May 6th, 2014

Real-Time Personalization with Ad Tech @Spotify

Kinshuk Mishra kinshuk@spotify.com @_kinshukmishra

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About Me• @Spotify since 2011 • Data and Backend engineering • With the Monetization team

May 12, 2014

Spotify in numbersStarted in 2006, available in 56 markets 20+ million songs, 20,000 added daily 24+ million active users, 6+ million subscribers 1.5 billion playlists

May 12, 2014

Monetization at Spotify• $1 billion paid to rights holders since launch

• 1 in every 4 Spotify users is a paying subscriber

• Ad revenue pays for free tier music streaming

• Build platform to power artist and music label promotions

What is our mission?

• Power the free tier with ads customers love. – Delight users, brands and artists with standout

promotional experiences. – Deliver the right message for every moment.

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Delight users, brands and artists with standout promotional experience

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• Ad formats need to be

- Native

- Actionable

- Scalable

- Engaging

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Spotlight

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Album promotion

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Sponsored Genre

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Sponsored Genre

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!(Right message for every moment)

Things we had to do

• Make ads context aware.

• Create measurable ad formats.

• Build tools to help brands understand their audience.

• Build flexible targeting infrastructure.

• Analyze user behavior to improve ad relevancy.

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Why is real time interesting?

• True assessment of the context - current view, last track, etc.

• Immediate feedback - ad clicked, followed a playlist, etc.

• Increased ad opportunities - hyperlocal, current mood, etc.

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source : http://nativemobile.com/tag/real-time-bidding

How to serve right message for every moment ?

• Fix the WTFs for ads in the music context - Realtime short window activity analysis

• Improve ad relevancy based on user behavior - (Realtime + Batch) long window activity analysis

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Some quick wins

• Fix the WTFs for ads in the music context - Fix the “Not Safe For Kids” ads problem. - Fix the relevancy of ads in your music session.

• Improve ad relevancy based on user behavior - Fix ad relevancy based on user’s musical taste.

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Targeting Architecture16

Desired overall design 17

Our requirements

• Scalable log collection • Capability to process logs in batch and realtime mode and aggregate user activity

• Capability to store user profiles and enable serve time lookups

• Ability to update and add new features to existing user profiles

Bigdata @Spotify and choices

• 700 node hadoop cluster • 400 GB service logs daily • 4.5 TB user data daily • 7500 hadoop jobs daily • 64 TB data generated daily • YARN Map-Reduce, Giraph, Storm, Spark, etc.

!

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Overall design and tech choices20

Source : http://tfosuccess.com/day-106-107-the-storm-arrived/

What is Storm?

• Real time stream processing • Like Hadoop without HDFS • Like Map/Reduce with many reducer steps • Fault tolerant and guaranteed message processing

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Storm @Spotify

• storm-0.8.0 • 22 node cluster • 15+ topologies • 200k tuples/second • ads, recommendation, analytics, monitoring, etc.

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source: http://storm.incubator.apache.org/

Use-case

Use real-time session genre information to control

ad serving in real-time

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Realtime activity analysis • Kafka -> Storm -> Memcached -> Targeting Backend

• Soft realtime

• Easy to scale

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Getting data25

Getting data across the globe26

Genre Topology27

May 12, 2014

Measuring effectiveness• Build ad quality scores from lower-level metrics

• Positive response, avoidance, etc.

• A/B test hypotheses, evaluate against quality scores

• Build quality score optimization into real-time system

May 6th, 2014

Want to join the band?https://www.spotify.com/us/jobs/ or https://twitter.com/Spotifyjobs !

Kinshuk Mishra kinshuk@spotify.com @_kinshukmishra