by Mandhir Gidda, Technical Director, Razorfish

1. Razorfish: Use of EMR for Marketing
Segmentation
© 2009 Razorfish. All rights reserved.

2. Agenda
• Who we are.
• Razorfish, ATLAS, Microsoft
• ATLAS What is it?, Problems
• AWS – EMR – Why move?
• EMR Solution Outline
• Benefits gained, Opportunities
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3. Who we are
– Razorfish London is a full-service digital agency.
– Founded in London in 1996
– We are now 250 people strong and experts at creative, design, social
media, digital media, analytics, technology, service operations and
user experience.
– We are part of one of the world's largest interactive agency networks
with more than 2,800 people.
– According to LinkedIn, Razorfish is the 31st most desirable employer in
the world (even beating Starbucks).
– For the last three years we’ve been the only agency recognised by
Forrester Research as a ‘leader’ in both the Media & Interactive
Marketing and Experience Design & Technology categories.
– We are Adobe’s ‘Digital Marketing Global Partner of the Year, 2012’
– We are No. 4 in the last Ad Age ‘Agency A-List’ - the highest ranked
digital agency.
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4. RF – Atlas - Microsoft
• Razorfish: Developed the ATLAS ad serving engine
• Atlas was seperated from Razorfish, but had a
symbiotic relationship
• Google bought DoubleClick
• Microsoft bought Aquantive Group
• Microsoft incorporated Atlas into MS Advertising
and Publishing
• Sold Razorfish to Publicis group
• RF continue to have a strong relationship with
Atlas, but have gone on to develop Razorfish Edge,
Insight On Demand (IoD), that use Atlas data
extensively.
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5. Atlas
•Razorfish: Developed the ATLAS ad serving
engine
• Single cookie & atlas tags
• 90% of Browsers
• Clickstream analysis of data, current and
historical, log file data. User are placed into
buckets - segmented
• Segmentation used to serve ads and cross
sell
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6. Problem
45 Terabytes of raw clickstream (log) data
45 Terabytes of raw clickstream and log data
Business logic and metrics against loosely structured data
• ROI
• Custom ROI base on complex, client specific business rules
• Rich Media and Analytics
Custom user profiling
Custom analysis of web surfing activity
Targeting
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7. Problem
• Giant Datasets
• Build infrastructure requires large
continuous investment
• Building for peak/holiday traffic
• Data mining apps / Physical DB’s at or
near limit
• Client expectations/data volumes
increasing
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8. Previously 2009
•Custom Distributed Log Processing Engine
• Sorted by cookie_id by time
• Need to segment granularly across larger no/ segments (Cust || Prospect)
•SQL
• 60 SQL Server boxes
• Shared resources (contention issues)
• In a DR configuration
•OLAP
• In house constrained
By the end of 2009 (x-mas holiday season), RF needed $500k to keep up with data
processing needs.
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9. AWS + EMR
• Efficient: Elastic infrastructure from AWS allows capacity to be provisioned as
needed based on load, reducing cost and the risk of processing delays.
• Configuration: Amazon Elastic MapReduce and Cascading lets Razorfish focus on
application development without having to worry about time-consuming set-up,
management, or tuning of clusters or the compute capacity upon which they sit.
• Ease of integration: Amazon Elastic MapReduce with Cascading allows data
processing in the cloud without any changes to the underlying algorithms.
• Flexible: EMR with Cascading is flexible enough to allow “agile” implementation
and unit testing of sophisticated algorithms.
• Adaptable: Cascading simplifies the integration of Hadoop with external ad
systems.
• Scalable: AWS infrastructure helps Razorfish reliably store and process huge
(Petabytes) data sets.
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10. AWS + EMR
AWS EMR Segmentation
•S3 Storage 45tb of log • Measurement of customer value • Actionable
data
• Measurement of customer affinity • Rules flexible /
customizable
• Joining 2.8 billion transactions against
known site categorization
information
• Unbalanced so there is a hit to the
reducers
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11. We import a lot of Atlas Data
24 servers
Cloud Storage
Upload 200 + GB
of data per day
( ½ Trillion ICA records )

12. We filter out the relevant cookies
Cloud Storage Elastic Mapreduce
100 Machine Cluster Created on demand. We filter for only the
transactions that we need to process (more than 3.5 billion)
( about 71 million unique cookies a day)

13. Filter by behavior
Filtered Transactions
SKU Table
Generate list of products that have been seen
( Match these cookies to 100,000’s of skus )

14. Match to their affinity
Join transactions
to site genre
information Sport
Enthusiast
70 million
Filtered Transactions
placements
Determinee profile information by the
types of sites the user has visited
( Cookies are matched to 3.5 billion ICA records )

15. …and run custom business rules
Join site
behavior to SKU Table
product info In market
Gamer
Filtered Transactions
Determine the types of products the
user is interested from what they have done on the site
( super–computing power determines some key categories )

16. We bring it all together
category affinity generation
In market
Gamer + Sport Enthusiast
+ Purchaser Home
Theater
( 1 of N “Personalization” segments )

17. Drive a personalized message
User recently purchased
a home theater system
and is now looking for Target Ad
sports games
( 1.7 million per day )

18. Each and every day
This all happens in about 8 hours every day
( not bad )

19. AWS + EMR
– Perfect clarity of cost
– No upfront infrastructure investment
– No client processing contention
– We couldn’t have done it.
– Without EMR/Hadoop process takes 3 days and heavy
reliance on manual processes. Now 5-8hrs
– Elasticity to complete a job faster if it’s worth the cost.
– We can meet our SLA’s
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20. Expanding Data Landscape
• EMR allows us to deal with the ever expanding number of
channels and user interactions with sites and data:
• Clickstream data available from tools like Atlas and
Doubleclick—who have cookied over 90% of the Internet
• Digital experience tracked through tools like Omniture,
Webtrends and Google Analytics
• Other channel data across touchpoints (email, call center,
mobile)
• Client Data
• Transactional data
• Survey-based (Nielsen’s)
• Social data available through open APIs (hosepipes)
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21. Thank you
•Mandhir Gidda
© 2009 Razorfish. All rights reserved.