IoTandDeepLearninginRetail:thehyper-relevant,competitiveretailerByPremCouture,CEO,ShareMyInsight,

Inapreviousposting,IdiscussedhowIoTconnectedstoresareabletocombineliveshopperjourneyandproductdatawithPOS,loyalty,socialmediaandotherdatasets.Also,howapplyingmachinelearningenablesrealtimeinsightsthatcantransformthecustomerexperience,enablecustomercentricmerchandisingandstreamlineoperations.Inthisposting,IwouldlikesharemythoughtsandexperienceonhowIoTinretailcanpowerbricksandmortarstorestocompeteinanomni-channelworldbybecominghyper-relevantacrossallcustomertouchpoints.

ASurgingWaveofDisruptionandOpportunityAspreviouslynoted,classicretailstrategiesandmethodologiesfordiscoveringandengagingcustomersareincreasinglyunmanageable,duetorapidlyevolvingcustomerinterestsandbehaviorpatternsandasevidencedby:

• theexponentialgrowthintheamountofshopperjourneys:fromresearchtopurchasetofulfillmentandcustomersupport,thenumberofpossiblejourneyshasgrownfrom40toamazeofmorethan800(Cisco)andfurtherincreasesovertime.

• theexpandingnumberofdatapoints(beyondspendanddemographics)andtherapidchangeinconsumerinterestsismakingthetraditionalrulesapproachtodataminingcustomerstobelessandlessmeaningful.

• theincreasingdemandforseamlessshoppingwithgreaterchoicesandlowerpricesacrossonline,in-store,andmobileplatforms,iscreatinga‘digitaldivide’betweenconsumerexpectationsandretailers’abilitytodeliver.

InnovationattheHeartoftheNewRetailRealityIfasensornetworkrepresentsournervoussystemandaDeepLearningplatformisourbrain,thenthepartthatmanagesretailprocessesfromsupplychaintomerchandisingandcustomercommunicationsissimilartothewayweengageandlearnfromtheenvironmentaroundus.Enablingcustomerstomakeeasyandcostefficientdecisionsfromawidearrayofchoicesiswhataconnectedretailerpreciselybecauseitcontinuouslylearnsandadaptstonewinformation.Someofthekeytechnologyadvancesthatmaketheabovepossibleinclude:

1. Retailsensordevicesthatactinasensorfusionmodeandlivestreamshopperandproductdatatocloudplatforms.

2. AIandDeepLearning:advancesinGPUacceleratedcomputingpowerenablesDeepLearningalgorithmstofindpatternsinlargeanddisparatedatasetsandtotransformdataintoinsight.

3. Storediagnosticscandetecthowproductplacement,brands,rangeassortment,pricing,personnelandstorelocationaffectshopperbehaviorandpurchasingdecisions.

4. Dynamic,automatedprocessescantriggeratkeymomentsonthepurchasedecisionpathandengagecustomersonthepreferredcommunicationchannel.

5. AnewevolutioninCRMmanageshyper-relevantandcontextualcustomerinteractions,deliversmoreefficientengagementsandoffersimmediatecustomersavings.

ProductivitySavingsforbothRetailerandCustomerAconnectedretailercanrealizeproductivitygainsacrossanumberbusinessareas,fromsupplychaintomerchandisingtomarketingactivities;further,helpresolveissueswhichretailershavebeenstrugglingwithforanumberofyears.Hereafewkeyareaswhereproductivitygainsaremostvisibleinaconnectedenvironment:StoreInventoryEfficienciesRetailersandFMCGpartnershavelongknownthatincorrectproductplacements,poorshelfmaintenanceandoutofstockconditionsallcontributetosignificantlossesinrevenues.RetailersandFMCGtackletheproblembyutilizingfieldmarketingagenciestoperiodicallycheckforcompliancewiththeagreedrange,shelfshareintheproductcategory,shareofcompetitors’shelfandpriceforeachitem.Noteworthyisthatatypicalcategoryauditsamplesonly2%to5%ofallstorelocationsatafrequencyof1timeperweekoreveryotherweek.AccordingtoECRwhenbuyerscan'tfindtheproducttheyarelookingforinitsusualplace,9%ofclientschooseanalternativeproduct,ordonotmakeapurchase.Outofstockisestimatedtocostaretailerapproximately4%ofsalesinlostrevenues.Incontrast,anIoTpoweredstorewithefficient,batterypoweredcamerasthatsendsproductimagestothecloudforproductrecognition,canprovideongoinginformationonconditionsandpredictwhenshelvesneedreplenishment.

ProductandInventorymanagementisoneofthekeyareaswhereIoTandDeepLearningcanmakeabigdifferencebymonitoringproductsandsignalingwhenerrorsoccurandreplenishmentactionsneedtobetaken,resultinginachievablegains:

• 2monthlyvisitsperstorebyafieldmarketingrepresentativeatayearlycostofapproximately$1,500percategory/storecanbesaved

• merchandiseplacementerrorsacrossallIoTconnectedstorescanbereducedby50%ormore

• timelystockreplenishmentcanreducelostsalesfromoutofstockproductsby1%-2%

CustomerCentricMerchandisingThe‘onesizefitsall’planogramdeployedacrossallstoresfailstoconsiderthatconsumersandtheirshoppingbehaviordiffersbypointofsaleandmanyotherfactors.

Didmovingthebakerysectiontothefrontofthestoreresultincustomersspendingmoretimeinthestore?Didmovingthewinesectionnexttothecheesecountercreatemorecrossshoppingbetweenthose2categories?Dowehavejusttherightamountofsalespeopleintheshoedepartmentatpeakshoppingtimesand,ifnot,areweloosingsales?SensorfusionandDeepLearningcanprovidealevelofdiagnosticsandinsightsthatuncoverwhichvariablesareworkingtogethertoinfluencehowshoppersmakepurchasingdecisions.Further,suggestplanogramsandproductassortmentsthattargetshopperpreferencesduringtheirshoppingjourney,aswellasoptimizingpricingstrategiesandforecastingdemandforbettercustomerservice.Bycontinuouslydetectingshopperjourneysacrossmerchandisezonesandapplyinglearningalgorithms,analyticscanpinpointareasofassortmentoptimization,rangelocalizationandbetterproductvisibility,resultinginashopperjourneybasedstorelayoutwithimprovedshoppingmetricsandreturnoneverysquaremeterofshoppingarea.

Basedonlivestoreexamples,herearesomeofthecapabilitiesandefficiencygainsobtainedfromimplementingtrackingsensorsinshoppingareas:

• Monitoringofkeymetricsineveryshoppingzone,withclearvisibilityintoover/underperformingzones

• Measuringtheeffectsonshoppingbehaviorbeforeandaftermerchandisechangesareputintoeffect,resultinginengineeredstorelayoutplansthatincreasetraffictopoorlyvisitedzonesbyupto3%

• Reducingtimefrictioninservicezonesbydetectingcongestionandalertingtheneedforadditionalpersonnel,resultinginincreasedsalesconversionsof1-3%

• IncreasingReturnonSpaceinspecificstorezones/categoriesbymorethan2%byflaggingtheneedforspacere-allocationandrangeplanning

• Fasterreactiontimetochangesinshoppingconditionsandidentifyingprobablecausese.g.Promoareatrafficdecreasedby15%becauseoflowinventoryconditionsandtheneedtoreplenishstock

Hyper-RelevantEngagements,byDesignInfluencingcustomersbygettinginsidetheirmindsduringthepurchasejourneyrepresentsanongoingchallengeformarketers.

Withincurrentmeans,marketingdepartmentpersonnelsuperviseopportunitiesforengagingcustomersandcreatetargetedmarketingcampaignsbasedontheirbestjudgment.Inaddition,usecommunicationchannelsthatareunabletoreachthecustomeratthemomentofmakingapurchasedecision.Thesetypesoflimitationsmean,forexample,thatawineoffermayreachacustomeronlyafterashoppingtripandwhenhomedrinkingwineatdinner.However,IoTenabledretailersthatarepoweredbyDeepLearninganalyticsareinapositiontodeliverrealtimesavingsduringtheshoppinglifecycle.DeepLearningistheenginethatprovideshypercontextualandrelevantinteractionsexactlyattherightmoment,therebyaddingalayerofefficiencythatishighlyvaluedbytheconsumer

PersonalizedadtriggeredandsentwhenJane,aluxurycategoryshopperwho‘Likes’LouisVuittononFacebook,waslocatedintheLVhandbagdepartment

Withanabilitytoknowwhatcustomersarelookingforandneedtoknowatagivenmomentduringtheshoppingjourney,marketerscanachieveanunparalleledlevelof‘responsetoconversion’metrics:

• Increasedcross-shoppingbetweenzonesby8-15%

• Increasedbasketsizeby1.25%intargetedcustomergroups

• Increasedvisitrepeatrate,shoppingfrequencyby1.5%

• Increasedsalesonpromoitemsupto4%

• Increasedsalesconversionsbyfloorpersonnelupto35%

• Moreinteractionsinserviceareasbetweenstorepersonnelandcustomersby15%

ConclusionBytestinganddeployingIoTandDeepLearningforbricksandmortarstores,retailersareabletoevolvetheirbusinessinachallengingnewenvironment.

Gettingitrightentailsknowingyourcustomersinamuchdifferentwaythaneverbefore,meetingtheirexpectationsastheychangeovertimeandbecominghyper-relevantacrossalltouchpoints.

Intrinsictosuccessisbecomingmorecostefficientonalloperationsandfindingtherightbalancebetweenpricing,productassortmentandcustomerservices-allofwhichdependsonadigitalizedphysicalenvironmentcapableofdetectingandadaptingtoconditionsastheychange.

AfewwordsaboutmyselfAstheCEOandprincipalarchitectatShareMyInsight(SMI),Ihavebeeninvolvedoverthepast10yearsindevelopingproprietarytechnologiesandapplicationsforbigdataanalyticsandstatisticalmodelsonconsumerbehavior.InthelastfewyearsIhaveseenretailersincreasinglystruggletocreatemeaningfulandrelevantcustomerengagements,largelyduetotraditionalstatisticalmethodsthatarebecomingobsolete.IbelievethatsensorfusionandDeepLearningtechnologiesarenowreadytoreplacetraditionalrulebasedmodels,enablinganewtypeofshoppingexperiencethatwillbenefitconsumers,brandsandretailers.Mycurrentfocusisonthedesigntoproductioncycleofavarietyofin-storesensorsthatlivestreamdatatotheSMImachinelearningplatformfordetecting,identifyingandputtingintoactioninformationforstoreoperations,merchandising,marketingandcustomercommunications.Iworkwitharangeofpartners,fromconsultantstomarketresearch,trademarketingandadagencies,tosolutionprovidersandintegrators.Feelfreetocontactmeatpcouture@cyscom.com