Using Advanced Analyics to bring Business Value
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Transcript of Using Advanced Analyics to bring Business Value
Copyright © 2013. Tiger Analy7cs
Using Advanced Analy7cs to bring Business Value
Two case studies in Digital Adver7sing
_________________________
Mahesh Kumar CEO, Tiger Analy6cs
Copyright © 2013. Tiger Analy7cs
Tiger Analy7cs
• Bou6que consul6ng firm solving business problems using advanced data analy6cs
• Focus areas – Digital adver6sing and Social Media – Marke6ng and Customer Analy6cs – Retail and CPG – Transporta6on
• Offices in Bay area, North Carolina, and India
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• Display Adver6sing through Real-‐6me bidding (RTB) – Background on RTB – Business problem: improve CTR – Solu6on Approach and Results
• Credit Card Customer Acquisi6on via Facebook Ads – Facebook ads plaOorm – Business problem: op6mal targe6ng and bidding – Applica6on to credit card marke6ng
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Overview – Two case studies
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Display Adver7sing through Real-‐7me bidding (RTB)
Case Study # 1
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Display Adver7sing – Real Time Bidding
Milliseconds to bid and load ad …
Waiting for ad from ad exchange
…
Male, 20-30 yrs, NYC Tech user
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Display Adver7sing – Real Time Bidding
Targeted Ads
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Real Time Bidding (RTB) for Display Ads
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Ad-‐Exchange Ad-network 2 Publisher (NYT.com) Tech section
Ad-network 1
Ad-network 3
Advertiser
Advertiser
Advertiser
Male, 20-30 yrs, New York, Tech user
• The en6re process takes less than 500 milliseconds • RTB share of online ads is es6mated to be $2B per year
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Business Problem
• Click-‐through rate (CTR) predic6on: Given a campaign line, what is the predicted CTR for an impression based on – User characteris6cs – Webpage characteris6cs
• Iden6fy impressions with highest CTR?
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Maximizing the CTR is Cri7cal For Cost Op7miza7on
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High CTR is good for everyone: users, advertiser, and publisher
High CTR
Relevant content for
Users
Revenue maximiza6on for
Publisher Relevant
audience for Adver6ser
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Sample data
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Data challenges
• Challenges – More than 5000 variables – Hundreds of millions of data points – Sparse and missing data – Clicks are very rare (typically 1 click in every 3,000 impressions)
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• Case sampling – Keep all impressions with clicks – Keep only a random sample of 1% non-‐clicks
• This reduced the data size by 100-‐fold, but predic6on accuracy was as good as when using all data
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Reducing the number of data sets
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Logis7c Regression results
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50 "
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1" 1001" 2001" 3001" 4001" 5001"
predicted!baseline!
K K K K K K 0% 20% 40% 60% 80% All data
• Top 20% of data got 232 out of 415 (56%) of clicks • A liY of 180%
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Insights – Final set of variables
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Twi\er – CTR Predic7on
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NLP
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Credit Card Customer Acquisi7on Through Social Media Marke7ng
Case Study # 2
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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 Pla`orm -‐-‐ targe7ng
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Facebook Ad Pla`orm -‐-‐ pricing
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Case study: credit card marke7ng
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Cash Back
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1,000,000 Impressions
300 Clicks
3 Applica7ons
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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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
• Iden6fy high performance segments – Sta6s6cally significant difference in ctr, cpc, cost per conversion, etc. – Use ctr as a proxy for conversion rate
• Ac6ons on high performance segments – Allocate higher budget – Increase bid price
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Data aggrega7on
Segment Level Data (Sparse and Noisy)
Iden7fy Important Dimensions (Using Sta7s7cal Models)
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Segment performance es7ma7on
Model Es7mates
Observed Performance
Prior Knowledge
Inferred Performance
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Bidding
Brand A
Brand B
Other Compe77on for Ad Space
Bid: $1.60
Bids
WIN
Bids will differ by Ad and Micro segment, and will change over
7me
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Budget Alloca7on • Increase budget for high
performance segments and reduce for low performance ones
– Business rules around minimum and maximum limits
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Methodology Segment Level Observed Data
Inferred Performance Indicators Based on priors, observed, model es6mates
Cost per Applica6on
Success Rate
Dynamic Budget Alloca7on Based on inferred performance indicators
and business constraints
Historical Campaign Data
Priors of Performance Indicators
Weighted Data Click vs. view through, card value, applica6on result, recency, delay in view-‐through appls
Cost per Acquisi6on
Model Performance as a func6on of targe6ng
dimensions
Model Es7mates of Performance Indicators
Dynamic Bid Alloca7on Based on observed/historical
Bid-‐Spend rela6onships
Con7nual 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 CPA of 25% across more than 100 brands
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Concluding remarks
• Online and social adver6sing are fast growing areas with – Plenty of data – A large number of interes6ng problems
• Predic6ve analy6cs can add a lot value in this business – Significant improvement in CTR means beber targeted ads – As much as 25% reduc6on in cost of media
• Our solu6ons are being used by several leading startups to serve billions of ads for Fortune 500 companies
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