Open analytics summit nyc

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


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


Social Media provides rich data to marketers


Ads on FacebookNewsfeed on Desktop Newsfeed on Mobile

Right Hand Side on Desktop

Sponsored Story

Image source: Facebook


Facebook Ad Platform -- targeting

5


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


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


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%


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)


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




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


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


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


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


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


Results: Increased CTR

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January February March April

25811141720232629258111417202326292581114172023262925811141720232629

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


January February March April

25811141720232629258111417202326292581114172023262925811141720232629

$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


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 ?

[email protected]

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