Predictive analytics is a hot topic and we are often asked how specifically marketers can use predictions to develop more profitable relations with their customers.
In this post, I’ll give you an overview of 13 predictive models you could use to increase revenues and delight your customers.
There are three types of predictive models marketers should know about:
Clustering models (segments)
Propensity models (predictions)
Collaborative filtering (recommendations)
I’ll go through each and give you a definition, as well as a total of 13 examples: Clustering is the predictive analytics term for customer segmentation. With clustering you let the algorithms, rather than the marketers, create customer segments. Think of clustering as auto-segmentation. Algorithms are able to segment customers based on many more variables than a human being ever could. It’s not unusual for two clusters to be different on 30 customer dimensions or more. We call these dimensions the cluster DNA. See below for an example of some of the factors that could make up a cluster’s DNA.