Girish Sathyanarayana, Senior Data Scientist at AppLift, " Business Value Through Data Science: A...
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Transcript of Girish Sathyanarayana, Senior Data Scientist at AppLift, " Business Value Through Data Science: A...
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Expert Service200+ employees from 40+ Nationalities
We are in 10 Global Locations: Berlin, Tokyo, Moscow, San Francisco,
Seoul, Bangalore, New York, São Paulo, Shanghai and Jakarta
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DataLift 360
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Our integrated platform is your single access point for successful mobile advertising campaigns
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Business Value Through Data Science
The Problem: If you are a DSP trying to run advertisers’ campaigns, how can you leverage Data
Science?
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Opposite Ends in Ad Tech Spectrum
Mobile AdvertiserData Scientist
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Different Views
• Advertiser (Business View): Running campaigns with a target CPI and scales target.
• Data Science: Build an accurate model that makes quality predictions.
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Forging Components
• Model predictions used to quantify the worth of an impression (bid value).
• How does this Data Science approach meet Business Needs.
• Depends on how well model generalizes.
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Ideal Scenario
• The perfect model would have good generalization (i.e. performance on unseen data).
• Good generalization at the campaign level is important:• Achieve a CPI < Target CPI.• Does not necessarily mean you’ll
achieve your scales objective.
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Realistic Scenario
• Realistically, it’s difficult to achieve perfect generalization.
• Most times, there is no good generalization at the campaign level.
• Some campaigns do well, some don’t.
• Does not solve the business problem.
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Bid Adjustment
• Use model output as central value around which we vary the actual bids.
• Vary bids by computing an error for budget delivery.
• Basically, a Feedback Control System.
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BID VALUE FROM MODEL BIDDER
ERROR PROCESSING
BLOCK
BID CORRECTION
ERROR
REFERENCE TARGET CPI & SCALE TARGET
REALIZED CPI & SCALESOUTPUT
Feedback Control System
Possible Outcomes
• Advertiser’s demands are met (i.e. target CPI and Scales).• Or algorithm flags that the campaign objectives are not
achievable with recommendations for achievable targets.
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Q&A
Thank you for listening!Any questions?
