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Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
Use of EMR for Marketing Segmentation
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Use of EMR for Marketing Segmentation

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by Mandhir Gidda, Technical Director, Razorfish

by Mandhir Gidda, Technical Director, Razorfish

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  • 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 Page 2 © 2012 Razorfish. All rights reserved.
  • 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 worlds 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. Page 3 © 2009 Razorfish. All rights reserved.
  • 4. RF – Atlas - Microsoft• Razorfish: Developed the ATLAS ad serving engine• Atlas was seperated from Razorfish, but had asymbiotic relationship• Google bought DoubleClick• Microsoft bought Aquantive Group• Microsoft incorporated Atlas into MS Advertisingand Publishing• Sold Razorfish to Publicis group• RF continue to have a strong relationship withAtlas, but have gone on to develop Razorfish Edge,Insight On Demand (IoD), that use Atlas dataextensively. Page 4 © 2009 Razorfish. All rights reserved.
  • 5. Atlas•Razorfish: Developed the ATLAS ad servingengine• Single cookie & atlas tags• 90% of Browsers• Clickstream analysis of data, current andhistorical, log file data. User are placed intobuckets - segmented• Segmentation used to serve ads and crosssell Page 5 © 2009 Razorfish. All rights reserved.
  • 6. Problem45 Terabytes of raw clickstream (log) data 45 Terabytes of raw clickstream and log dataBusiness logic and metrics against loosely structured data • ROI • Custom ROI base on complex, client specific business rules • Rich Media and AnalyticsCustom user profilingCustom analysis of web surfing activityTargeting Page 6 © 2009 Razorfish. All rights reserved.
  • 7. Problem• Giant Datasets• Build infrastructure requires largecontinuous investment• Building for peak/holiday traffic• Data mining apps / Physical DB’s at ornear limit• Client expectations/data volumesincreasing Page 7 © 2009 Razorfish. All rights reserved.
  • 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 constrainedBy the end of 2009 (x-mas holiday season), RF needed $500k to keep up with dataprocessing needs. Page 8 © 2009 Razorfish. All rights reserved.
  • 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. Page 9 © 2009 Razorfish. All rights reserved.
  • 10. AWS + EMR AWS EMR Segmentation•S3 Storage 45tb of log • Measurement of customer value • Actionabledata • 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 Page 10 © 2009 Razorfish. All rights reserved.
  • 11. We import a lot of Atlas Data24 servers Cloud Storage Upload 200 + GB of data per day ( ½ Trillion ICA records )
  • 12. We filter out the relevant cookiesCloud 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 behaviorFiltered 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 millionFiltered 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 togethercategory 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 dayThis 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 Page 19 © 2009 Razorfish. All rights reserved.
  • 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) Page 20 © 2009 Razorfish. All rights reserved.
  • 21. Thank you •Mandhir Gidda© 2009 Razorfish. All rights reserved.

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