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

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

  1. 1. 1
  2. 2. Expert Service 200+ employees from 40+ Nationalities We are in 10 Global Locations: Berlin, Tokyo, Moscow, San Francisco, Seoul, Bangalore, New York, São Paulo, Shanghai and Jakarta 2
  3. 3. DataLift 360 3 Our integrated platform is your single access point for successful mobile advertising campaigns
  4. 4. 4 Business Value Through Data Science The Problem: If you are a DSP trying to run advertisers’ campaigns, how can you leverage Data Science?
  5. 5. 5 Opposite Ends in Ad Tech Spectrum Mobile AdvertiserData Scientist
  6. 6. 6 Different Views • Advertiser (Business View): Running campaigns with a target CPI and scales target. • Data Science: Build an accurate model that makes quality predictions.
  7. 7. 7 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.
  8. 8. 8 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.
  9. 9. 9 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.
  10. 10. 10 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.
  11. 11. 11 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.
  12. 12. 12 Q&A Thank you for listening! Any questions?
  13. 13. www.AppLift.com Thank You! girish@applift.com

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