INFOSYS

CONSUMER,RETAIL,ANDLOGISTICS

JOURNAL:

SHAPINGTOMORROWWITHAI

Infosskn。wledgenstitute

KnowledgeInstitute

2InfosysConsumer,Retail,AndLogisticsJournal:ShapingTomorrowWithAI|ExternalDocument?2025InfosysLimited

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Tableofcontents

Foreword4

Executivesummary

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Trend1:AI-poweredrevenuegrowthmanagementdeliversaccurateinsights

7

Trend2:AIsolutionshelpmanageretailshrinkacrosstheorganization1

1

Trend3:Consumersbringonlineexpectationsintophysicalstores1

5

Trend4:MACHflexibilityhelpsenterprisesnavigateconstantdisruption

19

Trend5:UseAItodrivetransformationandcollaboration2

3

Trend6:TheriseofAIagentsthataresmart,proactive,andautonomous2

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Foreword

KarmeshVaswani

Executivevicepresidentandhead,consumergoodsandtech,retail,andlogistics

Infosyschairman—InfosysBPM,InfosysConsulting,EdgeVerve(Finacle)

Welcometoour2025Consumer,Retail,andLogisticsJournal.

Overthepastfewyears,wehavewitnessedanincredibleandinterdependentrevolutioninthetechnologylandscapeacross

semiconductors,datatechnology,andartificialintelligence(AI)technology.

ThisrevolutionisledbyAIandmachine

learning,whichinthecomingyearsholdsthepromiseofsignificantleapsinhumanproductivitycomparedtotherevolutionsofthepast100years.

ButAIisnotmerelyanenhancementtoexistingprocesses,systems,andpeople.Itisacatalystforrenaissanceandrenewal.

Itrequiresbusinessestocriticallyexamineenterpriseperformanceonthefive

fundamentalvectorsoftime,space,physicalcapital,humancapital,andthroughput.

Theymustalsoshiftfocusfromtalentmasstotalentdensity,masteringenterprisedatasets

tobecomeAI-ready.Andallinamannerthatwidensthecompetitivemoatofthebusiness,whilegivingtangibleimprovementsin

businessmetricsthatmatter.

Inthisjournal,weshareourviewofafewmultimodalAIcapabilitiesindifferentpartsofthevaluechaininconsumer,retail,andlogisticsindustriesthathavematuredin

termsofadoptionandvaluerealization.

Theseviewsreflectpartnershipsbetween

Infosysandselecttechnologyleadersin

servicingtheAI-ledrenaissancewithsome

ofourstrategicclientsintheconsumer,retailandlogisticsindustries.Wewillkeepbringingoutatregularintervalsnewinnovationsthathavescaledinadoptionandstep-change

improvementsinvaluerealizations.

Thepathaheadisoneofchange,evolution,andpositivetransformation,andIam

optimisticthattogetherwecanmakea

difference,andshapeabetterfutureforthegenerationstocome.

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Executivesummary

Theconsumerpackagedgoods(CPG),retail,andlogisticsindustriesareevolvingquickly,drivenbytechnologicaladvancements

anddigitalinnovationstoboostefficiencyandcustomersatisfaction.However,with

consumerdemandsandtechnology

constantlyshifting,theindustriesneedto

keepontopofthosefactorsandbereadytoadaptquicklytoremaincompetitive.

Thisjournalhighlightssixtransformativetrendsreshapingtheseindustries.Eachchapterexaminesthecurrentlandscape,identifyingchallengesandcomplexitieswithinbusinessandtechnologytrends.

Drawingonresearch,casestudies,and

real-worldapplications,thejournaloffers

recommendationstoequipleadersinCPG,retail,andlogisticswithacomprehensiveunderstandingoftheirindustry,explore

waystoaddresstheirpainpoints,andmakedecisionsinformedbythelatestthinking.

Wefirstlookatrevenuegrowthmanagementstrategies,withafocusonAI.Withconsumerspendingpatternssensitivetoexternal

factorssuchasinflation,CPGsneedtobe

nimbleintheirpricingstrategies,andit’sherethatRGMsolutions,poweredbyAI,canhelp.ThesestrategiescanaidCPGcompaniesin

buildingcapabilitiestoassessbrandpricing,improverevenuepredictability,anddrive

steadygrowth.

Next,welookattheeternalproblemof

shrinkage,andathownext-generation

retailersareusingAIforfaster,moreaccuratedata-driveninsightsinmanagingshrink

acrosstheenterprise,warehouse,andstorelevels.

Weexplorehowthisend-to-endvisibilityintotheirprocessesinrealtimecanhelpthem

pinpointareaswhereshrinkageiscausinglosses,andaddresstheirissuesaccordingly.

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Wealsolookathowthelinesbetween

physicalstoresanddigitalstoresareblurring,withretailersaimingtoprovideaseamlessandconsistentshoppingexperience

whethertheircustomersareinastore,athomeonalaptop,oroutandaboutusingamobilephone.Weconsidertherange

oftechnologiesavailabletoretailers,fromcutting-edgeAItoaugmentedreality

(AR),andhowretailerscandeploythosetechnologiesacrossallchannelstoattract,engage,andretaintheircustomers.

WenextturnourattentiontohowCPG

companiesneedtolookattheirinfrastructureandgetitreadytodeliverthoseseamless

shoppingexperiencesconsumersexpect.

Manycompaniesstillrelyoninflexible,

monolithicsystemsthatmakeitdifficulttointegratenewtechnologies.WeexplorehowadoptingaMACHarchitecture—

Microservices,API-first,Cloud-native,andHeadless—canhelpbusinessesaddnewtechnologiesseamlessly,embedding

flexibilityandresilience,andprovidingaplatformonwhichtoinnovate.

Underpinningmuchofwhatwecover

inthisjournalisAI,aground-breaking

technologythatisdrivingtransformation

acrossallsectors.However,companiesneedtomakesuretheyhavethebuildingblocks

ofgovernance,technology,talent,data,andstrategyinplace.Inthischapterwelookat

howCPGcompaniesmustrecognizethatAIisbecomingageneral-purposetechnologytobeembeddedacrosstheenterprise

technologystack.AdoptinganAI-first

mindsetmovescompaniestoward

enterprise

AIreadiness

,whetherit’sensuringtheyhaveavalidusecase,selectthosethatwilldeliversubstantialbusinessvalue,orbuildtalent

inAI.

Finally,weturntothecomingwaveofAI:AgenticAI,andtherolethistechnologycanplayinCPGcompanies.AIagentsarefullyautonomoussystemsthatpursuespecificgoalswithminimalhumanoversight.Withtheirabilitytodynamicallyengage,solve

complexproblems,andlearninrealtime,agenticAIoffersexcitingpotentialtoCPGcompaniesacrossarangeofusecases.

Keepingaclosewatchonthesetrendsandbeingreadytoactontheinsightsinthis

journalwillhelpCPG,retail,andlogistics

leadersnavigatetheevolvingbusinessandtechnologylandscape.ByembracingAI-

driveninnovation,digitaltransformation,andadaptablearchitectures,organizationscan

enhanceoperationalefficiency,drivegrowth,andthriveinanincreasinglydynamicand

complexmarket.

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Trend1:AI-poweredrevenuegrowth

managementdeliversaccurateinsights

TheimportanceofRGM

Consumerpackagedgoods(CPG)companiesmaketheeverydayproductsthatglobal

customerscherish.

ResearchfromInfosys

showsthatglobalretailsalesgrewby10%in2023—butthat’spartlybecausecompaniesraisedtheirprices.Inflationisexpectedtostayabovethepre-pandemicaverageof2.7%,

socontinuingtoincreasetheirpricescanbeariskystrategyforcompanies.Indifferent

markets,householdbudgetsaregettingtighter.

Forexample,retailsalesgrowthinChina

slowed

inNovember2024tojust3%year

overyear,whileinIndiaconsumersare

cutting

spendingasinflationsqueezes

middle-classbudgets.IntheUK,households

cutback

onpurchasesbyasmuchas15%,

between2021and2023,whileintheUS,consumersareturningto

storebrands

overwell-knownbrands.

Inthischallengingenvironment,retailersaredeployingrevenuegrowthmanagement

(RGM)strategiesto

helpboostrevenue

whilestayingintunewithwhatcustomerswant

andwhat’shappeninginthemarket.When

usedtogether,strategieslikeprice,packaging,whereproductsaresold,andpromotions

canprotectmarketshareandkeeprevenuesgrowingsustainably.

TraditionalRGMisfractured

TheRGMprocessisimportant,butit’s

fragmentedacrosstheorganization.ManyCPGcompaniesstilluseoutdatedmethodswithincompletedata,whichmeansthey

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don’tgettheinsightstheyneed.Togeta

fullview,businessesneedbothinternalandexternaldata,suchaswhatconsumersarebuying,howstrongtheirbrandis,andwhatcompetitorsaredoing.Tothriveintoday’sfast-movingenvironment,CPGleadersneedfaster,clearerinsightstomakebetterpricingdecisions.

AdoptingAI-poweredRGM

RGMsolutions,poweredbyartificial

intelligence(AI),canhelpCPGcompanies

buildthecapabilitiestoassessbrandpricing,improverevenuepredictability,anddrive

steadygrowth.

Forexample,InfosysworkedwithaBritish

consumercompanytouseanalyticsfor

comparingitsdetergentpriceswiththose

ofcompetitors.ThecompanyfoundthatitsDetergentA’spricingwasdoingwell,with

asmallleadoveracompetitor,sonoprice

changewasmade.Incontrast,DetergentB’spricingwasbehind,sothecompanychangeditspricetobemorecompetitive.

Withmachinelearning(ML),CPGcompaniescanusesimulationstotestpricingscenariosandcheckrevenueeffects.BasedonML

modeloutputs,managerscanthendecidewhethertomakechangesinreallifeacrossregions,channels,andretailstores.

StrategicRGMapproaches

AsAIcontinuestoevolve,CPGcompaniesmightbenefitfromconsideringthesebestpracticesforthoughtfulpreparationandimplementation.

Ensuredatareadiness

Recent

research

fromInfosysshowsthatonly17%ofcompaniesbelievetheirdataisreadyforAI.ForanAI-poweredRGMplatformto

delivertransformativeresults,companies

needtrusteddatafromeverypartofthe

business.Thismeansbothstructuredandunstructureddatashouldbecollected,

cleaned,labeled,andstoredsecurelyinoneplaceforseamlessaccess.ThevalueofthisdataisevenclearerwhenitisshowninAI-powereddashboards.

Forexample,imagineaglobalCPGcompanyusinganAI-poweredplatformtodiscover

thatane-commerceproductpagehada

77%dropinadspending.TheAIsuggests

increasingtheadspendandestimatesa

dollaramount.So,whatwouldbetheresultforthecompany?AI-powereddashboards

canturncomplicateddataintoeasy-to-

understandinsightsandclearactionstotake.

UnlockRGMwithintegration

Lookingatprice,pricepack,channel,and

promotionstogethergivesaclearpicture

forsettingthemostoptimalpricesthatalignwithbrandequity.CPGcompaniescanrealizestrategicobjectivesbyusingAItoidentify

RGMopportunities,suchas:

?Strategicpricingmatchesconsumerexpectationswiththecompany’smarketshare.

?Price-packarchitecturefindsgapsinpackpricingtowinmorecustomersandgainmarketshare.

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?Channelassortmentchoosesthebest

mixofproductsforeachchannel,selectingfromthecompany’sfullrangeofproducts.

?Tradepromotionmeasuresreturnoninvestmentandboostseffectivenessforfuturepromotions.

WhencompaniescombineallpartsofRGM,theycangrowmarketshare,increaseprofits,andbuildstrongerchannelpartnerships.

Forexample,InfosysworkedwithanItalianmultinationalfoodcompanytouseRGM

analyticstoimprovepricing,promotions,andexpansionplans.Thecompanyuncovered

between$70millionand$100millioninincrementalannualfreecashflow.

Maketherightpricingdecisions

Thestandardelasticitymodelhaslongbeenkeyforsettingtacticalpricingandpromotionstrategies,buttoday’scompetitivemarket

demandsmore.Brandsarenowusing

AI-

drivenRGMtools

togobeyondoldpricingmodels.Theycansimulatestrategicpricingdecisions,likefiguringouttheStrategicPriceIndex(SPI)comparedtocompetitors,and

designabetterpromotionplan.WithAI-

drivenRGMtools,CPGcompaniescanfactorin:

?Consumerbehavioranalysistolearnhowpeoplereacttopromotions,productplacements,andonlineshopping.

?Brandequityalignmenttocombinebrandtrackerinsightswithdataaboutdifferentgroupsofpeople,matchingpricingwithmarketsharegoals.

?Competitorintelligencetogain

insightsfromeconometricmodelswithbenchmarksandsyndicateddata.

Optimizeprice-packstructures

Forprice-packarchitecture,AImodelscananalyzetherelationshipbetweenprice

pointsandproductpackagingfordifferentsizesorbundleofferings.Thegoalistomeetconsumerneedsandmaximizeprofitability.Forexample,AItoolscanrunsimulations

toidentifythemosteffectivewaystoofferproductsinvarioussizes,packaging,or

bundles.

CPGcompaniescanservedifferentcustomergroups,suchasprice-sensitive,value-

drivencustomers,whiledrivingincrementalsales.AIcanwithreasonableaccuracy

identifystrategicpricingandpackagingcombinations.Thishelpsreinforcebrandequityandmakesproductsstandoutincompetitivemarkets.

Ensuretherightproductmix

Forchannelandmixassortment,AImodelscanhelpCPGcompaniestargetthemost

profitablecustomergroupsandreduce

inefficiencies.Forexample,AIcanrun

simulationstoalignproductswithhow

customersshoponline,inconvenience

stores,oratbig-boxstores.Shelfspacecanbemaximizedbypickingthebestitemsforshelves—especiallypopularorhigh-profitones—toboostsalesandmakeproductseasiertofind.Thisway,CPGcompaniescansimplifytheirproductmixesandbettermeetwhatcustomerswant.

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Evaluatepastpromotions

CPGcompanieshavevaluablehistoricaldataabouttradepromotions.Usinganalytics,

managerscanlookbackatprevious

promotionstoseewhatworkedbest.Theycanthenusetheseinsightstorecommendmoreeffectivepromotionsinthefuture.

Thistypeofdata-drivenplanningcanhelpbusinessesgetbetterreturnsontheirtradeinvestments.

Withpredictiveanalytics,managerscanalsolookahead.AI/MLcanpredicthowfuture

promotions,likediscountsduringslowtimes,mightwork.Thiscapabilityhelpsbrands

planmorestrategicallyandspendtheir

budgetsonpromotionsthatarelikelyto

drivethehighestreturns,whilephasingoutunderperforminginitiatives.

RGMistransformational

AI-drivenrevenuegrowthmanagementisagame-changer.IthelpsCPGleadersmakefaster,smarterdecisionswithprecision.Byreducingrisksinpricing,assortment,and

promotions,companiesunlocknewprofitstreams.Theseprofitscanfuelresearchanddevelopment,sparkinnovation,andelevateproductstothenextlevel.RGMisn’tjustatool—it’sastrategicadvantagethatdrivessustainedrevenuegrowthandkeepsbrandsaheadofthecompetition.

Revenuegrowthmanagementsolutions,poweredbyartificialintelligence,canhelpCPGcompaniesbuildthecapabilitiestoassessbrandpricing,improverevenuepredictability,

anddrivesteadygrowth.

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Trend2:AIsolutionshelpmanageretailshrinkacrosstheorganization

Socialandeconomicimpact

Retaillossandshrinkagehavereachedan

unprecedentedvolumeacrossregions.Moreindividuals,whethercustomersoremployees,areresortingtoretailtheftandfraudto

counterhighinflation.

IntheUK,theftfromstoresreachedanall-

timehighofmorethan20millionincidentsin2023-24,costingretailers￡2.2billion($2.8billion),upfrom￡1.8billionthepreviousyear,

accordingtotheBritishRetailConsortium

.

IntheUS,theNationalRetailFederation

reportedthattheftaccountedfor$112billioninlossesin2022.Theftaccountsfor

66%

of

retailshrink,impactingsales

revenues

.

Whenpeoplethinkaboutretailshrinkage,theyoftenpictureshopliftingorpaymentfraud.Butnotallshrinkagecomesfrom

crime.Manyretailersincurlossesfrom

operationalissuessuchasdamagedgoodsduringhandling,inventorytrackingerrors,orwarehousemis-picks.

The

USNationalRetailFederation

foundin2022that26%ofshrinkcomesfromadministrativeprocessfailuresandpoorcontrols.Retailersmanagevastsupplychains,multiplepartners,andcomplexinventorysystems,andtheyoftenlack

real-time,end-to-endvisibilityintothese

processes.Another8%ofshrinkisclassifiedas“unknownloss,”highlightingtheblindspotsthatexistacrosstheenterprise,warehouse,

andstorelevels.

UsingAItoreduceshrink

Next-generationretailersarenowturningtoartificialintelligence(AI)forfaster,more

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accurate,data-driveninsights.Whileretailershavelongreliedontraditionallosspreventionmethods—suchasclosed-circuitTV(CCTV)camerasandelectronicarticlesurveillance

tags—AIsolutionscanhelpidentifyshrinkacrosstheenterprise,warehouse,andstorelevels.

Howtomanageshrink

Enterpriselevel

ManyorganizationsstrugglewithAIreadinesstomanageshrink.

Recentresearchby

Infosys

foundthatonly15%ofrespondentsexpressedconfidenceinhavingthe

necessaryelementsforsuccessfulAIprojects,includingabusinesscase,clearusecases,

andgovernanceapproval.Yetawell-definedAIstrategyisessentialtobuildingthedatainfrastructureneededforAI-poweredloss

prevention.

Forexample,AI-enableddashboardsactas

acontrolcenterthatprovidesvisualized,

real-timeupdates.AImonitoringcandetectanomalies,trackdiscrepancies,andalertuserstotheseissues.Drawingfromlargelanguagemodels(LLMs),generativeAIsolutionscan

suggestinsightsandrecommendresponsesforlosspreventionteams.

Enterprisedatacanalsobeleveragedfor

differenttypesofmachinelearning(ML)

modeling.Forexample,unsupervisedMLcanscannetworktrafficforunusualactivityandflagsuspiciousevents.Lookingaheadwithpredictiveanalytics,reinforcementlearningandmodelsimulationscanforecasttheareasmostatriskforfuturelosses.

Warehouselevel

Warehousesanddistributioncentersuse

CCTVcamerasandinventorymanagementsystemstopreventlosses,creatingvolumesofdata.Butrawdataaloneisn’tenough.AI

andadvancedanalyticsmakeitpossibleto

processdatafasterandwithgreateraccuracy,turningexistingsecurityinvestmentsinto

sharpertools.Takecomputervision,for

example—aformofAIthathelpsmachinesinterpretimagesandvideos.Appliedto

securityfootage,itcandetectsuspicious

behaviorinrealtime,givinglosspreventionmanagersthechancetoactbeforetheft

occurs.

Thesefacilitiesarehighlyrestrictedareas,andtheyarebeehivesofactivitywithequipmentoperators,pickers,packers,andthird-party

logisticsprovidersinconstantmotion.Dronesandroboticsaddanotherlayerofcontrol,

conductingproductqualityinspectionstoidentifyspoilage,mishandling,orimproperstoragebeforetheycauselosses.Meanwhile,losspreventionexpertstapintoaccess

controldatafrombiometricscannersandkeycards,usinganalyticstopinpointweakspotsandpreventunauthorizedentry.

Storelevel

Aspublicspaces,storesfacethewidest

varietyoftheftandfraudscenarios,involvingcustomers,employees,andvendors.

Sometimes,multiplepartiescollude.

Forexample,onepersonmightdistract

employeeswhileanorganizedcrimegroupstealsmerchandise.Employeesmightalsoworkwithvendorsordeliverydriverstosteal

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inventoryinexchangeformoneyorgoods.Advancedanalyticscanhelpstoreleaders

withlaborschedulingandtaskmanagement.Inlargestores,theycanbetteridentifywhereemployeesarestationed,suchasatregisters,onthesalesfloor,orinstockrooms.

?Pointofsale(POS)fraud

Cashierfraud,oftencalledsweethearting,happenswhenemployeesgive

unauthorizeddiscountsorfreeitemstofriends,family,orcoworkers.Commonmethodsincludenotscanningproducts,overridingtheprice,andprocessingfakerefunds.

Togetaheadofthistypeoffraud,Infosysworkedwithaleadingfootwearand

apparelmanufacturertoreducePOS

fraudusingAI.Byapplyingquantitative

andbehavioralanalysis,theAIidentifiedpatternsonPOStransactions.Some5,000suspiciouscaseswereflaggedtothelosspreventionteam,reducingpotentialfraudby20%andsavingapproximately$8

millioninrevenue.

?E-commercefraud

Onlineshoppingsurgedduringthe

Covid-19pandemic,andcriminalshave

becomemoreadvancedandmore

organized

toexploitreturnpoliciesand

shippinggaps

.Commonfraudtechniquesincludefakerefundingservicesand

“missingintransit”scamsthatmanipulatereturnlabels.

InfosyshelpedaclientuseAI-poweredanalyticstodetect$500,000inpotentialreturnfraud.TheAIrevealedthat80%ofsuspectedfraudsterswerefirst-timebuyerswithsimilarshoppingandreturnpatterns,leadingthecompanytoplace

239customersonarestrictedlist.

Peopleplayarole

Reducingshrinkisn’tjustaboutAI—italsodependsonemployees.Onlinetraining

programsandprocessenhancementscanteachthemthebestwaystohandledifficultsituations.Withouttraining,acashiermightunintentionallycommitfraud,suchas

Whileretailershavelongreliedontraditionallosspreventionmethods—suchasCCTVcamerasandelectronicarticlesurveillancetags—AIsolutionscanhelpidentifyshrinkacrosstheenterprise,

warehouse,andstorelevels.

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approvingarefundwithoutareceipttocalmanupsetcustomer.Whilethismightseem

minor,itcanimpactacompany’sbottomline.

Aglobalsportswearmanufacturerfoundthat23%ofin-storecashrefundslackedoriginal

receiptsinaspecificregion.Infosyshelped

thecompanyuseAIanalyticstomonitor

keyperformanceindicatorsandstore-level

trends.Withinamonth,theAIsolutionhelpedthecompanyidentifythepotentiallossof

revenue,helpingpreventfurthershrink.

Thewayahead

Withtherightmixofexpertise,analytics,andtechnology,retailerscanshiftfrompreventinglossestoactivelymanagingandminimizingshrink.Thekeyistostayfocusedonwhat

matters—protectingrevenue,safeguardingbrandreputation,andpreservingcustomer

trust.AIandadvancedanalyticsarecatalystsforfaster,data-drivendecisions.Shrinkisa

realitytoday,butwithanAI-poweredstrategy,itdoesn’thavetodefinethefuture.

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Trend3:Consumersbringonlineexpectationsintophysicalstores

Shapedbyonlineexperiences

Physicalstoresoffermorethanjust

transactions—theycreateambience,

encouragediscovery,andprovidespacesforsocialconnection.

Thedesireforin-personexperiencesis

reflectedinretailtrends.Forecastspredict

thatphysicalstoreswillaccountforalmost

80%of

globalretailsalesin2025

.Some

brandsareexpandingaggressively,suchasanIrishfashionretailerthatisplanningto

reach

530storesby2026

,andanIndiankitchen

appliancemakerisaimingto

growitsstore

countby30%

overthenextfouryears.

Atthesametime,otherbrandsareclosingstores.In2024,a

Frenchsupermarketgroup

closed768stores,whilean

Australianpizza

chain

shutdown205stores.

Thiscontrastshowstheinfluenceof

e-commerceonthephysicalenvironment.Customerscontinuetodefineshopping

experiences—bringingtheironline

expectationsofhighlycustomized,

contextualized,andpersonalizedshoppingwiththemintobricks-and-mortarstores.

Tostayrelevant,retailersmustrethinkhowtheyengageshoppers,introducedigitaltechnologies,andcreateeasyshopping

experiencesacrosschannels.

Innovationmeetsfixedlimits

Consumertrendsandexpectationschangequickly,forcingretailerstokeepinnovatingforcompetitiveness.Butbricks-and-mortarstoreshavereal-worldconstraints—fixedlayouts,highrealestatecosts,andlogisticalchallenges—thatlimithowquicklytheycanadapt.Conversely,digitalplatformscan

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updatetheiralgorithms,interfaces,and

personalizedrecommendationsinstantlyandinresponsetocustomerneeds.

Elevatetheexperience

Retailersareusingartificialintelligence(AI)

toimprovethestoreshoppingexperience.

Forexample,a

USfoodanddrugretailer

introducedAI-poweredshoppingcarts;andaEuropeanbeautyretailerinstalledinteractivemirrorsforcustomerstovirtuallytryon

makeupbeforebuying.Theseinnovations

helpphysicalstoresincreaserevenues,

standoutfromcompetitors,andmakein-

personshoppingnotjustanalternativetoonlineshopping,butthepreferredcustomerexperience.

Digitalsolutionsforstores

Retailersneedto