An important part of the Data Science Foundation’s remit is enabling the flow of new ideas. As the foundation has a wide user base, active members benefit from the opportunity of peer review across many sub-disciplines. An example is Pivotal iQ, data science specialists and leaders in the field of propensity Modelling. They wanted to demonstrate how millions of micro data points can be connected to build a picture of customer opportunity. Publishing a white paper with the Data Science Foundation offered Pivotal iQ the opportunity to demonstrate to potential clients the tools they can offer to help them better understand the customer environment. In this article, we’ll shed more light on how Pivotal iQ implements Propensity Modelling to find customer opportunities in data. Hopefully, this case study will help you implement Propensity Modelling in your own business or research using the best industry standards embodied in Pivotal iQ tools.
Articles Published by HG Data
Propensity modelling is a statistical approach and a set of techniques which attempts to estimate the likelihood of subjects performing certain types of behaviour (e.g. the purchase of a product) by accounting for independent variables (covariates) and confounding variables that affect such behaviour.