Data Science

Typical data science analysis'

Recommendation engines – increase cross-sell and repeat purchases by identifying other products in which a customer or prospect is likely to be interested

Web analytics - advanced click-stream, golden path analysis, viewer engagement, segmentation, and more.

Cross-channel marketing attribution – move beyond the skewed input of last click analysis to accurately determine campaign impact effectiveness across all channels

Influencer analysis – understand whose actions have impact in the network to encourage the behavior of peers for purchases, attrition, or just engagement.

Practical machine learning tricks - KDD 2011

Link

At first glance, this might appear to be a "Hello-World" machine learning problem straight out of a textbook or tutorial: we simply train a Naive Bayes on a set of bad ads versus a set of good ones. However this is apparently far from being the case - while Google is understandably shy about hard numbers, the paper mentions several issues which make this especially challenging and notes that this is a business-critical problem for Google.

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There are many useful suggestions in this post.