Open Analytics Summit NYC - Tiger

7/28/2019 Open Analytics Summit NYC - Tiger

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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


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Tiger 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, and

India


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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

HighCTR

Relevant contentfor Users

Revenuemaximization for

PublisherRelevant

audience forAdvertiser


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Case study: credit card marketing

Cash Back

1,000,000

Impressions300

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




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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 overtime


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Budget Allocation Increase budget for high

performance segments and reducefor low performance ones

Business rules around minimumand maximum limits

Constrained Multi-Armed BanditProblem


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Methodology

Segment 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

Historical

Campaign Data

PriorsofPerformance

Indicators

Weighted DataClick vs. view through, card value, application

result, recency, delay in view-through appls

Cost per

Acquisition

Model Performanceas a function of targeting

dimensions

Model Estimates ofPerformance 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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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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Copyright 2013 Tiger Analytics

Questions / Comments ?

[email protected]

www.tigeranalytics.com

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Open Analytics Summit NYC - Tiger

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