open analytics summit nyc
TRANSCRIPT
Copyright © 2013. Tiger Analytics
Predictive Analytics in Social Media and Online Display Advertising
_________________________
Mahesh KumarCEO, Tiger Analytics
April 8th, 2013
_________________________Co-authors: Pradeep Gulipalli, Satish Vutukuru
Copyright © 2013. Tiger Analytics
Tiger Analytics
• Boutique consulting firm solving business problems using advanced data analytics
• Focus areas– Digital advertising and Social Media marketing– Retail merchandising– Transportation
• Team of 20 people based in California, North Carolina, and India
Copyright © 2013. Tiger Analytics
Social Media provides rich data to marketers
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Ads on FacebookNewsfeed on Desktop Newsfeed on Mobile
Right Hand Side on Desktop
Sponsored Story
Image source: Facebook
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Facebook Ad Platform -- targeting
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CTR and the Size of Audience Vary Inversely
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• Broadly defined interests result in low CTR.• Narrowly defined precise targets can generate high CTRs.
Sports
Basketball
NBA
Lakers
Kobe Bryant
Kings
Football
NFL College High School
Low CTR
High CTR
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Maximizing the CTR is Critical For Cost Optimization
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High CTR is good for everyone: users, advertiser, and publisher
HighCTR
Relevant content for Users
Revenue maximization for
PublisherRelevant
audience for Advertiser
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Case study: credit card marketing
Cash Back
1,000,000Impressions
300Clicks
3Applications
1Approval
Conversions are rare events when compared to clicks. The challenge is to be able to make meaningful inferences based on very little data, especially early on in the campaign.
Click-through rate0.03%
Conversion rate1%
Approval rate33%
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Background
• Objective: Given a target budget, maximize the number of approved 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)
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Cross-conversions
Impression shown and application filled need not be for the same card
Ad for Card 1
Ad for Card 2
Application for Card 1
Application for Card 2
Application for Card 3
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Micro Segments
1 Segment 50 Segments
50 x 2 = 100 Segments
2 Genders 4 Age Groups
100 x 4 = 400 Segments
25 Interest Clusters
400 x 25 = 10,000 Segments
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Methodology
• 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 price
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Segment performance estimation
Model Estimates
Observed Performance
Prior Knowledge
Inferred Performance
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Bidding
Brand A
Brand B
Other Competition for Ad Space
Bid: $1.00
Bid: $1.60
Bids
WIN
Bids will differ by Ad and Micro segment, and will change over
time
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Budget Allocation• Increase budget for high
performance segments and reduce for low performance ones
– Business rules around minimum and maximum limits
• Constrained Multi-Armed Bandit Problem
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MethodologySegment Level Observed Data
Inferred Performance IndicatorsBased on priors, observed, model estimates
Cost per Application
Success Rate
Dynamic Budget AllocationBased on inferred performance indicators
and business constraints
HistoricalCampaign Data
Priors of Performance
Indicators
Weighted DataClick vs. view through, card value, application result, recency, delay in view-through appls
Cost per Acquisition
Model Performance as a function of targeting
dimensions
Model Estimates of Performance Indicators
Dynamic Bid AllocationBased on observed/historical
Bid-Spend relationships
Continual monitoring and analysis
Business Constraints
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Results: Increased CTR
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January February March April
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0.00%
0.01%
0.01%
0.02%
0.02%
0.03%
0.03%
0.04%
0.04%
0.05%
0.05%
0.06%
0.06%
0.07%
0.07%
0.08%
0.08%
aCTR
• Overall increase in CTR by 50% across more than 100 brands
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January February March April
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$0.00
$0.10
$0.20
$0.30
$0.40
$0.50
$0.60
$0.70
$0.80
$0.90
$1.00
$1.10
aCPC
Results: Lower costs
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• Overall decrease in CPC of 25% across more than 100 brands
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Concluding 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 to serve billions of ads for Fortune 500 companies
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Questions / Comments ?
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