the predictive casino

Confidential Page1 11/28/16

ThePredictiveCasino

By

AndrewPearson

©AndrewW.Pearson2016


ThePredictiveCasino...........................................................................................................1ExecutiveSummary..................................................................................................................3DataLakevs.DataWarehouse................................................................................................3InternetofThings(IoT)............................................................................................................8StreamProcessing....................................................................................................................8Real-timeTechnology............................................................................................................11Analytics.................................................................................................................................12SocialMediaMonitoringandAnalytics.................................................................................15TheCustomerJourney...........................................................................................................17

Pre-Arrival.........................................................................................................................................17ClickstreamAnalysis.....................................................................................................................17Personalization.............................................................................................................................18

ArrivalandStay................................................................................................................................20GeofencingApplications...............................................................................................................20FacialRecognition.........................................................................................................................20AugmentedReality.......................................................................................................................20Mobileadvertising........................................................................................................................21e-Commerce.................................................................................................................................22RetailEdgeAnalytics.....................................................................................................................22SocialCommerce..........................................................................................................................22

Departure.........................................................................................................................................23FacilitiesandLaborManagement..........................................................................................23

AugmentedReality...........................................................................................................................24Geo-fencing......................................................................................................................................24Security.............................................................................................................................................25Hosts,Hostesses,andPitBosses......................................................................................................26

Conclusion..............................................................................................................................26References.............................................................................................................................27


ExecutiveSummaryThe predictive casino is a casino or an integrated resort that takes into account everything that ishappeningonthecasinopropertybythepatrons,customers(we’llconsiderthesethepeoplewhohaven’tsignedupforaplayercardyetandare,therefore,mostlyuntrackable),theemployees,andthevendors.The predictive casino utilizes all of the data associated with all of these individuals to make betterbusiness decisions for the company as a whole. Descriptive analytics, diagnostic analytics, predictiveanalytics, prescriptive analytics and the newest entrant into the growing field of analytics—edgeanalytics—areexploited throughout theenterprise to reachas real-timean ITenvironmentaspossible.Thepredictivecasinocominglesstructured,semi-structured,andunstructureddatatoprovidecustomerinsightsdowntothegranularlevelandutilizestheseinsightstoimprovecustomerintelligence,marketingautomation, analytics, and socialmediamarketing. Extrapolating one customer’s experience to that ofthousand,tenthousand,ahundredthousand,even,potentially,amillioncanprovide insights intobothshorttermlaborneeds,andlong-termsupplyandoperationalneeds.

Thepredictivecasinocullsdatafromthefollowingsources:

• GamingdatafromBally’s,IGTorotherin-housegamingsystems• CustomerRelationshipManagement(CRM)systems• Patronmanagementsystems• TransactiondatafromPOS(F&B,spa,clubandretailoutlets)systems• Hotel’sreservationsystems• Casinowebsiteclickstreams• Callcentersystems• Surveillanceandsecuritydatasets,includingfacialrecognitiontechnologyinformation• RFIDchips,forbothcasinochipsorotherdevicestrackinginventory• IoTsensors• Geo-locationdatafromin-houseandon-busWi-Fisystems• Tablegamesrevenuemanagementsystems• AngelEyecardshoedata• SocialmediadatafromWeChat,Facebook,Weibo,Twitter,Jeipang,etc.,etc.• Socialmedialisteninghubs• HRandERPsystems,includingvirtualroster• Weatherdata

All of this information can be fed into a data lake, where it can be utilized by a multitude of casinopersonnel, including patron management, marketing (including social media marketing), security, callcenter/customerservice,pitbosses,hosts,slotmanagers,dealers,HR,hotelmanagement,retail,spa,andF&Bclerks,allthewayuptothecasino’stopexecutives,aswellasthehighestC-levelsuite.

DataLakevs.DataWarehouseJamesDixon,“ChiefGeek”atPentaho,iscreditedwithcoiningthephrase“DataLake”.Dixonpostedthateachspecializeddatamartinadatawarehousecouldbelikenedtoabottleofwater.Thedatawasreadyforuseinasmall,identifiablecontainer.Incontrast,adata“lake”wasamassive,intermingledrepositoryofalldatainitsrawestform.

Adatalakeisahuborarepositoryofallofthedatathatacasinohasaccessto,wherethedataisingestedandstoredinasclosetotherawestformaspossible,withoutenforcinganyrestrictiveschemaonit.Thisprovidesanunlimitedwindowintothedataforanyonetorunad-hocqueriesandperformcross-sourcenavigationandanalysisonthefly.Successfuldata lake implementationsrespondtoqueries inreal-timeandprovideusersaneasyanduniformaccessinterfacetothedisparatesourcesofdata.DataLakesretainall data, support all data types and all users, aswell as adapt easily to changes,while providing fasterinsightsintothedata.


Inrecentyears,businessesingeneralandcasinocompaniesinparticularhavecometotherealizationthatdatawarehouses,while perfectly able to handle the BI and analytics needs of yesterday, don’t alwayswork in today’s complex IT environments, which contain structured, unstructured and semi-structureddata. Normal relational databases worked fine while business users were restricted to proprietarydatabases and the scopeofworkwas limited to canned reports andmodest dashboards that includedlimiteddrilldownfunctionality,but,today,withtheinclusionofsomuchunstructureddatacomingfrommobile,social,weblogs,andsemi-structureddatabeingcreatedfromamultitudeofsources,limitationsabound.Standarddatawarehousesrequirebuilt-in,understandableschemas,butunstructureddata,byitsverydefinition,doesn’thaveadefinableschemathat isaccessibleandunderstandableineverycase.Datalakeshavebeenaresponsetotheselimitations.

AccordingtoPWC’s(2014)articleDatalakesandthepromiseofunsiloeddata:

Job number one in a data lake project is to pull all data together into one repositorywhile giving minimal attention to creating schemas that define integration pointsbetweendisparatedatasets.Thisapproachfacilitatesaccess,buttheworkrequiredtoturnthatdata intoactionable insights isasubstantialchallenge.While integratingthedata takes place at the Hadoop layer, contextualizing the metadata takes place atschemacreationtime.

Data lakes support a concept known as “late binding, or schemaon read, inwhich users build customschema into their queries. Data is bound to a dynamic schema created upon query execution” (PWC,2014).“Thelate-bindingprincipleshiftsthedatamodelingfromcentralizeddatawarehousingteamsanddatabaseadministrators,whoareoftenremotefromdatasources,tolocalizedteamsofbusinessanalystsanddatascientists,whocanhelpcreateflexible,domain-specificcontext.ForthoseaccustomedtoSQL,thisshiftopensawholenewworld”(PWC,2014).Foracasinocompany,thismeansthatdepartmentsasdifferent as call center personnel, patronmanagement people, on floor hosts and pit bosses, hotel orretailclerks,securitypersonnel,andmarketinganalystcanaccessandutilizedatadrawnfromthesamedatalake,justpersonalizedforthemand,potentially,theircustomers.

Figure1:DataLakeforacasinoproperty

Today’s IT environment is nothing like the IT environment of even three years ago. Real-time datamanagement capabilities have brought a whole new level of data available to customer intelligence,


customerinteraction,patronmanagement,HR,ERP,gaming,andsocialmediasystems.Today,oneofthebiggestchallengesforITdepartmentsisscalability.WithaHadoopback-endeddatalake,businessescandynamicallyscaleupordown,accordingtotheiruniquestorageneeds,evendepartmentalstorageneeds.Over the past few years, the cost of storage has plummeted and virtual servers can be spun up veryquicklyandquiteinexpensively(relativetotheoutrightpurchaseofhardware).Withthisinstantaccesstodata,awholenewworldofreal-timeinteractionshasbeenmadepossible.

Theconceptof“EdgeAnalytics”—theprocessingofanalyticsatthepointorveryclosetothepointofdatacollection—exponentiallyincreasestheabilitytousepredictiveanalyticswhereitcanbeutilizedbest—atthemeetingpointofcustomerinteractionanddata. Inshort,edgeanalyticsbringsanalyticstothedataratherthanvice-versa,which,understandably,canreducecostandincreaseitsexploitabilityasthedataisanalyzedclosetowhere itcanmakethemostdifference.Thisalsoreduces latency,whichcouldbethedifferencebetweenusefulanduselessanalytics.

As BernardMarr (2016) argues in his articleWill ‘Analytics on The Edge’ Be The Future Of Big Data?,“Ratherthandesigningcentralizedsystemswhereall thedata issentbacktoyourdatawarehouse inarawstate,whereithastobecleanedandanalyzedbeforebeingofanyvalue,whynotdoeverythingatthe‘edge’ofthesystem?”

Marr (2016)usestheexampleofamassivescaleCCTVsecuritysystemthat iscapturingreal-timevideofeedsfromtensofthousandsofcameras.“It’slikelythat99.9%ofthefootagecapturedbythecameraswillbeofnouseforthejobit’ssupposedtobedoing—e.g.detectingintruders.Hoursandhoursofstillfootage is likely tobecaptured foreverysecondofusefulvideo.Sowhat’s thepointofallof thatdatabeing streamed in real-time across your network, generating expense as well as possible complianceburdens?”(Marr,2016).Thesolutiontothisproblem,Marr(2016)arguesisfortheimagesthemselvestobeanalyzedwithinthecamerasatthemomentthevideo iscaptured.Anythingfoundtobeout-of-the-ordinarywill triggeralerts,whileeverythingdeemedunimportantwilleitherbediscardedormarkedaslowpriority,therebyfreeingupcentralizedresourcestoworkondataofactualvalue(Marr,2016).

Foracasino,thisphilosophycanhelpinamultitudeofways;edgevideoanalyticsmighthelpinplaceslikeSingapore, where the integrated resorts located there are required to check the passports of eachincoming guest. With cameras that are able to compare the faces of entering customers against thecasino’s patron database, the patron records of returning customers could be quickly uploaded to asecuritypersonnel’scomputer,probablyseveralmomentsbeforetheactualpersonstepsuptotheguardstation.Ratherthanwaitingforarecordtobepulledup,thesecuritypersonnelwouldsimplyconfirmordenythepatroninlessthanhalfthetimeitwouldnormallytake.Thiscouldcutdownonthelonglinesatthefrontof thecasinothatarereallycostlychokepoints.Thecasinomakesmoneywhengamblersaregambling,notwhentheyarestandinginlinetogetin.Asagreatsidebenefit,thepatronswillprobablyseethisasacustomerserviceimprovementaswell.

Besides theobvioususebyacasinosecurity team, forbothpatronand,potentially,perimetersecurity,edgeanalyticscouldalsobeusedtospothighrollersventuringontoaproperty,oruncoveringproblemgamblers.

Edgeanalyticscanalsohelp intheretailspace.“Largeretailerscouldanalyzepointofsalesdataas it iscaptured, and enable cross selling or up-selling on-the-fly, while reducing bandwidth overheads ofsendingall salesdata toa centralizedanalytics server in real time” (Marr, 2016).As today’s integratedresortsarealso,inmanycases,hugeretailmalls,retailedgeanalyticscould,potentially,becomepartofamarketing and/or analytics package the casino company makes available to its retail clients. First tierretailers like Chanel or Tiffanymight not be interested in suchpackages, but the smaller retailerswhodon’t have sophisticated in-house CRM systems might be. After all, there’s no reason why casinocompaniescan’tmonetizethedataitlegallycollectsthroughoutitsvastproperties.

Edgeanalytics,ofcourse,goeshand-in-handwiththeInternetofThings–“thenetworkofphysicalobjectsthatcontainembeddedtechnologytocommunicateandsenseorinteractwiththeirinternalstatesortheexternal environment” (Gartner, 2013).IoT technology costs are coming down, broadband’s price has


dropped,whileitsavailabilityhasincreasedandthereisaproliferationofdeviceswithWi-Ficapabilitiesand censors built into them. Smart phone penetration is also exploding. All of these individualtechnologicaladvancesweregoodfortheIoT,together,however,theyhavecreatedaperfectstormforit(Morgan,2014).Withlessthan0.1%ofallthedevicesthatcouldbeconnectedtotheInternetcurrentlyconnected (Marr, 2015), there is tremendous growth potential and thosewho embrace it now shouldhave the firstmoveradvantage thatcouldproveenormouslyvaluable in termsofROI in thenear termfuture.

The predictive casino sees the IoT as a gateway to more efficient business practices and processes.Bringing together all of the parts needed for the predictive casinos is not easy, however. Previousattempts of broad-based data integration have forced users to build data sets around commonpredeterminedschemas,oraunifyingdatamodel,somethingnotpossiblewhenunstructuredandsemi-structureddataareaddedtothemix.Thisiswherethedatalakecomesin.Unlikethemonolithicviewofasingleenterprise-widedatamodel,thedatalakerelaxesstandardizationanddefersmodeling,resultinginanearlyunlimitedpotentialforoperational insightanddatadiscovery.Asdatavolumes,datavariety,and metadata richness grow, so, too, do the benefits. Today, data is coming from everywhere, frombusinessmainframes,corporatedatabases,logfiles,cloudservices,APIs,RSSfeeds,aswellasfromsocialmedialivestreams;mostofthisinformationdoescontainmeaning,ifyouknowwhereandwhattolookfor.Adatalakemakesiteasiertoreadandunderstandthisdata,atleastthat’sthetheorythatisbeingtestedoutbyseveralforward-thinkingcompaniesrightnow.

With a data lake, the data is ingested and stored in as close to the rawest form as possible, withoutenforcinganyrestrictiveschemaatopit.NoOLAPcubesareusedtomanipulatethedata.Thisprovidesanunlimitedviewofthedataforanyonewithintheorganizationwhohasbeengivenaccesstoit.Theuserwillbeabletorunad-hocqueriesandperformcross-sourcenavigationandanalysisonthefly.Successfuldatalakeimplementationsrespondtoqueriesinreal-timeandprovideusersaneasyanduniformaccessinterface.DataLakesretainalldata,supportalldatatypes,andadapteasilytochanges,whileprovidingfasterinsights.

With a normal datawarehouse, a casino needs to decide on the structure (schema) of the datawhencreatingthewarehouse—beforeanythingisevenpopulatedwithdata(schema-on-write).WithaHadoop-baseddata lake,however,acasino justhastostorethedataandstructure it later,atatimewhen it isneededforeachqueryorusecase(aschema-on-readframework).Table1revealsthemaindifferencesbetweenthetwosystems.

DATAWAREHOUSE vs. DATALAKE

Structured,processes DATA Structured/semi-structured/unstructured,raw

Schema-on-write PROCESSING Schema-on-read

Expensiveforlargedatavolumes

STORAGE Designedforlow-coststorage

Lessagile,fixedconfiguration AGILITY Highly agile, configure andreconfigureasneeded

Mature SECURITY Maturing

Businessprofessionals USERS Datascientists,etal.

Table1:DifferencesbetweenaDataWarehouseandaDataLake

Usingadatalakesolution,acasinocan:

• Gainvisibilityonapatron’strueidentitybycullingthroughgovernmentandsocialmediarecordsduringthesignupprocess

• Gain a comprehensive view of patron behavior by automatically cleansing and consolidatingdisparategamingandnongamingactivityintoasingleversionofthetruth


• Understand patron activity and behaviors—using segmentation to analyze data gathered atmultipletouchpointsandthensegmentingpatronsaccordingtotheirsubtlegamingdifferences

• Identify the greatest drivers of patron value and projecting how those drivers will affectprofitability and revenue projections months into the future, with enough lead time to takecorrectiveactions,ifnecessary

• Create highly effective personalized promotions that are tailored to appeal to a casino’smostvaluablepatronsbyautomatingandpersonalizingmarketingcampaignsonarecurringbasis

• Add socialmedia as a customer service andmarketing channel, utilizing automated Facebook,Twitter, etc., bots to disseminate IR-related information, like room rates, restaurant optionsand/orreservations,etc.,etc.

• Maximizepatron satisfactionandprofitabilitybyoptimizingvaluable resources tomeetpatronneedsateverypropertytouchpoint

• Getvaluablecustomerinsightsintothehandsofpatron-facingemployees,decisionmakersandotherswhocanexploitit,withhigh-endreportinganddatavisualizationcapabilities

• Geo-locateapatronfromthemomentheorshearrivesonpropertyandincludethisparticularindividual’s specific gaming play information into models that adjust table games revenuemanagementinclosetorealtime

• Understand real-time casino floor traffic flows so that casino personnel can be utilized moreefficiently

• CapturePoS fraudandAnti-Money Laundering (AML) activity in real-time, thereby rootingoutcriminalsandreducingcosts

• Associateonlineadmarketingwithpatronconversions.

Data coming frommobile and social media sources likeWeChat,Weibo, Facebook, YouTube, Twitter,YouKu, etc., tend to be highly unstructured, while data coming from CSVs, XML and JSON feeds areconsidered semi-structured.NoSQL databases are also considered semi-structured, while text withindocuments,logs,surveyresults,ande-mailsalsofallintotheunstructuredcategory.Datacominginfromtheplethoraofcasinosourcesystem,undoubtedly,canfeedintoadatalakeandthenbeutilizedinwaysthatarealmostimpossibleforanormalrelationalDW,evenonewithin-memorycapabilities.

Highly structured patron data could be combinedwith unstructured data coming in from socialmediafeeds.IfapatrontweetsfromthetrainheadingtowardsZhuhaioracarcrossingtheNevadaborder,whyshouldn’tthecasinomarketingdepartmentbealerted?SettingupJSONfeedsforTwitterusersaccountsisaverysimpleprocessandmanyothersocialmediacompaniesofferAPIsthatallowaccesstocustomeraccounts. These are two-way systems as well, and the casino’s marketing department should includesocialmediaasachanneltoconnectwithpatronsandpotentialcustomers,andtheseareprobablythechannels that customers want to be connected through. Facebook and Twitter bots can be set up toanswerbasicquestionslikehotelroomrates,orconcertcalendars,oramultitudeofthingsabouttheIR.Thesecanbeautomatedanswersthatcancontainlinkstothehotelreservationpage.

Withquickandeasyaccessibilitytoacasino’sdata,customerconversionratescanbeimproved,revenuecan be increased, and customer churn can be predicted and, hopefully, reduced asmuch as possible.Customeracquisitioncostscanbeloweredaswell.Byutilizingthecomplexwebofcustomerdatacominginfromseveraldifferentchannels—mobile,socialmedia,customerloyaltyprograms,transactiondata,e-commerce weblogs, IoT sensors, amongst others—a casino can also work more productively. Byunderstandingcustomerpatternsandbehavioracrossthewholespectrumofthebusiness—frompatronpickup,topatronnavigationthroughouttheproperty,toroomandrestaurantchoice,tofinalcarorbustriphome—acasinocanusetheircustomerbehaviorpatternsto,potentially,mapoutitshumancapitalandinventoryneedsaswell.

Obviously,creatingasingleversionofthetruthisahighlysoughtafter,butextraordinarilydifficultthingtoaccomplish.However, if one couldmakeconsiderableprogress towards it, itwould solveaworldofproblems that have been vexing casino analytics departments for decades, and it is certainly aworthy


goaltostrivefor.Itisalsoanecessaryconditionforpersonalizationmarketing,whichwillbediscussedintheCustomerJourneysectionofthispaper.

InternetofThings(IoT)Inhisarticle,TheInternetofThingsisFarBiggerThanAnyoneRealizes,DanielBurrisarguesthat,“Ofallthetechnologytrendsthataretakingplacerightnow,perhapsthebiggestoneistheInternetofThings;it’stheonethat’sgoingtogiveusthemostdisruptionaswellasthemostopportunityoverthenextfiveyears.”AccordingtoHitachi,thethreeelementsthathavebecometheenablerofthenewerainclude:

• Enterprisecloud—This isanecessarycomponentand thescalabilityof thecloud,whichallowsmassiveamountsofcompute,storageandnetworkingtobeputonlineupataffordableprices,laysthefoundationforIoT’sneededframework.

• Big data analytics—New technologies are allowing companies to better understand data andmakemoredata-drivendecisions.Someofthesenewtechnologies includepredictiveanalytics,datamanagementforbigdatainahighlydistributedmanner,andbeingabletoblendstructuredand unstructured data from a variety of internal and external sources to bring context whensolvingcomplicatedproblems.

• Streaming data and real-time analytics—To be effective in a dynamic, ever changing, process-drivenworld requires the ability toprocessdata in real time.Manyuse cases that dependondata require fast analysis rather thanwaiting for data to be processed in batch. For example,whenmanaging the flowof traffic ina congestedcity,datamustbeanalyzed in real timeandactionsmustbeimmediatelytakentodecreasedelays.TheabilitytoactuponstreamingdataisbecomingaprerequisiteformanyIoTapplications.

Foracasinoproperty,IoTimplementationscouldbeaslargeascreatingamicroenergygridforamassiveintegrated resort, to as small as capturing the face of every card that is turned over on a baccarat orblackjack table (which, tobe fair, isalreadyoccurring). Inbetween, thepotential to captureandutilizeproperty-widesensordatacanhelpacasinoincountlessways,everythingfromunderstandingcustomerflow patterns, to improving marketing messages, to better labor management and inventory control.Theseandotherpotentialusecaseswillbediscussedthroughoutthisarticle.

StreamProcessingAsKaiWähner(2014)explainsinhisarticleReal-TimeStreamProcessingasGameChangerinaBigDataWorldwithHadoopandDataWarehouse,“Streamprocessingisrequiredwhendatahastobeprocessedfastand/orcontinuously, i.e. reactionshavetobecomputedand initiated inreal time.”Wähner (2014)adds:

“Streamingprocessing”istheidealplatformtoprocessdatastreamsorsensordata(usuallya high ratio of event throughput versus numbers of queries), whereas “complex eventprocessing” (CEP) utilizes event-by-event processing and aggregation (e.g. on potentiallyout-of-ordereventsfromavarietyofsources—oftenwithlargenumbersofrulesorbusinesslogic). CEP engines are optimized to process discreet “business events” for example, tocompare out-of-order or out-of-stream events, applying decisions and reactions to eventpatterns, and so on. For this reason multiple types of event processing have evolved,describedasqueries,rulesandproceduralapproaches(toeventpatterndetection).

Stream processing acts on real-time streaming data feeds, using ‘continuous queries’ (i.e. SQL-typequeries that operate over time and buffer windows) (Wähner, 2014). With its ability to continuouslycalculate mathematical or statistical analytics on the fly within the stream, streaming analytics is anessentialpartofstreamprocessing.“Streamprocessingsolutionsaredesignedtohandlehighvolumeinrealtimewithascalable,highlyavailableandfaulttolerantarchitecture”(Wähner,2014).Faulttolerantis one of the keys here, since actions based upon real-time processing require as near perfect faultsafeguardsaspossible.


“In contrast to the traditional database model where data is first stored and indexed and thensubsequently processedby queries, streamprocessing takes the inbounddatawhile it is in flight, as itstreams through the server” Wähner (2014). Stream processing can also connect to an external datasource,therebyaddingawholenewdimensiontoanalyticalprocesses.

Wähner(2014)arguesthatoneoftherecentdevelopmentinstreamprocessingmethodsistheinventionofthe“livedatamart,”which“providesend-user,ad-hoccontinuousqueryaccesstothisstreamingdatathat’s aggregated in memory.” “Business user-oriented analytics tools access the data mart for acontinuously live view of streaming data. A live analytics front ends slices, dices, and aggregates datadynamicallyinresponsetobusinessusers’actions,andallinrealtime”notesWähner(2014).Foracasino,streaming data could be coming in from facial recognition software, fraud or anti-money launderingsolutions,PoSsystems,slotandtablegamesdata,patroncard information,campaignmanagementandredemption systems, employee/labor utilization data, as well as social media feeds. Although, not anabsolutenecessity,streamprocessingdoeshelpconsiderablywithpersonalizationmarketing.

As Wähner (2014) explains, a stream processing solution has to solve the following eleven differentchallenges:

1. “Processingmassiveamountsofstreamingevents(filter,aggregate,rule,automate,predict,act,monitor,alert)

2. Real-timeresponsivenesstochangingmarketconditions3. Performanceandscalabilityasdatavolumesincreaseinsizeandcomplexity4. Rapid integration with existing infrastructure and data sources: Input (e.g. market data, user

inputs, files, history data from a DWH) and output (e.g. trades, email alerts, dashboards,automatedreactions)

5. Fast time-to-market for application development and deployment due to quickly changinglandscapeandrequirements

6. Developerproductivitythroughoutallstagesoftheapplicationdevelopmentlifecyclebyofferinggoodtoolsupportandagiledevelopment

7. Analytics: Live data discovery andmonitoring, continuous query processing, automated alertsandreactions

8. Community(component/connectorexchange,education/discussion,training/certification)9. End-userad-hoccontinuousqueryaccess10. Alerting11. Push-basedvisualization.”

Forcasinos,real-timestreamingcanhelpinthefollowingways:

• CustomerService:o Geo-locateapatronwhenheorshesignsontothecasino’sin-houseWi-Fi,whetherthat

isonacasinobus,whilewanderingthroughanintegratedresort,inahotelroom,oratanevent’scenter

o Video analyticswith facial recognition technology can spot and/or confirm a patron’strueidentityandalertanyneededcasinopersonnel

o Socialmediacustomerservicecancutdownonnormalcustomerserviceexpenses,aswellasallowcasinorepstomakehighlypersonalizedoffersaccordingtosimilarguestexperiences

• E-Commerceo ClickstreamanalysiscouldallowpersonalizedofferstoreturningguestswhenonlyanIP

addressisknowno Improve accurate attribution analysis so that themarketing department understands

whichadvertisingisassociatedwithwhichuser,makingitmuchmorequantifiable• Hosts:

o Hosts can be alerted when a VIP, or even a premium mass player, steps onto thepropertyorevenontooneofthecasino’sbuses


• Hotel:o Hotelroomrevenuemanagement;offertherightpricetotherightpersonattheright

time,ontherightchannelo Offerhigh-profitupgradestothosemostlikelytouseandpayforthem

• HumanCapitalManagement:o Employeeschedulescanbeadjustedaccordingtolabormanagementsneedsalmost in

real-timeo Casinos can take the guesswork out of hiring employees by building templates that

showwhatmodelemployeeslookslikeintermsofskills.• PatronManagement:

o Customer relationship management (CRM) systems can target its messaging to onlythosepatronswhoaremostlikelytorespondtoapromotion,therebybecominghighlychannel-specific

o The amount of promotions available and channels through which to market throughincreases considerably as campaign lift canbe assessed in termsof hours rather thandaysorweeks

o Customeracquisitionisacceleratedbecausebusinessusersthroughoutthepropertycanquicklyderiveanswerstothefollowingquestions:

§ Whichcombinationsofcampaignsaccelerateconversion?§ Whatbehaviorsignalschurn?§ Dowebsearchkeywordsinfluencedealsize?§ Whichproductfeaturesdousersstrugglewith?§ Whichproductfeaturesdriveproductadoptionandrenewal?§ Whatdrivescustomerstousecostlysaleschannels?

o Customerinteractiondatacanquicklybeturnedintobusinessopportunitieso Powerful recommendation engines can ingest data from a multitude of sources and

thenbemadeavailabletofrontlinestaff,whocanreactinnearrealtime• PitBosses/FloorManagers:

o Facial recognitiontechnologyallows for immediateknowledgeofpatronsenteringthecasino,whichcanimprovecustomerserviceforbothVIPsandpremiummassplayers

o Facialrecognitioncanspotself-excludedpatronsandalertsecurityaboutthepotentialproblemgambler

o Tablegameminimumscanberaisedor loweredaccordingtoalmostup-to-the-minutedemandforecasts

o TGRMmodelscanbefedwithadditionaldata(likereservationcheck-in,mobile,locationand/orsocialmediadata)sothatactivitybeyondthetablegamefloorcanbeaddedtothesepredictivemodels

o Slotflooroptimizationo Casinofloormapsthatrevealfloortrafficcanassistintheopeningandclosingoftables

• RetailandF&B:o Outliers in data sets thatmight point to fraudulent activity on POS systems can push

alertstomanagersinrealtimeo Retailers can better target market merchandise, sales, and promotions and help

redesignstorelayoutsandproductplacementtoimprovethecustomerexperienceandincreasesales

• Security:o UncoverAMLactivityo Spotblacklistedplayerstryingtogambleo Reduceexposuretohighriskpatronso Uncovergamingscamso Counter surveillance scanners can spot theuseofunauthorizedvideocamerason the

casinofloor


o Cyberattackpreventiono Regulatorycomplianceo Spotdealingpatternsthatmightrevealthecardshaven’tbeenshuffledproperly,orat

allo Motion-detectingsystemscanspotroulettecheatstryingtoaddchipsaftertheroulette

wheelhasbeenspun,aproblemmanycroupiersareunabletocatchasithappens.

Real-timeTechnologyAs The Cluetrain Manifesto1points out, “Real-time marketing is the execution of a thoughtful andstrategic plan specifically designed to engage customers on their terms via digital social technologies”(Macy and Thompson, 2011). Adding to that description,Wikipedia notes that real-timemarketing is,“marketing performed ‘on-the-fly’ to determine an appropriate or optimal approach to a particularcustomerataparticulartimeandplace.Itisaformofmarketresearchinboundmarketingthatseeksthemost appropriate offer for a given customer sales opportunity, reversing the traditional outboundmarketing (or interruption marketing) which aims to acquire appropriate customers for a given 'pre-defined'offer.”Real-timemarketingcanbeinexpensivecomparedtothecostoftraditionalpaidmedia,however, because “Expensive research, focus groups, and awareness campaigns can be replaced withonlinesurveys,blogcomments,andtweetsbyanyoneoranybusiness”(MacyandThompson,2011).

In his article How Real-time Marketing Technology Can Transform Your Business, Dan Woods (2011)makesanamusingcomparisonofthedifferingenvironmentsthatmarketers facetodayascomparedtowhattheir1980scounterpartsmighthavefaced:

Technologyhaschangedmarketingandmarketresearchintosomethinglesslikegolfandmore likeamulti-player first-person-shootergame.Crouchedbehindahut, thestealthymarketers, dressed in business-casual camouflage, assess their weapons for sendingoutbound messages. Email campaigns, events, blogging, tweeting, PR, ebooks, whitepapers, apps, banner ads, Google Ad Words, social media outreach, search engineoptimization. The brave marketeers rise up and blast away, using weapons not to killconsumersbuttoattract themtotheirsites, totheiroffers, totheircommunities. If theweaponswork,yougetincomingtraffic.

Today’smarketingexecutivedoesn’t have time for amarket research study in this sort of first-person-shooter game, Woods (2011) argues. “The data arrives too late and isn’t connected to the modernweaponsofmarketing.Theworldisnowburstingwithdatafromsocialmedia,webtraffic,mobiledevices,andtripwiresofallkinds”(Woods,2011).Companieshavemassiveamountsofevidenceaboutconsumerbehavior coming at them constantly. The challenge is tomake sense of the data in time tomatter, tounderstand how consumer attitudes and behavior are changing and how they are being changed bymarketingandadvertisingefforts.

Woods(2011)contendsthatthechallengeinunderstandingthemodernconsumerismakingsenseofallof the customerdata, coming in fromvastunstructuredand various sources. Someof this informationexplainsthebroadfluctuationsinmassopinion,whileotherevidenceexplainswhatconsumersaredoingona companyWebsite (Woods,2011).Others still explainwhat consumershavedone,enmasseorasindividuals(Woods,2011).

Successfulmobileadvertisingrequiresthreething—reach,purityandanalytics;reachcanbefosteredbyaccessing accounts throughmultiple platforms likeblogs, geofencing applications,OTT services,mobileapps,QRcodes,pushandpullservices,RSSfeeds,search,socialmediasites,andvideo-casting,amongstother things. “Purity” refers to the message and its cleanliness; if the data is unstructured anduntrustworthyitis,basically,uselessanddatagovernanceisparamountforreal-timeadvertisingtowork

1TheCluetrainManifestoisasetof95thesesorganizedandputforwardasamanifesto,orcalltoaction,forallbusinessesoperatingwithinwhat is suggested tobeanewly connectedmarketplace. The ideasput forwardwithin themanifestoaim toexamine theimpactoftheInternetonbothmarkets(consumers)andorganizations.


properly.Analyticsisthethirdingredientandit“involvesmatchingusers’interests—implicitandexplicit,context,preferences,networkandhandsetconditions—toadsandpromotions inrealtime”(Sharmaetal.,2008).

Successfulmarketingisaboutreachingaconsumerwithaninterestingofferwhenheorsheisprimedtoacceptit.Knowingwhatmightinterestaconsumerishalfthebattletomakingthesaleandthisiswherecustomeranalyticscomesin.Customeranalyticshasevolvedfromsimplyreportingcustomerbehaviortosegmentingcustomersbasedonhisorherprofitability,topredictingthatprofitability,toimprovingthosepredictions (because of the inclusion of new data), to actually manipulating customer behavior withtarget-specificpromotionaloffersandmarketingcampaigns.Thesearethechannelsthatreal-timethrivesinandthisiswhereacasinocangainapowerfulcompetitiveadvantagewhenusingit.

Forareal-timeplatformtowork,datamustbegatheredfrommultipleanddisparatesources,whichcaninclude Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Social CRM(SCRM) platforms, geofencing applications (like Jiepang and Foursquare), Over-The-Top services (likeWhatsApp and WeChat), mobile apps, augmented reality apps, and other mobile and social mediasystems. This data must be collected and then seamlessly integrated into a data warehouse that cancleanseitandmakeitreadyforconsumption(Sharmaetal.,2009).

AfewindustryexamplesfromInformationWeek’sIn-memoryDatabases,IBM,Microsoft,Oracle,andSAPAreFightingtoBecomeYourIn-memoryTechnologyProviders.DoYouReallyNeedtheSpeed?(Henschen,2014b)mightshedsomelightonthesubjectofreal-timemarketingandcustomerinteractions,including:

• Online gaming company Bwin.party uses in-memory capabilities to handle 150,000 bets persecond.Thiscomparestotheirnormalsystemrateof12,000betspersecond

• ForretailservicescompanyEdgenet,“in-memorytechnologyhasbroughtnear-real-timeinsightinto product availability for customers of AutoZone, HomeDepot, and Lowe’s. That translatesintofewerwastedtripsandhighercustomersatisfaction”(Henschen,2014)

• ConAgra,an$18billion-a-yearconsumerpackagedgoodscompany“mustquicklyrespondtothefluctuatingcostsof4,000rawmaterialsthatgointomorethan20,000products,fromSwissMisscocoa to Chef Boyardee pasta” (Henschen, 2014) and an in-memory system assists them inmaterialforecasting,planning,andpricing

• ConAgraalsotapsitsin-memorysolutiontomakecompanypromotionsmorerelevantbyusingfasteranalysis,whichallowsConAgraanditsretailercustomerstocommandhigherpricesinanindustrynotoriousforrazor-thinprofitmargins

• Maple Leaf Foods, a $5 billion-a-year Canadian supplier ofmeats, baked goods, and packagedfoods, finds thatprofit-and-loss reportswhich“used to take15 to18minutesonconventionaldatabasesnowtake15to18secondsontheirin-memoryplatform”(Henschen,2014)

• Temenos, a banking software provider that uses IBM’s in-memory-based BLU Acceleration forDB2system,reportsthatqueriesthatusedtotake30secondsnowtakeone-thirdofasecondthankstoBLU’scolumnarcompressionandin-memoryanalysis.

ForTemenos,inparticular,thatdifferenceinspeedmeansthatmobilecustomerscanquicklyretrievealloftheirbankingtransactionsontheirmobiledevices,ratherthanjusttheirlastfive,whichcouldmeanthedifference between handling customer issues on a mobile device rather than in a company store(Henschen,2014). “Onlineormobile interactioncosts thebank10 to20cents tosupportversus$5ormoreforabranchvisit” (Henschen,2014).Thecostsavingsare,obviously,substantial.Savingsof thesekinds are probably available to a large integrate resort as their business processes are vast andwide,coveringamultitudeofareas,includingcustomercallcenters.

AnalyticsSinceadata lake isa repositoryofall thedata thatacasinohasaccess to, it theoreticallyshouldallowanyonewiththeproperaccesstoittorununlimitedad-hocqueriesandperformcross-sourcenavigationandanalysisonthefly.Successfuldatalakeimplementationsrespondtoqueriesinreal-timeandprovide


usersaneasyanduniformaccessinterfacetothedisparatesourcesofdata.Thiswindowintoacasino’sdatashouldallowuserstoutilizeanyoneofthefollowingfourdifferenttypesofanalytics,including:

• Descriptiveanalytics—Whathappened?• Diagnosticanalytics—Whydidithappen?• Predictiveanalytics—Whatcouldhappen?• Prescriptiveanalytics—Whatshouldhappen?

Figure2showstheAnalyticValueEscalator,whichexplainshowthesefourtypesofanalyticsprocessescanbeused.

Figure2:AnalyticsValueEscalator

Foracasinocompany,descriptive,predictiveandprescriptiveanalyticscanbeusedtouncoverwhathashappened,why ithappened,whatcould,potentially,happenandwhatshouldhappen(seeFigure3formore use cases). Descriptive analytics could include pattern discovery methods such as customersegmentation,i.e.,cullingthroughapatrondatabasetounderstandapatron’spreferredgameofchoice.Simpleclustersegmentationmodelscoulddividecustomersintotheirpreferredchoiceofgamesandthisinformationcanbegiventothemarketingdepartmenttocreate listsofbaccaratplayers forabaccarattournament,forexample.

ANALYTICVALUEESCALATOR

VALU

E

DESCRIPTIVE

ANALYTICS

Whathappened?

DIANOGSTIC

ANALYTICS

ANALYTICS

Whatshouldhappen?

UsingalgorithimstooptimizesuchthingsasTableGamesRevenueManagement,hotelroomrates,slotflooroptimiztion,andlaborutilization

MiningdatatodeterminewhatcausedaspikeinWebtrafficoverthepastmonth

Usingalgorithmsanddatatopredictwhichcasinopatronsaremostlikelytouseaparticularoffer

UsingGoogleAnalyticstotrackwebsitetraffic,suchaspageviewsandnumberofvisitors

Whydidithappen?

Whatcouldhappen?

OPTIM

IZATION

ANALYTICS

PREDICTIVE

PRESCRIPTIVE

VALU

E

INFORMATION

VALU

E

DIFFICULTY


Table2UnderstandingAnalyticsSource:Descriptive,predictive,prescriptive:TransformingassetandfacilitiesmanagementwithanalyticsIBM2

Marketbasketanalysis,whichutilizesassociationrules,wouldalsobeconsideredadescriptiveanalyticstechnique.Casinoscanusemarketbasketanalysistobundleandofferpromotions,aswellasgaininsightintogaming,shoppingandprettymuchanykindofpurchasingbehavior.

Diagnosticanalyticsisaformofadvanceanalyticsthatexaminesdataorcontenttoanswerthequestion,“Whydid it happen?”Diagnostic analytics attempts to understand causation andbehaviors byutilizingsuchtechniquesasdrill-down,datadiscovery,dataminingandcorrelations.Buildingadecisiontreeatopawebuser’sclickstreambehaviorpatternscouldbeconsideredaformofdiagnosticanalytics.

AccordingtoCharlesNyce(2007),“Predictiveanalytics isabroadtermdescribingavarietyofstatisticalandanalytical techniquesused todevelopmodels thatpredict futureeventsorbehaviors. The formofthese predictive models varies, depending on the behavior or event that they are predicting. Mostpredictivemodelsgenerateascore(acreditscore,forexample),withahigherscore indicatingahigherlikelihoodofthegivenbehaviororeventoccurring.”

Datamining,whichisusedtoidentifytrends,patterns,and/orrelationshipswithinthedata,canthenbeused todevelopapredictivemodel (Nyce,2007).Predictionof futureevents is thekeyhereand theseanalysescanbeusedinamultitudeofways,includingforecastingcustomerbehaviorthatcouldleadtoacompetitive advantage over rivals. Gut instinct can sometimes punch you in the gut and predictiveanalytics can help factor in variables that are inaccessible to the human eye or intellect. Often theunknowneventofinterestisinthefuture,butpredictiveanalyticscanbeappliedtoanytypeofunknown,whetherthatisinthepast,thepresentand/orthefuture.

Predictiveanalyticsusesmanytechniquesfromdataminingtoanalyzecurrentdatatomakepredictionsaboutthefuture,includingstatistics,modeling,machinelearning,andartificialintelligence.Forexample,logistic regression can be used to turn amarket basket analysis into a predictor so that a casino canunderstandwhatitemsareusuallypurchasedtogether.Ofcourse,theoldbeeranddiapersmarketbasketstorywouldn’tfitforacasino,butgleaningdatafromthecasinofloorcouldrevealsecondfavoritegamesthatpatronsliketoplay.Thiscouldbeusefulinformationwhenapatronishavingarunofbadluckonhis

2https://static.ibmserviceengage.com/TIW14162USEN.PDF

Whatthe

userneeds

toDO

Whatthe

userneeds

toKNOW

How

analytics

gets

ANSWERS

Whatmakes

thisanalysis

POSSIBLE

UNDERSTANDINGANALYTICS

•Increaseassetreliability

•Reducelaborandinventorycosts

•Predictinfrastructurefailures

•Forecastfacilitiesspacedemands

Definitions,sampleapplicationsandopportunities,andunderlyingtechniques

Descriptive

WhatHAShappened?

Predictive Perscriptive

•Alerts,reports,dashboards,business

intelligence

•Predictivemodels,forecasts,

statisticalanalysis,scoring•Businessrules,organizationmodels,

comparisons,optimization

•Increaseassetutilization

•Optimizeresourceschedules

WhatCOULDhappen? WhatSHOULDhappen?

•Howtoincreaseassetproduction

•Wheretooptimallyrouteservice

technicians

•Whichstrategicfacilitiesplan

providesthehighestlong-term

utilization

•Howtoanticipatefailuresfor

specificassettypes

•Whentoconsolidateunderutilized

facilities

•Howtodeterminecoststoimprove

servicelevels

•Thenumberandtypesofasset

failures

•Whymaintenancecostsarehigh

•Thevalueofthematerialsinventory

•Standardreporting-What

happened?

•Query/drilldown-Whereexactlyis

theproblem?

•Adhocreporting-Howmany,how

often,where?

•Predictivemodeling-Whatwill

happennext?

•Forecasting-Whatifthesetrends

continue?

•Simulation-whatcouldhappen?

•Alerts-whatactionsareneeded?

•Optimization-Whatisthebest

possibleoutcome?

•Randomvariableoptimization-

Whatisthebestoutcomegiventhe

variabilityinspecifiedareas?


or her favorite game. Perhaps amarketingoffer for a gameheor she sometimesdabbles inwouldbeappreciatedratherthananofferonhisorherfavoritegame,asthatmightnotbeseeninsuchapositivelightwhilethepatronisinthemidstofalosingrun.

For a casino company, predictive analytics can also be used for CRM, collection analysis, cross-sell,customer retention, direct marketing, fraud detection, product prediction, project risk management,amongstotherthings.

Prescriptive analytics tries to optimize a key metric, such as profit, by not only anticipating what willhappen, but also when it will happen and why it is happening. Furthermore, “prescriptive analyticssuggestsdecisionoptionsonhowtotakeadvantageofafutureopportunityormitigateafutureriskandshowstheimplicationofeachdecisionoption.Prescriptiveanalyticscancontinuallytakeinnewdatatore-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing betterdecisionoptions”(Wikipedia).

Prescriptive analytics can ingest a mixture of structured, unstructured, and semi-structured data, andutilize business rules that can predict what lies ahead, as well as advise how to exploit this predictedfuture without compromising other priorities. Stream processing can add an entirely new element toprescriptiveanalyticsaswell.

SocialMediaMonitoringandAnalyticsSocialtechnologiesallowindividualstointeractwithlargegroupsofpeopleatalmostanylocationintheworld,atanytimeoftheday,atmarginal,ifnotnocostatall(Chuietal.,pg.5,2012).Withadvantageslikethese,itisnotsurprisingthatsocialmediahasbecomesowidespreadthatalmostoneinfourpeopleworldwideuseit.Itisactuallysurprisingthatthefigureissolow.

Businessesarequicklyrecognizingthepowerofsocialmedia.“Thousandsofcompanieshavefoundthatsocial technologies can generate rich new forms of consumer insights—at lower cost and faster thanconventional methods” (Chui et al., pg. 2, 2012). In addition to this, businesses can watch what“consumers do and say to one another on social platforms, which provide unfiltered feedback andbehavioraldata(i.e.,dopeoplewho“like”thismoviealso“like”thisbrandofvodka?).”

The power of socialmediamarketing really comes to lightwhen one compares it to broadcasting andtelephoneandemailmarketingmodels.IntheirbookMarketingCommunications:IntegratingOfflineandOnlinewithSocialMedia,P.R.SmithandZeZook(2011)showjusthowpowerfulsocialmediamarketingcan be. Smith and Zook (2011) looked at the target audiences for three different types of marketingplatforms—broadcastnetwork,telephoneandemailnetwork,andsocialmedia.

AccordingtoSmithandZook(2011),“Broadcastnetworkisbasedona‘onetomany’model(e.g.,oldTVadvertising). It is the Sarnoff network (after David Sarnoff, the broadcasting legend). A hypotheticalSarnoffnetworkwith20viewershasascoreof20.Thenetworkscoreissimplythenumberofnodes(i.e.,audiencemembers)”andthisequatestoapaltrysumoftwentyindividuals.

Thetelephoneandemailnetwork isbasedontheMetcalfmodel (namedafterBobMetcalf,oneof theinventorsoftheInternet)andthis isa“manytoeachother”model(SmithandZook,2011).Thismodelallowseveryoneinthegrouptoconnectwitheveryoneelse(SmithandZook,2011).Becauseanymemberofthegroupcancontactanyoneelseinthegroup,thetotalnumberofpotentialcontactsis20squared,or400(SmithandZook,2011).Obviously,thisisamuchmorepowerfulcommunicationmodelthantheSarnoffmodelasthenetworkscoreisthenodenumbertothepowerof2,whichis400.Agoodnumber,butitstillpalesincomparisontothesocialnetworkmodel.

NamedafterDavidReed (whonoticed thatpeople in social situationsusuallybelong tomore than justone network), the social networkmodel is a “many belong to numerous networks”model (Smith andZook,2011).“ThepossiblevalueofaReednetworkistwotothepowerofthenumberofnodesonthenetwork,”explainSmithandZook(2011).Ifyoutakethesamegroupof20peopleinasocialsituation,a“Reednetworkgeneratesascoreof2tothepowerofthenode”(SmithandZook,2011),whichgenerates


a network score of over onemillion people; obviously, this is a number exponentially higher than thenumberofpeople reachedby theSarnoffandMetcalfmodels.This is thepowerof socialmediaand itcannot be underestimated.When coupledwithmobile, that number can be even greater and, just asimportantly,thereachcanbelightningfast.

Social media analytics is “the practice of gathering data from blogs and social media websites andanalyzing that data to make business decisions. The most common use of social media is to minecustomersentiment”(Rouse).

SocialmediaanalyticsevolvedoutofthedisciplinesofSocialNetworkAnalysis,MachineLearning,DataMining,InformationRetrieval(IR),andNaturalLanguageProcessing(NLP)(MelvilleandLawrence,2009).It isa“powerfultoolforuncoveringcustomersentimentdispersedacrosscountlessonlinesources.ThisanalysisisoftencalledSocialMediaListeningorOnlineListening.Theanalyticsallowmarketerstoidentifysentimentand identify trends inorder tobettermeet their customer’sneeds” (Wikipedia).As Figure3shows,thereareamultitudeofusecasesforsocialmedialistening.

Figure3:SocialMediaListening

Acasinocanuseadatalakeandpredictivetechnologytoanswersuchquestionsas:

• Howcanacasinomeasurethebenefitsofsocialmedia?• Howshouldacasinoorganizeitssocialmediapresenceorpresences?• Howshouldacasinospreadsocialmediausagethroughoutitsorganization?• Howhassocialmediachangedtherelationshipbetweenacustomerandacasino?

Casinoscanalsousesocialmediainthefollowingways:

• AddinginteractivitytoaWebsite• BrandandAnti-brandmanagement• Brandloyaltyenhancement• Buildingfanbases• Crisismanagement• Discoveringimportantbrandtrends• DrivingtraffictoaWebsite• Engagingcustomersandpotentialcustomers• Harvestingcustomerfeedback• Marketingtoconsumers


• Reputationmanagement• Socialshopping

Web2.0changedtherelationshipbetweenacompanyand itsconsumersforever. Inshiftingthepowerawayfromthecorporationandintothehandsoftheconsumer,Web2.0empoweredtheconsumerwitha voice unheard of in the history of advertising. The voice of the online consumer is now part of themessage and consumers are fully vested participants in the conversation (Outing, 2007). This factshouldn’tfrightencasinoexecutives,itshouldactuallyexcitethem.

Acasinocompanyisonlyasstrongasitsweakestcustomerrelationshipandmobileandsocialmediaareproving to be highly valuable marketing tools because they create a two-way relationship between acompany and its consumers. This dialogue can help a casino property in amyriad ofmarketingways,includingprovidinganeasyavenueforuserstoopt-intocompanyCRMdatabases.Sincetheseconsumershaveimplicitlyagreedtoreceiveacompany’smarketingmaterial,theyareusuallythetypeofconsumersmarketers pay a lot ofmoney to reach because they are knowledgeable, empowered and,more oftenthannot,highlymotivatedtobuy.

WithasimpleTweetDeckfeed,acasinocanhaveareal-timefeedonitsTwitteraccounts,utilizingtheminwaysthatcould improveCRM,customer intelligence,and,potentially,security.Allowingusers to tweetsecurity issues they might find throughout the property is advantageous because these tweets couldincludegeo-locationdata.HotelreservationscouldalsowatchtheTwitterfeedsforcustomerswantingtoknowaboutroomrates.Concertgoersmightalsowanttoknowwhatticketsareavailableforanupcomingconcert. There are amultitude of otherways that a creative casino could utilize Twitter,Weibo and awholehostofothersocialplatforms.

“Analyticsarecriticalforenablingorganizationstomaketherightdecisionsaboutwhen,where,andhowto participate in socialmedia. It isn’t enough to just listen; organizations much insert themselves andbecomepartoftheconversation,”arguesStodder(2012).Smartcompanieswillstartviralcampaigns,forexample,usingTwitterHashtags fora topic; thecampaigncouldbeacomponentofa largermarketingstrategy” (Stodder, 2012). In Macau and/or China, Weibo and WeChat can also be used to seedconversations.Thecasinocould“thenmonitorsocialmediatoseewhatpeoplesayandanalyzehowthecampaignisplayingamonginfluencersandacrossnetworks,”Stodder(2012)recommends.

TheCustomerJourney

Pre-ArrivalThecustomerjourneystartswaybeforethecustomerentersthecasino.Itbeginsthemomentacustomernoticesanadvertisementforthecasinoand/orintegratedresort,whetherthatadvertisementisonTV,onawebsiteoronabillboard.Itcanbewhilebrowsingthecasino’swebsite,connectingwithitssocialmediaaccounts,oreventhemomentthecustomeractuallyentersthecasino.Fromcheckingintothehotel,togambling (or learning how to gamble), to eating in the IR’s restaurants, drinking in its bars, enjoying ashoworanevent,throughtothemomentthepatron leaves.Throughoutthiscustomer journey,adatalakecanhelpcollect,analyze,visualizeandlivestreamrecommendationcontent.

ClickstreamAnalysisWhenapersonsurfsawebsite,heorsheleavesbehindadigitaltrail.Clickstreamanalysisistheprocessof collecting, aggregating, reporting and analyzing the browsing behavior of a web surfer to betterunderstand the intentions of users and their interests in specific content or products on a website.Clickstreamanalysis(alsocalledclickstreamanalytics)istheprocessofcollecting,analyzingandreportingaggregatedataaboutwhichpagesawebsitevisitorvisits—andinwhatorder.Thepaththevisitortakesthoughawebsiteis,basically,theclickstream.

Thereare two levelsofclickstreamanalysis, trafficanalyticsande-commerceanalytics.Trafficanalyticsoperatesat theserver leveland trackshowmanypagesareserved to theuser,how long it takeseachpagetoload,howoftentheuserhitsthebrowser'sbackorstopbuttonandhowmuchdataistransmitted


before the user moves away from the website. E-commerce-based analysis uses clickstream data todetermine the effectiveness of awebsite as a channel-to-market. It is concernedwithwhat pages thebrowser lingerson,whatheor sheputs inor takesoutof a shopping cart,what itemsarepurchased,whether or not the buyer belongs to a loyalty program and uses a coupon code, aswell as his or herpreferredmethodsofpayment.

Utilizing clickstream analysis, a casino can help build aMasterMarketing Record for each customer inreal-time. This allows the casino to test scenarios and options for the website, as well as developpersonalized responses for individuals. The system should include a combination of social listening,analytics,contentpublicationanddistribution,andtracking,aswellasastrongworkflowandrulesenginethat isgearedaroundstronggovernance.Allof theseapplicationsarebuilt toultimately feedaMasterMarketingProfile—acentralizedcustomerrecordthatpullsinalldatabasedondigitalactivitythatcanbeidentified by a single customer ID. Through this clickstream analytics, personalization marketing canbegin,andassociatingthisactivitywithacustomeronceheorshewalksthroughthefrontdoorshouldbeacasino’sprimarygoal.Thiscanbedonebyenablingnewuserstologintohisorheraccountviawebormobileapplicationslikeacasino’sWeChatapp.

PersonalizationToday,“Personalization”—theprocessofutilizinggeo-location,mobileapp,Wi-Fi,andOTTtechnologytotailormessagesorexperiencestoanindividualinteractingwiththem—isbecomingtheoptimumwordinaradicallynewcustomer intelligenceenvironment.Eventhoughthispersonalizationcomesataprice—privacy—it isapricemostconsumersseemmorethanwillingtopay ifarecognizedvalue isreceived inreturn.Fortheretailer(and,potentially,theintegratedresort),“personalization”requiresaninvestmentin software analytics, but retailers should recognize that this price must be paid because highlysophisticated consumers will soon need an exceptional shopping experience to keep them frompurchasing products online, or at another retail establishment (that will, undoubtedly, offer suchservices).

To compete in this highlycompetitive industry, casinocompanies are recognizing theimportance of personalizationwhen it comes to customerinteractions. Most casinos todayhave customer loyalty programsthatareapartofaCRMand/oraSCRM initiative to provide theirguestswithanintimateexperiencethat will make them want toreturn again and again and again.Mobile and socialmedia channelsare some of the best ways toreachthesecustomers.

Currently, there is an bigdisconnect between whatcompanies think they aredelivering in terms ofpersonalization and whatconsumersareexperiencing.Inhisarticle Study finds marketers areprioritizing personalization… butare further behind then theyrealize, Andrew Jones (2015)

Figure4:PersonalizationthroughoutthecustomerjourneySource: VB Insight, Marketing Personalization: MaximizingRelevanceandRevenue


argues that, “Although two-thirds of themarketers surveyed rate their personalization efforts as ‘verygood’ or ‘excellent,’ just 31 percent of consumers reported that companies are consistently deliveringpersonalizedexperiences.”3

“Aside fromthisdisparity, thereport finds thatpersonalizationstrategies todayare immature. It showsthat91percentofthemarketerssurveyedareprioritizingpersonalizationoverthecomingyear,yetmanystill rely on basic segmentation strategies,” Jones (2015) notes. This isn’t that surprising as manycompanies are struggling with the ability to not just capture the information necessary forpersonalization,butalsocreatingDWsthatcansilothedataproperly,thendeliveringittohighlycomplexanalyticalprogramsthatcanmakesenseofallthatdata.It’s likefindinganeedleinahaystackforeachandeverycustomerinadatabase;aherculeantask,nodoubt.

It isobviousthatacreatingaconsolidatedcustomerview isanecessarycomponentofpersonalization,but,unfortunately,“mostmarketerstodayareworkingwithcustomerdatathatisdecentralized,spreadacross the organization in multiple databases that are updated in batch processes. To find success,marketersmustprioritizeconsolidatingdataintoasingledatabase,”statesJones(2015)andthisiswhereadatalakecomesin.

Another important step to bringing personalization efforts up to a user’s expectation will be usingbehavioraldata.“Inordertocreatethesetypesof[personalized]customerexperiences,marketersmuststrategicallycollectandutilizecustomerdata,includingreal-timesignalsofintent,whicharetypicallynotcapturedtoday,”arguesJones(2015).Figure5listsouttheidentity-relateddatasourcesthatcanbeusedfor personalization and it is a considerable amount of data that must be culled through, siloed, andunderstood.

Figure5:Identity-relateddatasourcesusedforpersonalizationSource:VBInsights4

3 http://venturebeat.com/2015/12/14/study-finds-marketers-are-prioritizing-personalization-but-are-further-behind-than-they-realize/(accessed26November2016)


With customers attitudes towards personalized content being shaped by recommendation engines likeAmazon,Pandora, andNetflix, consumersarebecomingmoreused to receivingwhat theywant,whentheywant it, onwhatever channel theywant it on (Jones, 2015).Casinosand integrated resortsmustkeep this in mind when developing personalization programs. The consumer has become highlysophisticated and he expects the level of sophistication he receives on platforms like Amazon andPandora,don’twastehistimewithnon-matchingoffersorhewillgodownthestreettoacompetitor’sproperty.

ArrivalandStayOnceacustomershasarrivedinMacau,LasVegas,Manila,oranycasinocity,heorshewillleaveatrialthatcanbepickedup inamultitudeofways, includingviasocialmedia,geo-fencingapplications, facialrecognitiontechnology,simplybyacheck-inatthehotel,oraswipeofapatroncardinaslotmachineorat a casino table. The following technology can spot, track and help a casino better personalize acustomer’svisittoacasinoproperty.

GeofencingApplicationsToday,most smartphoneshavegeofencingcapabilities,whichare featuresofa softwareprogramthattapintoGPSorRFIDtechnologytodefinegeographicalboundaries.Basically,geofencingprogramsallowan administrator to set up triggers—usually SMS push notifications or email alerts—sowhen a devicecrosses a “geofence” and enters (or exits) a set boundary, a user is notified (TechTarget, 2011).ApplicationssuchasFoursquareandChina’sJiepangusegeofencingtolocateusers,aswellashelpthemfindtheirfriendsand/orcheckintoplaces.

Perhapsoneof thebestusesof location-basedservices is in theMeetings, Incentive,ConferencingandExhibition(MICE)space.ThemassivesizeofsomeIRexhibitionhallscanmakefindingaparticularboothor floor section a daunting proposition. Indoor mobile communication technology with locationawareness technologycanhelp conference-goersnavigateavast conference floor (Giaglisetal., 2002).Also, before arriving at a conference, amobileuserwouldbeable to registerhis personal preferencesand,onceheenterstheexhibitionhall,aroutemapwouldbesenttohisorhermobilephone.Vendorappointmentscouldevenbesetupsothattheyarelocatedneareachothersothattheconference-goerwouldn’t have to run around frantically trying to make meetings that are spread out all over theconventionfloor(Giaglisetal.,2002).

FacialRecognitionRapidadvancements infacial-recognitiontechnologyhavereachedthepointwhereasinglefacecanbecompared against 36million others in just one second.A systemmadebyHitachi Kokusai Electric andreportedbyDigInfoTVshownatasecuritytradeshowrecentlywasabletoachievethisblazingspeedbynotwastingtimeonimageprocessing—ittakesvisualdatadirectlyfromthecameratocomparethefacein real time.Thesoftwarealsogroups faceswith similar features, so it'sable tonarrowdown the fieldveryquickly.Theusefulnesstothecasino’ssecurityenforcementisprettyobvious.

AugmentedRealityTarunWadhwa(2013)statesthataugmentedrealityworksby“displayinglayersofcomputer-generatedinformationontopofaviewofthephysicalworld.”Itis“atechnologythatalterstheperceptionofrealitybydistortingit,allowingescapefromit,andenhancingit—allatthesametime”(Wadhwa,2013).

4 http://insight.venturebeat.com/report/marketing-personalization-maximizing-relevance-and-revenue?utm_source=vb&utm_medium=refer&utm_content=editorial-post&utm_campaign=personalization-report(accessed26November2016)


AnalystsnowpredictthattheARmarketwillgrowfromroughly$181millioninrevenuesin2011tonearly$5.2billionby2016.56By2017,morethan2.5billionmobileaugmentedrealityappswillbedownloadedtosmartphonesandtabletsannually;3.5timesthenumberofAngryBirdsdownloadsin2011.Anticipatedmarket growth and trending investments have led theHarvardBusiness Review to predict thatARwillsoonhaveanimpactoneverythingfromadvertising,tolocationservices,tohealthcare,torelationships,totheverynatureofknowledge”(Deloitte,2013).

AccordingtoitspressreleaseGartnerSaysAugmentedRealityWillBecomeanImportantWorkplaceTool,“Augmented reality is the real-time use of information in the form of text, graphics, audio and othervirtualenhancementsintegratedwithreal-worldobjects.”TuongHuyNguyen,principalresearchanalystat Gartner, states that, “AR leverages and optimizes the use of other technologies such as mobility,location, 3D content management and imaging and recognition. It is especially useful in the mobileenvironmentbecause it enhances theuser's senses viadigital instruments to allow faster responsesordecision-making”(Gartner,2014).APokemonGo! typeofappthatcontainedproperty-wide3Dcontentcould help patrons navigate through a large integrated resorts, which could cut down on the timegamblerstaketoreachtheirgameofchoice,whetherit’saslotmachineoratablegame.

MobileadvertisingSuccessful mobile advertising requires three things—reach, purity and analytics. Analytics “involvesmatchingusers’interests–implicitandexplicit,context,preferences,networkandhandsetconditions—to

adsandpromotionsinrealtime”(Sharmaetal.,2008). Stuart Elliot, a columnist for the NewYorkTimes states that,“If the20thCenturywasknown in marketing circles as the advertisingcentury,the21stCenturymaybetheadvertisingmeasurement century. Marketers areincreasingly focused on the effectiveness oftheirpitches, trying to figureout the returnoninvestment for ad spending” (Elliot, 2007).Because of the unique nature of the mobileuser, marketers now have much morepowerful–and effective–ways to measure thesuccessoftheiradvertisingcampaignsaswell.

By assigning a digital fingerprint to a particularmobile user, casinos can know in real-timeexactlywhatthatuserisclickingon.AperFigure6,auser’s IPaddresscouldbethefirstpointofcontact,withtheirdeviceIDandusercookieIDthe second and third. From there, a casinoshouldaimtogettheirSocialID,hisorheremailaddress, which will reveal their social activityandpotential e-commerce activity,which for a

5“ResearchandMarkets:GlobalAugmentedRealityMarketForecastbyProductforGaming,Automotive,Medical,Advertisement,Defense,E-Learning&GPSApplications—ExpectedtoGrowto$5,155.92Millionby2016,”BusinessWire,December7,2011,http://www.businesswire.com/news/home/20111207005613/en/Research-Markets-Global-Augmented-Reality-Market-Forecast(Retrieved:17November2016)6JuniperResearch,“PressRelease:Over2.5BillionMobileAugmentedRealityAppstoBeInstalledPerAnnumby2017,”August29,2012,http://www.juniperresearch.com/viewpressrelease.php?pr=334(Retrieved:17November2016)

Figure6:IdentityFunnel:AnonymoustoKnownSource:VBInsights


casinowouldprobablyonlybepurchasesof such thingsashotel rooms.Thiswouldbeagreat startingpoint for a personal profile that could then be augmented with patron gambling and on propertypurchasingbehavior.

Inthismobileadvertisingworld,patronswouldbeseenasparticipantsratherthanasa“targetaudience.”Withblogs,vlogs,socialnetworks,messageboards,andviralmessaging,advertisersarenowfacedwitharadicallydifferentmarketinglandscapethantheywerejustafewshortyearsago.Thisisanewlandscape,alandscapeinwhichcasinomarketersarenolongerincompletecontrolofthemessage,anditisaworldthatmustbeembracedandnotfeared.

Mobileadvertisingcanrevolutionizethewaycasinosconnectwiththeircustomers.Amulti-screen,alwaysonstrategythatfollowsamarketer’saudiencethroughouthisorherdigitaldayshouldbedeveloped.Thetechnology’s inherent ability to create a highly personal, instant two-way connection betweenpurchaser—or potential purchaser—and seller is something that has never been seen before in thehistoryofcommerce.

e-CommerceWitha fewclickstrokes,acasino’secommercedepartmentcancreateaclickpathanalysis thatrevealscustomerinteractionsonthecasino’sseveraldifferentwebsites.Descriptiveanalyticalfunctionalitiescanthenprovideadeeperunderstandingofthecustomerjourney.Columndependencies(standardinmostoftoday’s Data Integration software tools) can visually display the strength of a relationship betweenattributeswithinanydataset.Thishelpsusersbetterunderstandthecharacteristicsof theirdataand isoftenusedtohelptargetfurtheranalytics.

Columndependencies canhighlight relationshipsbetween job titleandpurchaseamount, age, locationandproductselection,transactiontypeandfrequency.Decisiontreescanbebuilttoautomaticallyhelpusersunderstandwhatcombinationofdataattributesresult inadesiredoutcome.Thestructureofthedecisiontreereflectsthestructurethatispossiblyhiddeninacasino’sdata.

Arecommendationenginecanalsopredictaperson’sinterestbasedonhistoricaldatafrommanyusers.This is useful in increasing client engagement, recommending more relevant choices and increasingcustomer satisfaction. For example, recommendations can predict interest in casino games, products,hotelrooms,andservices.

RetailEdgeAnalyticsBrick-and-mortarstoresarelookingforanycompetitiveadvantagetheycangetoverweb-basedretailers,andnear-instantedgeanalytics—wheresalesdata,images,couponsused,trafficpatterns,andvideosarecreated—provides unprecedented insights into consumer behavior. This intelligence can help a retailerbettertargetmerchandise,sales,andpromotionsandhelpredesignstorelayoutsandproductplacementto improvethecustomerexperience.Onewaythis isaccomplished is throughuseofedgedevicessuchasbeacons,whichcancollectinformationsuchastransactionhistoryfromacustomer’ssmartphone,thentarget promotions and sales items as customers walk through the store. As Integrated Resorts havebecomemassiveshoppingmalls,thisisaservicethattheIRcanoffertoitsretailclients.

SocialCommerceTheB2CmarketingcloudcompanyEmarsysbelievesthatsocialcommercewillsoonbecomeamainstaychannelforconsumers.Althoughmanyretailersdon’tknowexactlywhattodowithsocialcommerceyetthe social channels are boldly leading the way. In 2015, Pinterest launched the ‘Buy it’ button andInstagramcreatedanexpandedadprogramcalled‘InstagramAds.’Atthesametime,othersocialmediachannelssuchasTwitterandYouTubeareexploring‘BuyItNow’featuresandamultitudeofmobileappsarepoppingupthatalloweasye-commercepurchases.Andwhynot?ItmakescompletesensetosimplifythepurchasingprocessandFacebook isalready jumping into the frayasanewservice insideFacebookMessenger allows users to readily swap money with others. Casinos should embrace this bold new


frontier and a data lake would simplify the purchasing process, as well as the purchasing behaviorcollectionprocess.

A recentMarketingWeek article entitled Social commerce: Howwilling are consumers to buy throughsocialmedia?Includesthefollowinginterestingstatistics:

Morethanhalf(56%)ofconsumerswhofollowbrandsonsocialmediasitessaytheydosotoviewproducts.Dubbed‘socialshoppers’,researchshowsthattheseindividualsvisitsocialnetworks as part of their everyday shopping behavior, and use images that they see onsocialmediasitestoinspirepurchases.Nearlyathirdofonlineshoppers(31%)saytheyareusingthesechannelstobrowsefornewitemstobuy,Facebookisthemostpopularnetworkpeopleareusing(26%),followedbyInstagram(8%)andPinterest(6%).

Aspreviouslymentioned, integrated resortshavebecomemassive retail outlets and casino resorts canturn store windows into 24/7 retail platforms via technologies like interactive touch screens and QRcodes.AccordingtotheJWT(2012),“Inlate2012,PayPalrolledoutatestinAmsterdam’sDe9Straatjesshopping district in which retailers posted QR codes on their storefronts, enabling shoppers with thecampaign’smobileapptoscanthecodeforpurchaselinkstoproductsinthewindow.”

InGermany, shoppersoutsideanAdidasNEOLabel store “weredirected toamobileURL that linkedasmartphonewithashoppingbagonthewindow;consumerscouldthendragproducts intothebagandbuythemorsavethemforlaterpurchase”(JWT,2012).

Today,whenaconsumeruseshisorhermobiledevicetotakeapictureofaQRCodeinanad,onastoreshelf,offaTVorachocolatebar,orinahundredotherplaces,heorshegreatlyenhancesthemarketingcompany’soff-linemarketingstrategybecauseanassociationbetweentheprintadvertisementandthetransaction that follows is created (Sharma et al., 2008). This is data that can be added to a patron’spurchasingbehavior,whichcanalsobecomeintelligenceforamarketingcampaign.

Itemspurchasedthiswaycouldbedelivereddirectlytoabuyer’shomeaddress,whichwouldreducetheneed to stock up on so many items and the consumer would probably like this feature as he or shewouldn’thavetolugtheitemhomeafteratriptoanintegratedresort.

Snapchatcouldalsobeaninterestingplatformtoselloutlastminutehotelrooms.IfBetfaircanteamupwith Snapchat to sell bets on premier league games, why can’t Integrated Resorts utilize the sametechnologytoselllast-minuterooms?

DepartureOncethepatronleavestheproperty,themarketingcyclebeginsanew.RFMmodelscanprojectthetimeat which a patron is likely to return and social media should be checked for any comments, likes oruploads.Allofapatron’scapturedinformationcannowbecomepartoftheMasterMarketingProfilethatwillbethebasisforfuturemarketingefforts.Combingthedaily,weeklyandmonthlyMasterMarketingProfileswillalsoallowthecasinotodevelopinsightfulmacroviewsofitsdata,viewsthatcouldhelpwithfacilities,labormanagementandvendorneeds

FacilitiesandLaborManagementBydefinition, a data lake is amassive, intermingled repository of all of a company’s data in its rawestform. By understanding customer patterns and behavior across thewhole spectrum of the business—from outside marketing activities, to patron pickup, to patron navigation throughout the property, toroomandrestaurantchoice,toapatron’sbuyingandgamblinghabits,toallpatronincidentalscharges,tofinal car or bus ride home—a casino can use these customer behavior patterns tomap out its humancapitalneeds,allthewaydowntotheseatonthebusthattakesadealerhomeattheendofashift.

As facilities and assets become more IT-like—instrumented, intelligent and interconnected—theconvergenceofphysicalanddigitalinfrastructuresmakestheirmanagementincreasinglycomplex.Andina physical world outfitted with millions of networked sensors, vast amounts of facilities- and asset-


generateddatamakeextractingmeaning increasinglydifficult. To takeadvantageof theopportunity totransform facilities and asset management, organizations need analytics capabilities that can identifyoperatinganomaliesinrealtime,aswellaspredictoutcomesanddeliveroptimizationmodels.

Organizations need analytics solutions that can extract meaning from huge volumes of data to helpimprove decisionmaking, handlewide varieties of data and data sources fromwithin and outside theenterprise, and keepupwith the rapid velocity of data inmotion. They need capabilities for analyzinghistorical and real-time data, as well as forecasting for the future, to distill what’s valuable, detectpatternsandrevealinsightstheymaynotevenhavethoughttoaskabout.Withsuchsolutions,theycanachieve benefits ranging from increased revenue to lowered operating expenses, enhanced serviceavailabilityandreducedrisk.

Thefollowingtechnologywillproducedatathatcouldbeincludedinadatalakethatwillhelpoptimizeacasinoorintegratedresort.

AugmentedRealityGartner (2014) believes “AR technology has matured to a point where organizations can use it as aninternaltooltocomplementandenhancebusinessprocesses,workflowsandemployeetraining.”Gartner(2014) also believes that “AR facilitates business innovation by enabling real-time decision-makingthroughvirtualprototypingandvisualizationofcontent”(Gartner,2014).

WearableARdevicescan“allowuserstoaccessstandardizedsetsof instructionsforaparticulartask inrealtime,triggeredbyenvironmentalfactorsandoverlaidontheuser’sfieldofvision”(Deloitte,2013).Researchhasshownthatoverlaying3Dinstructionsoverareal-lifeprocesscanreducetheerrorrateforanassembly taskby82percent,withaparticularlystrong impactoncumulativeerrorsduetopreviousassemblymistakes(Deloitte,2013).Thismaynotworkonthecasinofloor,wherehosts,dealersandpitbosses have to interact with patrons, but it certainly can be useful in physical plant and propertymanagementdepartments.

“AR allows for improved senses and memory through the capture and enhancement of the user’sperspective.Byrecordingvideo/audio,capturingimagesandremovingelementsthatobscurethesenses,AR technology allowsusers’ eyes to act as cameras, and canenhance the senses inwaysnot availablenaturally,suchasnightvisionortheabilitytozoominonfar-awayobjects”(Deloitte,2013).

AR’s benefits include the “potential to improve productivity, provide hands-on experience, simplifycurrent processes, increase available information, provide real-time access to data, offer newways tovisualizeproblemsandsolutions,andenhancecollaboration”(Deloitte,2014).

Geo-fencingAsTechTarget(2011)explains,geofencinghasmanyuses,including:

• Assetmanagement—AnRFIDtagonapalletcansendanalertifthepalletisremovedfromthewarehousewithoutauthorization

• Compliancemanagement—Networklogsrecordgeo-fencecrossingstodocumenttheproperuseofdevicesandtheircompliancewithestablishedrules

• Fleetmanagement—whenatruckdriverbreaksfromhisroute,thedispatcherreceivesanalert• Human resourcemanagement—An employee’s smart card will send an alert to security if an

employeeattemptstoenteranunauthorizedarea• Marketing—A retail business can trigger a text message to an opted-in customer when the

customerentersadefinedgeographicalarea.

For indoor location-basedservices suchas “libraries,museums,exhibitions,andhypermarkets, locationawarenesscanbecomeacrucialdeterminantofasuperiorqualityofservice,derivingfromtheabilitytolocatetheco-ordinatesofapersonoranobjectwithreasonableaccuracyatalltimes,andthusprovidespatially-aware services” (Giaglis et al., 2002). Forexample, location-based services canhelp customers


whoareshoppinginasupermarketoratahypermarket;byregisteringashoppinglistbeforeshearrivesatthestore,ashoppercan,uponenteringthestore,receiveasuggestedroutethatwillguidehertotheproductsshewants(Giaglisetal.,2002).Thishelpsnotonlytheshopperbutalsotheretailerbecauseitwillknowtheexactpositionsoftheircustomersatalltimes,whichcanhelpwithworkforceallocationandshelfreplenishment(Giaglisetal.,2002).Customerswon’tlingerinastoreaslongastheynormallywouldeitherbecausetheywillknowexactlywhereeveryproducttheywanttopurchaseislocated,meaningthestore would probably be less crowded than it would be otherwise. With some casinos today beingmassive integrated resorts, indoor location-based services can help patrons navigate through anenormouspropertytothespecificslotmachinetheymightwanttoplay.Withlocation-awaretechnology,casinoscaneasilycreatepersonalguidedtoursforvisitors.

SecurityAdatalakewouldbeusefulforthefollowingsystemsandfraudulentbehavior:

• Angel Eye—In Asian casinos, card switching is a serious problem—cheaters bring speciallymarkedcardsfromhomeandreplacethemoverthecourseofseveralgamesthroughsleightofhand. Angel Eye is a customized baccarat-dealing shoe that reads invisible ink printed on thebacksofthecardsbeforethey’redealt.Itthenrecordsthedealandshowsthedealer—meaningthat if a player pulls a switch, it won’t match up with what the Angel Eye recorded and thecheater will be caught instantly. Dealer hand speed can also be calculated, which could helpmanagementunderstandtheirfastestandslowestdealers.

• RFID—Modern casino chips in high denominations are now fitted with RFID transmitters thatbroadcastuniquefrequenciesbasedontheirvalue.Whenthetimecomestocashtheminorusethematthetable,they’repassedoveraRFIDreadertoensurethey’relegit.Thistechnologyalsoallowscasinostoensurethatchipsarenotbeinggiventoblacklistedpeopleonthesly.

• NORA—Casino cheats rarely work alone, so identifying that moment when two or moresuspiciouspeoplewalk in isvital foracasinotostopcheatingbefore itstarts.OneofthemostinterestingpiecesofsoftwaretheyusetoaccomplishthisiscalledNORA,shortforNon-ObviousRelationship Analysis. It starts with surveillance footage from the casino floor, which is runagainstadatabaseofknowncheats.Oncethey’re identified,ahugepublicrecordscrawl isrunautomatically to findconnectionsbetweenothercasinoguestsandevenemployees. Itdoesn’tprovide definitive results, but it does give investigators a head start in identifying potentialcheats.

• TableEye 21—Blackjack is one of the most popular table games for cheaters, as the house’sadvantageisn’tlargeandthereareanumberofthingsunscrupulousplayerscandotoswingthegametheirway.Onecasino inventionthat isworkingtoeliminatethatkindofchicanery is theTableEye 21, an ambitious way to catalog and track blackjack players, game by game. Usingoverheadvideocameras,thesystemtracksplayer’sbets,theirwinningpercentage,aswellasawhole host of other variables, letting casinos get a statistical analysis of their projectedperformanceandalertingthemiftheystartgoingoutsideofthatprojection.Fromthere,casinoemployeeswillmonitorthesuspectforevidenceofcheatingand/orcardcounting.

• SIN—Onetool that is inuse is theSurveillance InformationNetworkorSIN,createdbyViisageTechnology. This private networking tool links up over a hundred US casinos nationwide andallowsthemtocomparenotesoncheaters,highrollersandother importantplayers,aswellasalerteachothertonewscamsbeingrun.

• Counter Surveillance Scanners—As casino technology gets better and better, so too does thetechnology that cheaters use. Small video cameras have been employed to run all kinds ofcheating gambits, so now casinos are taking things a step further into the realm of counter-surveillance. Video cameras release a specific high-pitched frequency when they’re used, sosomecasinosarenowinstallingcounter-surveillancescanners,sensitivemachinesthatcanpickup on that frequency and report it to themonitoring room. Considering the sheer number ofcamerasalreadyoperatingonthegamblingfloor,thesemachinesneedtobeincrediblysensitive


topickuponemore,butthecostoflettingadeckofcardsbephotographedbeforeit’sdealtcanbeinthemillions.

• Motion-DetectingatRouletteWheels—Modernsurveillancecamerasdomorethanjustdumblyrecordfootage—allkindsofalgorithmsarerunonvideotocatchcheaters.Oneinterestingusageof software at casinos is to catch roulette cheaters. A common roulette cheating method isputtingchipson the tableafter theball is inplay,whichgivesaminoradvantage inpredictingwhere and when it will land. It’s very difficult for the croupier to keep track of this, so newsoftware analyzes the speedof theball in thewheel, thenmonitors thebetting area tomakesurenochipsaremovedaftertheballreachesacertainspeed.

This is obviously an enormous amount of data thatwould be need to siloed into individual stacks anddeploying a data lake to make sense of all of it would be preferable to a normal DW, which wouldprobablybeoverwhelmedbythereal-timeprocessingrequiredtocaptureanduncoverallofthischeatingbehavior.

Hosts,Hostesses,andPitBossesTableGamesRevenueManagement(TGRM)isasystemthatisusedbyalotofcasinostoraiseandlowertablegamesminimumsinanattempttomaximizerevenue.InMacau,wheretablegamesmakeup70%-80ofacasino’srevenue,TGRMismore importantthanever.Tangem,aTGRMsystemthat iscurrentlybeing used bymany casinos around the world, can either be fed with additional data so that activitybeyond thegaming floor canbeadded toTangem’spredictivemodels. This couldevenallow real timeadjustmentstotablegameminimumsthatwouldbeaffectedbysuchthingsasreservationcheck-in,geo-locatingdata,mobilecheck-ins,socialdata,weather,trafficand/orbordercrossinginformation.

DatacapturedonthegamingfloorbysystemslikeAngelEyeandvideoanalyticsprogramscanbeusedtoaccessnotonlythetablegamesminimumhandprices,butitcanalsobeutilizedtounderstandemployeeproductivity.Gamepacestatisticdashboardscaneasilybecreatedtounderstanddealerspeedsothatthecasinocanknowwhichdealersarethemostproductiveandwhichmightneedadditionaltraining.Alertscanbesetupsothatpitbossescanunderstandwho isoncoursetomeetdailyandmonthlytargetsaswellaswho is lagging.Patternscanbeanalyzedso thatadeeperunderstandingofwhycertaindealersand/ortablesareperformingwellornotsowell.

Real-timeprocessingsystemscanalsohelphostsandhostesskeepup-to-theminuterecordstokeeptrackofcomplimentariesthattheyhandouttocustomers.

ConclusionWithadata lake,businesseventscanbeprocessed inrealtime,enablingacasinoand/oran integratedresortmakefasterandmoreinformeddecisionsacrosstheentirespectrumofthebusiness,fromgamingtoretail toF&B,spa,andanyotherformofentertainmentoffered.Thisenablesthemosteffectiveandstrategicoutcomeswithregardstoriskmanagement,customerengagement,andmarketing.It isnotaneither-or question—Data Lake vs. Relational DataWarehouse; a data lake doesn’t necessarily have toreplacearelationaldatawarehouse(andprobablyshouldn’t)butwiththeexplosionindatasourcesanddata content, it is probably one of the best ways to wrangle data so that it can become actionableintelligence.

Data lakes are also one of the bestways for casino operators to gain the in-depth understanding of apatron that is needed to deliver personalization marketing. In today’s marketing world, demandingpatrons will require nothing less than deep customer understanding. Theywant offers that they havepreviously shown a preference for to be delivered on the channels they prefer,when andwhere theywant it. Short of this, casino companies will probably see their customers headed to a competitor, acompany that has made the investment in personnel and resources to deliver this kind of actionableintelligence.


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