Girish Sathyanarayana, Senior Data Scientist at AppLift, " Business Value Through Data Science: A Mobile Ad Tech Case Study"

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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 &
SCALES
OUTPUT
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?

13. www.AppLift.com
Thank You!
girish@applift.com