Ad Yield Optimization @ Spotify - DataGotham 2013
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May 12, 2014
Ad Yield Optimization @ Spotify
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
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• 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?
• getFreeTierUsers() / getAllUsers() > 0.70 • getSpotifyPayoutToMusicLabels() = $$$ • Great medium for promotions and announcements
Why are Ads important?
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Native Ads
The problem
How do we optimize the ad yield on Spotify platform?
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?
What is unique about us?
• Rules triggering ad breaks are unique
• We also log user activity and audio streaming data
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
Our Hadoop infrastructure
700 nodes in our hadoop cluster
Some constraints
• Fast real-time lookup service
• Consistent results
• Ability to handle additional targeting
• Ability to scale
The solution
Use subset of impression data then filter and extrapolate it using a simple model in a service
But how?
Now begins the fun part… Lets dive deeper to solve this problem
What was the big picture going be like?
Hadoop Ad impression log
Postgres DB Booked Campaigns
Forecas4ng engine
Forecast Query
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
Some challenges…
• Organic growth in inventory • Cold start • Seasonality
Organic growth in inventory
Ad impression inventory in a growing market
Organic growth in inventory?
Ad impression inventory in a market with high conversion to premium
Cold start
Ad impression inventory in a newly launched market
Seasonality
Ad impression inventory dip in early Q1
Volume of data
• Billions of ad impressions per month • Terabytes of relevant forecasting data
Data overload?
Sampling
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
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
Day of the Month
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Growth
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
Growth Factor
Keep it simple
Extrapolation
• Population (15 million) -> Sample (150,000)
• Scaling factor is 100
• Total Available inventory = scaling factor * available inventory for sample
Other features
• Ad Frequency capping
• Day of the week and time of the day filtering
• View per user (VPU) capping
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
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
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