Open analytics summit nyc

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  • Facebook alone has 845 Million users, very significant reach and comparable to TV, but it is coupled with interactivity. You can now have a dialogue with customers and build a story around your brand with social. Almost 4 billion pieces of content shared each week
  • CTR is a Not a sole predictor of social media campaign, but from cost perspective, CTR optimizes. CTR best metric for optimization.
  • Open analytics summit nyc

    1. 1. Copyright © 2013. Tiger AnalyticsPredictive Analytics in Social Mediaand Online Display Advertising_________________________Mahesh KumarCEO, Tiger AnalyticsApril 8th, 2013_________________________Co-authors: Pradeep Gulipalli, Satish Vutukuru
    2. 2. Copyright © 2013. Tiger AnalyticsTiger Analytics• Boutique consulting firm solving business problems usingadvanced data analytics• Focus areas– Digital advertising and Social Media marketing– Retail merchandising– Transportation• Team of 20 people based in California, North Carolina, andIndia
    3. 3. Copyright © 2013. Tiger AnalyticsSocial Media provides rich data to marketers
    4. 4. Copyright © 2013. Tiger AnalyticsAds on FacebookNewsfeed on Desktop Newsfeed on MobileRight Hand Side on DesktopSponsored StoryImage source:Facebook
    5. 5. Copyright © 2013. Tiger AnalyticsFacebook Ad Platform -- targeting5
    6. 6. Copyright © 2013. Tiger AnalyticsCTR and the Size of Audience Vary Inversely6• Broadly defined interests result in low CTR.• Narrowly defined precise targets can generate high CTRs.SportsBasketballNBALakersKobe BryantKingsFootballNFL College High SchoolLow CTRHigh CTR
    7. 7. Copyright © 2013. Tiger AnalyticsMaximizing the CTR is Critical For Cost Optimization7High CTR is good for everyone: users, advertiser, and publisherHighCTRRelevant contentfor UsersRevenuemaximization forPublisherRelevantaudience forAdvertiser
    8. 8. Copyright © 2013. Tiger AnalyticsCase study: credit card marketingCash Back1,000,000Impressions300Clicks3Applications1ApprovalConversions are rare events when compared to clicks. The challenge is to be able to makemeaningful inferences based on very little data, especially early on in the campaign.Click-through rate0.03%Conversion rate1%Approval rate33%
    9. 9. Copyright © 2013. Tiger AnalyticsBackground• Objective: Given a target budget, maximize the number ofapproved customers• Separate budget for 5 different credit cards in the US• Each card has different value• Account for cross-conversions• Two bidding methods– Cost per click (CPC)– Cost per impression (CPM)
    10. 10. Copyright © 2013. Tiger AnalyticsCross-conversions Impression shown and application filled need not be for thesame cardAd for Card 1Ad for Card 2Application for Card 1Application for Card 2Application for Card 3
    11. 11. Copyright © 2013. Tiger AnalyticsMicro Segments1 Segment 50 Segments50 x 2 =100 Segments2 Genders 4 Age Groups100 x 4 =400 Segments25 Interest Clusters400 x 25 =10,000 Segments
    12. 12. Copyright © 2013. Tiger AnalyticsMethodology• Identify high performance segments– Statistically significant difference in ctr, cpc, cost per conversion, etc.– Use ctr as a proxy for conversion rate• Actions on high performance segments– Allocate higher budget– Increase bid price12
    13. 13. Copyright © 2013. Tiger AnalyticsSegment performance estimationModel EstimatesObserved PerformancePrior KnowledgeInferred Performance
    14. 14. Copyright © 2013. Tiger AnalyticsBiddingBrand ABrand BOther Competition for Ad SpaceBid: $1.60BidsWINBids will differ by Ad and Microsegment, and will change overtime
    15. 15. Copyright © 2013. Tiger AnalyticsBudget Allocation• Increase budget for highperformance segments and reducefor low performance ones– Business rules around minimumand maximum limits• Constrained Multi-Armed BanditProblem
    16. 16. Copyright © 2013. Tiger AnalyticsMethodologySegment LevelObserved DataInferred Performance IndicatorsBased on priors, observed, model estimatesCost perApplicationSuccessRateDynamic Budget AllocationBased on inferred performance indicatorsand business constraintsHistoricalCampaign DataPriorsofPerformanceIndicatorsWeighted DataClick vs. view through, card value, applicationresult, recency, delay in view-through applsCost perAcquisitionModel Performanceas a function of targetingdimensionsModel Estimates ofPerformance IndicatorsDynamic Bid AllocationBased on observed/historicalBid-Spend relationshipsContinual monitoring andanalysisBusinessConstraints
    17. 17. Copyright © 2013. Tiger AnalyticsResults: Increased CTR17• Overall increase in CTR by 50% across more than 100 brands
    18. 18. Copyright © 2013. Tiger AnalyticsResults: Lower costs18• Overall decrease in CPC of 25% across more than 100 brands
    19. 19. Copyright © 2013. Tiger AnalyticsConcluding remarks• Online and social advertising are fast growing areas with– Plenty of data– A large number of interesting problems• Predictive analytics can add a lot value in this business– Significant improvement in CTR means better targeted ads– As much as 25% reduction in cost of media• Our solutions are being used by several leading startups toserve billions of ads for Fortune 500 companies19
    20. 20. Copyright © 2013. Tiger AnalyticsQuestions / Comments ?mahesh@tigeranalytics.comwww.tigeranalytics.com20

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