residentialseparationby wealthand
the variationin consumercharacethnicity.Consequently,
teristicsacrossdifferentstoremarketareasis substantially
withineachmargreaterthanthevariationin characteristics
ket area.This,coupledwiththe averagingoutof individual
stochasticbehavior,mayaffordus witha betteropportunity
betweenpricesensitivityandmarket
to detectrelationships
areacharacteristics.
A simpleexamplewill help clarifythesepointsfurther.
betweenpricesensitivityandconConsidertherelationship
sumerwealth.Withconsumerpaneldata,we couldattempt
to measurepricesensitivityforeachconsumerandthenperforma cross-sectionalregressionof price sensitivityon a
Store-LevelPriceElasticity
wealthmeasurefor each household.Givenpreviousfindin thisconings, we wouldexpecta veryweakrelationship
sumer-level data.To reducemeasurement
noise, we might
averagepricesensitivityoverconsumersof exactlythesame
wealthlevel andregresstheseaverageson ourwealthmeasure.This amountsto averagingthe regressionerrorterms
verticallyat eachof somegroupof wealthpoints.
Ourstore-leveldatadividestheconsumerpopulationinto
groupsby marketarea.Wecomputetheaveragepricesensitivityfor each marketareaandregressthis on the average
wealthfor eachmarketareagroup.Thisis analogousto averagingthe regressionerrorsboth vertically(acrossconsumersof the same wealthlevel) and horizontally(across
consumersof differentwealth levels in the same market
area).Thus,the successof ourempiricalanalysishingeson
the trade-offbetweenreducingnoise in the dependentvariableandthe loss of informationthroughaggregationof the
variablesto marketareas.
independent
Inthenextthreesectionswe will describeourmethodand
results.We adopta two-stepprocedure.First,we estimate
store-specificprice elasticitiesfor each of the 18 product
categories.2Second,we relatethese store/product
category
andcompetitivevariables.
elasticitiesto demographic
OBTAINING
PRICEELASTICITY
ESTIMATES
Obtainingpriceelasticityestimatesfor an entirecategory
is a complicatedtask involvinga numberof decisionson
bothaggregationandmodelspecification.In a typicalgrocery category,supermarkets
carryupwardsof 250 UPCs.
Ourapproachis to builda detaileddemandmodelat a low
level of aggregationto avoidthe extremeassumptionsand
possibleaggregationbias thatwouldaffecta highlyaggregatedanalysis.This detaileddemandmodelcan be usedto
measurethe sales responseto changesin the priceof individualitemsandsubaggregates
or to predicttheresponseof
the whole categoryto a uniformprice changeacrossall
itemsin the category.
We concentrateon measurementof the category-level
pricesensitivity,whichis at thecoreof theretailer'scategomostretailershavefairlyunirypricingproblem.Currently,
formpricingpolicies(e.g., theyfollowor leadthe competition), whichare not customizedto store-specificclientele.
Exploringsystematicpatternsof pricesensitivityon thecategorylevel canopenthepossibilityof improvingprofitabilpricing.Reity by store-specificeverydayandpromotional
tailersalso may be interestedin individualitem or brand
elasticities,whichcan be used to predictpatternsof differentialpromotional
response.
DemandModelSpecification
Our aggregationscheme startswith the total of 4636
UPCs thatmakeup the 18 categories.Even with our 160
weeksof data,it wouldbe difficultto preciselymeasure83
x 4636 store-UPCelasticitycombinations.Furthermore,
2An alternativeapproachwould a one-step procedurein which the elasticity coefficients in the demandmodel in (1) are made functions of the demographicvariables.To implement a one-step strategy,we would need to
make specific distributionalassumptionsregardingthe store elasticity random component or asymptotic argumentswhich requirea large numberof
categories. Our approachis a nonparametricfixed effects one, which estimates a store elasticity component in the first step.
21
the pricesof manyUPCsareperfectlycorrelated,because
theyarepricedandpromotedtogether(e.g.,differentflavors
or formsof the samebrand).Therefore,we formedaggregatesin eachcategory,consistingof top-sellingbrandsand
aggregatesof otherUPCson thebasisof size, form,flavor,
andtype.As shownin Table1, we formed265 itemswithan
averageof 15 aggregatesper category(columnlabeled
"Number
of Items")fromthe4636 UPCs.
Theaggregation
schemevaried,dependingon thelevelof
andconcentiation
differentiation
in thecategory.Forexamin
the
ple,
refrigerated
juice category,the top ten selling
UPCsaccountfor morethan53%of dollarsales volume,
whereasin ready-to-eat
breakfast
cerealsthetoptenaccount
forless than17%of sales.Incategorieswithdispersedsales,
we aggregated
acrosseithersizesand/orflavors/forms
to get
to a pointwherethetop 10 to 20 itemswouldaccountforat
least50%of sales.
Thechoiceof modelspecificationalsorequiresa number
of tradeoffs in orderto maketheproblemtractablefroman
Forexample,we mighthavestarteconometricperspective.
ed with a flexiblefunctionalformfor the utilityfunction
overall brandsin eachof our 18 categories.Wethencould
derivea hugedemandsystemthatwouldfeaturehundreds
of
of thousands
of parameters.
Instead,our
pricesandhundreds
approachis to specifya simplelog-lineardemandmodelin
whichthelog of unitsalesfor eachitemis regressedon the
log of priceof thatitemandotheritemsin the samecategory. In addition,we includepromotionalvariablesand a
laggedsales volumetermin each item equation.Thus,we
specifym log-lineardemandequationsfor each of the m
itemsin a category.The parameters
fromthis demandsystemarethenusedto definea categorypriceelasticity,as describedsubsequently.
Thebasicdemandsystemstartswiththevectorof log unit
sales(standardized
by size) in storej duringweekt, qjtand
thevectorof log pricesof eachof them itemsin eachcategory, Pjt (throughout,we denote the logs of variables in
lowercase).Eachelementof thevector,qjt,is denotedqijt,i
= 1,...,I, whereI is thetotalnumberof itemsin the category; in the equations,i will alwaysdenotethe index of an
item.Thepriceof eachof the itemsin eachcategoryis defined as the geometricshareaverageor the Divisiaprice
indexof all of UPCsthatmakeupthatitem.Forexample,in
therefrigerated
juicecategory,thereare12itemsthatareaggregatesof the 108UPCs.Foreachweekandstore,we computethe share-weighted
averageof the log priceof eachof
the UPCsin eachitemto formthe Pjtvector,whichis a 12
X 1 vector.
For each category,we define a standardlog-lineardemandsystem:
(1) q
=
oa + Tj + (N + Aj)pj + (qj,-I + A Dealjt+ B Fjt+
e
Lis a m X 1 vectorof ones,Aj= kjI, = 4<I,A = diag(8),B
= diag(O).The a vectorallowsfor item-specificintercepts,
andthe rTj
is a store-specificintercept.Dealjtis a
parameter
vectorof dummyvariablesthatindicatewhetherthereis a
temporary
pricereductionoranin-storecoupon.Fjtis a vector of featureindicatorvariablesobtainedfromIRI'sInfoScansampleof DFFstoresin the Chicagoarea.Themodel
JOURNAL
OF MARKETING
FEBRUARY
1995
RESEARCH,
22
in (1) is estimatedon thefirst80 weeksof ourstoredata,reservingtheremaining80 weeksfor modelvalidation.
The demandsystemin (1) allows for a full patternof
crosselasticities,as capturedby the elementsof the N matrix, N = [ir'], along with a lagged value of the dependent
serialcorrelation
to accommodate
variablethatis introduced
behavior.The
introduced
by forwardbuyingor inventorying
on
the ownelaseffect
a
allows
for
matrix
store-specific
Aj
ticities.The modelin (1) makesthreebasicsets of restrictionson thecategorydemandmodel.Weallowonlytheown
(ri' + kj) to varyacrossstores,while
elasticityparameters
we restrictthecross-elasticity
(N) so theyarethe
parameters
sameacrossstores.
Featureanddeal effectsare allowedto be item-specific
but not store-specific.The lagged sale effect is the same
acrossitemsin the samecategory.Thepriceelasticitymodels fit the dataverywell, with R2rangingfrom.76 to .93.
Largefeatureeffects are observedin most categories.To
extenvalidatethis modelingapproach,we experimented
formulations
thatare,to varyingdesivelywithalternative
grees,less restrictivethanthe modelin (1). Ourmodelperformswell relativeto theseless restrictivemodels,as measuredby both in-sampleand out-of-sampleyardsticksof
performance.3
Category-LevelPrice Elasticities
in (1) canbe usedto preThe priceresponseparameters
dictresponseto a widevarietyof pricechangeson boththe
item andcategorylevel. Sinceourfocusis on the retailer's
categorypricingproblem,we derivea categoryelasticity
measureby computingthe categoryvolumeresponseto a
changein all pricesin the category,holding
proportionate
the promotional
policy variablesconstant.For a particular
store,the demandsystem(1) canbe writtenas
qj= a + Fjpj
The intercepta includesthe feature,deal, andlaggedq
variables,whicharenot changedin the priceresponsepartial derivative,andrj = N + Aj, whichis the store-specific
pricecoefficientmatrix.Becausethedemandsystemis written in logs, ourcategoryelasticitymeasurecanbe computed by addinga constantto the vectorof log prices,p + hi,
andobservingits effecton log volumein thecategory.
3Withoutrestrictions on the variation in cross-elasticity patterns, we
would be faced with the problem of estimating m2 X J price parameters
(where J is the total numberof stores) versus the m2 + J parametersemployed in (1). To assess the validity of these restrictions,we also estimated
a completely unrestrictedmodel that allows the cross-elasticities to vary
across stores. We use out-of-samplepredictivevalidationand the Schwarz
(1978) model selection criterionto comparethe restrictedand unrestricted
models. The unrestrictedmodels have between 30 and 60 times the number
of parametersas the restrictedmodel in (1) and, as might be expected, they
show a somewhatsmallerin-sampleMSE. The lower in-sampleMSE is almost entirelyattributableto a fuller set of brand-storeintercepts;freeing the
restrictionson the cross-priceelasticity terms results in little improvement
in fit. However,the out-of-samplepredictiveperformanceof the unrestricted model is much inferior to the restrictedmodel. The restrictedmodel
shows an average 55% reductionin out-of-sampleMSE, with a minimum
of 14% and a maximumof 97%. This suggests that there is a greatdeal of
sampling variabilityin the parameterestimates in the unrestrictedmodel.
The Schwarz (1978) model selection criterion also favors the restricted
model.
Totalcategoryunitvolumeis writtenas V(p) = Zexp(qi)
= Zexp{ai+ Yij'P},where-Yij
is the ith rowof Fj.The categoryelasticityforstorej is definedas thepercentagechange
in the categoryvolumeproducedby a uniform1%increase
in the prices of all items in the category.In differential
of lnVwithrespect
terms,we musttakethepartialderivative
to h, whereh is the constantof proportionality;
we increase
p to p' = p + hL.We areconsideringthe local or derivative
as h -> 0.
measure,so we musttakethelimitof thisderivative
(2)
Icj=
lnV(p +h)lh=o=
y'j = w'j
wj is the vectorof volumeshares,w. = Qij/V,whereQ. is
theunitvolumeforitemi in storej. Thecategorypriceelasticity measurein (2) providesan estimateof the expected
changein categoryunitvolumefora 1%uniform
percentage
increasein the pricesof all the itemsin the category.For
each category (c = 1, ..., C) and each store (j = 1, ..., J), we
use (2) to computea categoryelasticity,lcj.Weshouldnote
thatthese are short-runelasticities;the long-runelasticity
wouldbe scaledup by the factor1/(1-4).
on the level and
Table1 presentsdescriptiveinformation
dispersionof the categoryelasticityestimates.The categoriesaresortedfirstintofoodandnonfooditemsandthen
rankedon the basis of the size of elasticities.The average
deviacategoryelasticityis -1.06, andthe averagestandard
tionacrossstoresin thesamecategoryis .33, indicatingthat
thereis a greatdeal of variationacrossstoreswithinthe
samecategory.
Table1 presentsthe averageownpriceelasticityin additionto the categoryelasticity,whichis drivenby the degree
of substitutability
amongcategoriesin the same storeand
betweencompetingstores. Substitutionor switchingbetweenbrandsin the samecategoryhas a smalleffecton the
categoryelasticity(it canhavesomeeffectif the volumeof
purchaseschange).Thissuggeststhatthecategoryelasticity
shouldbe smallerthantheitemorbrandelasticity,as shown
in Table1, whichis the case in 13 out of the 18 categories.
for thosecategoriesin whichthereis intense
Furthermore,
storecompetitionand widerretaildistribution(e.g., drug,
conveniencestores,anddiscountstores),we shouldexpect
highercategoryelasticitiesdrivenby substitutionacross
stores.This is the case in the soft drinkand bath tissue
categories.
Figure1 is a mapof the Chicagoareawith each of the
DFF storelocationsmarkedwith a dot.Aroundeach store
to thesize of
locationis a circlewhoseradiusis proportional
the price elasticityestimatesfor the soft drinkcategory.
variationin the size of elasticities,clustering
Considerable
of similarsize elasticities,andsmoothtransitionfromone
geographicareato the next areapparentin the figure.The
challengenowis to relatethisvariationin elasticitiesto market areacharacteristics.
RESULTSFOR INDIVIDUALCATEGORIES
Giventhe categoryelasticityestimates,we examinethe
betweentheseestimatesandmeasuresof conrelationship
A regressionrelasumerand competitivecharacteristics.
tionshipbetweenthe elasticityestimate,nj, andthe independentvariablesis postulated.
(3)
ncj = xj'Pc + eCj
ej
- iid
N(0,c)
c = 1, 2,... 18
Store-LevelPrice Elasticity
There are 18 category-level regressions each with 83 store
observations on consumer and competitive characteristics
(these are in the vector xj). Because the ncjare based on regression coefficient estimates (see (2)), they could be heteroskedasticover stores in the same category. This regression equation assumes that the estimates are homoskedastic
within a category and heteroskedasticacross categories. To
check this assumption,we computed the standarderrorsof
the n,c using standardregression theory and (2). We found
very little variationin these standarderrorsacross stores for
each of the 18 categories,justifying our errorspecification
in (3). We now turn to the constructionand selection of independentvariables.
23
Figure 1
BY DFF LOCATION
SOFT DRINKPRICEELASTICITIES
0
0
?
Selection of IndependentVariables
Because of the comprehensiveamountof backgroundinformationavailable for each tradingarea, we spent considerable effort identifying a parsimoniousmodel of price sensitivity that containedboth consumer and competitivecharacteristics. Our intent was not so much to find the one true
model but to identify factors consistent with the household
productionand economics of informationapproaches.Relating specific demographicvariablesto economic concepts
is difficult. For example, a numberof demographicvariables
could be relatedto wealth or to the opportunitycost of time.
We use multiple variables as proxies for key concepts and
anticipatethat some collinearity will exist between the demographic variables. The final model that we report here
contained 11 predictorvariables.
Consumer characteristics: Seven of the predictor variables were consumer characteristics.The percentageof the
populationover 60 years of age (ELDERLY),the percentage
of the populationwith a college education(EDUC), and the
percentage of households with five or more members
(FAM_SIZE)relate to opportunitycosts. Elderlypeople and
largerhouseholds were expected to be more price sensitive,
leading to negative relationships with price elasticity. The
log of median income (INCOME)and the percentageof the
homes with a value greaterthan $150,000 (HOUSE_VAL)
are related to income constraints on purchasingbehavior.
We also included the percentage of women who work
(WORK_WOM).If this variable representsthe higher opportunitycosts of working women, then we would expect it
to be positively relatedto price elasticity. If, however,many
workingwomen face budgetconstraints,then the sign might
reverse.We tried to isolate demographicsthat might capture
differentiallevels of storageand transportationcosts, but we
could find no systematic relationshipsto price sensitivity.
In addition, we included the percentage of consumers
who were black and Hispanic (ETHNIC)as an independent
variable. This variable is best regardedas an intermediate
construct that captures underlying characteristicssuch as
certainpatternsof income, household composition, and personal wealth for which economic theory makes price response predictions.
We experimentedwith model specifications in which the
ETHNIC variable was replaced by associated measures
(e.g., percentagebelow the povertyline, unemploymentlevels, telephone ownership, percentage of singles); however,
ETHNIC capturedsomething additional,so we retainedthe
?
0
variable. In addition, the ETHNIC variable has obvious
value as a predictorin the event that this analysis was used
to forecastprice sensitivity at locations for which we do not
have detailed scannerdata.
Competitivecharacteristics: Four of the predictor variables were competitivecharacteristics.We divided the competition into two sources: traditional supermarkets and
warehouse or superstoreoperations.Distance in miles and
drivingtime were closely related.We calculatedthe average
distancein miles to the nearestfive supermarketcompetitors
(SUPER_DIS) and the distanceto the nearestwarehouseoperation(WARE_DIS).Distance in urbanareaswas scaled by
a factor of two to reflect greatercongestion and density.We
also calculated the sales volume (a proxy for store size) of
each store relative to both the supermarket(SUPER_VOL)
and warehouse(WARE_VOL)competitors.
Examinationof the distributionof the independentvariables across each of the 83 stores shows that there is considerablevariationin both the demographicand competitive
characteristics.For example, there are DFF stores in market
areas in which less than 5% of the adult populationis college-educated,whereasother locations have more than 50%
of college-educated consumers. Figure 2 shows the difference in the EDUC variable on the map of DFF locations.
Some DFF stores are located right next to both supermarket
and warehousecompetition,and others are located as far as
JOURNALOF MARKETING
RESEARCH,FEBRUARY1995
24
Figure 2
BY DFFLOCATION
COLLEGEEDUCATED
PERCENTAGE
We now turnto an analysis of the directionof the effects
of each independentvariable. In inteipreting these coefficients, it is importantto rememberthat the dependentvariable is a negative number.A positive coefficient indicates
that as the associated independent variable increases, the
price elasticity moves closer to zero. These coefficient distributionscan be summarizedas follows:
variablesreducepricesensitivi*TheEDUCandHOUSE_VAL
ty, as evidencedby the overwhelmingmajorityof positive
coefficients.
variablesin*TheETHNIC,FAM.SIZE,and WORK_WOM
creasepricesensitivityforthemostpart
variablesarea mixedbag,with
*TheINCOMEandELDERLY
almostequalnumbersof positiveandnegativecoefficients.
increasesprice sensitivity as expected, and
*WARE_VOL
reducespricesensitivity.
WARE_DIS
variableshaveweakand
*TheSUPER_DISandSUPER_VOL
mixedeffects.
18 miles from warehouse competition and 4 miles from
majorsupermarketcompetition.In addition,most of the intercorrelationbetween the 11 independentvariablesis below
.5 in absolute value, with the exception of a .89 correlation
between HOUSE_VALand EDUC.
Results of CategoryRegressions
The 18 category regressions of elasticities on this set of
independentvariables are listed in Table 2. The categories
are listed in alphabeticorderacross the columns of the table.
Below each coefficient is the standarderror,and the bottom
two rows give the adjustedR2 and standarderrorof the regression.The last column displays the varianceinflationfactors (VIF) for each independentvariable(see Weisberg1985
for a definition of the VIF).
Note that the VIF depends only on the independentvariables, which are the same in each of the 18 regressions.The
regressions have an average adjusted R2 of .67 (median,
.78), rangingfrom a low of .34 for toothpasteto a high of .86
for frozen entrees.The regressionsshow a remarkablygood
fit, especially in light of previous attemptsto explain variations in price response. In addition, our success in explaining variationsin price elasticity measures suggests that the
measurementerrorin these elasticities is low relativeto their
store-to-storevariation.
To assess the potential problems associated with
collinearity,we computedvarianceinflation factorsfor each
of the independentvariables.The only large VIFs were associated with the EDUC and HOUSE_VAL variables, reflecting their high degree of intercorrelation.Even these
VIFs were around7.0, which shows that there is still considerableindependentvariationin these variables.All other
VIFs are 4 or below, with most below 3. Thus, the diversity
in the DFF marketarea allows us to isolate effects that are
difficult to find in the relativelyhomogenous cities in which
scanner panel data is usually collected (e.g., Sioux Falls,
SD, and Springfield,MO, for ERIM data and Marion, IN,
and Rome, GA, for the IRI academic data sets).
Although Table 2 provides informationabout the direction of the effects and the statisticalstrengthof the evidence,
it does not directly address the substantiveimpact of the
variables.To better gauge the relative importanceof demographic versus competition variables, Table 3 presents the
results of a series of four sets of regression equations. It
startsin column 2 with the root mean squarederror(RMSE)
for the base model, which is a simple category-levelmean.
The rest of the columns presentthe ratio of RMSE from regressions with differentsets of independentvariablesto this
base model. The addition of the demographicvariables to
the base model results in a large decrease in RMSE (the column marked DemographicVariables).On the other hand,
the competitionvariablesadd little eitherincrementallyover
the demographic variables (Demographic 1Competition
Variables)or comparedto the base model alone (Competition Variables).This is an interestingresult, given retailers'
typical focus on their competition.
Despite the large qualitative differences between the
product categories, the regression coefficients consistently
cluster aroundpositive or negative values for many of the
independentvariables.However,there are a few exceptions;
for example, althoughmost of the coefficients for the EDUC
variable are positive and most of the coefficients for the
ETHNIC variablesare negative, there are a few significant
coefficients of the opposite sign.
For other independentvariables, such as the ELDERLY
variable,there are a numberof significantpositive and negative coefficients. The elderly may display a preferencefor
Table 2
INDIVIDUAL
CATEGORY
REGRESSIONRESULTS
Independent
Variables
ELDERLY
Bath
Tissue
Bottled
Juice
Grahams/
Saltines
Canned
Seafood
Canned
Soup
1.119
(.463)
2.136
(.833)
1.830
(.617)
-.942
(.399)
.051
(.296)
-.70
(.436)
-1.660
(.824)
.635
(.586)
.347
(.236)
1.398 -1.405
(.777)
(.671)
.819
(.323)
1.646
(.434)
-.754
(.112)
.074
(.099)
-.152
(.123)
-.233
(.108)
.558
(.610)
-.566
(.232)
.817
(.205)
-.081
(.165)
-.170
(.146)
-.206
(.174)
.325
(.066)
.624
(.497)
.133
(.189)
.227
(.167)
.884 -1.764
(.780)
(.715)
-1.882
(1.476)
-.126
(1.049)
Cereal
Cookies
Dairy
Cheese
.341
(.539)
.365
(.399)
1.978
(.452)
.630
(.343)
-.121
(.152)
-.257
(.134)
.424
(.130)
-.600
(.115)
.860
(.334)
-.074
(.127)
-.453
(.113)
FAMILY.SIZE -.745 -1.658
(.964)
(.829)
-.023
(.809)
-.498
(.235)
-.303
(.208)
1.285
(1.492)
WORK-WOM -1.475
(.625)
.464
(.524)
.100
(.967)
-.556
(.463)
-.697
(.505)
-.609
(.956)
.371
(.680)
1.172
(.265)
-.000
(.008)
.273
(.127)
.605
(.139)
.001
(.004)
.727
(.262)
EDUC
ETHNIC
INCOME
-.339
(.537)
.394
(.147)
Fabric
Softener
Frozen
Entree
Frozen
Juice
.433
(.576)
Laundry
Detergent
-2.158
(.557)
Liquid Dish
Detergent
Paper
Towels
-.645
(1.007)
.190
(.849
.289
(.628
-.631
(.383)
-.487
(.239
.022
(.161)
.702
(.339)
.176
(.211
-2.439
(1.52
-.386
(.985
1.34
(.270
-.606
(.648)
.466
(.480)
-.759
(.183)
-1.271
(1.360)
.015
(.219)
-.148
(.194)
-.535
(.412)
-.241
(.157)
-.063
(.139)
.597 -1.906 -2.877
(.422) (1.201) (1.392)
-2.056
(.997)
-.703
(1.161)
-2.587
(2.435)
-.340
(.646)
.539
(.177)
.299
(.752)
-1.118
(1.578)
.233
.796
(.206)
(.433)
.006
(.007)
.007
(.014)
-.087
(.051)
-.184
(.107)
.056
(.059)
.605
(.186)
-.255
(.273)
.079
(.075)
.849 -1.365
(.902)
(.778)
1.300
(.214)
.819
(.248)
.009
(.006)
-.100
(.046)
-.004
(.002)
.010
(.019)
-.006
(.007)
.028
(.053)
.019
(.008)
.030
(.061)
.004
(.006)
-.021
(.044)
Refrigera
Juice
WARE_VOL
-.022
(.042)
-.049
(.036)
.628
(.144)
-.010
(.005)
.010
(.036)
SUPERJDIS
-.042
(.026)
-.003
(.022)
-.003
(.021)
.041
(.040)
.001
(.019)
.019
(.021)
-.014
(.039)
-.007
(.028)
-.004
(.011)
.054
(.032)
-.008
(.037)
.042
(.026)
-.043
(.031)
.003
(.065)
.010
(.009
-.081
(.067
-.038
(.040
SUPER_VOL
.063
(.114)
.017
(.098)
-.193
(.096)
-.229
(.177)
-.045
(.085)
-.055
(.093)
-.072
(.175)
.043
(.124)
.034
(.050)
-.263
(.142)
-.293
(.165)
.031
(.118)
-.325
(.289)
-.151
(.180
5.990
.765
(1.307) (1.124)
2.576
(1.096)
.195
(2.022)
-592
(.969)
1.120
(1.057)
-9.043
(2.000)
.363 -1.449 -4.110
(1.421)
(.572) (1.628)
1.692
(1.886)
-.305
(1.351)
-.057
(.138)
-.768
(1.573)
-6.180
(3.301)
-2.481
(2.060
HOUSE.VAL
WARE_DIS
Intercept
.136
(.171)
.013
(.005)
-.003
(.005)
-.031
(.066)
.008
(.004)
-.114
(.031)
-.045
(.034)
-.004
(.008)
-.093
(.065)
RMSE
.142
.122
.119
.220
.105
.115
.217
.155
.062
.177
.205
.147
.171
.359
.224
AdjR2
.417
.782
.698
.782
.771
.782
.852
.804
.450
.854
.595
.492
.646
.524
.810
JOURNALOF MARKETING
RESEARCH,FEBRUARY1995
26
Table3
RMSEBY MODELSPECIFICATION
RELATIVE
Demographic
BaseRMSE
Constant
BathTissue
BottledJuice
CannedSeafood
CannedSoup
Cereals
Cookies
Grahams/Saltines
DairyCheese
FabricSoftener
FrozenEntrees
FrozenJuice
LaundryDetergent
LiquidDish Detergent
PaperTowels
RefrigeratedJuice
Soft Drinks
SnackCrackers
Toothpaste
.187
.263
.474
.218
.221
.247
.569
.351
.084
.466
.324
.207
.289
.523
.517
.391
.367
.374
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Average
.338
1.0
Category
some product categories, such as condensed soup, which
may counterbalanceother factors that would lead to greater
price sensitivity.
It is unreasonableto expect all categories to display the
same patternof coefficients. There is tremendousvariation
in the size, level of aggregation,type of product,inventory
patterns,and degree of complementarityor substitutability
with other products across the 18 categories. Furthermore,
we have estimated216 regressioncoefficients in the category regressions and it is possible to obtain a numberof significantcoefficients of the opposite sign from the population
value, even when testing at the 5% level.
Therefore,we conductedan exploratoryprincipalcomponent analysis to assess commonalityacross categoriesin the
elasticity estimates.The first principalcomponentof the 18
X 18 correlationof the categoryelasticities across the stores
explains some 54% of the total variation.Any one of the
other factors explains little of the total variation.The first
eigenvectorweights every categoryexcept one with positive
weights, suggesting that there is a strong common component that might be interpretedas an overall store elasticity.
The common patterns in regression coefficients across
categories naturallyleads to considerationof methods for
pooling the information across categories, both to obtain
more precise estimates of the common patternand to better
summarizethe centraltendencies in the data.
In the next section, we explore pooling methodsfor combining informationacross categories.
POOLEDANALYSIS
All pooling strategiesrely on a set of assumptionsthattie
together each of the individual category regressions. We
startby introducingnotation and errorassumptionsfor the
system of category regressions that form a multivariateregression system.
Variables
.78
Competition
Variables
Demographic/Competition
Variables
.46
.47
.56
.55
.47
.38
.45
.74
.39
.67
.71
.60
.71
.44
.63
.48
.89
.87
.87
.94
.93
.83
.90
.84
.86
.94
.93
.91
.92
.86
.91
.89
.85
.83
.92
.76
.46
.46
.55
.48
.46
.38
.44
.74
.38
.63
.71
.59
.69
.43
.61
.47
.81
.58
.89
.56
(4)
Yl = al + Xpi + e1
' = (e', .....e ) - MVN(O,A In)
2
2
A = diag(a
1cr, ... a c)
Yc =Ta
+ X3c + EC
The specification in (4) allows for category-specific intercepts. Ycis a N (numberof stores) X 1 vector of elasticities
for the cth category expressed in terms of deviations from
the mean category elasticity. X is the N X k matrix of values of the k independentvariables, ,Pc is the cth category
slope coefficient vector, and cc is the N X 1 vector of error
terms for the cth category.In our case, there are C = 18 categories, k = 11 independentvariables, and N = 83 stores.
Note that our regression specification has a separateintercept for each category;this is true even for the randomcoefficient approachdiscussed subsequently.
This means that we seek only to explain the variationin
elasticities across stores. We do not attemptto identify factors thatcould explain differencesin mean elasticities across
categories. Admittedly, understanding differences in the
level of elasticities across categories is an interestingtopic,
though it is not our focus here.
We make two key assumptions about the correlation
structureof the errorterms.First, we assume that errorsare
independentacross stores within a given category, that is,
that e, -~ MVN(0, or In). This is reasonablebecause of our
careful selection of relevantindependentvariables.Second,
we assume that errorsacross categories for the same store
are independent.Our analysis of the correlationmatrix of
category-level regression residuals justifies this second
assumption.
One standardapproachto pooling informationacross categories would be to restrictsome or all of the regressioncoefficients so they were the same across each of the C cate-
Store-LevelPriceElasticity
goryregressionsin (4). The evidencereportedin section6
suggeststhatsuch severepoolingrestrictionsare not warranted.Wemightexpectthatthe coefficientswoulddisplay
consistentpatternsin sign,butwe see no a prioritheoretical
reasonswhy the coefficientsshouldhavethe samemagnitudeacrosscategories.To formallytest the poolingrestriction,Ho:P1i= ... = Pc, we computedlikelihoodratiotestsof
this restrictionin the system(4). The restrictionis rejected
at the .001 level.
thepooledresultsin theconIt is alsopossibleto interpret
textof a randomcoefficientmodel,in whicheachof thecategorycoefficientvectorsis viewedas a drawfroma superthatis, ic are distributediid with
populationdistribution;
= Vp.Tocharacterize
meanE[13c]= 1 andvariance,Var(13c)
the centraltendencyor commonalityamongthe categories,
we wouldlike to makean inferenceaboutthe meanof this
randomcoefficient distribution.The pooled regression
stacksup the C regressionsin (4) into one largeregression
systemwithcommonslopecoefficients.y = Xp3+ u, where
y is a CN X 1 vectorof elasticities,X is a CN X (C + k) matrixof independent
variables,P is the vectorof categoryink
commonslope coefficients.The least
and
is
the
tercepts,
of
the
commoncoefficientvectorin the
estimates
squares
pooled regressionapproachis a consistentestimateof 13.
However,if one subscribesto the randomcoefficientview,
thenthe errortermsin the pooledregressionwill displaya
In the standardrandom
specialformof heteroskedasticity.
reviewin
coefficientliterature
(see discussionandliterature
Judgeet al. 1985,Chapter13), variousmethodsof estimatarecoming the variancecomponentsin the errorstructure
binedwith a feasibleGLSapproachto produceasymptoticallyefficientestimatesof 13.
Ourapproachis to avoidspecificassumptionsregarding
the formof the heteroskedasticity
inducedby the random
and use insteadthe generalhetcoefficientinterpretation
eroskedasticestimatorof the variancecovariancematrixof
theleastsquaresestimatorproposedby White(1980).Thus,
we use ordinaryleast squaresand computeadjustedstandarderrorsthatareasymptotically
justified.
It is importantto note thatthe asymptoticarguments
requiredfor use of our adjustedstandarderrorsnecessitate
that the numberof observationsin the pooled regression
randomco(CN= 83 X 18 = 1494)to be large.Thestandard
efficientapproachespursuedin the econometricsliterature
areasymptoticin the numberof categories.A moreformal
of any
analysisthatdoes not requireasymptoticarguments
sort can be obtainedby the use of Bayesianhierarchical
andRossi(1993)conducta hierarchimodels.Montgomery
cal analysisusing Gibbssampling,whichprovidesresults
verysimilarto thosepresentedhere.
Table4 presentsthepooledestimatesof 1 alongwiththe
standarderrorsand 90% confiheteroskedastic-consistent
denceintervals.The pooledanalysisreinforcesthe conclusions from individualcategoryregressions.On average
across categories, the ETHNIC, FAM_SIZ, and
variableshavenegativecoefficients,leadingto
WARE_VOL
high price sensitivityfor highervaluesof these variables.
TheEDUC,HOUSE_VAL,
andWARE_DIS
variableshave
positivecoefficients.
27
Table4
RESULTSPOOLEDACROSSCATEGORIES
(3
Variable
(StandardError)
ELDERLY
EDUC
ETHNIC
-.082
(.25)
.76
(.17)
-.27
90% Confidence
Interval
(-.50, .33)
(.48, 1.0)
(-.43,-.11)
(.10)
INCOME
-.071
(.076)
FAMILY_SIZE
-.84
(-.20, .054)
(-1.7,-.0018)
(.50)
WORK.WOM
HOUSE_VAL
WARE_DIS
WARE_VOL
SUPER_DIS
SUPER_VOL
-.29
(.28)
.54
(.071)
-.00042
(.00024)
-.065
(.017)
.012
(.012)
-.056
(.048)
(-.75, .17)
(.42, .66)
(-.00083,.00003)
(-.093,-.037)
(-.0077, .032)
(-.13, .023)
FOR RETAILPRICING
IMPLICATIONS
In thissection,we exploretherelevanceof ourresultsfor
boththeeverydayandpromotional
pricingpoliciesof theretailer.Dominick'scurrenteverydaypricingpolicyallowsfor
somecustomization
to the storelevel, with the assignment
of storesto one of threepricezones.Thepricezonesessentiallydictatethe everydaypricingof all itemsin the store,
withthepromotional
in a uniformmanner
pricesdetermined
acrossthe chain.Ourresultssuggestthatboth theirprice
zonesandthe relianceon uniformpromotional
policiescan
be improved.
ThethreeDFFpricezonesaredefinedalmostentirelyby
theextentof nearbycompetition.
Thelowestpricezoneis a
zone,whichis aimedat achievingcloser
warehouse-fighter
paritywith largeEDLPwarehouseoperations.Consumer
characteristics
demographic
figureonlyminimallyin thedeof the pricezones.To explorethe potentialfor
termination
improvementin the existingeverydaypricingpolicy, we
constructa table showingthe relationshipbetweenstore
in eachof thethreepricezonesandthelevelof
membership
pricesensitivity.We computea share-weighted
averageof
eachof thecategoryelasticitiesforeachstoreanddividethe
storesinto the top quartileof pricesensitivity,the middle
50%,andthebottomquartile(high,medium,andlow in the
tablebelow).
Table5 shows no relationshipbetweenprice zone and
testfor independence
hasp =
pricesensitivity(chi-squared
.28 and Spearmanrankcorrelation= .20). This opens the
possibilityforanimprovedpricingpolicyin whichthemore
price-sensitivestores have lower everydayprices. Hoch,
in
Dr6ze,andPurk(1993)discusstheresultsof experiments
28
JOURNALOF MARKETING
RESEARCH,FEBRUARY1995
sureswill forceretailersandmanufacturers
to adopta more
database-driven
approachto pricing.
Table5
DFF PRICEZONESENSITIVITY
Zone I
Low Price
High Price
Sensitivity
Medium
Low
Total
2
6
1
9
3
Zone2 Zone
MediumPrice
High Price
14
25
10
49
4
11
10
25
Total
20
42
21
83
the DFF chain that confirmthe short-runprofitabilityof
sucha strategy.
Perhapsthe mostimmediatebenefitthatourresultsprovide the retaileris a methodto moreeffectivelyuse a proDFFconductsa chainwidepromotionalbudget.Currently,
motionalstrategyin whichpricesareloweredby a uniform
percentageacrossall storesin the chain.The purposeof
thesetemporary
pricecuts is to stimulatesalesof the item.
DFFon the
to reimburse
It is commonforthe manufacturer
basisof increasedsalesof an item.
If we viewthenarrowobjectiveof a pricepromotionas to
achievinga given percentageincreasein sales, then it is
clear that our price sensitivityestimatescan be used to
achievea givenpromotionallift at a lowertotalcost. This
for store-level
nonuniform
policyexploitsthe opportunities
BecauseDFF gets more bang for a
price discrimination.
storesthanin the
givenpricereductionin theprice-sensitive
stores,a simplestrategyfor rerelativelyprice-insensitive
ducingpromotioncostswouldbe to havesmallerpricecuts
in the highlysensitivestorescoupledwithlargecutsin the
insensitivestores.The benefitsof utilizinga customized
strategywill dependprimarilyon the variation
promotional
of the item
in storepriceelasticitiesas well the magnitudes
priceelasticity.
Toillustratehowlargethesebenefitsmightbe, let us first
consideran item in the refrigerated
juice categorythathas
devibothhighvariationin storepriceelasticities(standard
ationof .51)combinedwitha highlevelof elasticity(-2.24).
We selecttheTropicana64-ounceorangejuice item,which
in this
hasoneof thehighestmarketsharesof ouraggregates
category.To achievean expectedincreaseof 50%in unit
sales over the whole chain, DFF's uniformpromotional
strategywouldrequirea 10.84%pricecutata costmeasured
by reducedprofitsof $570 perweek.On the otherhand,a
customizedpromotional
strategythatadjuststhe amountof
the pricereductionfor each store,dependingon the price
elasticity,wouldhavea cost savingsof 15%.Cost savings
fromcustomizedpromotionalstrategieswouldbe minimal
in othercategoriesthatshowlow variationin pricesensitivity acrossstores.Forexample,in thefabricsoftenercategory, a customizedstrategyfor the promotionof the Downy
sheetsitemwouldresultin a cost savingsof onlyabout1%.
We do not meanto suggestthatthe currentpoliciesof
The optimalityof current
or suboptimal.
DFFareirrational
to the informationprorelative
be
should
judged
policies
cessing and analyticalconstraintsunderwhichDFF operates.Eventhoughthe scannerdatausedin this analysisis
availableto mostmajorgroceryretailers,theanalyticaltools
andsoftwareandhardware
technologyarenotreadilyavailWe expectthatcompetitivepresable or well appreciated.
CONCLUSIONS
As the consumerpackagedgoods industrygrows more
are investing
competitive,bothretailersandmanufacturers
stratesubstantialresourcesin developingmicromarketing
gies in an effortto capturenew economicrentsfromcustomizedpolicies. These efforts are predicatedon the assumptionthatthereare systematicand identifiabledifferencesin consumerbehavioracrossstorelocations.
Ouranalysisprovidessupportfor this view by demonrestratingthatpricesensitivitymeasuresaresystematically
of the consumersin the marketarea
latedto characteristics
andthe competitiveenvironment.
Typically,morethantwo
variationin elasticitiescanbe exthirdsof thestore-to-store
andcompetitivevariables.The
plainedby 11 demographic
and
directionof the relationshipbetween characteristics
price sensitivityfollows those predictedby a simpleecoas follows:
nomicframework.
Theresultsaresummarized
costs,so
(1) Moreeducatedconsumershavehigheropportunity
theydevoteless attentionto shoppingandthereforeareless
pricesensitive.
(2) Largefamiliesspendmoreof theirdisposableincomeon
groceryproducts,andthereforetheyspendmoretimeshoppingto gamertheirincreasedreturnsto search;theyarealso
morepricesensitive.
(3) Householdswith larger,moreexpensivehomeshavefewer
so theyareless pricesensitive.
incomeconstraints,
(4) Blackandhispanicconsumersaremorepricesensitive.
(5) Storevolumerelativeto the competitionis important,
suggestingthatconsumersself-selectfor locationandconvenienceor priceandassortment.
(6) Distancefromthe competitionalso matters.Isolatedstores
displayless pricesensitivitythanstoreslocatedcloseto their
costs.
increases
Distance
shopping
competitors.
of thecompetOurresultssuggestthatthecharacteristics
arenot all thatimportantas determinants
itiveenvironment
of storepricesensitivity(see Table3). It is reasonableto ask
measuresof
whythisis so. Wecouldbe utilizinginadequate
fornumerous
with
we
However, experimented
competition.
mulationsof competitionwithno success.Moreover,to our
knowledge,our competitivedata is more comprehensive
thananythingthathasbeenusedpreviously.
It is also possiblethaterrorin the competitivevariables
inducesbias andreducestheirpredictivepower.No doubt
thereis errorin ourmeasuresof competition,butprobably
no greaterthanthe errorfoundin the consumervariables
basedon censusdata.It may also be thatthe marketis alandas a resultthereareno returns
readyin priceequilibrium
for the consumerto searchfor, becauseretailers have alreadyadjustedtheirpricesto theirconsumersandthe competition.We find no evidenceto supportthis contention.
Acrosscategories,the averagecorrelationbetweenweekly
pricesattheskulevelis only .32.Althoughthere
competitor
is pricematchingin termsof everydayprices,promotional
pricingis not coordinated.Therefore,sizable gains from
consumerpricesearcharepossible.
Anotherexplanationfor the failureof competitivevariandReiss
ablesmaybe inferredfromtheworkof Bresnahan
(1991),who findthatonce a marketis servedby morethan
Store-LevelPrice Elasticity
three firms, the monopoly rents have virtually disappeared.
Thus, althoughit may seem that there is a good deal of difference in the competitionvariables,it may be that all DFF
locations face an extUemelycompetitive environment.
Although a metropolitanarea like Chicago has few geographically isolated stores (as are found in a rural area),
there are some DFF locations on the south side of the city
with very little competition.These stores are in economically disadvantagedurbanareas,in which few retailersare willing to locate. However, despite little competition, these
stores are among the most price sensitive in the chain.
Our results cast some doubt on the optimalityof the current pricing policies of grocery retailers.All the major retailers in Chicago divide their stores into a small set (typically less than five) of price zones. These pricing practices
are driven almost exclusively by local competition. On an
everyday basis, shelf prices will be at most a few pennies
differentthan the local competition.Although it does make
sense to charge different prices at different locations, depending on the extent of competition,our analysis suggests
that consumer characteristicsas well as the extent of competition are important and should inform the pricing
decision.
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