Weitere Ă€hnliche Inhalte Ăhnlich wie Open analytics summit nyc (20) Mehr von Open Analytics (20) KĂŒrzlich hochgeladen (20) Open analytics summit nyc1. Copyright © 2013. Tiger Analytics
Predictive Analytics in Social Media
and Online Display Advertising
_________________________
Mahesh Kumar
CEO, Tiger Analytics
April 8th, 2013
_________________________
Co-authors: Pradeep Gulipalli, Satish Vutukuru
2. Copyright © 2013. Tiger Analytics
Tiger Analytics
âą Boutique consulting firm solving business problems using
advanced data analytics
âą Focus areas
â Digital advertising and Social Media marketing
â Retail merchandising
â Transportation
âą Team of 20 people based in California, North Carolina, and
India
4. Copyright © 2013. Tiger Analytics
Ads on Facebook
Newsfeed on Desktop Newsfeed on Mobile
Right Hand Side on Desktop
Sponsored Story
Image source:
Facebook
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CTR and the Size of Audience Vary Inversely
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âą Broadly defined interests result in low CTR.
âą 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
High
CTR
Relevant content
for Users
Revenue
maximization for
Publisher
Relevant
audience for
Advertiser
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Case study: credit card marketing
Cash Back
1,000,000
Impressions
300
Clicks
3
Applications
1
Approval
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 rate
0.03%
Conversion rate
1%
Approval rate
33%
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Background
âą Objective: Given a target budget, maximize the number of
approved customers
âą Separate budget for 5 different credit cards in the US
âą Each card has different value
âą Account for cross-conversions
âą Two bidding methods
â Cost per click (CPC)
â Cost per impression (CPM)
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Cross-conversions
ï§ 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
âą Identify high performance segments
â Statistically significant difference in ctr, cpc, cost per conversion, etc.
â Use ctr as a proxy for conversion rate
âą Actions on high performance segments
â Allocate higher budget
â 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: $1.60
Bids
WIN
Bids will differ by Ad and Micro
segment, and will change over
time
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Budget Allocation
âą Increase budget for high
performance segments and reduce
for low performance ones
â Business rules around minimum
and maximum limits
âą Constrained Multi-Armed Bandit
Problem
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Methodology
Segment Level
Observed Data
Inferred Performance Indicators
Based on priors, observed, model estimates
Cost per
Application
Success
Rate
Dynamic Budget Allocation
Based on inferred performance indicators
and business constraints
Historical
Campaign Data
Priorsof
Performance
Indicators
Weighted Data
Click 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 Allocation
Based on observed/historical
Bid-Spend relationships
Continual monitoring and
analysis
Business
Constraints
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Results: Increased CTR
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âą Overall increase in CTR by 50% across more than 100 brands
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Results: Lower costs
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âą Overall decrease in CPC of 25% across more than 100 brands
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Concluding remarks
âą Online and social advertising are fast growing areas with
â Plenty of data
â A large number of interesting problems
âą Predictive analytics can add a lot value in this business
â Significant improvement in CTR means better targeted ads
â As much as 25% reduction in cost of media
âą Our solutions are being used by several leading startups to
serve billions of ads for Fortune 500 companies
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20. Copyright © 2013. Tiger Analytics
Questions / Comments ?
mahesh@tigeranalytics.com
www.tigeranalytics.com
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Hinweis der Redaktion 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.