Case Study: RazorFish User Segmentation with Cascading and Amazon Elastic MapReduce

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Amazon recently published a case study on how RazorFish “segments users and customers based on the collection and analysis of non-personally identifiable data from browsing sessions”.

From the case study:

Mark Taylor, Program Director at Razorfish, said, “With our implementation of Amazon Elastic MapReduce and Cascading, there was no upfront investment in hardware, no hardware procurement delay, and no additional operations staff was hired. We completed development and testing of our first client project in six weeks. Our process is completely automated. Total cost of the infrastructure averages around $13,000 per month. Because of the richness of the algorithm and the flexibility of the platform to support it at scale, our first client campaign experienced a 500% increase in their return on ad spend from a similar campaign a year before.”

Read more about how RazorFish uses Cascading to process big data.

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