I’ve spoken in some detail about what Sift Science does in my previous post. As a quick reminder, Sift Science helps e-commerce businesses fight fraud using the power of machine learning and predictive analytics. They collect diverse data, glean signals from it, and assign a ‘Sift score’ to every user which predicts how likely it is that a particular user is a fraudster.
In part 1 of this series on predictive analytics, we discussed the variety of data, particularly unstructured and semi-structured data, and the three types of predictive analytics. We also touched upon the approach of continual optimization of a predictive analytics system for it to become more and more accurate. With those basic concepts in place, we’re ready to take a tour of the various applications of predictive analytics as it’s used in businesses today. You can think of this as the hall of fame of predictive analytics. These businesses are playing “where the puck is going to be”