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Quantifying and Improving Promotion Effectiveness at CVS

Author(s): Kusum L. Ailawadi, Bari A. Harlam, Jacques César and David TrounceSource: Marketing Science, Vol. 26, No. 4 (Jul. - Aug., 2007), pp. 566-575Published by: INFORMS

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PracticePrize Report

Quantifying n d mproving Promotion

ffectiveness t V S

Kusum L. AilawadiTuckSchoolat DartmouthCollege,100 TuckHall, Hanover,New Hampshire03755,

[email protected]

BariA. HarlamCVSCorporation,One CVSDrive,Woonsocket,Rhode Island02895,[email protected]

JacquesCesar

MercerManagementConsulting,1

GrosvenorPlace,LondonSW1X7HJ,UK,[email protected] Trounce

MercerManagementConsulting,1 CaliforniaStreet,16thFloor,SanFrancisco,California 4111-5421,[email protected]

Marketing ScienceVol.26, No. 4, July-August2007,pp. 566-575issn 0732-2399 issn 1526-548X 71 6041 566

i n t J E E B

doi 10.1287/mksc.l060.0245©2007INFORMS

quantified he net unit and profit mpactof eachpromotionoffered n 2003by CVS,a leadingU.S.drugretailchain,and analyzedthe key drivers of variation n this net impact.We used this analysisto identify

the least effectivepromotionsand conducted a controlled field test to demonstrate he impactof eliminatingthem beforechainwideimplementation.Ourkey findingsare as follows. First,approximately 5%of the grosslift frompromotions s incremental or CVS.Further,orevery unit of grosslift, 0.16unit of some otherproductis purchasedelsewhere in the store.Still, more than 50%of promotionsare not profitablebecausethe lower

promotionalmarginis not sufficientlyoffset by incrementalunits. Second,there is substantialvariation n net

profit impactacrosscategories.Our field test shows that eliminatingpromotionschainwidein 15 of the worst

performingcategorieswill decrease sales by about$7.8million but will improveprofit by approximately 52.6million. This is very impressive given that CVSfrontstore sales in 2003 were approximately$9 billion while

the net profit impactof promotionswas -$25.3 million.Keywords:promotionprofitability; etailpromotions;retailpromotion mpactHistory:This paperwas receivedAugust 18,2005,and was with the authors 3 monthsfor 1 revision;

processed by GaryLilien.

1. The ManagerialProblem

1.1. Company BackgroundCVS is a U.S. drugstorechain with over 4,000 retailstores n 35 states. It sells over200categoriesof health,beauty,ediblegrocery,and generalmerchandiseprod-ucts. "Front tore"revenues (i.e., excluding prescrip-

tions) were over $9 billion in 2003. In recent years,competitionhas intensifiedsignificantly n this indus-

try,especially from mass merchants.It is telling, forinstance, that in Texas $0.72 out of every consumerdollar spent on health and beauty products goes tomass merchants.These productsare the mainstayofa drug chain.

CVS s a HILO high-low)retailer, .e., it offerspricepromotionson severalproductseach week. Approxi-mately 30% of its sales are made on promotion.The

company's researchshowed that direct competition

fromevery day low pricemass retailers ikeWal-Martis lowering consumers'referenceprices and hurtingthe price perceptionof CVS.Althoughthe company'spromotionalpricesarelow, regularprice pointsarein

many cases so much higherthan theirevery day low

price competition as to not be sustainable.Further,

effectiveness seems to vary widely among the tens ofmillions of promotionsthat CVSruns each year.The company has no intention of abandoningits

HILOpositioningbut it wants to ensure that its pro-motionalpricingdecisions are effectivein competingwith other retailers and in their net sales and profitimpactforthecompany.Todo so, it needs to (1)deter-mine which promotionsareeffective,which ones are

not, and why; (2) eliminate or modify ineffectivepro-motions;and (3)reinvest the savingsin morecompet-itive pricesand bettermerchandising.

566

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Ailawadi, Harlam, Cesar, and Trounce: Quantifyingand ImprovingPromotionEffectivenessat CVS

Marketing cience26(4),pp. 566-575,©2007 INFORMS 567

1.2. Research ObjectiveTheoverarchingpurposeof this projectwas to quan-tify and improvethe net impactof CVS'spromotions.We compiled data for each of the over 30 million

promotionsofferedin any of its approximately4,000stores during 2003 and (a) estimated the immediateor gross lift of the promotion;(b) decomposed the

gross lift into three components,i.e., currentperiodswitching from other brands in the store, stockpil-ing from future period category sales in the store,and incremental ift for the store; (c) estimated the"halo" ffectof thepromotion, .e., the extent to whichit increases sales of other product categories in thestore; (d) accounted for differences in promotionaland regular margins and the funding provided bythe manufacturer;nd (e) calculated the net unit and

profit impact of the promotion.We then conducted

a meta-analysisof how this net impact varies withcharacteristics f the promotion,brand,category,and

market,and identifiedthe most unprofitablepromo-tions as candidatesfor elimination. This analysisnot

only helped the company determine which promo-tions were particularlyineffective and which onesweremore effectivebut alsoprovideda deeperunder-

standing of why such variationexists, and how to

implementmore effectivepromotions.Promotionimpact is certainly not a new subject

for academicsor practitioners,with the studies basedon scanner data dating back to the eighties (e.g.,McAlister1986, Gupta 1988).Packaged goods man-ufacturersspend over 50% of their total marketing

budgets on promotions(CannondaleAssociates2000)and market researchcompanies generate significantrevenue from the reports they provide to manufac-turers quantifyingthe gross lift that manufacturers'

brandsget frompromotion.Further,academicshavespent considerableeffort in understandingwhy the

gross lift varies acrosscategoriesand brandsas wellas in decomposing that lift into brand switching,consumer stockpiling,and primarydemand compo-nents, at least for a few categories.

Why then did we need to conduct our own studyof promotioneffectiveness? The reason is that mostwork on promotionimpact takes the perspectiveofmanufacturers see Cooper et al. 1999, Srinivasan etal. 2004 for two exceptions). Further, ack of pub-licly available cost data has prevented analyses ofthe profit impact of promotion.Retailersmake deci-sions aboutpromotionsthatareoffered to consumers,

they experience the direct financial impact of thosedecisions,and effectiveness for a retailer s quite dif-ferent from effectiveness for a manufacturer.Anypromotion-inducedncrease n categoryconsumptionbenefits both manufacturersand retailers,but asidefrom that manufacturersbenefit by switching con-sumers fromcompetingbrandswhereasretailersben-efit by switchingconsumers fromcompetingstores.

Figure 1 depicts all the components of the grosslift for a promotedbrand in a given store in a givenperiod and highlights the componentsthat compriseincremental ift for the retailer.Along with this incre-mental lift within the category, he retailermust con-sider any "halo"effect that the promotionmay have

Figure ComponentsfGrossLift







Ailawadi, Harlam, Cesar, and Trounce: Quantifyingand ImprovingPromotionEffectiveness t CVS

568 Marketing cience26(4),pp. 566-575,©2007INFORMS

Figure Promotional atabase:KeyElements ndSources

on sales of other categoriesin the store to determinethe net unit impactof the promotion n the store.Fur-

ther, the retailer must account for regularand pro-motional margins, including manufacturer unding,to determine the net profit impact of the promotionin the store. Even after accountingfor manufacturer

funding,CVS'promotionalmargin s often lower than

regularmargin.Although there is a significant gross lift when a

promotionis offered, substantial variation seems toexist in the net impact of differentpromotions.Howmuch of the gross lift is incrementaland how muchis simply switched from other brands in the store or

pulled forward from consumers' later purchases atCVS?Are promotionson some products particularlyeffective in increasing sales elsewhere in the store?Does a net increase n units afteraccountingfor these

componentsimply a net increasein profit?In which

categoriesarepromotionsmost and least effective and

why? CVS needs answers to these questions, espe-cially in its core health and beautycategoriesthat are

rarely studied by academics with access to groceryproductdata.

1.3. Key ChallengesConductinga study of this scale and magnitudeforthe first time posed significantdata, organizational,andmethodologicalchallengeseven thoughit had thefull and enthusiasticsupport of senior management.We realized very quickly that real data in a com-

pany with such widespread operationsare not read-

ily available n the well-organized,clean,ready-to-use

form that academicsworking with a few categoriesfrom one or more marketsare used to.

First, we had to identify the sources within the

company that could help compile differentportionsof the data. This requiredbringing together peoplefrom differentorganizationalunits and locations,get-ting them to buy into the project,and convincingthem to share their knowledge and data even if

this revealed limitations in their existing decisionprocesses. Figure 2 summarizes the differentorga-nizational units involved in building the completepromotion database.Informationon consumer per-ceptionsof CVSpricesand value came fromthe mar-

ketingresearch eam,the point-of-saledatathat formthe backbone of our analysis and are routinelycol-lected by CVS were provided by store managersineach marketand the corporateIT team, the loyaltyprogram(called"ExtraCare"by CVS)panel data for

analysis of stockpiling were provided by the ExtraCareloyalty programteam, data on promotionchar-acteristicsrequired nput from the promotionsgroupand some manualcompilationfromweekly flyers in

each market,and data on marginsand vendor fund-

ing were provided by category managers and thefinancegroup.

Second, we faced a significantpolitical challengewithin the organization.CVS is a HILO retailerandits promotionalpricingstrategy s a cornerstoneof its

marketingposition. Many people viewed this projectwith skepticism and were concerned that it wasaimed at cutting their key sales lever promotions.It was thereforevery important to (1) ensure and

publicize strong support from the top, especiallythe








Marketing cience26(4),pp. 566-575,©2007INFORMS 569

executive vice presidentof marketingand merchan-

dising and the vice presidents of marketing and

merchandising; 2) highlight "improvementin ROIfrom promotion spending" as a criticalgoal for all

relevantgroups in the company'sannual process ofsettingand monitoring goals and priorities; 3) makethe data, methods,and analysis completely transpar-ent; (4) implement the project n stages; and (5) getactiveparticipationparticularly rom the promotionsand merchandisinggroupsat each stage.

The first and most criticalphase of the projectwasto quantify promotion impact. We ensured that allaffectedgroupswere awareof and had the opportu-nity to provide feedback on the data, assumptions,methodology,and analysis in this phase. Only thendid we enter the next stage of analyzing variationsin promotionimpact, identifying the least and mosteffective

promotionsand

recommendingchanges.To

furtherbuild confidence in (and of course to test)the validity of the recommendations,we designed acontrolledfield experimentin which the impact ofthe recommendedchangeswas tested and quantifiedbeforechainwide rollout. And the expected revenue

impact of the implemented changes was explicitlytakeninto consideration n revising targetsand bud-

gets for each group. In this way, all affectedpartiessaw that changeswere being made based not on thewhims of top managementor gut feel but on scien-tific measurementand tests that they were involvedin themselves.

A third challenge was that of database prepara-

tion. The nature of the data offered a key advantagethat eased this task. These are all objective,archivaldata so we did not have to deal with the complex-ity and potentialbiases involved in using more sub-

jective, perceptualdata. Further, he data warehouse

capabilitiesat CVS are quite impressive. The com-

pany's product line is organized in a hierarchyof

departments,categories,subcategories,and individ-ual items or SKUs. Each SKU has a unique codethat identifies ts category, ubcategory,manufacturer,brand,specificflavor,size, etc. The same code is used

throughoutCVS and by its vendors so it was easyto merge data from different sources by the SKUcode. However, some modifications were needed to

group items into what constitutes a product categoryfrom the consumer'sviewpoint. In most cases, sub-

categories(e.g., manual toothbrushes,power tooth-

brushes,toothpaste,dental floss, etc. within the oral

hygiene category)appropriatelydefine productcate-

goriesfromthe viewpointof the consumer.However,in some cases, subcategoriesaretoo narrow to reflectthe reality of how consumers switch between indi-vidual items. Forinstance,naproxyn,acetaminophen,ibuprofen, and aspirin are separate subcategorieswithin pain relievers but consumers view them as

substitutable.The research team worked with cate-

gorymanagers o combinesubcategorieswhereneces-sary,examiningswitching patterns n sales data in the

very few cases where the appropriate evel of aggre-

gationwas not obvious.There were other issues in database preparation

and storage that took a few months to tackle.Theseinclude (a) manuallycorrecting he point of sale datafor prices and discount depths of BOGO("buy one,get one free")promotions; b) allocatingtotalvendor

funding to individual brands and items and also to

promotionalversus nonpromotionalsales (since notall vendorfundingis tied to promotions);and (c)buy-ing and setting up a new server and other comput-ing resourcesdevoted to the large-scaledata storage,retrieval,and analysisrequiredfor this project.

The fourth challenge was identifying a suitable

methodology.Weneeded a methodologythat (a)pro-vides robustestimates of the grosslift and net impact;(b) is practicalfor CVS to implement on an ongo-ing basis for the evaluation of millions of promotionsoffered in its stores each year; (c) can be used with

only one year of point of sale data and or two yearsof panel databecause that is the most CVScan storeat any given time. Thecompanywantedto obtainrea-sonable estimates of the net sales and profit impactof each promotionbut, perhapsmore importantly, twanted to be able to comparedifferentpromotionalevents using the same methodin order to distinguishbetween poor and good performers,understandthecorrelates of variationin effectiveness,and use that

understandingn future

promotiondecisions.

As van Heerde et al. (2004)note, estimatinghow

many units of the gross lift come from other prod-ucts,otherperiods,and higherconsumption s crucialfor determining the net unit impact of promotions.However,we did not use theirmethodologybecausewe need the gross lift and net impact of individual

promotionsratherthan an averageeffect for a brand.Also, we found that their methodology could not

quantify stockpiling in a drugstorechain with only52 weeks of aggregatedata.Consumersshop at drug-stores less than once in two weeks (versus at leastonce a week at a grocerystore),and the average pur-chasefrequency or most productsat CVS is less than

twice a year.As a result,stockpilingeffects arespreadover a muchlonger periodthanthe 6-8 weeks forgro-cery stores (van Heerde et al. 2004, Mace and Neslin

2004).We thereforeuse two yearsof paneldatato esti-mate stockpilingbut follow the spirit of van Heerdeet al.'s (2004) approachin estimating switching andhalo rates.

1.4. Preview of Key FindingsBeforeproviding details of our analysis and the in-

sights it provided for CVS,we preview our key find-

ings. First,approximately45%of the gross lift is due









to switchingwithin the storeand 10% s due to stock-

piling futurepurchases n the store, eavinga substan-tial 45%as incremental ift for CVS.Second, there isa significanthalo effect of promotion for every unit

of gross lift, 0.16 units of some otherproductis pur-chased elsewherein the store.Third,despite the size-ablemagnitudeof the net unit impact,more than 50%of CVS's promotions are not profitablebecause the

company's promotional margin is often less than its

regular margin.Fourth,there is substantialvariationacrossbrands and categoriesbut, overall,promotionshave the worst net impact for health products andthe highest net impact for beauty products. Finally,a field test shows that eliminatingpromotionschain-wide in approximately15 of the worst performingcategoriesdecreases sales by about $7.8 million but

improves profit by approximately$52.6million. Thisis extremely impressive given that 2003 front store

sales were approximately$9 billion while the netprofit impactof promotionswas -$25.3 million.

2. Methodology for QuantifyingPromotion Impact

Complete details of the methodology that has beentested and put in place at CVS are available inAilawadi et al. (2006) but we provide an overviewbelow. We estimate the gross lift of each promotion,the percentage hatrepresents n-storeswitchingfromother items in the category (Figure1, box 1), the per-centage that is stockpiled and pulled forward fromfuture categorysales in the store (Figure1, boxes 4

and 5), and the halo effect on sales of othercategoriesin the store. As shown in the figure, subtracting hein-storeswitching and stockpiling components fromthe gross lift provides the incremental ift of the pro-motion,and addingin the halo effectprovidesthe netunit impactof the promotionin the store.

2.1. Gross LiftThe gross lift for a promoted item in a given storein a given week is equal to the unit sales of thatitem duringthe promotionalweek minus its baselineunit sales in that week. Like Abraham and Lodish

(1993), we estimate the baseline as a moving aver-

ageof the item's unit sales in

neighboring nonpro-motionalweeks. Initialanalysisshowed that baselines

computed using a fixed number of lagging weekswere not robust for highly seasonal and relativelyslowmoving items. We therefore use a combinationof lagging and leading weeks. A long lag is used for

very slow-moving items with little seasonalitysincemore weeks are necessaryto get a good estimate ofsales and the representativenessof earlier weeks isnot compromisedbecausethe item is not seasonal.Incontrast, horterperiodsare used for seasonal and rel-

atively fast-movingitems. Forhighly seasonalitems,

we use both lags and leads instead of just lags sothat the weeks remainrepresentative f the seasonforwhich baseline sales arebeing estimated.

2.2. SwitchingIf, for every unit increasein the gross lift of all pro-moted items in categoryc in stores in week t there sa correspondingunit increasein total categoryunitsin the store that week, then the promotion is not

switching any sales from nonpromoteditems in the

category.On the otherhand, if the gross lift is purelydue to switching from other items in the category,there should be no increase n totalcategoryunits inthe store. To illustrate,considera categorywith five

items, of which two are promotedin a given week.If the gross lift for the promoteditems is ten unitsand six units, respectively,and totalunits sold of the

categoryincreaseby sixteen, then the switchingper-

centage is zero. If, on the other hand, total categoryunits increaseby only ten units, then six units areswitchedfrom the nonpromotedtemsin thecategory,making the switching percentage37.5%of the total

grosslift.Thefollowing regressionof weeklycategoryunit sales on weekly categorygross lift allows us toestimate this switching percentage:

CategoryUnitscs,

= Ak+ PicsCategoryGrossLiftcsf eC8t,

where

CategoryGrossLiftcsf ]P GrossLift/cs,(1)

Pus=Pic+Pus-> #cs^N(0,crc2).We estimatethis randomcoefficientsmodel foreach

category using deseasonalized and first-differenceddata from all stores. j80cand /3lcare categoryfixedeffectsand Ptcs s a random effectfor each store.The

switching percentage or categoryc in stores is givenby 1- (j8lc j3|CS).ote that the independentvariableis the gross lift summed acrossall promoteditems in

categoryc in store s in week t. Thus,we accountforall promoted temsin the categorythoughwe assumethatthey have the same switching percentage.

2.3. StockpilingThe stockpiling component of the gross lift is the

percentagethat is taken from future category salesin the store. As noted earlier,this stockpilingeffectis spread over a fairly long period of time. We usetwo years of CVSloyalty programpanel data to esti-mate the reduction n futurecategorypurchaseswhenconsumers buy on promotionby comparingsubse-

quent purchases of promotionalbuyers with subse-

quent purchasesof nonpromotionalbuyers.1We order

1We do this because the time series of each panelistdoes not con-tain enough purchasesto reliablyestimatehis or her own futuredecrease n categorypurchaseaftera promotionalpurchase.









buyers into annual spending deciles and comparepromotionaland nonpromotionalbuyers within eachdecile. Thus,promotionalbuyers are comparedwith

nonpromotional uyerswhose annualspendingin the

category s similar to their own. For each purchase neachgroupin eachdecile, we calculatethe time untilthe next purchaseof the categoryat CVS and com-

pute the difference n averagetime between promo-tional and nonpromotionalpurchasesin that decile.We weight this differenceby each decile's averagenumber of category units per purchase occasion toobtainan overallaveragereduction n categoryunits.This reductiondividedby thegrosslift in thecategoryobservedin the panel is the stockpilingpercentage.

2.4. HaloIf, for every unit increasein the gross lift from pro-moted items in a given store in a given week, there

is a changein totalstore units (afteradjustingfor thenonswitchedportionof the gross lift), then the pro-motion has a halo effect on other categoriessold inthe store.Thus,we estimatethe effect of gross lift onstore(adjusted)unit sales:

AdjustedStoreUnitssf4

= A)+ E PdsTotalGrosslift*, + est,d=\

where

Total Gross Liftrfs, ]T Gross LiftlVfefied

(2)Afe= & + i8;s; j8;s~N(0,cr,2).

There are four independent variables in thismodel the total gross lift from all promoted itemsin departmentd in store s in week t where d goesfrom1 to 4 forthehealth,beauty,edibleproducts,and

generalmerchandisedepartments,respectively.Thus,we estimate separate halo effects for each depart-ment.As with switching,dataare deseasonalizedandfirst-differenced eforeestimation.We estimate fixedeffects /30and fid and random effects fi*dsor each

store,and {fid+ P*ds)s the halo effect of promotionsin departmentd in stores.

2.5. Net Unit and ProfitImpactThe net unit impactof the promotionin the store is:

StoreNet Unit Impact

= GrossLiftx (1- %Switching- %Stockpiling+ %Halo) (3)

Accounting for CVS's regular and promotionalmarginof the promoteditem inclusive of manufac-turerfunding,activity-basedcosts (ABC), he regular

and averagemarginof other itemsin thecategory(forswitchingand stockpiling,respectively),and the aver-

age marginof all itemsin the store(for halo)providesthe net profitimpactof the promotionin the store:

Store Net ProfitImpact

= Promo Units x (PromoPrice- Manuf.Price

- ABC+ NonpromoFunding+ PromoFunding)- Base Units x (RegularPrice- Manuf.Price

- ABC+ NonpromoFunding)- (%Switchingx Gross Liftx RegularCategory

Pricex RegularCategory%Margin)- (%Stockpilingx GrossLiftx Average Category

Pricex Average Category%Margin)

+ (%Halox GrossLiftx AverageStore Price

x AverageStore %Margin). (4)

Note that the (regular)manufacturer riceis unam-

biguous because that is clearly recorded for everyitem. Further,CVS has a well-establishedsystem for

estimating its activity-basedcosts. However, manu-facturerfunding has to be allocated some of this

funding is linked to promotions and is therefore

applied only to promotionalmarginwhile some of itis linked simply to sales and is thereforeapplied toboth promotionaland regularmargin.

3. Empirical Analysis: Promotion

Impact3.1. Overview of PromotionImpactTable 1 provides the starting point of our findingsregarding CVS promotion effectiveness. It summa-rizes the size of the gross lift as a percentageof base-line sales and the switching, stockpiling, and halorates. Thereare severalimportant nsightshere.First,neither the typical discount depth nor the size ofthe lift is much different from grocery stores (Blat-tbergand Neslin 1990,p. 351;Narasimhanet al. 1996).At CVS, the median gross lift due to promotion is

310%of baseline sales. Second, the median percent-age of the gross lift that is attributed to switchingfrom otheritems in the categorywithin the same storeis about 46%.This is much closer to the 35% or so

recentlyreportedby van Heerde et al. (2003)than the75%-80% eportedby Gupta(1988),Bell et al. (1999),etc. in theirelasticity decompositions.

Third,the percentageof the gross lift that is pulledforward from future CVS sales due to consumer

stockpilingis fairly small, at about 10%.Note, how-

ever,that some partof consumerstockpiling,which is







Ailawadi, Harlam, Cesar, and Trounce: Quantifyingand ImprovingPromotionEffectiveness t CVS572 Marketing cience26(4),pp. 566-575,©2007INFORMS

Table OverviewfPromotionmpact

StandardVariable Median deviation

Baseline nits per temperweekper tore) 0.86 6.0Baselinerofitper temperweekper tore) $) 1 29 5.1Promotionaliscountasa percent 30.0 14.5

ofregularrice)%)Grossift% asa percentfbaseline nits) %) 310 581

Switchingate portionfeveryunit fgross ift or 0.46 0.16a promotedtem hat s switchedrom ther temsin hecategory ithinhe store n hesameweek)

Stockpilingate portionfeveryunit fgross ift or 0.10 0.10a promotedtem hat s pulledorwardrom uture

categoryaleswithinhestoredue o consumer

stockpiling)Halo ate numberfunits f someotherproduct 0.16 0.17

soldelsewhere ithinhestore n hesameweekforeveryunit fgross ift ora promotedtem)

Netunitmpactper temperweekper tore, 1 05 12.7

computedsingEquation)Netprofitmpactper temperweekper tore, -0.62 13.5

computedsingEquation) ($)

taken fromfuturesales in competingstores,is incre-mentallift for CVS.Also note thatthe mix of productcategoriesfor CVS is quite different from a grocerychain.CVScarriesa muchhigherpercentageof healthand beauty productsthanfood products,and the for-mer are more need based. Even though they can bestored,consumersdo not stockpilethem unless theyforesee a need for them. Fourth,promotionsat CVShave a significanthalo effect thatvaries substantiallyacross departments.For every unit of gross lift, an

extra 0.16 unit of some other product is sold in thestore.

Table1 also provides an overview of the net unitand net profit impact of promotion.These numbersare in units and dollars,respectively,and the unit of

analysis is a week-long promotion on an individualitem or UPC. To put them in context,we also reportthe baseline units and profit per item per store perweek. Thus,the averageitem's baselineweekly salesin a CVSstore are 0.86 units and its baseline weeklyprofitis $1.29.A week-long promotionon an averageitem in a CVSstorecreatesa net increaseof 1.46unitsand decreasesprofitby $0.33.

The first take-away from thisanalysis

for CVS isthat although the net unit impact of a promotionispositive on average, the net profit impact is nega-tive. The substantialswitching and stockpilingcom-ponentsareone partof the reasonbut can not be the

only reason,because the net unit impact is positive.We find that after accounting for all manufacturer

fundingand allocating t to promotionaland nonpro-motional sales, CVS margin on promotionalsales isoften significantlyless than its regularmargin.Andof course all units sold during the promotion earnthe lower margin,not just the trulyincrementalunits.

This can make net profitimpactnegativeeven if netunit impactis positive.

Theseresultsaboutthe magnitudeandcompositionof promotionaleffects were very insightful for CVS.

They provided a rigorous analysis-basedestimateofthe incrementalportion of the gross lift ratherthanone based on anecdotal evidence or vendor suppliedinformation.

3.2. Variation in Promotion Impact Across

Departments

Figure3 shows how the impact of promotionvariesacross the four main product departmentsat CVShealth,beauty,edible grocery,and generalmerchan-dise products. The top chart depicts the medianvalues of baseline units, gross lift, incremental ift,and net unit impact for each department.As before,the unit of analysis here is a week-long promotionon a given item in a given store. As the chartshows,health products have the smallest gross and incre-mental lift in absolute terms as well as relative tobaseline units. Further, he halo rate is slightly nega-tive (-0.04), makingthe net unit impactsmallerthanthe incremental ift. Incontrast,beauty productsshowa much higher gross and incremental ift relativetobaseline units. And their net unit impactis substan-

tially higher than the incremental ift because of a

high halo rate. Indeed, the 0.30 halo rate for beautyproducts is higher than that for any of the other

departments.The gross and incremental ift for gro-cery products s high in absolute units but not relative

to baseline units, and with a small halo rate of 0.05net unit impactis only slightly biggerthan incremen-tal lift.

Figure3 shows not just the net unit impactbut alsothe net profit impact of promotionsin each depart-ment. Promotions have a substantialnegative net

profit mpactfor health and groceryproducts.Inbothof these departments, he net unit impactis not highenough to offset the smallermarginthatCVS makeson promotion.Incontrast,beauty productpromotionshave the highest net profit impact,with generalmer-chandisepromotions ustbreakingeven.

These differencesin promotionimpact across de-

partmentsareimportant orCVS,as theywant to flagthe most ineffectivepromotionsfor possible change.Among the bottom 15%of promotions n terms of net

profitimpact,51%are healthproductsand only 10%are beauty products.In contrast,among the top 15%of promotions,44%arebeauty productsand 26%arehealth products.Of course, there is plenty of varia-tion in net impacteven within departments. n orderto provide CVSwith a deeper understandingof thisvariation,we estimated a model of how promotion,brand,category,and marketcharacteristics re asso-ciated with the net impact of promotions.Detailed








Marketing cience26(4),pp. 566-575,©2007INFORMS 573

Figure VariationnPromotionmpact yDepartment

results of this analysisare available n Ailawadi et al.(2006).

4. Impact on Practice

4.1. Benefits for CVSThe primarypurpose of this projectwas to quantifythe impactof CVSpromotions,understand how and

why it varies,and use thatunderstanding o improvepromotionprofitability.Ouranalysisidentified which

promotions were the most and least effective and

why. Based on this analysis, we identified 15 cate-

gories where promotions consistently had negative

profit impactand at best a small

positivenet sales

impact. We recommendedthat promotionsof these

categories be significantly cut back or eliminated.Beforeany chainwidechangeswere made, however,CVS wanted to validate our analysis. We therefore

designed and implementeda controlled field test toassess the impactof eliminating promotionsin these

categories.Approximately400 CVS stores in 5 markets were

selected as experimentalstores in this field test for1 quarter,.e., 13 weeks. Theexperimental tores werechosenin consultationwith category,merchandising,

and store managers to be as representativeas pos-sible of the entire populationof CVSstores. Criteriafor experimentalstoreselection included coverageofeachmajorCVSregion,averagemarketsize, storesizeand age, no majorchangesin the store or marketarea

(e.g., store expansion or renovation),major compet-itive entry or exit, etc. Control stores were matchedon marketdemographics,storesales, margin, growthrate,etc.

Promotions of the 15 categories were discontin-ued in the experimentalstores whereas they were

promoted as originally planned in a matched set ofstoresin othermarketswithin the same region,whichserved as a control

group.For the

experimentalmar-

kets,the blocks of spacein theweekly promotion lyerused to featurepromotionsfor these categorieswere

replacedby nonpricemessagesand advertisingaboutthe same categories.Focus groups showed that con-sumers did not perceivedifferencesbetween the twosets of flyers.

Ouranalysispredicteda small decrease n the salesof the 15 categoriesin the experimentalstores rela-tive to control stores, but no loss of sales of other

categories and a significant increase in total profit.During the period of the field test, we trackedthe









number of transactionsper day, item level sales, and

profit in these experimental categories as well as inothercategories n the stores.In addition,we trackedconsumers'value ratingsof sales and specialsoffered

by CVS and purchase patterns of the panel of con-sumersin the CVSloyalty program.

The field test showed that in all but one of the

categoriesthe experimentalstores indeed had higherprofit even though category sales were lower. Fur-ther,there was no significantreduction n storetrafficor sales of other categories in the store. The paneldata supported these aggregate results while therewas a small decreasein penetrationof the categoriesamong programmembers,their store visits and their

purchasesof other productsat CVS were unaffected.

Finally,there was no significantdifferencebetween

experimentaland controlstores in consumers' value

ratings.A projectionof the results from the 13-week testin the experimentalstores to a 52-weekperiodchain-wide showed a net sales loss of $7.8millionbut a net

profitgain of $52.6million.Comparedwith total salesrevenue of approximately$9 billion, the sales loss isnot of concernto CVS.In contrast,given that the net

profit mpactof promotions n 2003 was -$25 million,a savings of $52.6 million in profit is highly signifi-cant.Inthe words of a senior CVSexecutive,"$52mil-lion dollarsin profit savings is the equivalentof $250million dollars of new business for the company "In 2005, CVS implemented these changes chainwideand is investing the profit savings in lower regular

pricesand othermerchandising upport. Earlyanaly-sis shows that actualchangesin CVS unit and dollarsharesin these categories,as trackedby InformationResources nc.reports,arein line with those that were

expectedbased on our analysis.Thecompanycontin-ues to carefully track not only aggregateshare and

profit data but also customer level perceptionsand

purchasebehavior.While this projecthas delivered on its major goal,

i.e., the improvedprofitabilityof promotions, t is also

showing significant impact along some other dimen-sions. First, it served as a stimulus for CVS to puttogether a comprehensive picture of how much oftotalvendor funding comes unencumberedwith anyrequirements,how much is tied to promotions,andhow much is tied only to sales volume. CVS obtainsvendorfunding in several forms throughout he year.This includes but is not limited to lump-sum pay-ments,promotionalallowances for featuresand other

merchandising, canbacks,and off-invoice discounts.Most of the funding is negotiatedbetween the cate-

gory managerand the vendor well before the begin-ning of the year, although funds come in and are

spent during the year. CVS category managers reg-ularly compile all vendor funding for their category,

so the total amounts were availablefor this project.However, since only a small percentage(less than

15%)of total vendorfundingis directly inkedto indi-vidualpromotions, he resthad to be allocated o pro-

motionaland nonpromotional ales and to individualpromotions.This allocationprocessis not perfect,but

going throughit has proved to be extremelyvaluablein guiding promotiondecisions and in vendor inter-actions.

Second, the fact base provided by this projectis

allowing CVS category managers to be better pre-pared for their interactionswith vendors. Insteadof

simply consideringgross lift of the promoted tem or

brand, they can now look at incremental ift in the

categoryand net impactin the storeas they negotiatemanufacturerunding and decide on their quarterlypromotionalcalendars.A third operationalbenefit,which is

just beginningto surface, s an

improvementin inventory management.The crestsand troughsofdemandareflatteningout in thecategorieswherepro-motions have been cut, making it easier to forecastsales and manageinventory.

It would be an exaggeration o claimthat we haveovercome all the challenges, particularlythe politi-cally chargedones around"promotion uts,"and thatinformationavailability, low, and utilizationis now

optimal. However, we can honestly say that signifi-cantprogresshas been made on all these dimensions.The reason for this progress is that the projectandthe subsequent field test have demonstrateda real,

quantifiable,and significant mpacton the company's

bottom line. The company's successful implementa-tion of the tested promotionchangeschainwideandits ongoing use of our promotionanalysismethodol-

ogy attests as nothingelse can, to this impact.

4.2. Benefits for IndustryPromotion pendingin the United Statesexceeds$200billion per year (PromotionMarketingAssociation

2003),so it is criticalthat businesses that arefundingpromotionsand implementingthem understandhoweffective they are both for the manufacturerwhosebrands arebeing promotedand the retailerwho actu-

ally sells those brands to consumers.Theimportanceof this activityis underscoredby the fact thatpromo-tion strategysessions are attendednot just by promo-tion managersand promotion agency executives,butalso by brandmanagers,directors of marketingand

promotion,and in the majorityof casesby CEOsand

presidents(PromotionMarketingAssociation2003).As one of the first attempts to assess promotion

impact (especially profit impact)from the viewpointof the retailer,our work has some important essonsfor managers.The first is the set of empiricalfind-

ings we have revealed about how promotionsworkfor a retailer.For instance,almost 50% of the gross









lift is switched from other brands in the store but

promotion-inducedconsumer stockpiling pulls for-wardonly 10%of future sales from the store,and thehalo effectof a promotionon sales of other products

canbe significant,as retailershave always hoped butnot necessarilydemonstrated.Perhaps most impor-tantly,our finding that more than 50% of CVS pro-motions have negative profit impact gives pause tothe conventionalwisdom thatpromotionsare bad formanufacturers ut good for retailers.Managersmustunderstandthe real impactof promotionsfor manu-facturersand retailersbefore they can take the next

step of trying to create "win-win"promotionstrate-

gies that makeboth partiesbetter off.The differences we observe across product cate-

gories are also importantfor managers.Health and

beauty productsare core to a drug chainbut are also

stronglyassociated with mass merchandiserswho

continue to grow their clout and share in the mar-ket. The contrastbetween these two departmentsisbothinterestingand surprising promotionsare least

profitablefor health products and most profitableforbeauty products.Bothmanufacturers nd retailersmust takenote of this contrastand try to understandit better.

Of course,our analysisis based on a single retailereven though it spans an unprecedented numberof promotions,brands, categories, and stores, andShankarand Bolton (2004) have shown that thereare significantdifferencesin pricing and promotionstrategyacross chains.Therefore, he opportunityfor

others to apply our models in their own businessesis perhaps even more important than the lessonslearnedfromour empiricalanalysis.Most of the dataused in our analysis are available within a com-

pany. Since scannersare now ubiquitous in all butthe smallest "mom and pop" stores, retailers rou-

tinely collect point-of-sale (POS) store data whichform the backboneof our analysis. Companies thathave a loyaltyprogramcanutilize it to quantifystock-

piling. Others can either try to estimate it from a

longer span of POS data or use our estimates andconduct a sensitivity analysis to assess worst- andbest-casescenarios.Compilingdata on costs,margins,etc.and

allocatingvendor

funding requiresignificant

effort,but our experience at CVS is that the effortis well worth the insights that such data provide.Theeconometricmodels we have developed aresum-marizedhere and fully describedin Ailawadi et al.

(2006).They provide an easily transferablemethod-

ology to conduct such analyses that is both practi-cal to implement and has been demonstratedto berobust. Indeed, the retailvalue engineering unit at

MercerManagementConsultinghas had many yearsof success using these types of models to drive finan-cial improvementat many retail businesses, includ-

ing severallarge grocersand drugstores n the United

States and in Europe.

AcknowledgmentsThe authors thank Pen-che Ho from the Tuck Schoolat DartmouthCollege and Matt Hamory, David Waller,and Andrew Youn from Mercer Management Consul-tants for their invaluableassistance with data preparation.The authors also thank Leigh McAlister and Ross Rizleyfrom the MarketingScienceInstitute,GaryLilien,and theINFORMS ocietyforMarketingSciencePracticePrize com-mittee for their comments and suggestions.

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Documents

Ailawadi, K. L., Harlam, B. A., César, J., & Trounce, D. (2007). Practice Prize Report—Quantifying and Improving Promotion Effectiveness at CVS. Marketing Science, 26(4), 566-575.pdf