Predictive models typically estimate the likelihood of future events, such as whether it will rain tomorrow or which customers are most likely to “churn” by cancelling their phone contract. In the case of the weather, we do not expect to change it; we just want to know how to adapt.
However, the goal for most use cases is to be more proactive; we want to understand what action to take to change the outcome in a favorable way.
In these cases prescriptive, not just predictive, analytics is required. The return on investment comes directly from knowing the impact of alternative treatments. By knowing the impact of each treatment, resources can be targeted where they will be most effective and withheld where they will have negligible effect or worse, have a negative effect.
This great objective of data science, to intelligently drive day-to-day business decisions based on data, is the purview of uplift modeling. This white paper will explain what uplift modeling is and why it can be much better than directly modeling the outcome.