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uplift modeling

Contributor(s): Melanie Luna

Uplift modeling is a type of data analytics that uses predictive modeling techniques to identify those individuals who can be positively influenced by an outreach effort.

Uplift modeling is often associated with political campaigns, advertising and healthcare. In any group of voters, potential customers or patients, for example, some individuals will be "on the fence" about a future course of action but can be influenced by talking to a candidate, stylist or physician's assistant. The goal of uplift modeling is to only expend time, money or effort on individuals that really need to be messaged before they will take a particular course of action.

Uplift modeling requires data scientists to build data models that will accurately identify the characteristics of individuals who require personal contact before making a decision.  Building the necessary control and test groups can take time, and refining the predictive models can take a lot of patience.  Because the criteria for identifying such individuals can be so nuanced, testing is often done incrementally. For this reason, uplift modeling is often called incremental modeling. 

This was last updated in October 2013

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