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CLICK PREDICTIONS FOR 500M MOBILE DEVICES
© 2008-2014
ADVERTISING IS A DATA BUSINESS
 Refinement of a commodity:
pixel real-estate
 Value creation: delivering
advertiser message to right
client in context
 KPI: quality and price
Telekom Shop
Bonn-City
Friedensplatz 4
53111 Bonn
0.5km
© 2008-2014
ADVERTISING IS A BIG DATA BUSINESS
 >1B unique devices
 ~40B impressions per day
traded through RTB WW
 40TB of new data per day
 ~500k impressions per second
 Latency: 100ms
© 2008-2014
GEOGRAPHIC DISTRIBUTION
50ms
RTT
50ms
RTT50ms
RTT
50ms
RTT
© 2008-2014
TECH CHALLENGE: SCALE AND COST
 Prediction latency: 3ms
 Volume: 500k impressions per
second
 = 300 predictions per core
= 1,500 cores
= 150-200 servers for bidding
Cloud Cost:
$200,000 per month
© 2008-2014
PREDICTION
 Multiple concurrent predictive
models
Likelihood to act (click, land,
browse, buy)
Demographic groups
Sociographic groups
Interest groups
Fraud detection
Site classification
 100’s ofTB input data sets for
training
Runs for many hours a day on even
more machines
Some refreshed hourly
© 2008-2014
DATA ENRICHMENT
 Multiple enrichments per bid
request
User
Location
Device
Weather
 10’s of Billion records with <1ms
access latency
 Data store with >1M IOPs/second
© 2008-2014
PREDICTIVE MODEL
 Predictive model has substantially cost impact
Every impression individually priced based on KPI estimation (e.g., click)
Basic filtering is insufficient
 Continuous model update in multiple time scales
1. Model refinement in real time
2. Full retraining at longer time scales (hour, day, week)
© 2008-2014
REAL-TIME BIG-DATA BUSINESS
 Technology and process
 Blue print invented by financial
world
 Layer-cutting integration
 Human touch points with
distributed ownership
 Scales when done right
Real-Time
Decisioning
Data
Acquisition
Storage
Batch
Analytics
Model
Testing
Time Series
and Cubes
Dashboard
and
Reports
© 2008-2014
THANK YOU
10
Dr.Volkmar Uhlig
CTO Adello
volkmar@adello.com

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Data Days 2014

  • 1. CLICK PREDICTIONS FOR 500M MOBILE DEVICES
  • 2. © 2008-2014 ADVERTISING IS A DATA BUSINESS  Refinement of a commodity: pixel real-estate  Value creation: delivering advertiser message to right client in context  KPI: quality and price Telekom Shop Bonn-City Friedensplatz 4 53111 Bonn 0.5km
  • 3. © 2008-2014 ADVERTISING IS A BIG DATA BUSINESS  >1B unique devices  ~40B impressions per day traded through RTB WW  40TB of new data per day  ~500k impressions per second  Latency: 100ms
  • 5. © 2008-2014 TECH CHALLENGE: SCALE AND COST  Prediction latency: 3ms  Volume: 500k impressions per second  = 300 predictions per core = 1,500 cores = 150-200 servers for bidding Cloud Cost: $200,000 per month
  • 6. © 2008-2014 PREDICTION  Multiple concurrent predictive models Likelihood to act (click, land, browse, buy) Demographic groups Sociographic groups Interest groups Fraud detection Site classification  100’s ofTB input data sets for training Runs for many hours a day on even more machines Some refreshed hourly
  • 7. © 2008-2014 DATA ENRICHMENT  Multiple enrichments per bid request User Location Device Weather  10’s of Billion records with <1ms access latency  Data store with >1M IOPs/second
  • 8. © 2008-2014 PREDICTIVE MODEL  Predictive model has substantially cost impact Every impression individually priced based on KPI estimation (e.g., click) Basic filtering is insufficient  Continuous model update in multiple time scales 1. Model refinement in real time 2. Full retraining at longer time scales (hour, day, week)
  • 9. © 2008-2014 REAL-TIME BIG-DATA BUSINESS  Technology and process  Blue print invented by financial world  Layer-cutting integration  Human touch points with distributed ownership  Scales when done right Real-Time Decisioning Data Acquisition Storage Batch Analytics Model Testing Time Series and Cubes Dashboard and Reports
  • 10. © 2008-2014 THANK YOU 10 Dr.Volkmar Uhlig CTO Adello volkmar@adello.com