Over the past few years we've all been hearing more and more about "Predictive Analytics" (PA for short).  It is one of those terms - like BI, CRM or ERP - that once coined, captures the essence of an area of business.

A simple definition of predictive analytics is that it's an activity which allows us to quantify future events or actions. This quantitification could be as straightforward as generating a list of customers who are likely, at one point in time, to behave in a certain way, e.g. to churn, to register, to buy or to respond to a particular mail, etc. Typically this list would be accompanied by a score which gives the probability (or propensity) that an event will occur. Alternatively the technique may give us a value, or set of values, e.g. a set of sales forecasts for a given product line in the coming week; furthermore the forecasts are often presented with confidence values, e.g. we might predict that we will sell 50 widgets on Monday, but we can say with 95% confidence that we will sell between 45 and 55.

So what's new?

One of the main characteristics of PA if that many of the tools, techniques and applications which it comprises are not actually particularly new. Credit scoring is one of the most well-known applications of PA and credit scoring has been around for over 50 years.