Games Analytics Industry Fourm 2 - Opera Solutions

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Transforming Raw Big Data into Extraordinary Performance

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Games Analytics Industry Fourm 2 - Opera Solutions

  1. 1. 1© 2012 Opera Solutions. All rights reserved.Opera SolutionsTransforming Raw Big Data intoExtraordinary PerformanceRichard Palmer9 May 2013GIAF2
  2. 2. 2© 2012 Opera Solutions. All rights reserved.Introduction to OperaSolutions
  3. 3. 3© 2012 Opera Solutions. All rights reserved.Opera SolutionsEmployees: 700 - Consulting Data Science SoftwareGlobal Network: North America Europe AsiaKey Focus:Data Scientists 230+Focus: Man + MachineConsumer Marketing, Finance, Gaming &Leisure, Government
  4. 4. 4© 2012 Opera Solutions. All rights reserved.What We BelieveHuman Behaviour can be expressed mathematicallyMachine intelligence is critical – but must be married to human insightIt’s not the data, it’s the signals in the dataMost organisations’ infrastructure, operations and scientific skills haven’tcaught up to Big Data
  5. 5. 5© 2012 Opera Solutions. All rights reserved.Topics for Today BIG Data & Predictive Analytics Video on Demand Case Study On-line Gaming Case Study
  6. 6. 6© 2012 Opera Solutions. All rights reserved.Big Data & PredictiveAnalytics
  7. 7. 7© 2012 Opera Solutions. All rights reserved.“Have I got a girlfor you!”The History of Predictive AnalyticsAppearance?TypeofRelationship?Arbitrary structureimposed on the datato make it solvable
  8. 8. 8© 2012 Opera Solutions. All rights reserved.What will the consumerrent next?If the consumer’s last rentals were:Netflix Prize - A Watershed Event
  9. 9. 9© 2012 Opera Solutions. All rights reserved.Complications We know nothing about the customer and there are 7 million of them Most customers have only rented a small number of Movies – less than 1%on average out of 17 thousand available movies Customers are biased in their feedback and may only rate movies they loveor hate
  10. 10. 10© 2012 Opera Solutions. All rights reserved.Breakthrough techniques –Factor Modellingusers (customers)items(movies)The objective of the Factor Models is to compute predictions for theseunseen events – and thus output a probability that a user will purchase/addto wishlist/rate etc. a particular item they have not yet experienced.Use all the data andlet the structureinside the datareveal itself
  11. 11. 11© 2012 Opera Solutions. All rights reserved.Approach unleashing a step-change in performance for our clientsN E T F L I X P R I Z E C O M P E T I T I O NV I D E O - O N - D E M A N DO P E R A T O R• 400% increase in take-rateover in-house solution (basedon most-popular title)• Out-performed 7 competingsolutions in a bake-off by awide marginTake-Rate (%)4XC R E D I T C A R DA S S O C I A T I O N• 20% lift in merchant behaviourmodel accuracy vs. in-housemodels (in-house modelsdeveloped by internal riskteams using MerchantCategory Code as inputvariable to models)Accuracy of MerchantBehaviour Model (%)0 2,000 4,000 6,000 8,000 10,000 12,000Dimensionality38.1%43.1%45.4%Merchant CodeAggregatedMerchant IDMerchant +Merchant CodeDataset ComplexityIn-houseModelOperaModel20% Lift inAccuracy1045In-House Opera
  12. 12. 12© 2012 Opera Solutions. All rights reserved.Customer factorsItem factorsCustomer biasItembiasGlobalmeanCustomer factorsItem factorsCustomerbiasItembiasGlobalmeanCustomer factorsItem factorsCustomerbiasItembiasGlobalmeanVSifNfcficciVUbbR 1ˆifNfcficciVUbbR 1ˆifNfcficciVUbbR 1ˆSummary – human behaviors can be expressed mathematically and whencombined with machine intelligence data can provide valuable Signals
  13. 13. 13© 2012 Opera Solutions. All rights reserved.Video on DemandCase Study
  14. 14. 14© 2012 Opera Solutions. All rights reserved.ViewersConverting BIG Data to small data – the ‘dimensional reduction journey’RAW CONSUMPTION DATA SPARSE MATIRX DIMENSIONAL REDUCTION2.3 Billion rows of dataContent & Time of ViewFactormodels, clustering and neuralnetworks toextract NaturalClusters fromconsumptiondata1 0 0 1 10 0 0 0 00 0 1 0 00 1 0 0 00 1 0 0 011 Million Viewers14K of VoD Items~0.13% filledVery high dimensionalrepresentation9 Natural Clusters of customerbehaviour (Archetype)TimeSignalGenerator
  15. 15. 15© 2012 Opera Solutions. All rights reserved.1 2 3 4 5 6 7 8 9Series xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxSeries xxxV I E W E R A R C H E T Y P E SColour code:HighMediumLowArchetypes display highly distinctive content viewing behaviourSERIES
  16. 16. 16© 2012 Opera Solutions. All rights reserved.And have distinct demographic signaturesArchetype 4 Archetype 5HaveChildrenABC1 Housewives16-34MaleHaveChildrenABC1 Housewives16-34Male
  17. 17. 17© 2012 Opera Solutions. All rights reserved.1. Drive higher costper impression viamore accuratetargeting2. Drive higheradvertiser share-of-wallet via superiorcampaign ROI3. Drive higherViewer satisfactionvia more relevantAdsApplication – targeting of advertising based on predicted demographicsegment• Content information;Viewer Archetype,series participation,flicking, etc.• Time information,including intensity,Time of ViewArchetype, intensityetc.Viewing Behaviour• Type and number ofhardware devices• Type of software (i.e.,browsers used)• ISP informationDevice Information• Location• Travel behaviour• Regionaldemographics (e.g.,income, education)Locality Information• Page hits• Referral sites andsections• On site search terms• Off site search termsBrowsing BehaviourMicro-targeted AdsMicro-targeting signals made available through Opera’s Signal HubTM (examples)Increased ValueVoD Viewer Micro-targeting1,500 signals in total
  18. 18. 18© 2012 Opera Solutions. All rights reserved.Example Demographic Prediction: Female Age 16 to 34AUC KSTest 0.85 0.58# UnregisteredTargetable Vod ViewsEstimated RecallLots Very good%PositivesCaptured% Negatives CapturedFemales behavinglike females(positives)Males behavinglike females(negatives)Cut-off at 80%correctly classified
  19. 19. 19© 2012 Opera Solutions. All rights reserved.Online GamingCase Study
  20. 20. 20© 2012 Opera Solutions. All rights reserved.Driving customer loyalty through in improving player recommendations• 3-4% increase inaverage weeklygameplay• 50% increase is averagerating50k games15 million users
  21. 21. 21© 2012 Opera Solutions. All rights reserved.Simple standalone solutionProduct/ContentManagerPlayersOperaRecommendationSolutionOperation types:New UserRegistrationConsumeRecommendationRateGameOtherActionsAdd GameRemoveGameReviewStatisticsBackupSystemInstallUpdatesInputOutputStatisticControlOtherITManagerOperaSolutions
  22. 22. 22© 2012 Opera Solutions. All rights reserved.Questions

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