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Using Advanced Analyics to bring Business Value
1.
Copyright © 2013.
Tiger Analy7cs Using Advanced Analy7cs to bring Business Value Two case studies in Digital Adver7sing _________________________ Mahesh Kumar CEO, Tiger Analy6cs
2.
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
3.
Copyright © 2013.
Tiger Analy7cs • 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 3 Overview – Two case studies
4.
Copyright © 2013.
Tiger Analy7cs Display Adver7sing through Real-‐7me bidding (RTB) Case Study # 1
5.
Copyright © 2013.
Tiger Analy7cs 5 Display Adver7sing – Real Time Bidding Milliseconds to bid and load ad … Waiting for ad from ad exchange … Male, 20-30 yrs, NYC Tech user
6.
Copyright © 2013.
Tiger Analy7cs 6 Display Adver7sing – Real Time Bidding Targeted Ads
7.
Copyright © 2013.
Tiger Analy7cs Real Time Bidding (RTB) for Display Ads 7 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
8.
Copyright © 2013.
Tiger Analy7cs 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? 8
9.
Copyright © 2013.
Tiger Analy7cs Maximizing the CTR is Cri7cal For Cost Op7miza7on 9 High CTR is good for everyone: users, advertiser, and publisher High CTR Relevant content for Users Revenue maximiza6on for Publisher Relevant audience for Adver6ser
10.
Copyright © 2013.
Tiger Analy7cs Sample data 10
11.
Copyright © 2013.
Tiger Analy7cs 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) 11
12.
Copyright © 2013.
Tiger Analy7cs • 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 12 Reducing the number of data sets
13.
Copyright © 2013.
Tiger Analy7cs Logis7c Regression results 13 - " 50 " 100 " 150 " 200 " 250 " 300 " 350 " 400 " 450 " 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%
14.
Copyright © 2013.
Tiger Analy7cs 14 Insights – Final set of variables
15.
Copyright © 2013.
Tiger Analy7cs Twier – CTR Predic7on 15 NLP
16.
Copyright © 2013.
Tiger Analy7cs Credit Card Customer Acquisi7on Through Social Media Marke7ng Case Study # 2
17.
Copyright © 2013.
Tiger Analy7cs Social Media provides rich data to marketers
18.
Copyright © 2013.
Tiger Analy7cs Ads on Facebook Newsfeed on Desktop Newsfeed on Mobile Right Hand Side on Desktop Sponsored Story Image source: Facebook
19.
Copyright © 2013.
Tiger Analy7cs Facebook Ad Pla`orm -‐-‐ targe7ng 19
20.
Copyright © 2013.
Tiger Analy7cs Facebook Ad Pla`orm -‐-‐ pricing 20
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Tiger Analy7cs Case study: credit card marke7ng The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. Cash Back The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. 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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Tiger Analy7cs 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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Tiger Analy7cs 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 23
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Tiger Analy7cs Data aggrega7on Segment Level Data (Sparse and Noisy) Iden7fy Important Dimensions (Using Sta7s7cal Models)
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Tiger Analy7cs Segment performance es7ma7on Model Es7mates Observed Performance Prior Knowledge Inferred Performance
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Tiger Analy7cs 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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Tiger Analy7cs 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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Tiger Analy7cs 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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Tiger Analy7cs Results: Increased CTR 29 • Overall increase in CTR by 50% across more than 100 brands
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Tiger Analy7cs Results: Lower costs 30 • Overall decrease in CPA of 25% across more than 100 brands
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Tiger Analy7cs 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 31
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Tiger Analy7cs Ques7ons / Comments ? mahesh@7geranaly7cs.com www.7geranaly7cs.com 32
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