Back in the early 2000s, Business Intelligence professionals were talking about the novelty of automated cross-advertising. An example could be advertising greeting cards to someone who just purchased a gift or vice versa. If you bought a present then you must need a greeting card, so how about some of the choices right there, presented to you before or after you check out. While this wasn’t predictive analytics, it was still based on past shopping behavior. Market baskets are a more sophisticated version of shopping behavior analysis.
Modern predictive analytics is more comprehensive – it is an analysis of a pattern across a time span aimed at predicting what you are likely to do or need next. There is, probably, an infinite number of ways to analyze various data to try to predict human behavior – past shopping data, weather pattern data, seasonal data, income level data, credit score data. Creative analysis of combinations of these data and many more factors can uncover what’s about to happen before it happens.
However, many contemporary predictive analysis examples still amount to little more than simple cross-advertising that can, potentially, be achieved without the associated expense. It’s hardly necessary to go through extensive data analysis to “predict” that I am likely to need a greeting card to go with that gift I just purchased. It would be wise to understand the real business need for analytics. If the goal is to develop a competitive edge in the modern marketplace, then predictive analytics is likely the way to go, and it is a journey that takes methodology and patience.
Predictive analytics and advertising: the science behind knowing what women (and men) want