Tiger Analytics is a boutique consulting firm that uses predictive analytics to solve business problems in digital advertising, social media marketing, retail, and transportation. The document discusses how Tiger Analytics uses predictive models and micro-segmentation of audiences to optimize online display advertising and social media campaigns for clients. By analyzing past user behavior and applying machine learning, the company is able to increase click-through rates by 50% and decrease costs per click by 25% for its clients.

1. Copyright © 2013. Tiger Analytics
Predictive Analytics in Social Media
and Online Display Advertising
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Mahesh Kumar
CEO, Tiger Analytics
April 8th, 2013
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Co-authors: Pradeep Gulipalli, Satish Vutukuru

2. 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

3. Copyright © 2013. Tiger Analytics
Social Media provides rich data to marketers

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Ads on Facebook
Newsfeed 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
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
• 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: $1.60
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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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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• Overall increase in CTR by 50% across more than 100 brands

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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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20. Copyright © 2013. Tiger Analytics
Questions / Comments ?
mahesh@tigeranalytics.com
www.tigeranalytics.com
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