PricingIntelligence2.0ABriefGuidetoPriceIntelligenceandDynamicPricing

ByMihirKittur

TableofContents

Preface

Chapter1:WelcometotheNewWorldofPricing

Chapter2:ThePriceMatchTrap

Chapter3:AnIntroductiontoPricingIntelligence

Chapter4:IntroductiontoDynamicPricing

Chapter5:TheHumanFactor

Chapter6:TheFutureofPricingIntelligenc

Chapter7:HowtoGetStarted

TheAuthor

MihirKitturisaCo-founderandChiefInnovationOfficeratUgam.Heoverseessales,marketingandinnovationandworkswithleadingretailersandbrandswithinsightsandanalyticssolutionsaroundtheircategorydecisionstoimproveoverallbusinessperformance.

ThePreface

Withtoday’schaoticbuyingclimate,we’reallveryawareofhowmuchretailersarevyingforconsumers’limitedattentionspanandtheoverabundanceofchoicesavailabletothem.Themobile,technologyandsocialrevolutionhaveledtotheriseofthesupershopperwhoisarmed,informedandvocal.Mostconsumerstodaybegintheirshoppingjourneysonlineandarelookingforthebestprices.They’realsoacclimatizedtodynamicallychangingprices.Pricewarsoccurinrealtimenow,butsomeretailersandbrandsaren’treadyforthisnewreality.

PriceIntelligenceandDynamicPricingareemergingasmust-havecapabilitiesthatretailersneedinordertostayrelevanttotheirconsumersandremaincompetitiveandhaveanedge.

Knowingthisclimate,we’rethankfulthatyoupickedthisbookandarrivedatthispage.ThiseBookwasdevelopedforAmazonandisanabbreviatedversionofamuchmorein-depthbookonthistopiccalledPRICINGINTELLIGENCE2.0:TheEssentialGuidetoPriceIntelligenceandDynamicPricingthatweencourageyoutodownloadhere.

Wehopeyoufindthisbookausefulread,andwelcomeyourcommentsandfeedbackatebookfeedback@ugamsolutions.comor(415)320-8426.

Thankyou,MihirKittur

Chapter1:WelcometotheNewWorldofPricing

OverviewYourcustomersaremoreempowerednowthaneverbefore.Armedwithsmartphonesandcomparison-shoppingengines,eventhemostloyaloneswillgoelsewhereifyou’renotofferingthe“rightprice.”

Ifyouarejustgettingstartedtryingtowrapyourheadaroundthenewworldofpricing,thegoodnewsismostoftheretailworldisstillplayingcatch-upwiththenextgenerationofPricingIntelligence.

AreYouintheMiddleofaPriceWar?Retailers,aswellastheanalystsandjournalistswhocoverthem,areextremelyfondofcombatmetaphors.

Describinganearly2014discountingfrenzyonhigh-endshampoobrands,TheWallStreetJournaldeclaredtherewasa“BigHairWar”betweenProcter&GambleandUnileveroverfolliclesintheUnitedStatesandWesternEurope.1TheJournalalsoreportedthatP&GisnowinTarget’s“crosshairs”formakingitcheaperformega-rivalAmazon.comtoshipPampersdiapersandBountypapertowels.InsiderssaythatthegiantretailerhasretaliatedbydevotinglessendcapspacetoP&Gbrands.2

Isallthisbattlegroundtalkabitmelodramatic?Perhaps.Butthefightformarketshareisendlessandrelentless–anditpaystofullyunderstandwhomyouarefightingforandagainstinordertobuildandprotectyourcompetitiveedge.

Theworldofretailisnotforthemeek.IntheAgeofMoreChoice,youcan’taffordtositonthesidelineswhileyourcompetitorsplaytheprice-changinggame.TheAmericanobsessionwithshoppingfordealscaneasilytemptretailerstochase

customersatanycost,launchingpricewarsthatultimatelymightnotbeintheirbestlong-terminterest.AstheMITSloanManagementReviewhasnoted,thereareusuallynowinnersinapricewar:“Thelosersareoftenforcedoutofbusiness,andthesurvivorshavebeenknowntosufferalong-termsqueezeinprofitability.Pricewarsbeginwhencompetitorsaggressivelyandrepeatedlysetpricesbelowestablishedlevels.”3

Insomecases,companiesthatinitiatepricewarsengageinself-destructivebehavior,whichleadstodownwardpricingspiralsthatalterindustrystructures,”wrotePatrickReinmoeller.“Instudyingpricewarsthattookplacebetween1980and2013inindustriesincludingairlines,telecomsandfinancialservices,Isawthatpricewarswereinvariablylinkedwithseriousdropsinfinancialperformance.Indeed,whenpricewarserupted,mostcompaniesfoundthemselvesincommoditytraps:Profitsnarrowedconsiderably,andweakcompetitorshaddifficultystayinginbusiness.”Inawarofattrition,bothsidescomeoutbadlybeatenandworseoffthanwhentheystarted.

There’sanotherwayforpricestogo,ofcourse.Andthat’sup.

Selectivelyraisingprices,ifhandledtherightway,willnotleadtocustomerinsurrection.Wepromise.Thinkaboutyourownconsumerexperience.Whenyouarecruisingdownthesupermarketaisles,you’lllikelyfindeitherCokeorPepsiproductsonsalefor99centsfora2-literbottle.Stopataconveniencestoreatmidnightandyou’llhavenoissuesforkingover$1.50fora16-ouncebottle.Wantthatsamebottleataballgameorconcert?It’snow$4.Atthemovies,your32-ouncefountaindrinkis$6inasouvenirplasticcup.Ifyou’readevotedsodadrinker,youknowthepriceofthirstvariesbasedonwhereyouare,theavailability(orlack)ofcompetition,andwhetheryouarewillingtowaitforeitherofthosefactorstochange.Youwon’tstopdrinkingsodabecausethosearetheacceptedanduniversalrulesofthegame.Evenifyou’renotinthebeveragebusiness,thesesamepricingprinciplesapplytoyourcustomers.

Chapter2:ThePriceMatchTrap

OverviewAmazonismakingmillionsofpricechangeseachday.Tryingtomatchtheireverymoveisafruitless(andimpossible)exercise.Youneedtoplayyourowngame.Ifyoutaketheprice-matchingtrendtoitslogicalconclusion–everyretailer’spriceseventuallybeingthesame–youneedtogiveyourcustomersamorecompellingreasontokeepbuyingfromyouandonlyyou.

AvoidingthePriceMatchTrap:Q&AWithKevinSterneckertInthecurrenthyperactivepricingenvironment,manytopretailers,likeBestBuy,haveadoptedprice-matchingguaranteesastheirfirstlineofdefense.Customersinbrick-and-mortarstoreswhofindcheaperonlinepricescanoftengetthosepriceshonoredbyastoremanager.Someretailersevenofferapricematchafterthefact–ifacustomershowsupwithinaweekwithproofofabetterdeal.

Wantingtoavoidbeingundercutbyevenafewpennies,manymajorretailerscontinuetoexpandtheirpricematchingpoliciesandproudlyannounceeachnewrevisionintheiradvertising.

Pricematchinginanyformisuniversallyviewedasavictoryforconsumers,butforretailers,it’saracetothebottom.Toexplorewhy,wetalkedwithretailanalystKevinSterneckert,aformervicepresidentofresearchforGartnerandanindustryexpertonPricingIntelligence.

AccordingtoSterneckert,retailerswhotrytocompetewithAmazononprice“areshowinguptoagunfightwithapixiestick.”

Q:HowfaraheadisAmazonintheareaofPricingIntelligence?KS:Let’sputtechnologyasideforaminute.Iwouldsaythatthey

aresixtoninemonthsaheadinstrategicthinking.It’sgoingtotakeeducationandpainforanothersixtoninemonthsbeforeleadingretailersbegintosay,“We’vegottodosomethingdifferent.”Thenit’sgoingtotakeanothersixto12monthstoinstallthetechnologythat’sgoingtoleadtoamorecompetitivesetofcapabilities.I’mnottalkingaboutmatchingAmazon.I’mtalkingaboutgoingtoagunfightwithagun–andtoday,peopleareshowinguptoagunfightwithapixiestick.

Q:Whichretailersareaggressivelytryingtocatchup?KS:Thelargestcompanieswiththemostdirectcompetitiveimpactarecertainlyworkingaggressively.Walmart,Staples,Target,Macy’sandTescoareamongthoseworkingaggressively.Theyrecognizethethreat,buttodaytheyaretakingmoreofareactionarypositionthantheyaretakingastrategicproactiveposition.

Mostofthesecompaniesarestillinveryearlystages.Theyarethinkingrules-based,theyarethinkinglookingatthecompetitor,lookingattheirvolume,understandingelasticityandthenmatchingpricesontheelasticitems.Insteadofbeingapriceleader–andthatreallyiswhatAmazonhasdone.

Q:Whichretailersarefarbehind?KS:Thebulkofotherretailersarefarbehind.Andit’snotatechnologyrace.It’sastrategic-thinkingrace.Manyretailersgetandunderstandoptimizingpricesforbrickandmortar,yettheyhaveforsomereasondecidedthattherightstrategyistomatchtheironlinepricewiththeirin-storeprice.Ifthat’syourstrategy,itisaveryflawedstrategy.

TheSolution:HowRetailersCanSurviveandThriveThetrick,accordingtoSterneckert,isunderstandingandinfluencingthecustomerthroughhisorhershoppingbehavior.

“Thecustomercaresaboutcertainitems[intermsofpricesensitivity]andtheydon’tcareaboutothers.Youtrulycantapintowhatthecustomerexpectsandyoucansteerthecustomerinverypredictablewaystobuycertainitems–andtonotbuyotheritems,”hesays.

“Let’ssayyouhavetwodifferentsizesoflaundrydetergent,the128-ounceandthe96-ounce.Ifyouhavemoreprofitonthe128-ounce,youcaninfluencethecustomertobuythatitemjustbymakingtheper-unitpricingmorefavorable.Youcanalsoreversethatandmakethecustomerwanttobuythe96-ounceitemifthat’swhereallyourprofitis.Thiselasticitymethodologytrulyisthewaythatretailerscanwin.”

SterneckertusedtobeinchargeofpriceoptimizationforH-E-BGroceryStores,aregionalsupermarketchaininTexasandNorthernMexicothathasachievedgreatersalespersquarefootthanWalmart.

“It’sbecauseH-E-Bhassaidwe’regoingtotakepriceoffthetable,”theanalystreveals.

“We’regoingtounderstandourcustomers.We’regoingtostudythem.We’regoingtomakesuretheydon’tgotoourcompetitionbecauseofprice.We’renotgoingtosaythatwe’regoingtomatch,butwearegoingtoberightontheitemsthatthecustomercaresabout.Andwe’regoingtoofferthecustomerthingstheycan’tgetanywhereelse.”

“Inmymind,thebestquoteofthecenturyaboutbeingcompetitivecomesfromSamWaltonhimself.Andhesaid,‘Ifyouwanttocompetewithme,dowhatIdon’tdo.’”

Toys“R”UsandTargetbothofferstore-exclusiveLegosets.KmartletsyoudresslikeformerCharlie’sAngelactressJaclynSmith.Macy’sandNordstromhavedealswithMadonnaon“TrustorDare”shoes.Whenyourstoreistheonlyplacetobuyanitem,youarenolongercompetingjustonprice.

Differentiationdoesnothavetobebasedonproductchoiceorassortment.Itcanalsoinvolveauniqueapproachtocustomerservice.ZapposCEOTonyHsiehhasadoptedtheunorthodoxpolicyofhavinghiscallcenterrepresentativesdirectdisappointedcustomerstothreedifferentcompetitorwebsitesifZapposisoutofstockonacertainsizeorstyleofshoe.

“Yes,welosethattransaction,”heexplainedtoanaudienceataSouthbySouthwestInteractiveconference.“Butwe’renottryingtomaximizeeverysingletransaction.We’retryingtobuildalifelongrelationshipwitheachofourcustomers–onecallatatime.”4

Whenyouareofferingitemsthatcanbeboughtfromseveralothercompetitors,usingDynamicPricing,whichistheactofpricingitemsbasedonvariablemarketconditions,youcanensurethatcustomersperceiveyourbrandasbeingfair.WiththerightPricingIntelligencesolution,you’llknowwhichhighlyprice-sensitiveitemsneedtobediscounted,whichonescanremainunchangedandwhichonesareripeforincreasingprofits.

Yes,Amazonisfaraheadoftheretailpack.Butthere’sgoodnews:AccordingtoaJanuary2014studybyRISNews,mostofthatpackissittingonthecouch.5

ConsiderthesefindingsaboutthecurrentuseofRetailPriceIntelligence:

Only23%ofsurveyedretailersareusingPriceIntelligencesoftwarerightnow.Anadditional29%ofretailersplantodeployPriceIntelligencetoolsin2014.Astunning42%havenoplanstousePriceIntelligencesoftwareatallthisyear.

Asmentionedearlier,eventhemostsophisticatedtechnologyisuselesswithouttherightstrategicthinking.Butifyouwanttostopbeingreactiveandstartbeingproactivewithyourpricing,there’sstilltimetogetonboard.

ActingontherightPricingIntelligencewillhelpyouavoidthePriceMatchTrap.

Chapter3:AnIntroductiontoPricingIntelligence

TheMyth:Store-BasedRetailersOnlyNeedStored-BasedIntelligenceAsstrangeasitmightseeminthecomputerage,thepencil-and-paperapproachtointelligencegatheringishardlyextinct.

CompetitorPriceMonitoringhasbeenaroundinvariousformsalmostaslongasretailitself.Thisisprimarilybecausewhetheryouarerunningaconsumerelectronicsstoreoraneighborhoodlemonadestand,yourcustomerswilllikelyflockelsewhereiftheycanconvenientlygetthesameproductsatalowerprice.

Traditionally,brick-and-mortarretailershavesentemployeesintocompetingstoreswithachecklistofkeyproductsforpricecomparisonandthendecidediftheirpricingneededtobeadjustedaccordingly.Retailerscannowoutsourcethiscumbersometasktomysteryshoppersorretaildatacollectioncompanies;however,theystillcan’tavoidputtingpeople“ontheground”sincenotallstoresputalltheirpricesonline.

Conventionalwisdomamongstore-basedretailershasbeenthatonlyphysicalvisitstocompetingstoreswillproducethemostmeaningfulcompetitivedata.Indeed,thatmethodisstillimportant,butbrick-and-mortarretailersalsoneedtoincludeonlinepricemonitoringontheirradar.Withveryfewexceptions,onlineretailpricesnowreflectin-storeprices.

Onlycaringaboutpricingdatafromphysicalstoresislikepretendingyourcustomersdon’tknowabouttheInternet.Youneedtobethinkingaboutpricingthewayyourcustomersandcompetitorsthinkaboutpricing.Youneedtobelookingatthesamenumberstheyare.

Amazonismakingpricechangesmorethanamilliontimesaday.WalmartandTargetevaluatethepricingontheirKeyValueItems(KVIs)everytwohours.

Bygatheringonlineprices,retailerscanregularlyandaccuratelymonitoralltargetedcompetitiveproductsinsteadoffocusingonaselectfew.Withoutthelimitationsofphysicalstoreprice-checks,thereisvirtuallynolimittothenumberofSKUsthatcanbemonitoredonlineacrossanynumberofrelevantcompetitors.Onlinepricemonitoringgivesretailersaholisticviewofthemarketplace–includingcomparisonsoftheoriginalproductprice,theMSRP,thepromotionalpriceandthepricewithandwithoutshipping.

AccordingtoarecentonlineshoppingstudybyWorldPay,aglobalpaymentcompanythatprocessestransactionsin120differentcurrencies,56%of

customerswillabandontheirshoppingcartswhenpresentedwith“unexpectedcosts”likeshippingortaxesatcheckout.6

Itiscriticaltomakesureyoualwaysmonitorcompetitorpriceswithshippingincluded.Thereisawidevarietyofshippingpoliciesonline:

WhatDoes“FREE”ShippingMean?MinimumPurchasesRequiredByRetailers

*BothBelkandTargetofferfreeshippingforstore-brandedcreditcardholders

**Ordermustbeunder20lbs.Source:Retailerswebsites

Therearemanyothershippingfactorstoconsiderwhentryingtounderstandthepsychologyofyourcustomers.Mostfreeshippingpoliciesdonotincludelargeorbulkyitems,suchasfurnitureorlumber.Manyretailerswillalsoofferfreedeliverytoanyoftheirstoresforcustomerpickup.Lastly,AmazonPrimeoffers“free”two-dayshippingfor$99peryear.

Forsmallerpurchases,adiscountofafewdollarswillbeneutralizediftheshopperneedsto“givethemoneyback”atcheckoutinshippingcosts.Mostpeopleareevenwillingtoabsorbaminimalconveniencefee–payingasmallamountmore–ifitmeansgettingtheirpurchasesnow.

Customerswhocomparepricesinphysicalstores(alsoknownasshowrooming)paycloseattentiontohowshippingaffectstheirbottomlinesforonlinepurchases.Makesureyou’repayingcloseattention,too.

TheFourStagesofPricingIntelligence:TurningNumbersintoActionThismaysoundobvious,butwhenyou’remakingasalad,it’soptimaltousethefreshestlettuce,tomatoesandcucumbersavailable.Nomatterhowgoodofachefyoumaybe,usingwiltedvegetableswillresultinarottensalad.

ThesameprincipleappliestoPricingIntelligencedata.Youneedtorefineyourrawdatasoit’sreadyforyouranalyststoturnitintorealintelligence–inaccuratedata

willleadtofaultypricingrecommendations.

Herearethestepsthatareneededtoturnyournumbersintoaction:

1. GatheringPrices–Webcrawlerscontinuouslyscrapecompetitorsitesforproducts,modelnumbers,pricesandothercharacteristics.

2. EnrichingtheData–Usingautomatedtoolsandretailcategorymanagerexpertise,yourproductsarematchedor“mapped”tothesameorsimilarproductssoldbycompetitors.Pricecomparisonsareonlyvalidifyouaremakingapples-to-applescomparisons.

3. Analysis&Recommendations–Usinghistoricalsalesdata,retailanalystsbuildpricingmodelsthatexplainpastperformanceandpredictfuturetrends.Thepricingformulasdeterminetheoptimalpricewheresalesandprofitswillbehighest.

4. TakingAction-Analystsrecommendthatpricesberaised,loweredorkeptthesamebasedoncompetitorpricechangesandyourownconsumerdemandandexpectations.

Unfortunately,mostpricingdataisnotreadytousewhenit’sfirstdeliveredbywebcrawlers.Forexample,computerscaninstantlycomparethepricesofeveryiPodintheuniverseaslongastheUPCcodesarelisted.ButgiventhatAppleseldomdiscountstheirproducts,themoresignificantquestionis:WhichcompetingMP3playersaremostcomparable–whichoneswillbemostlikelyattachedtoyourcustomers’earbudsiftheygowithPlanB?

Makingmattersevenmorecomplicatedisthatshoppingforconsumerelectronics(andmanyothercategories)ofteninvolvesthreesetsofprices:Manufacturer’sSuggestedRetailPrice(MSRP),salepriceandthesecret“clickhere”price.

Manyproductpagesone-commercesitesshowyoutheirlistpriceandtheninviteyoutomoveyourmouseovertheitemorclickonashoppingcartto“SeePriceatCheckout.”ThereasonretailersdothisistoavoidMinimumAdvertisedPrice(MAP)violations.Someofthemorepremiumbrandsforbidstoresfromadvertisingtheirproductsbelowacertainpricethreshold–toavoidcheapeningtheirbrandequity.

Manyretailershavebeenhappilyusingautomatedwebcrawlerstogathercompetitorprices,butmostofthesetoolsarerapidlybecomingantiques.Yourtechnologynowneedsto“see”thesehiddenprices.Itneedstocapturethisdeepdatabyreplicatingthebehavioroftheonlineshopper.

Onceyou’verefinedyourdatawiththerightautomatedtoolsandanalystexpertise,youcanthenactonyourintelligence.

PricingIntelligencetoolsidentifythebestopportunitiesforincreasingmargins,givingyouasnapshotofwhichproductsarethemostpricesensitiveatanygivenmoment.

TakingAction:WhatCanBeLearnedFromPricingIntelligence?Duringthe2013holidayshoppingseason,wetookanextensivelookatpricingdatafrom15majorU.S.retailersacross13categories,includingclothing,toys,consumerelectronics,fragrances,cameras,kitchenappliancesandvacuumcleaners.(Youcanreadthefullreport,“Revealed:RetailStrategiesofthe2013HolidaySeason,”here.)

NovemberandDecemberof2014wasabannertimetobepurchasingclothesforgirls.

Ugam’sseven-weekpricinganalysisrevealedthatgirls’clothingwasthemostfrequentlyandheavilydiscountedholidayproductcategory,withBelkdepartmentstoresloweringpricesonawhopping98percentoftheiritems.Target,ontheotherhand,ransalesononly40percentofitsgirls’clothing–stillasizableselectionforbargainhunters.

Fromabrick-and-mortarperspective,theaggressiveBelkstrategymightseemmostrelevanttonationalretailerswithlocationsthroughouttheSouthernstates,wheretheregionalchainisbased.However,Belk.comwouldhavepoppedupforshopperssearchingforclothesontheWeb–andtheirfreeshippingforordersof$99ormoreshouldbefactoredinbyretailersplanningtheirownapparelstrategies.

PercentageofGirl’sClothingonSale–2013HolidaySeason

Onthestingiersideofthespectrum,theproductcategorywiththeleastamountofpricevolatilitywasvideogames.2013wasanexceptionallybriskyearforvideogamesaleswiththereleaseofthenextgenerationXboxandPlayStationconsoles.

VideoGameAssortment&Prices–2013HolidaySeason

TheyellowbarsinthegraphaboveshowgreatfluctuationinvideogameassortmentandpricesforretailersfromAmazontoToys“R”Us.Amazon,nothinderedbyshelfspace,boastednearly70%morechoicesthanWalmart.Butthebluedotssayitall.

Thebluedotsmarktheaveragevideogametitlepriceoverthebusiestsevenshoppingweeksoftheyear.Notethealmostnegligible$5differentiationinpricebetweentheseretailers,whoareusuallynotbashfulaboutsluggingitoutoverlowprices.

Videogameenthusiastsarenotknownfortheirpatience.Theyputtheirnamesonwaitinglistsandstandinlinesatmidnightfortheprivilegeofbeingthefirsttoplay

thenewestconsolesandgames.Ingeneral,videogamersarelikeiPhonefansinthattheyarewillingtopaythe“goingprice”forwhattheywant–whateverthatpricemaybe.

Strategiccompetitiveintelligencecanalsohelpyoudeterminewhensignificantlyloweringpriceswillincreaseprofits.

Chapter4:AnIntroductiontoDynamicPricing

OverviewDynamicPricingisNOTthesameaspricematching.Basedonsupplyanddemand,consumersocialsignals(e.g.productreviews,Facebooklikes),theweather,andeventhetimeofday,therearealsoopportunitiestoraiseyourpriceswithoutgettingyourcustomersupset.Manyretailershavealreadybecomeextinctinthisharshcompetitiveenvironment.DynamicPricinghelpsyoustaynimbleinaconstantlychangingdigitalworld.

SliceofReality:WhatAreYourCustomersWillingtoPay?

Howmanypiecesofpizzadoyouusuallyeatinonesitting?Doespriceimpacthowhungryyouare?

AtStevestonPizzainVancouver,BritishColumbia,theirspecialC6gourmetpiecostsawhopping$450–that’s$56.25perslice–forheapingportionsoflobster,blackAlaskancodandRussianOsetracaviartoppingthemozzarella.7CanadiannewsmagazineMaclean’scalleditthe“World’sMostExpensivePizza.”8

Incontrast,Domino’sPizzarecentlyadvertisedanonlinespecialforalargethree-toppingpizzafor$10(caviarwasnotatoppingoption).Atthisprice,youcouldbuy45Domino’spizzasforthecostofoneStevestonC6.

WhycanSteveston’schargesomuchforlunch?BecausecustomerswhoorderaC6knowtheycan’tgetoneanywhereelse.Perhapstheyhavethedisposableincometoorder$450pizzaseverydayoritmaybeaone-timeindulgencetoknockoffabucketlist.Eitherway,thispricepointexistsbecausepeoplearewillingtopayit.

That’sthephilosophybehindDynamicPricing.Retailerswhoknowtheir

customers’preferences,spendinghistory,tastesanddesirescanestablishtherightpricefortheminsteadofreflexivelymatchingthepriceoftheircompetitors.

Sterneckertputsitmoresimply:“Ifyouhaveathousandunitsthatmoveamonthonanitem,andyourcompetitionlowerstheprice,butyouarestillsellingathousandunits,whydoyouneedtolowertheprice?Youdon’t.”

DynamicPricingisbasedonsupplyanddemand,customerexpectationsandeventhetimeofthedayorweatherconditions.Sportsfansnowacceptthatmanyoftheirfavoriteteamsfluctuateticketpricesbasedonthepopularityofopponents,whetherthehometeamiscompetitiveenoughtomaketheplayoffs,andofcourse,whetherornotit’sgoingtobesunnyandwarm.

BasedongatheringaccurateandtimelyPricingIntelligencefromyourownstoresandcompetingretailers,thereareconstantopportunitiestoraise,lowerorkeeppricesthesame.

“It’sokayifsomebodyisbeatingyouonpriceifyourcustomersdon’tcare,”saysSterneckert,notingthatthepricesensitivityofitemsvariesbyproductcategory,timeofyear,customerdemographics,storelocationandnumerousotherfactors.

Consumersarenotstingyaboutsharingwhattheylikeandwhattheydon’tlike–andwhethertheythinktheirpurchasesareworththeprice.There’sanever-growingsupplyofconsumerproductreviewsandsocialmediasentimenttodeterminebuyingtrendsandwhatproductsmightbecometomorrow’shotsellers.

Herearesomeofthefactorsthatdeterminecustomers’expectationsatcheckout:

ProductAvailability–Doesacompetitorcarrythesameitem,andifso,atwhatprice?Isitoutofstock?Location–BuyingapackofguminNewYorkCitywillbemoreexpensivethanthesameguminthesuburbs.ConsumerSegment–Whatistheirdiscretionaryincomeandspendinghistory?InstantGratification–Nobodycaresaboutfreeshippingwhenthey“need”theitemrightnow.ProductPopularity–Isthisitemflyingofftheshelves?Oristhesize,styleorcolorinlowdemand?

Forgetaboutpricematching,despiteallthehype.Youjustneedtomeetyourcustomers’expectationsforwhattheyperceivetobefair–andwhenpossible,offerthemsomethingtheycannoteasilygetelsewhere.

That’salessonthatStevestonPizzatooktoheart.Encouragedbythepositive

buzzgeneratedbytheC6,theyrecentlyaddedthepricierC7,a“BestofSeas”concoctioncoveredintigerprawns,lobsterratatouille,smokedsteelheadtrout,RussiancaviarandItalianwhitetruffles.

Yes,thereisevenamarketfora$725pizza,althoughnowordwhythewhitetruffles–araregourmetmushroomunearthedbysniffingpigs–belongonanocean-themeddish!

LessonsFroma“Smart”VendingMachine:WhenisitOKtoRaisePrices?It’ssummertimeandmanyofusareheadingtothebeach.Afterafewhoursoflounginginthesun,howbadlydoyouusuallywantanicecolddrink?Howmuchmoreareyouwillingtopayforthatdrinkovertheregularsupermarketprice?

In1999,theCoca-ColaCompanytestedvendingmachinesthatwouldautomaticallychargehigherpricesforcoldbeverageswhenthetemperaturegothotter.AccordingtoTheNewYorkTimes,thevariablepricingvendingmachineswereoutfittedwithaheat-sensorandacomputerchip.9

Eventhoughconsumersoftenpaymoreforcoldsoftdrinksatthebeach,therewasabacklashagainsta“smart”vendingmachinedoingthesamething.“What’snext?”sniffedonebeverageindustryexecutive,“AmachinethatX-rayspeople’spocketstofindouthowmuchchangetheyhaveandraisespricesaccordingly?”

ArchrivalPepsiCoalsorippedintotheplan,eagertoportraytheirbrandasfightingtokeeppriceslow.“Webelievethatmachinesthatraisepricesinhotweatherexploitconsumerswholiveinwarmclimates,”aspokespersonsaid.“AtPepsi,wearefocusedoninnovationsthatmakeiteasierforconsumerstobuyasoftdrink,notharder.”

Coca-Colaabandonedtheexperimentbecausemanycustomersfelttheywerebeingtakenadvantageof.

UnlikeintheCokestory,retailersdonothavetocomeacrossasthebadguywhenraising(orsimplynotlowering)prices.Thekeyisknowingwhencustomerswon’tnoticeorcare.Forexample,whenpeoplepaytwiceasmuchforsodaatthemovies,therearenoprotests.

Customersalreadyhavetheexpectationthatconcessionpriceswillbehigherinthetheater,whereapopcornanddrinkcaneasilydoublethecostofaticket.

Thetrickforretailersismeetingcustomerexpectationsandfiguringoutwhattheybelieveisareasonablepricetopay.

DynamicPricing–raising,loweringorkeepingpricesthesamebasedonchangingconditions–isnotthesamethingaspricematching.

Sohowcanretailersraisepricesandstillbeperceivedasofferingvalue?

Acommonpromotionaltacticduringback-to-schoolseasonorThanksgivingweekisloweringpricesonKeyValueItems(KVIs)–usuallyabout10%oftheitemsinthestore–whilemodestlyincreasingpricesoneverythingelse.Shoppersareattractedbythedealsonthehottestproductsandinevitablybuyotherthingsonimpulsewhilethey’reinthestore.ThelowermarginsonthoseKVIsaremorethanbalancedoutbyhigherpricesontheremaining80-90%ofproductsinthestore.

Psychologically,acustomerfeelsgoodaboutgettingtheirspecialitematabargainpriceandwilllikelynotnoticetheslightlyhigherpricesoneverythingelseinhercart.

HerearefourunconventionalwaysyoucanuseDynamicPricingtoincreaseyourprofitmarginsbyupto3%:

1. KnowTheSourcesofYourWebsiteTraffic–Didacustomerarriveonyoursiteafterarandomsearchordidsheorhereturnasarepeatcustomer?Trafficfromcomparison-shoppingenginesisbelievedtobefarmoreprice-sensitive–thatis,theseshopperswillquicklysearchelsewhereiftheydon’timmediatelylikewhattheysee.Customerswhoclickalinkonablogoractonareferralorproductrecommendationaremoremotivatedtoshopwithyoubecauseofreputation,selection,serviceorotherreasons.

2. ReactOnlytoCompetitorsWhoImpactYourSales–SomewhereinKansas,someoneissellingthesameexactitemasyououtoftheirgarage,buthasonlythreeofthem.Don’tworryaboutthatguy.Onlymonitorandrespondtothepricesofretailerswhoposeastatisticallysignificantcompetitivethreat.Alargecompanythatyouperceivetobeacompetitormaynotbeinacertainproductcategory.Analyzingtrafficoncompetitors’productpagescannarrowdownwhoyoureallyneedtokeeponyourradar.

3. LearnWhichProductsYou’reReallyCompetingAgainst–Whenyouarecomparingprices,considerlookingbeyondexactbrandorproductmatches.Thinkaboutfunctionality.Forexample,abeachenthusiastlookingforflip-flopsmayalsobeconsideringruggedsandalsorwatersocks.It’smoreeffectivetolookatbest-sellingitemsinacategoryasareference.

4. UnderstandthePurchasePathforYourProducts–Consumersareleavingbehindplentyofclueswhytheyvisitaproductpageandwhatsealsthedealforthem.Youmaychoosetorewardyourmostloyalandprofitablecustomerswithexclusiveoffersonlyforthem–increasingthelikelihoodthey’llbeback.

DynamicPricingisimplementedbyusingananalytics-drivenRulesEngine.Analystsorcategorymanagerscancreateasetofconditionalrulesthatdictatehowyourpriceswillchangeinresponsetocompetitor’spricechangesorothermarketconditions.

Chapter5:TheHumanFactor

SixStepstoImmediatelyImproveYourProductMatchingAccuracyBeforeyoucancomparepriceswithyourcompetitors,youneedtomakesurethatyou’recomparingthesameorsimilarproducts.Thisiscalled“productmatching”or“productmapping.”

Automatedmappingisrelativelysimpleinsomecategories,suchaselectronics,wheresoftwarecaneasilycomparethemodelnumbersonaTVortablet–althoughafewretailersmaymakepricematchingdifficultbystockinglotsofexclusiveproducts.

Thisexerciseisfartrickierwhenitcomestoclothingorhomefurnishings,wheretherearemorevariationsinstylesandcolors.

Takealookatthedifferentmirrorspicturedbelow.Becausethemodelnumbersarenotuniversal,youneedtochoosethemostrelevantproductcharacteristicsasyourbasisofcomparison.

Whichmirrorstylesshouldbegroupedtogetherassimilarproducts?Howthickcantheframebe?Whichmaterials?Shouldasquaremirrorbecomparedwitharectangularmirrororarethereasignificantnumberofshopperswhoarerectanglepurists?

Categorymanagerswithexperienceinhomedécorwillknowalotmorethanacomputerabouthowcustomersthinkwhencomparingsimilar,butnotquitethesame,mirrors.

Withoutanyhumanintervention,theaccuracyrateofautomatedproductmatchingisgenerallylow–dippingbelow50%inseveralcategories.Withtheappliedknowledgeofacategorymanagerorcategoryresearcher,product-mappingsystemscandeliverupto98%accuracy.

Belowisalookattheindustryaveragesforautomatedmappingaccuracybeforeanalystsfine-tunetheresults:

Astheexpressiongoes,“Almostonlycountsinhorseshoesandhandgrenades.”Evenwhenaretailerhitsthe90thpercentile,heorshecontinuestoshootfor100percent.

Asisthechallengewithrefininganycomputersearch,automatedproductmappingincludesnumerousirrelevantandredundantlistingsthatdilutethevalueofyourPricingIntelligence.AquicksearchforcoffeemakersonAmazonproduces31,109listingsalone.BestBuyservesup1,185andWalmarthas1,043.

Howmanydoyoureallyneedtocareabout?Beforeyourpricingorassortmentanalystsdeterminewhichsuggestedmatchesareonesthatmattertoyourbottomline,therawdataneedssomehumanfiltering.

1. Removeduplicatesandirrelevantitemsfromtheresultsstream.Sometimesitemsareinadvertentlylabeledwiththewrongmodelnumberandaremiscategorized.Aricemaker,forexample,maywindupwiththeblenders.

2. Establishwhichattributesorfeaturesaremostimportant.Yoursearchcanbenarrowedbybrand,size,shape,material,color,etc.

3. Normalizeunitsofmeasurement.AKingsizebedis76”x80”andisalsoknownasanEasternKingbed.ACaliforniaKingbed,marketedtowardtallerpeople,is72”x84”.Makesureyourmeasurementsareuniformwithyourdescriptions.

4. Identifywhichprivatelabelproductfeaturesmattermost.Trackingdownnon-brandedproductmatchescanbelikeherdingcats.Ifyouaresellingrefrigerators,choosewhichfeaturesyourcustomerscareaboutmost:freezerspace,icemakers,slideoutshelves,etc.

5. Knockofftheaccessories.Ifyousearchforconsumerelectronics,yourpotentialmatcheswillhavelotsoffalsepositivesthatarebatterychargers,protectivecases,cords,etc.

6. Identifypossibleoverlookedcategories.Sometimesyourproductmaybecategorizedintwodifferentareas.Forexample,afoldingfabricchairmightbelistedunderlawnfurnitureorbeachfurniture.Ahammockmightbewithcampinggearorwithpatiofurniture.

Whydoesthismatter?Youcannotmakesmartdata-drivendecisionsunlessyouareconfidentintheaccuracyofyourmatches.

TheAnalystFactor:TurningYourPricingDataIntoInsightsAsmentionedabove,youcan’tmakesmartdata-drivendecisionsifyourdataisquestionable.Previously,weexploredwhyproductmatching–alsocalledproductmapping–canmakeallthedifferenceingivingyouanaccuratesnapshotofhowyoucomparetothecompetition.

Ifyou’renotsurethatyou’resellingthesameexactproduct(orasimilarenough

product)asyourcompetitor,thenyoumayaswelltossyourpricecomparisonsinthetrash.

However,onceyouareconfidentthatyourPricingIntelligencedataisaccurate,youneedtofigureouthowandwhenyoushouldactonit.Sohowdoanalyststurndataintoinsightsandpricingrecommendations–andultimately–bettersalesresults?

Therearetwomainapproachesforturningnumbersintoaction:

1. SettingUpAutomatedRules–DeployingsimpleStrategyRules(e.g.,IfCompetitorAlowersthepriceonProductAtoX,welowerourpricetoY;IfCompetitorBisoutofstockonProductA,weincreaseourpricetoZ.)StrategyRulesareidealifyouwanttokeeptabsonpricechangesforKVIsatspecificcompetitorsandwanttoalwaysbewithinacertainrange.

2. BuildingaPricingModel–Developingasophisticatedmathematicalmodeltooptimizepricingenablesretailerstotakethemanyfactorsthatcontributetothebuyingprocessbeyondpriceintoaccount.Theequationmayincorporatearangeofinputs,includingaretailer’shistoricsalesdata,historicalcompetitorpricing,inventory,productpagecontent,Webtrafficandpromotionsdata.Themodelmayalsoconsidercustomerreviews,productratings,sociallikes,etc.,usingconsumersentimentanalysistotranslateratingsintopricinginsights.

Theretailexperienceandexpertiseofanalystsareinvaluableforpursuingthesecondapproach.Ananalystfirstwalksthroughthebuyingprocessinthecustomer’smindandthencreatesahypothesisthatattemptstoexplainsalestrends.

Let’ssaythatyouaresellingluggage,forexample.Herearesomeofthequestionsthatmayimmediatelycometomind:

Doluggagesaleshistoricallypeakjustbeforesummervacation?Dodufflebagsalesspikebeforecollegebeginsinthefall?Whatcolorsuitcasesaremostpopularwithmenvs.women?Dochild-sizedrollingbagsflyofftheshelvesbeforeFebruaryandAprilschoolvacations?WhatisthemosthighlyratedluggagebasedonproductreviewsontravelwebsitesorthebestvaluelistedinConsumerReports?

Allofthesequestionscanbeansweredbycreatingvariables–suchascolor,time,gender,age,peakdemand,qualityofreviews–andindependentlycomparingthosevariablestopricesovertime.Throughtrialanderror,theanalystcandeterminewhichvariableshavethegreatestinfluenceonsalesand

incorporatethosefactorsintoaregressionequation.Thisgraphshowstheoutputofalogarithmicequationcalculatingwhichpricepointsresultinmaximumluggagerevenue,basedonadepartmentstorechain’smostinfluentialvariables.

LuggageSalesvs.Price

*ThisgraphisasimulationoftheSales-Pricerelationship.Itisastatisticalmodelthatdoesnotreflectactualluggagepricesorannualsales.

Hereisthepricingmodelexpressedasanequation:

Everystoreisdifferentandwillhaveadifferentequationanddifferentsetsofvariables.

Log(LuggageSales)=0.29-0.66(Log)(A)+0.01(B-A)+0.25(C)+0.02(D)+0.06(E)

KEYA=RetailerPrice

B=CompetitorPriceC=NumberofImagesonProductPage

D=NumberofAmazonReviewsinLastTwoMonthsE=NewnessofProduct(NumberofWeeks)

Don’tworryaboutthemath–that’swhyyouhireanalysts!

TheimportantthingtoknowisthatasyourPricingIntelligencegatheringandprocessingbecomesmoresophisticated,youwillbeabletobetterunderstandandhaveagreaterinfluenceoveryoursalesresults.Dependingonwhatyouwanttolearn,analystscanhelpyoudeterminethe“Why,”the“WhatIf,”andthe“What’sNext?”

Chapter6:TheFutureofPricingIntelligence

HowlingSuccess:TheValueofProductReviewsforDynamicPricingIn2009,aninnocuoust-shirtgraphicofthreewolveshowlingatthemoonattractedtheattentionofonlineshopperBrianGovern,aRutgersUniversitylawstudentbrowsingonAmazon.

Inawhimsicalmood,Governtappedoutasatiricalproductreview,claimingthe“ThreeWolfMoon”shirtwasmagicandmadehimirresistibletowomen.

OtherAmazonreviewerspickeduponthethemeandwrotetheirownfunnyreviewspraisingthemysteriouslife-changingpowersofthewolves.Withindays,thejokewentviral–andalthoughGovernhimselfneverboughtashirt,itsoonbecameabestselleronAmazon.

AccordingtoTheNewYorkTimes,theMountaint-shirtcompanyinKeene,NewHampshire,wentfromsellingtwotothreeshirtsperdaytoselling100everyhour.Thewolveswoundupspendingnearly200daysonAmazon.com’sTop100list.10

Noteverycustomerreviewisgoingtolaunchaproductintotheretailstratosphere,butonlinecomments–those

ofthenon-facetiouskind–containvaluableinsightsabouttheextremesoftheshoppingexperience.Thinkaboutit:peopleonlybothertosharetheirthoughtswithacompanyiftheyareeitherverypleasedorveryunhappy.Fewreviewersbothertowriteaboutanaverageexperience.

SmartDynamicPricinginvolvesextractingconsumerdemandsignals–valuablecustomerdatafromreviewsaswellassocialmediamessages–andfactoringthemintopricingdecisionsforbothpersonaloffersandforecastingfuturepurchasingtrends.ItisanextralayerofintelligenceappliedtoDynamicPricing,whichusesaRulesEnginetoautomaticallyraise,lowerorkeeppricesthesamebasedonsupplyanddemand,theweatherandeventhetimeofday.

DespitetheconcernaboutfakeAmazonreviews–notthesillykindlikethewolvesbutwhencompaniesshamelesslyreviewtheirownproducts–thenumbersdon’tlie.AhugeamountofconsumerreviewsacrosstheWebindicatesanorganicdemandforthatitem.

BelowisagraphshowingtheincreaseinsearchenginetrafficfortheLGElectronicsHBS-730BluetoothHeadsetinrelationshiptothegrowingnumberof

positivereviews.Peopleinnatelytrustwhattheirfellowconsumersthinkmorethanacompany’sofficialmarketingmaterials.

PersonalizedPricing:AConversationwithRetailAnalystKevinSterneckertGPSismostcommonlyassociatedwithdrivingornavigation,butitcouldsoonbecomeanevenmorevaluableshoppermarketingtool.Earlierthisyear,AppleintroduceditsiBeacontechnologytogiveretailerstheabilitytosharecustomizedmessagesaboutspecialdealsandproductinformationbasedonwherecustomersarewalkinginthestore.11

TheiBeaconiscurrentlybeingtestedattheAppleStoreinManhattanaswellasalimitednumberofDuaneReadedrugstoresinNewYork.ThedeviceusesgeofencingandBluetoothsignalsemittedfromspecificshelvesforanewlevelof“micro-location”targeting.AtDuaneReade,theiBeaconcanwooshopperswithimpulsepurchaseincentives–suchas25%offnewnailpolishcolorsorofferingumbrellasalesonarainyday.12

Whenapharmacydetectsthatacustomerhasenteredthestore,and“knows”fromloyaltycarddatathatshehasasweettooth,offeringheracoupononDoveChocolatemakessense.Butsendingheranalertassheiswalkingdownthecandyaisleistheoptimalpersonalizedoffer.

Couldretailerssoonbemakingsimilarreal-timeofferstoeverycustomer?Toexplorethisbravenewworldofpersonalizedpricing,wesatdownforanotherchatwithretailanalystKevinSterneckert,aformervicepresidentofresearchforGartner.

Q:Howadvancedareretailersnowwithpersonalpricingandwherearethingsheaded?KS:We’reseeingretailerstodaybeginningtooffercouponsordiscountsto

consumerclasses–soforexample,tomybestcustomerswhoarefemaleages39-45,I’mgoingtoofferthem20%offallfragrances.OrI’mgoingtogive15%offallDr.DreBeatsheadphonestomenages18-24.

WhereIbelievewe’reheadedisofferingspecificpromotionstoindividualconsumers.Weseethisalreadybeginningtooccur.Forexample,CVSPharmacyusesaverysophisticatedCRMapproachandhasbeensendingtailoredcustomcouponbookstohouseholds.Theyarebeginningtoeliminatetheprintedcouponbooksandbeginningtodirectlycommunicatewithconsumersrelativetotheindividualhousehold.

Sotheseprofilingsystemsbegintounderstandnotjustthatyou’reacollegestudent,butyou’reawealthycollegestudentandyouprefertheschoolsuppliesthatareinourlocation,butyoualsodon’tuseourover-the-counterdrugsection.Sowe’regoingtoencouragethatover-the-counterdealduringappropriatetimesforallergy,coldandfluorpainreliefmedicine.We’regoingtoprobetounderstandexactlywhatitwilltaketogetyoutobuyinanothercategory.

Q:Doesapersonalizedpricingofferneedtobedonethroughanapporrewardscardoristhereanotherway?KS:Retailersareusinglotsofways,buttherehastobesomekindofengagementwiththeconsumertobegintolearnconsumerbehaviors.Someareusinganapp,somearetyingthatapptoaloyaltycard,butit’salwaysanopt-inproposition.Whenyou’reclosetooneofthosestores,amessagewillpopupandsay,“Hey,here’sanofferforyourightnow!”

Q:DoyouthinkiBeaconswillbesuccessfulatshoppingmalls?KS:We’rejuststartingtoseestoresexperimentwiththebeacons.Again,therealtestofthiswillbe:“Howrelevantwilltheretailerbewiththeconsumer?”Ifyou’reonlydoinggenericoffers,thenthisisnotreallygoingtobepopular.Butiftheyapplysomeveryintelligentlisteningsolutionsandtrulypersonalizetheoffers,thentheiBeaconhasanopportunitytoreallytakeoff.

Thetechnologyofthebeaconisn’twhat’sgoingtomakeithappen–it’sgoingtobetheintelligencebehinditthatunderstandsandextendsrelevancetotheconsumerinanengagingway.

Thewidespreadadoptionofpersonalizedpricingwouldbeagamechanger:Theluxuryofalwaysknowingwhatyourcompetitionischargingmayeventuallydisappear.Therewillbeapublicpriceandthenperhapstherealpriceforyou–acustomizedcalculationbasedonyourdemographic,spendinghistory,brandloyalty,yourcompetitiveoptionsonthemarketandaslewofotherdemandsignals.

Rightnow,gatheringPricingIntelligenceisrelativelyeasy.It’slikebeingagasstationowneratabusyintersection.Allhehastodotokeeptabsonthecompetitionislookouthiswindowatthegiantsignabovethepumps.Butwhatwouldthegasstationownerdoifthosepricesweren’tposted–ifthecustomerswereprivatelygiventhepricebeforehandontheirsmartphones?

Theservicestationownerwouldneedtofigureoutwhathiscustomerswerewillingtopaypergallon,basedonthecustomer’sneeds,desiresandresourcesinsteadofjustautomaticallymatchingthepriceacrossthestreet.Likeanyretailer,hewouldneedtodevelophisownpricingdemandmodel–beyondsettinguprulesrespondingtotheotherguy.

Howthesechallengeswillallshakeoutisuncertain.Whatisclearisthatstayingaheadofthecompetitionrequiresalignmentwiththeneedsofthecustomer.Standingstillisnotanoption.

Chapter7:HowtoGetStarted

4QuestionstoConsiderWhenGettingStartedwithaPricingIntelligenceSolutionWhetheryouaretryingPricingIntelligenceforthefirsttime,haveexperienceditbutaretryingDynamicPricingforthefirsttimeorhaveexperiencedbothbutarelookingtoswitchvendors,herearesomehelpfulquestionstoaskyourselfasyougetstarted.

1. WhichCategoriesAndSKUsShouldIMonitor?2. WhichCompetitorsShouldBeOnMyRadar-AndHowMany?3. HowFrequentlyShouldIMonitor/ChangePrices?4. WhatAreMyMatchingRulestoCompareMyProductsWithMy

Competitors’Products?

WhichCategoriesAndSKUsShouldIMonitor?KnowingwhichcategoriesandSKUsyoushouldfocusondependsonhowyoudefineyourbusiness.Whencustomersthinkofyourstore,whichitemsdotheyinstantlyassociatewithyou?Inwhichcategoriesareyouexpectedtoattackandinwhichonesshouldyoumerelyplaydefense?

WhichCompetitorsShouldBeOnMyRadar–AndHowMany?Thereisnoone-size-fits-allanswertothisquestion.Thenumberofcompetitorstomonitorwilldependonthecategoriesandthislistwillkeepchangingasretailersaddandremovenewitemstoandfromtheirassortments.Theonlycompetitorsyoushouldcareabout,however,aretheonesyourcustomerswouldlikelyturntoforprice-sensitiveKeyValueItems(KVIs).

Foradepartmentstore,themultiplelistsofcompetitorstomonitorwillbedifferentforshoes,electronics,apparel,etc.It’salsoimportanttonotethatthereisnouniversallistofKVIs–thisalsovariesbyindividualstoreandcanonlybedeterminedbystudyingyourcustomers.Agoodruleofthumbisfocusingonthesixtoeightcompetitorsmostsimilartoyou.

HowFrequentlyShouldIMonitor/ChangePrices?SomemajorretailersusingDynamicPricingareregularlycheckingcompetitors’pricesoneverysingleitemtheyoffer.Frequencydependsontheitem’simportanceandpricesensitivity.KVIsaretypicallyreviewedeverytwohours,whileotherproductsarereassessedeveryweekoreverymonth.Ultimately,thedecisionkeepscomingbacktohowdependentsalesareonthepriceofagivenitemandhowoftencompetitorsarechangingtheirprices.

WhatAreMyMatchingRulestoCompareMyProductswithMyCompetitors’Products?Whencomparingyourpricestothecompetition,itisessentialthatyoumakesureyouarecomparingthesameproducts.Thisiscalledproductmapping.

Butappearancescanbedeceiving.Takealookatthefoodscalesbelow.

ComparisonoftwoproductswiththesameUPC

Onfirstglance,withtheexceptionofthesilvertray,theyappeartobethesamescale:SameUPCcode,samesize,samedigitalscreenandsamebase.SowhydoesWasserstrom.com’sversioncost60%morethanthemodelonAmazon?Whenthereisalargepricedifferentialbetweenthesameitematdifferentretailers,anautomatedmappingsystemcanalertpricinganalyststoinvestigatefurther.

ItturnsoutthatthechromefoodtrayontherightisapprovedbytheNationalSanitationFoundation(NSF)formeetingthepublichealthstandardsforschoolsandhospitals.Theplasticoneontheleftdoesnotsharethatdesignation.

Withinyourchosencategory,therewillbemanydiscrepancieslikethiswhencomparingsimilarproducts.Youcan’talwaysdependonUPCcodesormodelnumbersforproductmapping.Sometimestherearenouniversalnumbers,whichisthecaseforgenericorprivatelabelproducts.

Regardlessofthecategory,youneedtodefinewhichproductfeaturesorattributesyourcustomerscareaboutmost.Forexample,ifyouaresellingfurniture–anotoriouslydifficultcategorytomatch–youmaydecidethatthekindofmaterial(fabric,wood,metal,glass,leather)isthemostimportantattributewhencomparingitems.Oritmaybethenumberofdrawersorthedimensions.

BigPicture:WhatDoYouWanttoAchieve?Itcan’tbestatedenoughthatgatheringbusinessintelligenceisworthlessifyoucan’tactonthatintelligence.Herearesomeofthebigpictureretailquestionsyoucananswerbycloselykeepingtabsonyourcompetitors’prices:

HowcanItakeadvantageofcompetitorinventory?Howcompetitivearemyprices?Whenarecompetitorschangingtheirprices?

HowcanIincreasemymargins?AmImarkingthepricedowntoosoonortoomuch?

WouldlearningtheanswerstotheabovequestionsbeenoughtoachieveyourcurrentbusinessgoalsordoyouneedtoimplementDynamicPricingaswell?

UsingaRulesEngine,DynamicPricingallowsyoutoraise,lowerorkeeppricesthesamebasedontheconstantlychangingcircumstancesofthemoment.Pricerecommendationscanvarybasedonsupplyanddemand,howcustomersfindyou(directtraffic,comparisonshoppingengines,organicsearchorsearchenginemarketing),consumersocialsignals(productreviews,Facebooklikes,etc.)andeventheweather.

HowWouldYouLiketoConsumeYourData?Everycompanyhasitsowncultureandpreferredwayofdoingthings.RegardlessofwhichPricingIntelligencevendoryouchoose,theyshouldbeabletodeliveryourdataandinsightsinthemostuser-friendlyformatcustomizedforyourneeds.Youroptionsshouldinclude:

DatafeedsAPIintegrationintoyourBusinessIntelligenceorPointofSalesystemsDashboardsExceloutputsAlertfeedsPricerecommendationfeeds

WhatShouldBeCoveredinYourService-LevelAgreement?YourSLAforanyPricingIntelligenceorDynamicPricingsystemsneedtocoverhowtoverifytheaccuracyofyourdata,whattoexpectfromtheonboardingprocess,andthetimelineforsettingupthesystemanddeployingit.

1.ConfirmingtheAccuracyofYourData

WhenhundredsofthousandsofSKUsaremappedandcrawledeachday,theopportunityforerrorscanbesignificant.Criticalerrorscancreepintoyourdataandthenintoyouractionableinsights.Yourcompetitors’ever-changingcategorypagesandthecomplexstructureofmarketplacewebsitesaddtothischallenge.

Insightsandcriticalpricingdecisionsbasedonfaultydatacouldexposeyoutogreatrisk.Whenresearchingasolutionprovider,lookintothestrengthoftheirQualityAssurance(QA)algorithmsandprocessestomanagedata.Oftenprovidershaveaparallelprocessthatonlysamplescrawlingandmappingaccuracy,whichmaybegrosslyinadequate.

Matureprovidersofferacomprehensive,rule-baseddataintegritychecksystemthatdoesformat,factual,timing,andlogicalchecksoneachdatapoint.MakesuretothoroughlyinvestigatetheQAprocessandhaveyoursolutionproviderdemosamplerunsusingtheirsystems.

Don’tforgettoaskvendorshowtheyidentifyandmapsimilarcompetingproducts,andaskthemtoexplaintheirongoingprocessformappingnewproducts.Youneedtoknowyourcoverage,whichisthepercentageofyourproductsthatmatchacompetitor’sproducts.Unlessyourcompetitorsstockasignificantnumberofexclusiveproductsorprivatelabelproducts,yourcoveragepercentagegenerallyshouldbeveryhigh.

Therewillbesituationswhenacompetingretailercarriesthesameproductsasyou,butindifferentpacksizes.Asophisticatedproductmatchingsystemshouldbeabletoidentifythesecasesandtranslatethepricesperunit.Beawarethattherearenowseveralproductmatchingsystemsonthemarketthatcannothandledifferentpacksizes.

Ifyouinitiallydonotseehighcoverageforexactproductmatches,youshouldthendetermineyournumberofsimilarproductmatches.Forexample,let’ssaythatyourstorecarriesthefollowingfruit:

Andyourcompetitorcarriesthesefruits:

Onyourfirstattempttomeasurecoverage,youwouldfindonlytwoexactmatches:oneredappleandonepineapple.However,ifyouredefineyourmatchingrulestolookforsimilarproducts,youwouldlearnthatbothyouandyour

competitorshareaheavyfocusonapples.

Youcannotmakesmartdata-drivendecisionsunlessyouareconfidentintheaccuracyofyourmatches.

2.AssessingtheOnboardingProcess

Awell-managedonboardingprocesssetsyouupforsuccesswhileprovidingastandardforongoingchangesinproductsandcompetitors.Somesolutionsaredo-it-yourselfwithallsortsofuserconfigurableoptions,whileotherplatformsthatofferexcitingfeaturesmaynotbehelpfulatalliftheyarenoteasilyunderstoodbynontechnicalusers.

Awell-plannedonboardingprogramoffersdifferentlevelsofhandholdingforvarioususertypes.Forexample,one-to-onesessionswithon-callguidanceinthefirstfewweekscanhelpensuresuccess.Makesurethatthevendor’sprogrammanagerunderstandsmerchandisingandpricingandisnotsimplyahigh-techtooluser.Yourtrainingandonboardingneedtobemoreapplication-orientedandrelevanttosolveyourbusinessneeds.

Besuretocheck:

Whatistheonboardingtimeline?Whatisrequiredofyou?Whatwillthesolutionprovidertakecareof?Willthesolutionproviderbeonsite?Ifso,whowillbeonsiteandforhowlong?Willtheybereturningregularly?Whathappenswhentheretailerbringsonnewpersonnel?Howwilltheybetrained?Whowillbeheadinguptheonboardingprocess?Whatistheirexperience?

ThisisabriefguidetoPricingIntelligence.WeencourageyoutodownloadthefulleBook–PRICINGINTELLIGENCE2.0:TheEssentialGuidetoPriceIntelligenceandDynamicPricingformoreexamplesandanin-depthlookathowyoucangetsmarteraboutpricing.

AboutUgamUgamisagloballeaderinmanagedanalytics.Combiningaproprietarybigdataplatformwithaglobalteamofinsightsandanalyticsexperts,Ugam’suniqueofferingempowersclientswiththeconfidencenecessarytotakeactionthatimpactstheirbusiness.ClientstrustUgambecausetheydeliverunmatchedcustomerexperienceandspecificresults.Thattrustisalsobasedondeepdomainexpertise,end-to-endservice,innovationandthehighestqualityofinsightsandanalytics,whichenableUgamtotransformbigdataintobiginsightanddirectaction.Asaresult,nineofthelargest25retailers,manyoftheworld’slargestbrandsandonlinemarketplaces,and12ofthetop25marketresearchfirmsturntoUgamtodaytohelpimprovetheirbusinessperformance.www.ugamsolutions.com

Footnotes(1)“Procter&GambleandUnileverEscalateBigHairWar,”WallStreetJournal,Feb.24,2014.http://online.wsj.com/news/articles/SB10001424052702304434104579378923001137120

(2)“P&G’sAmazonPactPromptsRetaliation,”WallStreetJournal,Feb.26,2014.http://online.wsj.com/news/articles/SB20001424052702304703804579380792664369028

(3)“HowtoWinaPriceWar,”MITSloanManagementReview,March18,2014.http://sloanreview.mit.edu/article/how-to-win-a-price-war/

(4)SouthBySouthwestInteractive2009OpeningRemarksbyTonyHsieh,Part1,SXSWYouTubeChannel:http://youtu.be/63WFjoFiXns

(5)RISNews:“PricingIntelligenceGoestoWar,”Jan.3,2014.http://risnews.edgl.com/retail-research/Pricing-Intelligence-Goes-to-War90346

(6)“AreYouGivingYourCustomersWhatTheyReally,ReallyWant?:Aglobalresearchprojectexploringconsumerattitudestowardsonlineshopping.”WorldPay,2013.http://www.slideshare.net/mattheweveritt8290/consumer-attitudes-towards-online-shopping-a-global-study-from-worldpay

(7)http://stevestonpizza.com/pizzas.html

(8)“World’smostexpensivepizza:$450andafulldayinthemaking,”Maclean’s,June18,2012.http://www.macleans.ca/society/life/worlds-most-expensive-pizza-its-in-vancouver/

(9)“Variable-PriceCokeMachineBeingTested,”TheNewYorkTimes,October28,1999.http://www.nytimes.com/1999/10/28/business/variable-price-coke-machine-being-tested.html

(10)“ThinkaT-ShirtCan’tChangeYourLife?ASkepticThinksAgain,”TheNewYorkTimes,May24,2009.http://www.nytimes.com/2009/05/25/nyregion/25towns.html?_r=0

(11)“Retail’sNextBigBet:iBeaconandthePromiseofGeolocationTechnologies,”Wired.com,May14,2014.http://innovationinsights.wired.com/insights/2014/05/retails-next-big-bet-ibeacon-promise-geolocation-technologies/

(12)“Geofencing:CanTextingSaveStores?”TheWallStreetJournal,May8,2012.http://online.wsj.com/news/articles/SB10001424052702303978104577362403804858504