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Ad Yield Optimization @ Spotify - DataGotham 2013

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Ad Yield Optimization @ Spotify - DataGotham 2013

  1. 1. May 12, 2014 Ad Yield Optimization @ Spotify
  2. 2. I’m Kinshuk Mishra •  Work on distributed systems and data science problems •  Lead architecture for ads backend platform at Spotify •  You can find me @_kinshukmishra
  3. 3. 3 •  Started in 2006 •  Currently has over 24 million users •  6 million paying users •  Available in 28 countries •  Over 300 engineers, of which 100 in NYC What is Spotify?
  4. 4. •  getFreeTierUsers() / getAllUsers() > 0.70 •  getSpotifyPayoutToMusicLabels() = $$$ •  Great medium for promotions and announcements Why are Ads important?
  5. 5. 5 Native Ads
  6. 6. The problem How do we optimize the ad yield on Spotify platform?
  7. 7. The type of questions we have Find the total available audio ad impressions on iOS platform between 9/12/2013 and 9/13/2013 in NYC metro area for male users in the age-group of 18-35, and who typically listen to hip-hop music genre?
  8. 8. What is unique about us? •  Rules triggering ad breaks are unique •  We also log user activity and audio streaming data
  9. 9. Different approaches •  Simulate ad delivery by replaying user events and triggering ad breaks •  Pre-compute impression aggregates for different dimensions and build a complex model to combine those •  Use subset of impression data then filter and extrapolate it using a simple model
  10. 10. Our Hadoop infrastructure 700 nodes in our hadoop cluster
  11. 11. Some constraints •  Fast real-time lookup service •  Consistent results •  Ability to handle additional targeting •  Ability to scale
  12. 12. The solution Use subset of impression data then filter and extrapolate it using a simple model in a service
  13. 13. But how? Now begins the fun part… Lets dive deeper to solve this problem
  14. 14. What was the big picture going be like? Hadoop   Ad  impression  log   Postgres  DB   Booked  Campaigns   Forecas4ng    engine   Forecast  Query  
  15. 15. High level forecasting engine algorithm Log   data   Load  Data  Cache   Campaign   data  daily Once a minute Submit  Forecast   query   Wait  for   query   Apply  filter  criteria   to  dataset   Count  available   impressions   Apply  growth  and   other   extrapola4on   factors  
  16. 16. Some challenges… •  Organic growth in inventory •  Cold start •  Seasonality
  17. 17. Organic growth in inventory Ad impression inventory in a growing market
  18. 18. Organic growth in inventory? Ad impression inventory in a market with high conversion to premium
  19. 19. Cold start Ad impression inventory in a newly launched market
  20. 20. Seasonality Ad impression inventory dip in early Q1
  21. 21. Volume of data •  Billions of ad impressions per month •  Terabytes of relevant forecasting data Data overload?
  22. 22. Sampling
  23. 23. Caching 9/12/2013   9/11/2013   9/10/2013   9/09/2013   9/08/2013   9/07/2013   Log   data   Load  Data  Cache   Campaign   data  daily Once a minute 9/13/2013   9/14/2013  
  24. 24. Optimizing data retrieval •  We analyzed our data access pattern and found over 75% of our campaigns are targeted by age and location. •  So we mapped location to a list of users sorted by age using SortedSetMultimap •  Optimized user lookup by location and age-group to O(kLgN) from typical O(kN) where, N : Total users for a location k : constant
  25. 25. Day of the Month 1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   Growth
  26. 26. How to find available inventory for sample population? 1.  Take all user ad impressions by applying “day of the month” substitution 2.  Apply filters by ad-type, location, age, gender, platform, etc. 3.  Count the total impressions for all the users who match 4.  Read booked impressions for the similar target criteria from the cache 5.  Inventory available = total impressions – booked impressions
  27. 27. Growth Factor Keep it simple
  28. 28. Extrapolation •  Population (15 million) -> Sample (150,000) •  Scaling factor is 100 •  Total Available inventory = scaling factor * available inventory for sample
  29. 29. Other features •  Ad Frequency capping •  Day of the week and time of the day filtering •  View per user (VPU) capping
  30. 30. What worked for us? 1.  Fast lookups 2.  Simple models scaled well 3.  Deterministic algorithms easier to debug 4.  Adding new targeting features was easy 5.  Forecasting engine agnostic to changes in ad server
  31. 31. What didn’t work that well? 1.  Campaign level forecasts difficult without simulation 2.  Cold start is a real problem when there is no proxy dataset 3.  Forecasting inventory for new ad types can be challenging
  32. 32. What we’ve learnt •  Think data volume •  Consider Sampling •  Choose appropriate time window •  Analyze data access patterns and optimize for it •  Use deterministic algorithms •  Analyze data trends and factor those in computation •  Simple models scale well
  33. 33. May 12, 2014 Email - Kinshuk@spotify.com https://twitter.com/Spotifyjobs Thanks!

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